placestats

Redistribution vs. Researchers

Fades after adjusting for income

Across 44 countries, Redistribution and Researchers rise and fall together at r = +0.58 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.20, which could easily be chance (q = 0.48, 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 0.21 in r.

Comparing countries across the middle half of redistribution, 25% to 38.2% reduction in the Gini, researchers is better by 1,563 per million people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 482 per million people.

Link before adjusting (r)+0.584p = 3.2e-5 · 44 countries
After adjusting for national income+0.202p = 0.194 · likely range [-0.10, +0.47]
p, allowing for regions0.271standard error 0.163 · 6 regions
Check for luck (q)0.475across 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.

Redistribution · % reduction in the Ginilink with income +0.50 · moderate
Researchers · per million peoplelink with income +0.85 · proxy

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

Redistribution · % reduction in the Gini →Researchers · per million people →2.67%48.3%2599,472Australia: 27.7% reduction in the Gini, 4,569 per million peopleAustria: 41.4% reduction in the Gini, 6,634 per million peopleBelgium: 47.7% reduction in the Gini, 5,990 per million peopleBulgaria: 26% reduction in the Gini, 2,401 per million peopleBrazil: 13.8% reduction in the Gini, 903 per million peopleCanada: 29.3% reduction in the Gini, 5,624 per million peopleSwitzerland: 21.2% reduction in the Gini, 6,108 per million peopleChile: 8.68% reduction in the Gini, 707 per million peopleChina: 6.22% reduction in the Gini, 2,107 per million peopleCosta Rica: 12.6% reduction in the Gini, 508 per million peopleCzechia: 43.5% reduction in the Gini, 4,505 per million peopleGermany: 38.7% reduction in the Gini, 5,926 per million peopleDenmark: 37.5% reduction in the Gini, 8,838 per million peopleSpain: 34.4% reduction in the Gini, 3,654 per million peopleEstonia: 33.5% reduction in the Gini, 4,800 per million peopleFinland: 47.2% reduction in the Gini, 8,315 per million peopleFrance: 42.2% reduction in the Gini, 5,369 per million peopleUnited Kingdom: 29.7% reduction in the Gini, 4,473 per million peopleGreece: 36.9% reduction in the Gini, 5,283 per million peopleCroatia: 33.1% reduction in the Gini, 2,440 per million peopleHungary: 29.4% reduction in the Gini, 4,567 per million peopleIndia: 2.67% reduction in the Gini, 259 per million peopleIreland: 41.2% reduction in the Gini, 5,753 per million peopleIceland: 35.7% reduction in the Gini, 5,953 per million peopleItaly: 36.2% reduction in the Gini, 2,855 per million peopleJapan: 34.1% reduction in the Gini, 5,609 per million peopleKorea, Rep.: 18.2% reduction in the Gini, 9,472 per million peopleLithuania: 29.7% reduction in the Gini, 3,704 per million peopleLuxembourg: 35.7% reduction in the Gini, 5,359 per million peopleLatvia: 28.1% reduction in the Gini, 2,191 per million peopleMexico: 3.77% reduction in the Gini, 438 per million peopleNetherlands: 34.2% reduction in the Gini, 6,563 per million peopleNorway: 39.5% reduction in the Gini, 7,451 per million peopleNew Zealand: 28.7% reduction in the Gini, 5,435 per million peoplePoland: 41.9% reduction in the Gini, 3,675 per million peoplePortugal: 36.5% reduction in the Gini, 5,983 per million peopleRomania: 38.1% reduction in the Gini, 1,082 per million peopleRussian Federation: 27% reduction in the Gini, 2,639 per million peopleSlovak Republic: 48.3% reduction in the Gini, 3,507 per million peopleSlovenia: 42.4% reduction in the Gini, 5,616 per million peopleSweden: 33.1% reduction in the Gini, 8,911 per million peopleTurkiye: 18.6% reduction in the Gini, 2,645 per million peopleUnited States: 22.1% reduction in the Gini, 4,937 per million peopleSouth Africa: 12.8% reduction in the Gini, 444 per million peopleBELCHECHLDNKFININDKORMEXPOLROUSVKSWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5843.2e-544
By rank (Spearman), not adjusted+0.5270.00044
Standard (Pearson), adjusted for national income+0.2020.19444
By rank (Spearman), adjusted for national income+0.2420.11844

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 44 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: +0.382 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.137 (without FIN) to +0.349 (without KOR). 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: KOR (+0.147).

Fragile: this link may not hold up.
  • leave-one-out range spans 0.21 in r

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.

nr
Europe & Central Asia31+0.28

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.

nr
Poorest third15+0.42
Middle third14-0.32
Richest third15+0.40

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.

nr
Christians36+0.40

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 = +0.196 after dropping 2 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 13years. 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 = -0.018, 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 redistribution moved did not tend to see their researchers move with it.

Across countries (adjusted)+0.202for comparison
Within countries+0.052each country vs itself
…and vs the same year elsewhere-0.018q = 0.958 · 34 countries
Year-on-year changes+0.046q = 0.688
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.

387 country-years (one country in one year), 34 countries, 2011–2023, about 11 years each on average. Share of the variation that happens within countries over time: 4% for Redistribution, 7% for Researchers. 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

0.20−0.2-100+5years Researchers trails Redistribution →lag -10: r = 0.007 (n = 390)lag -9: r = 0.013 (n = 387)lag -8: r = 0.014 (n = 392)lag -7: r = -0.000 (n = 392)lag -6: r = 0.008 (n = 398)lag -5: r = 0.001 (n = 390)lag -4: r = -0.025 (n = 390)lag -3: r = -0.061 (n = 390)lag -2: r = -0.049 (n = 385)lag -1: r = -0.041 (n = 390)lag +0: r = -0.018 (n = 387)lag +1: r = 0.041 (n = 362)lag +2: r = 0.106 (n = 326)lag +3: r = 0.175 (n = 286)lag +4: r = 0.147 (n = 262)lag +5: r = 0.103 (n = 219)

Same year (lag 0): -0.018. 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 (+0.17 at lag +3) is higher than the same-year one by 0.16. That gap is not a finding. It is the largest of 16 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


% reduction in the Gini
per million people		Years
Slovak Republic SVK48.3%3,5072023
Belgium BEL47.7%5,9902023
Finland FIN47.2%8,3152023
Czechia CZE43.5%4,5052023
Slovenia SVN42.4%5,6162023
France FRA42.2%5,3692023
Poland POL41.9%3,6752023
Austria AUT41.4%6,6342023
Ireland IRL41.2%5,7532023
Norway NOR39.5%7,4512022, 2023
Germany DEU38.7%5,9262022, 2023
Romania ROU38.1%1,0822023
Denmark DNK37.5%8,8382022, 2023
Greece GRC36.9%5,2832023
Portugal PRT36.5%5,9832023

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
World Bank, World Development Indicators

Sources

Redistribution

Reduction in the Gini coefficient from taxes and transfers

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator · comparable from 2011 only

The Gini before taxes and payments minus the Gini after them, as a share of the first. It shows how much of the inequality produced by the market the government removes. It is built from the before-tax Gini above, so their pair is built from the same numbers and flagged. No "better" direction is set: a country may redistribute little because its market incomes are already equal.

Researchers

Researchers in R&D (per million people)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

Researchers counted as full-time equivalents (two half-time researchers count as one), from national research surveys that many countries run only now and then. So coverage is thin outside the OECD.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2024. 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 6 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 Redistribution and Researchers

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

More links with Redistribution or Researchers