Infrastructure score vs. Slum share of urban population
Fades after adjusting for income
Across 183 countries, Infrastructure score and Slum share of urban population rise and fall together at r = -0.77 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.35, which could easily be chance (q = 0.067, 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. Slum share of urban population is one of the measures Infrastructure score is built from, so the two are bound to match. Their link is arithmetic, not a relationship between two things.
Comparing countries across the middle half of slum share of urban population, 0.572% to 44.3% of urban population, infrastructure score is worse by 0.914 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.261 typical gaps.
Link before adjusting (r)-0.770p = 3.3e-37 · 183 countries
After adjusting for national income-0.350p = 1.3e-6 · likely range [-0.47, -0.22]
p, allowing for regions0.014standard error 0.103 · 7 regions
Check for luck (q)0.067across 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
Slum share of urban population · % of urban populationlink with income -0.78 · 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.
Built from the same numbers. Slum share of urban population is one of the measures Infrastructure score is built from, so the two are bound to match. Their link is arithmetic, not a relationship between two things.
Every country as a dot
best-fit line line after adjusting
Four ways to measure the link
Method
r
p
Countries
Standard (Pearson), not adjusted
-0.770
3.3e-37
183
By rank (Spearman), not adjusted
-0.775
6.3e-38
183
Standard (Pearson), adjusted for national income
-0.350
1.3e-6
183
By rank (Spearman), adjusted for national income
-0.253
0.001
183
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 183 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: +0.421 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.376 (without PNG) to -0.319 (without GNQ). 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: GNQ (+0.031).
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.
n
r
East Asia & Pacific
28
-0.07
Europe & Central Asia
49
-0.17
Latin America & Caribbean
30
-0.13
Middle East, North Africa, Afghanistan & Pakistan
21
-0.23
Sub-Saharan Africa
46
-0.42
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.
n
r
Poorest third
61
-0.44
Middle third
61
-0.28
Richest third
61
-0.17
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.
n
r
Christians
106
-0.39
Muslims
49
-0.49
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.
n
r
France
25
-0.52
Never a European colony
21
-0.11
Spain
19
-0.51
United Kingdom
57
-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 = -0.350 after dropping 0 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.
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.
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 2012–2024. 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 Slum share of urban population
This page shows that two measures go together. It does not show that Infrastructure score changes Slum share of urban population, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Infrastructure score or Slum share of urban population