Gender Development Index vs. Slum share of urban population
Fades after adjusting for income
Across 173 countries, Gender Development Index and Slum share of urban population rise and fall together at r = -0.63 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.31, which could easily be chance (q = 0.089, 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 gender development index, 0.931 to 0.993 ratio of women's HDI to men's, slum share of urban population is better by 16.2% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 7.02% of urban population.
Link before adjusting (r)-0.630p = 1.6e-20 · 173 countries
After adjusting for national income-0.309p = 3.6e-5 · likely range [-0.44, -0.17]
p, allowing for regions0.014standard error 0.091 · 7 regions
Check for luck (q)0.089across 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.
Gender Development Index · ratio of women's HDI to men'slink with income +0.65 · high
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.
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
-0.630
1.6e-20
173
By rank (Spearman), not adjusted
-0.576
1.1e-16
173
Standard (Pearson), adjusted for national income
-0.309
3.6e-5
173
By rank (Spearman), adjusted for national income
-0.132
0.084
173
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 173 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: +0.320 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.338 (without STP) to -0.288 (without MLI). 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: STP (-0.028).
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
-0.19
Europe & Central Asia
47
-0.02
Latin America & Caribbean
25
-0.36
Middle East, North Africa, Afghanistan & Pakistan
21
-0.69
Sub-Saharan Africa
44
-0.39
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
58
-0.42
Middle third
57
-0.14
Richest third
58
+0.05
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
101
-0.28
Muslims
48
-0.38
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
24
-0.18
Never a European colony
20
-0.51
Spain
18
+0.22
United Kingdom
56
-0.25
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 = -0.222 after dropping 11 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 23years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.
Within countries over time, there is no link that is clearly more than chance (r = -0.128, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose gender development index moved did not tend to see their slum share of urban population move with it.
Across countries (adjusted)-0.309for comparison
Within countries-0.380each country vs itself
…and vs the same year elsewhere-0.128q = 0.175 · 153 countries
Year-on-year changes—q = —
Read with care.
Almost all of this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.
Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.
1,652 country-years (one country in one year), 153 countries, 2000–2022, about 11 years each on average. Share of the variation that happens within countries over time: 9% for Gender Development Index, 8% for Slum share of urban population. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.
Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.
With one measure years behind the other
Same year (lag 0): -0.128. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.
The largest value on this curve (-0.22 at lag +8) is higher than the same-year one by 0.09. That gap is not a finding. It is the largest of 21 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.
The data
ratio of women's HDI to men's
% of urban population
Years
Ukraine UKR
1.04
1.1%
2023, 2018
Qatar QAT
1.04
0%
2023, 2022
Antigua and Barbuda ATG
1.03
2.65%
2023, 2022
Mongolia MNG
1.03
17.9%
2023, 2022
Moldova MDA
1.03
6.5%
2023, 2018
Latvia LVA
1.03
0.593%
2023, 2022
Dominican Republic DOM
1.02
11.2%
2023, 2022
Russian Federation RUS
1.02
2.62%
2023, 2022
Estonia EST
1.02
0.345%
2023, 2022
Lithuania LTU
1.02
0.509%
2023, 2022
Uruguay URY
1.02
1.3%
2023, 2022
St. Lucia LCA
1.02
0.441%
2023, 2022
Panama PAN
1.01
16.3%
2023, 2022
Jamaica JAM
1.01
0.883%
2023, 2022
Poland POL
1.01
4.22%
2023, 2022
United Nations Development Programme, Human Development Report 2025 World Bank, World Development Indicators
United Nations Development Programme, Human Development Report Office · via Our World in Data · CC BY 3.0 IGO · updated annual
The HDI worked out for women divided by the HDI worked out for men. It is above 1 where women score higher, as they do in several countries on health. So no "better" direction is set: equality is the aim, and equality is the middle of the scale, not one end of it.
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 2012–2023. 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 Gender Development Index and Slum share of urban population
This page shows that two measures go together. It does not show that Gender Development Index changes Slum share of urban population, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Gender Development Index or Slum share of urban population