placestats

Infrastructure score vs. Redistribution

Fades after adjusting for income

Across 45 countries, Infrastructure score and Redistribution rise and fall together at r = +0.52 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.05, which could easily be chance (q = 0.91, 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 0.18 in r.

Comparing countries across the middle half of redistribution, 23.2% to 38.1% reduction in the Gini, infrastructure score is better by 0.156 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0121 typical gaps.

Link before adjusting (r)+0.519p = 0.000 · 45 countries
After adjusting for national income+0.047p = 0.760 · likely range [-0.25, +0.34]
p, allowing for regions0.853standard error 0.245 · 7 regions
Check for luck (q)0.913across 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.

Infrastructure score · typical gaps (standard deviations)link with income +0.90 · 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

Infrastructure score · typical gaps (standard deviations) →Redistribution · % reduction in the Gini →0.5271.722.67%48.3%Australia: 1.5 typical gaps (standard deviations), 27.7% reduction in the GiniAustria: 1.37 typical gaps (standard deviations), 41.4% reduction in the GiniBelgium: 1.64 typical gaps (standard deviations), 47.7% reduction in the GiniBulgaria: 1.25 typical gaps (standard deviations), 26% reduction in the GiniBrazil: 1.18 typical gaps (standard deviations), 13.8% reduction in the GiniCanada: 1.56 typical gaps (standard deviations), 29.3% reduction in the GiniSwitzerland: 1.7 typical gaps (standard deviations), 21.2% reduction in the GiniChile: 1.25 typical gaps (standard deviations), 8.68% reduction in the GiniChina: 1.66 typical gaps (standard deviations), 6.22% reduction in the GiniCosta Rica: 1.19 typical gaps (standard deviations), 12.6% reduction in the GiniCzechia: 1.48 typical gaps (standard deviations), 43.5% reduction in the GiniGermany: 1.65 typical gaps (standard deviations), 38.7% reduction in the GiniDenmark: 1.65 typical gaps (standard deviations), 37.5% reduction in the GiniSpain: 1.55 typical gaps (standard deviations), 34.4% reduction in the GiniEstonia: 1.45 typical gaps (standard deviations), 33.5% reduction in the GiniFinland: 1.48 typical gaps (standard deviations), 47.2% reduction in the GiniFrance: 1.7 typical gaps (standard deviations), 42.2% reduction in the GiniUnited Kingdom: 1.6 typical gaps (standard deviations), 29.7% reduction in the GiniGreece: 1.57 typical gaps (standard deviations), 36.9% reduction in the GiniCroatia: 1.28 typical gaps (standard deviations), 33.1% reduction in the GiniHungary: 1.5 typical gaps (standard deviations), 29.4% reduction in the GiniIndia: 0.527 typical gaps (standard deviations), 2.67% reduction in the GiniIreland: 1.43 typical gaps (standard deviations), 41.2% reduction in the GiniIceland: 1.53 typical gaps (standard deviations), 35.7% reduction in the GiniIsrael: 1.31 typical gaps (standard deviations), 23.2% reduction in the GiniItaly: 1.36 typical gaps (standard deviations), 36.2% reduction in the GiniJapan: 1.45 typical gaps (standard deviations), 34.1% reduction in the GiniKorea, Rep.: 1.72 typical gaps (standard deviations), 18.2% reduction in the GiniLithuania: 1.29 typical gaps (standard deviations), 29.7% reduction in the GiniLuxembourg: 1.59 typical gaps (standard deviations), 35.7% reduction in the GiniLatvia: 1.28 typical gaps (standard deviations), 28.1% reduction in the GiniMexico: 1.13 typical gaps (standard deviations), 3.77% reduction in the GiniNetherlands: 1.63 typical gaps (standard deviations), 34.2% reduction in the GiniNorway: 1.68 typical gaps (standard deviations), 39.5% reduction in the GiniNew Zealand: 1.51 typical gaps (standard deviations), 28.7% reduction in the GiniPoland: 1.24 typical gaps (standard deviations), 41.9% reduction in the GiniPortugal: 1.6 typical gaps (standard deviations), 36.5% reduction in the GiniRomania: 1.45 typical gaps (standard deviations), 38.1% reduction in the GiniRussian Federation: 1.32 typical gaps (standard deviations), 27% reduction in the GiniSlovak Republic: 1.4 typical gaps (standard deviations), 48.3% reduction in the GiniSlovenia: 1.42 typical gaps (standard deviations), 42.4% reduction in the GiniSweden: 1.57 typical gaps (standard deviations), 33.1% reduction in the GiniTurkiye: 1.19 typical gaps (standard deviations), 18.6% reduction in the GiniUnited States: 1.53 typical gaps (standard deviations), 22.1% reduction in the GiniSouth Africa: 0.709 typical gaps (standard deviations), 12.8% reduction in the GiniBELCHECHNFRAINDKORMEXNORPOLSVKZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5190.00045
By rank (Spearman), not adjusted+0.3470.02045
Standard (Pearson), adjusted for national income+0.0470.76045
By rank (Spearman), adjusted for national income+0.0290.85345

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: +0.471 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.009 (without GRC) to +0.190 (without CHN). 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: CHN (+0.143).

Fragile: this link may not hold up.
  • leave-one-out range spans 0.18 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+0.18

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 third15-0.20
Middle third15-0.05
Richest third15-0.20

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+0.22

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 = +0.042 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 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
Korea, Rep. KOR1.7218.2%2024, 2022
Switzerland CHE1.721.2%2024, 2022
France FRA1.742.2%2024, 2023
Norway NOR1.6839.5%2024, 2022
China CHN1.666.22%2024, 2011
Denmark DNK1.6537.5%2024, 2022
Germany DEU1.6538.7%2024, 2022
Belgium BEL1.6447.7%2024, 2023
Netherlands NLD1.6334.2%2024, 2023
Portugal PRT1.636.5%2024, 2023
United Kingdom GBR1.629.7%2024, 2023
Luxembourg LUX1.5935.7%2024, 2023
Greece GRC1.5736.9%2024, 2023
Sweden SWE1.5733.1%2024, 2023
Canada CAN1.5629.3%2024, 2023

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

Infrastructure score

Infrastructure score

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

The average of 5 measures from 1 publisher. 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: Electricity access, Slum share of urban population, Mobile subscriptions, Fixed broadband, Internet users.

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 2011–2024. 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 Infrastructure score and Redistribution

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

More links with Infrastructure score or Redistribution