placestats

Slum share of urban population vs. Wellbeing score

Fades after adjusting for income

Across 103 countries, Slum share of urban population and Wellbeing score 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 +0.07, which could easily be chance (q = 0.58, 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: sign flips once outliers are trimmed.

Comparing countries across the middle half of slum share of urban population, 0.0796% to 21.4% of urban population, wellbeing score is worse by 0.407 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 0.0584 typical gaps the other way.

Link before adjusting (r)-0.522p = 1.5e-8 · 103 countries
After adjusting for national income+0.073p = 0.468 · likely range [-0.12, +0.26]
p, allowing for regions0.381standard error 0.077 · 7 regions
Check for luck (q)0.580across 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.

Slum share of urban population · % of urban populationlink with income -0.78 · high
Wellbeing score · typical gaps (standard deviations)link with income +0.80 · high

Both of these rise and fall closely with national income, so a strong link between them before adjusting was close to certain. Read the adjusted figure, not the first one.

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

Every country as a dot

Slum share of urban population · % of urban population →Wellbeing score · typical gaps (standard deviations) →0%92.5%-1.981.89Albania: 2.7% of urban population, -0.617 typical gaps (standard deviations)Andorra: 0% of urban population, 0.512 typical gaps (standard deviations)Argentina: 14.5% of urban population, 0.216 typical gaps (standard deviations)Armenia: 8.39% of urban population, -0.118 typical gaps (standard deviations)Australia: 0.035% of urban population, 1.25 typical gaps (standard deviations)Austria: 0.0466% of urban population, 1.26 typical gaps (standard deviations)Azerbaijan: 26.9% of urban population, -0.0603 typical gaps (standard deviations)Belgium: 0% of urban population, 0.818 typical gaps (standard deviations)Burkina Faso: 87.9% of urban population, -0.476 typical gaps (standard deviations)Bangladesh: 51.5% of urban population, -0.561 typical gaps (standard deviations)Bulgaria: 0.197% of urban population, -0.572 typical gaps (standard deviations)Bosnia and Herzegovina: 0.313% of urban population, -0.087 typical gaps (standard deviations)Belarus: 0.004% of urban population, 0.732 typical gaps (standard deviations)Bolivia: 46.6% of urban population, -0.361 typical gaps (standard deviations)Brazil: 14.9% of urban population, 0.123 typical gaps (standard deviations)Canada: 1.1% of urban population, 1.06 typical gaps (standard deviations)Switzerland: 0% of urban population, 1.55 typical gaps (standard deviations)Chile: 7.32% of urban population, 0.19 typical gaps (standard deviations)China: 26.3% of urban population, 0.913 typical gaps (standard deviations)Colombia: 9.7% of urban population, -0.125 typical gaps (standard deviations)Cyprus: 0.272% of urban population, -0.0176 typical gaps (standard deviations)Czechia: 0.0273% of urban population, 0.726 typical gaps (standard deviations)Germany: 0% of urban population, 0.981 typical gaps (standard deviations)Denmark: 0% of urban population, 1.89 typical gaps (standard deviations)Algeria: 13.2% of urban population, -0.205 typical gaps (standard deviations)Ecuador: 57.8% of urban population, -0.164 typical gaps (standard deviations)Egypt, Arab Rep.: 3.84% of urban population, -1.08 typical gaps (standard deviations)Spain: 0.046% of urban population, 0.816 typical gaps (standard deviations)Estonia: 0.345% of urban population, 0.622 typical gaps (standard deviations)Ethiopia: 64.3% of urban population, -0.726 typical gaps (standard deviations)Finland: 0% of urban population, 1.87 typical gaps (standard deviations)France: 0% of urban population, 0.717 typical gaps (standard deviations)United Kingdom: 0.163% of urban population, 1.07 typical gaps (standard deviations)Georgia: 7.08% of urban population, -0.493 typical gaps (standard deviations)Ghana: 33.5% of urban population, -0.73 typical gaps (standard deviations)Greece: 0.05% of urban population, -0.284 typical gaps (standard deviations)Guatemala: 37.6% of urban population, 0.352 typical gaps (standard deviations)Croatia: 0.527% of urban population, 0.0883 typical gaps (standard deviations)Haiti: 51.1% of urban population, -0.461 typical gaps (standard deviations)Hungary: 7.2% of urban population, 0.266 typical gaps (standard deviations)Indonesia: 19.4% of urban population, -0.192 typical gaps (standard deviations)India: 5.41% of urban population, -0.736 typical gaps (standard deviations)Ireland: 8.5% of urban population, 0.894 typical gaps (standard deviations)Iran, Islamic Rep.: 44.7% of urban population, -0.684 typical gaps (standard deviations)Iraq: 