placestats

Education score vs. Redistribution

Holds up after adjusting for income

Across 45 countries, Education score and Redistribution rise and fall together at r = +0.47 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.21 (q = 0.048, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak positive link remains. Caution: much weaker within income groups than across them (mostly a gap between richer and poorer countries).

Comparing countries across the middle half of redistribution, 23.2% to 38.1% reduction in the Gini, education score is better by 0.117 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0587 typical gaps.

Link before adjusting (r)+0.466p = 0.001 · 45 countries
After adjusting for national income+0.206p = 0.179 · likely range [-0.10, +0.47]
p, allowing for regions0.006standard error 0.050 · 7 regions
Check for luck (q)0.048across 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.

Education score · typical gaps (standard deviations)link with income +0.81 · proxy
Redistribution · % reduction in the Ginilink with income +0.50 · moderate

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

Every country as a dot

Education score · typical gaps (standard deviations) →Redistribution · % reduction in the Gini →0.5841.442.67%48.3%Australia: 1.03 typical gaps (standard deviations), 27.7% reduction in the GiniAustria: 1 typical gaps (standard deviations), 41.4% reduction in the GiniBelgium: 1.25 typical gaps (standard deviations), 47.7% reduction in the GiniBulgaria: 0.794 typical gaps (standard deviations), 26% reduction in the GiniBrazil: 0.731 typical gaps (standard deviations), 13.8% reduction in the GiniCanada: 1.06 typical gaps (standard deviations), 29.3% reduction in the GiniSwitzerland: 0.92 typical gaps (standard deviations), 21.2% reduction in the GiniChile: 0.979 typical gaps (standard deviations), 8.68% reduction in the GiniChina: 0.674 typical gaps (standard deviations), 6.22% reduction in the GiniCosta Rica: 0.629 typical gaps (standard deviations), 12.6% reduction in the GiniCzechia: 1.08 typical gaps (standard deviations), 43.5% reduction in the GiniGermany: 0.938 typical gaps (standard deviations), 38.7% reduction in the GiniDenmark: 0.941 typical gaps (standard deviations), 37.5% reduction in the GiniSpain: 0.983 typical gaps (standard deviations), 34.4% reduction in the GiniEstonia: 0.841 typical gaps (standard deviations), 33.5% reduction in the GiniFinland: 1.02 typical gaps (standard deviations), 47.2% reduction in the GiniFrance: 0.907 typical gaps (standard deviations), 42.2% reduction in the GiniUnited Kingdom: 1.19 typical gaps (standard deviations), 29.7% reduction in the GiniGreece: 1.31 typical gaps (standard deviations), 36.9% reduction in the GiniCroatia: 0.985 typical gaps (standard deviations), 33.1% reduction in the GiniHungary: 0.83 typical gaps (standard deviations), 29.4% reduction in the GiniIndia: 0.605 typical gaps (standard deviations), 2.67% reduction in the GiniIreland: 1.18 typical gaps (standard deviations), 41.2% reduction in the GiniIceland: 1.26 typical gaps (standard deviations), 35.7% reduction in the GiniIsrael: 0.804 typical gaps (standard deviations), 23.2% reduction in the GiniItaly: 0.94 typical gaps (standard deviations), 36.2% reduction in the GiniJapan: 1.04 typical gaps (standard deviations), 34.1% reduction in the GiniKorea, Rep.: 1.03 typical gaps (standard deviations), 18.2% reduction in the GiniLithuania: 0.928 typical gaps (standard deviations), 29.7% reduction in the GiniLuxembourg: 0.66 typical gaps (standard deviations), 35.7% reduction in the GiniLatvia: 0.923 typical gaps (standard deviations), 28.1% reduction in the GiniMexico: 0.636 typical gaps (standard deviations), 3.77% reduction in the GiniNetherlands: 1.27 typical gaps (standard deviations), 34.2% reduction in the GiniNorway: 1 typical gaps (standard deviations), 39.5% reduction in the GiniNew Zealand: 1.44 typical gaps (standard deviations), 28.7% reduction in the GiniPoland: 0.952 typical gaps (standard deviations), 41.9% reduction in the GiniPortugal: 1.02 typical gaps (standard deviations), 36.5% reduction in the GiniRomania: 0.584 typical gaps (standard deviations), 38.1% reduction in the GiniRussian Federation: 0.85 typical gaps (standard deviations), 27% reduction in the GiniSlovak Republic: 0.801 typical gaps (standard deviations), 48.3% reduction in the GiniSlovenia: 1.18 typical gaps (standard deviations), 42.4% reduction in the GiniSweden: 0.968 typical gaps (standard deviations), 33.1% reduction in the GiniTurkiye: 1.03 typical gaps (standard deviations), 18.6% reduction in the GiniUnited States: 1.11 typical gaps (standard deviations), 22.1% reduction in the GiniSouth Africa: 0.665 typical gaps (standard deviations), 12.8% reduction in the GiniBELCHLCHNGRCINDLUXMEXNZLROUSVKUSA

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.4660.00145
By rank (Spearman), not adjusted+0.3640.01445
Standard (Pearson), adjusted for national income+0.2060.17945
By rank (Spearman), adjusted for national income+0.1440.35145

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 45 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: +0.259 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.143 (without LUX) to +0.275 (without ROU). 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: ROU (+0.069).

Fragile: this link may not hold up.
  • much weaker within income groups than across them (mostly a gap between richer and poorer countries)

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+0.15

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+0.01
Middle third15+0.08
Richest third15+0.15

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+0.24

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 = +0.242 after dropping 1 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.

Loading…

Over time, within countries

Everything above compares countries with each other at one moment. This section compares each country only with itself over 0years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Not enough history inside each country to look at this over time. That needs countries measured for several years in a row, and at least one of these measures is measured too rarely.

The data


typical gaps (standard deviations)
% reduction in the Gini		Years
New Zealand NZL1.4428.7%2023, 2022
Greece GRC1.3136.9%2023
Netherlands NLD1.2734.2%2023
Iceland ISL1.2635.7%2023, 2019
Belgium BEL1.2547.7%2023
United Kingdom GBR1.1929.7%2023
Ireland IRL1.1841.2%2022, 2023
Slovenia SVN1.1842.4%2023
United States USA1.1122.1%2022, 2023
Czechia CZE1.0843.5%2023
Canada CAN1.0629.3%2023
Japan JPN1.0434.1%2023, 2021
Australia AUS1.0327.7%2024, 2020
Korea, Rep. KOR1.0318.2%2024, 2022
Turkiye TUR1.0318.6%2023, 2022

uis; undp_hdr; wdi
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

Education score

Education score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 10 measures from 3 publishers. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Primary completion, Expected schooling, Mean years of schooling, Adult literacy, Pupil-teacher ratio, Primary enrollment, Out of school, primary, Secondary enrollment, Tertiary enrollment, Researchers.

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.

How this was computed

Data used: most recent value in 2010-2025; values come from 2011–2025. 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 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Education score and Redistribution

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

More links with Education score or Redistribution