placestats

Redistribution vs. Slum share of urban population

Fades after adjusting for income

Across 44 countries, Redistribution and Slum share of urban population rise and fall together at r = -0.66 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.45, which could easily be chance (q = 0.054, 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.

Comparing countries across the middle half of redistribution, 25% to 38.2% reduction in the Gini, slum share of urban population is better by 4.69% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 3.4% of urban population.

Link before adjusting (r)-0.660p = 1.1e-6 · 44 countries
After adjusting for national income-0.450p = 0.002 · likely range [-0.66, -0.17]
p, allowing for regions0.006standard error 0.098 · 6 regions
Check for luck (q)0.054across 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
Slum share of urban population · % of urban populationlink with income -0.78 · high

“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 →Slum share of urban population · % of urban population →2.67%48.3%0%26.3%Australia: 27.7% reduction in the Gini, 0.035% of urban populationAustria: 41.4% reduction in the Gini, 0.0466% of urban populationBelgium: 47.7% reduction in the Gini, 0% of urban populationBulgaria: 26% reduction in the Gini, 0.197% of urban populationBrazil: 13.8% reduction in the Gini, 14.9% of urban populationCanada: 29.3% reduction in the Gini, 1.1% of urban populationSwitzerland: 21.2% reduction in the Gini, 0% of urban populationChile: 8.68% reduction in the Gini, 7.32% of urban populationChina: 6.22% reduction in the Gini, 26.3% of urban populationCosta Rica: 12.6% reduction in the Gini, 3.55% of urban populationCzechia: 43.5% reduction in the Gini, 0.0273% of urban populationGermany: 38.7% reduction in the Gini, 0% of urban populationDenmark: 37.5% reduction in the Gini, 0% of urban populationSpain: 34.4% reduction in the Gini, 0.046% of urban populationEstonia: 33.5% reduction in the Gini, 0.345% of urban populationFinland: 47.2% reduction in the Gini, 0% of urban populationFrance: 42.2% reduction in the Gini, 0% of urban populationUnited Kingdom: 29.7% reduction in the Gini, 0.163% of urban populationGreece: 36.9% reduction in the Gini, 0.05% of urban populationCroatia: 33.1% reduction in the Gini, 0.527% of urban populationHungary: 29.4% reduction in the Gini, 7.2% of urban populationIndia: 2.67% reduction in the Gini, 5.41% of urban populationIreland: 41.2% reduction in the Gini, 8.5% of urban populationIceland: 35.7% reduction in the Gini, 0% of urban populationItaly: 36.2% reduction in the Gini, 0.02% of urban populationJapan: 34.1% reduction in the Gini, 2% of urban populationKorea, Rep.: 18.2% reduction in the Gini, 4.5% of urban populationLithuania: 29.7% reduction in the Gini, 0.509% of urban populationLuxembourg: 35.7% reduction in the Gini, 0.0173% of urban populationLatvia: 28.1% reduction in the Gini, 0.593% of urban populationMexico: 3.77% reduction in the Gini, 17.6% of urban populationNetherlands: 34.2% reduction in the Gini, 0% of urban populationNorway: 39.5% reduction in the Gini, 0% of urban populationNew Zealand: 28.7% reduction in the Gini, 0% of urban populationPoland: 41.9% reduction in the Gini, 4.22% of urban populationPortugal: 36.5% reduction in the Gini, 0.059% of urban populationRomania: 38.1% reduction in the Gini, 2.52% of urban populationRussian Federation: 27% reduction in the Gini, 2.62% of urban populationSlovak Republic: 48.3% reduction in the Gini, 0.066% of urban populationSlovenia: 42.4% reduction in the Gini, 0.527% of urban populationSweden: 33.1% reduction in the Gini, 0.336% of urban populationTurkiye: 18.6% reduction in the Gini, 14.1% of urban populationUnited States: 22.1% reduction in the Gini, 0.0932% of urban populationSouth Africa: 12.8% reduction in the Gini, 24.2% of urban populationBELCHECHNCRIFININDIRLMEXSVKZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6601.1e-644
By rank (Spearman), not adjusted-0.5803.6e-544
Standard (Pearson), adjusted for national income-0.4500.00244
By rank (Spearman), adjusted for national income-0.3830.01144

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: +0.210 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.501 (without IND) to -0.390 (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 (+0.060).

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 Asia31-0.34

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 third15-0.47
Middle third14-0.38
Richest third15+0.23

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
Christians36-0.41

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 = -0.330 after dropping 6 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


% reduction in the Gini
% of urban population		Years
Slovak Republic SVK48.3%0.066%2023, 2022
Belgium BEL47.7%0%2023, 2022
Finland FIN47.2%0%2023, 2022
Czechia CZE43.5%0.0273%2023, 2022
Slovenia SVN42.4%0.527%2023, 2022
France FRA42.2%0%2023, 2022
Poland POL41.9%4.22%2023, 2022
Austria AUT41.4%0.0466%2023, 2022
Ireland IRL41.2%8.5%2023, 2022
Norway NOR39.5%0%2022
Germany DEU38.7%0%2022
Romania ROU38.1%2.52%2023, 2022
Denmark DNK37.5%0%2022
Greece GRC36.9%0.05%2023, 2012
Portugal PRT36.5%0.059%2023, 2022

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.

Slum share of urban population

Population living in slums (% of urban population)

UN-Habitat · CC BY 4.0 · updated quarterly

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 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 Slum share of urban population

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

More links with Redistribution or Slum share of urban population