49.3% of urban population, -0.726 typical gaps (standard deviations)Iceland: 0% of urban population, 1.78 typical gaps (standard deviations)Italy: 0.02% of urban population, 0.438 typical gaps (standard deviations)Jordan: 23.4% of urban population, -0.666 typical gaps (standard deviations)Japan: 2% of urban population, 0.552 typical gaps (standard deviations)Kazakhstan: 0.794% of urban population, 0.304 typical gaps (standard deviations)Kenya: 40.5% of urban population, -0.678 typical gaps (standard deviations)Kyrgyz Republic: 2.44% of urban population, 0.225 typical gaps (standard deviations)Korea, Rep.: 4.5% of urban population, 0.475 typical gaps (standard deviations)Kuwait: 0% of urban population, 0.607 typical gaps (standard deviations)Lebanon: 4.53% of urban population, -1.54 typical gaps (standard deviations)Libya: 16.6% of urban population, -0.158 typical gaps (standard deviations)Lithuania: 0.509% of urban population, 0.51 typical gaps (standard deviations)Luxembourg: 0.0173% of urban population, 0.652 typical gaps (standard deviations)Latvia: 0.593% of urban population, 0.212 typical gaps (standard deviations)Morocco: 10.9% of urban population, -0.382 typical gaps (standard deviations)Moldova: 6.5% of urban population, -1.29 typical gaps (standard deviations)Maldives: 34.8% of urban population, 0.0845 typical gaps (standard deviations)Mexico: 17.6% of urban population, 0.335 typical gaps (standard deviations)North Macedonia: 0.278% of urban population, -0.171 typical gaps (standard deviations)Mali: 92.5% of urban population, -0.328 typical gaps (standard deviations)Malta: 0.0375% of urban population, 0.15 typical gaps (standard deviations)Myanmar: 58.3% of urban population, -0.547 typical gaps (standard deviations)Montenegro: 8.77% of urban population, 0.239 typical gaps (standard deviations)Mongolia: 17.9% of urban population, 0.000193 typical gaps (standard deviations)Malaysia: 0.2% of urban population, 0.215 typical gaps (standard deviations)Nigeria: 48.5% of urban population, -0.611 typical gaps (standard deviations)Nicaragua: 5.52% of urban population, 0.0736 typical gaps (standard deviations)Netherlands: 0% of urban population, 1.54 typical gaps (standard deviations)Norway: 0% of urban population, 1.82 typical gaps (standard deviations)New Zealand: 0% of urban population, 1.46 typical gaps (standard deviations)Pakistan: 56% of urban population, -0.299 typical gaps (standard deviations)Peru: 45.1% of urban population, -0.348 typical gaps (standard deviations)Philippines: 35.9% of urban population, -0.119 typical gaps (standard deviations)Poland: 4.22% of urban population, 0.505 typical gaps (standard deviations)Portugal: 0.059% of urban population, 0.195 typical gaps (standard deviations)West Bank and Gaza: 19.5% of urban population, -0.644 typical gaps (standard deviations)Qatar: 0% of urban population, 0.756 typical gaps (standard deviations)Romania: 2.52% of urban population, 0.197 typical gaps (standard deviations)Russian Federation: 2.62% of urban population, -0.00878 typical gaps (standard deviations)Rwanda: 38.3% of urban population, -0.867 typical gaps (standard deviations)Singapore: 0% of urban population, 0.779 typical gaps (standard deviations)Serbia: 1.42% of urban population, 0.105 typical gaps (standard deviations)Slovak Republic: 0.066% of urban population, 0.318 typical gaps (standard deviations)Slovenia: 0.527% of urban population, 0.55 typical gaps (standard deviations)Sweden: 0.336% of urban population, 1.7 typical gaps (standard deviations)Thailand: 2% of urban population, 0.256 typical gaps (standard deviations)Tajikistan: 0.05% of urban population, 0.104 typical gaps (standard deviations)Trinidad and Tobago: 8.63% of urban population, 0.0637 typical gaps (standard deviations)Tunisia: 7.64% of urban population, -0.73 typical gaps (standard deviations)Turkiye: 14.1% of urban population, -0.508 typical gaps (standard deviations)Ukraine: 1.1% of urban population, -0.244 typical gaps (standard deviations)Uruguay: 1.3% of urban population, 0.449 typical gaps (standard deviations)United States: 0.0932% of urban population, 0.943 typical gaps (standard deviations)Uzbekistan: 7.1% of urban population, 0.617 typical gaps (standard deviations)Viet Nam: 32.5% of urban population, 0.469 typical gaps (standard deviations)South Africa: 24.2% of urban population, -0.515 typical gaps (standard deviations)Zambia: 48.3% of urban population, -1.67 typical gaps (standard deviations)Zimbabwe: 54.9% of urban population, -1.98 typical gaps (standard deviations)BFADNKECUEGYFINLBNMDAMLINORZWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5221.5e-8103
By rank (Spearman), not adjusted-0.7095.2e-17103
Standard (Pearson), adjusted for national income+0.0730.468103
By rank (Spearman), adjusted for national income-0.1460.144103

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 103 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: +0.449 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.027 (without MLI) to +0.131 (without TJK). 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: TJK (+0.058).

Fragile: this link may not hold up.
  • sign flips once outliers are trimmed
  • direction is not consistent across regions

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
East Asia & Pacific13+0.05
Europe & Central Asia48-0.25
Latin America & Caribbean13-0.48
Middle East, North Africa, Afghanistan & Pakistan14+0.21

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 third35-0.06
Middle third34+0.19
Richest third34-0.23

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
Christians61+0.08
Muslims28-0.02

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.

nr
Never a European colony15-0.48
Spain12-0.34
United Kingdom25+0.09

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 = -0.032 after dropping 7 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


% of urban population
typical gaps (standard deviations)		Years
Mali MLI92.5%-0.3282022, 2010
Burkina Faso BFA87.9%-0.4762022, 2010
Ethiopia ETH64.3%-0.7262022
Myanmar MMR58.3%-0.5472022
Ecuador ECU57.8%-0.1642022
Pakistan PAK56%-0.2992022
Zimbabwe ZWE54.9%-1.982022
Bangladesh BGD51.5%-0.5612022
Haiti HTI51.1%-0.4612022, 2014
Iraq IRQ49.3%-0.7262022
Nigeria NGA48.5%-0.6112022
Zambia ZMB48.3%-1.672022, 2010
Bolivia BOL46.6%-0.3612022
Peru PER45.1%-0.3482022
Iran, Islamic Rep. IRN44.7%-0.6842022

World Bank, World Development Indicators
ivs; whr

Sources

Slum share of urban population

Population living in slums (% of urban population)

UN-Habitat · CC BY 4.0 · updated quarterly

Wellbeing score

Wellbeing score

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

The average of 3 measures from 2 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: Self-reported happiness, Interpersonal trust, Life satisfaction.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2022. 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 Slum share of urban population and Wellbeing score

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

More links with Slum share of urban population or Wellbeing score