Do Human capital (PWT) and Slum share of urban population go together across countries?

Across 138 countries the link before adjusting for anything is r = -0.79, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.79.

Does the link hold within countries over time, not just across countries?

Comparing each country only with itself across 2000-2022, and setting aside what happened worldwide in each year, the link is -0.071 (128 countries, 1,465 country-years). Using year-to-year changes, it is —.

Does Human capital (PWT) cause Slum share of urban population?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Human capital (PWT) vs. Slum share of urban population

Fades after adjusting for income

Across 138 countries, Human capital (PWT) and Slum share of urban population rise and fall together at r = -0.79 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.37, which could easily be chance (q = 0.055, 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 human capital (PWT), 2.16 to 3.3 points, slum share of urban population is better by 32.7% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 16.6% of urban population.

Link before adjusting (r)-0.793p = 4.2e-31 · 138 countries

After adjusting for national income-0.374p = 6.8e-6 · likely range [-0.51, -0.22]

p, allowing for regions0.006standard error 0.091 · 7 regions

Check for luck (q)0.055across 18,622 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.

Human capital (PWT) · index (1 = no schooling)link with income +0.82 · 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.

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	-0.793	4.2e-31	138
By rank (Spearman), not adjusted	-0.789	1.4e-30	138
Standard (Pearson), adjusted for national income	-0.374	6.8e-6	138
By rank (Spearman), adjusted for national income	-0.323	0.000	138

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 138 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: +0.419 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.392 (without MAR) to -0.339 (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 (+0.034).

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	17	-0.54
Europe & Central Asia	40	-0.37
Latin America & Caribbean	22	-0.01
Middle East, North Africa, Afghanistan & Pakistan	15	-0.12
Sub-Saharan Africa	37	-0.28

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	46	-0.49
Middle third	46	-0.14
Richest third	46	-0.25

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	86	-0.22
Muslims	34	-0.61

By last European coloniser

	n	r
France	20	-0.45
Never a European colony	15	-0.72
Spain	18	+0.35
United Kingdom	43	-0.20

By official ideology

	n	r
Nationalist	19	-0.42
No strong official ideology	97	-0.39

By party system

	n	r
A few parties banned	20	-0.68
No parties banned	101	-0.29

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 = -0.374 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.

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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 = -0.071, 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 human capital (PWT) moved did not tend to see their slum share of urban population move with it.

Across countries (adjusted)-0.374for comparison

Within countries-0.474each country vs itself

…and vs the same year elsewhere-0.071q = 0.430 · 128 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.

1,465 country-years (one country in one year), 128 countries, 2000–2022, about 11 years each on average. Share of the variation that happens within countries over time: 4% for Human capital (PWT), 9% 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): -0.071. 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.

This curve peaks at the edge of the window (lag +10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.

The data

	index (1 = no schooling)	% of urban population	Years
Slovak Republic SVK	3.89	0.066%	2023, 2022
Korea, Rep. KOR	3.87	4.5%	2023, 2022
United Kingdom GBR	3.85	0.163%	2023, 2022
United States USA	3.83	0.0932%	2023, 2022
Croatia HRV	3.81	0.527%	2023, 2022
Czechia CZE	3.78	0.0273%	2023, 2022
Canada CAN	3.77	1.1%	2023, 2022
Norway NOR	3.75	0%	2023, 2022
Switzerland CHE	3.75	0%	2023, 2022
Germany DEU	3.71	0%	2023, 2022
Denmark DNK	3.67	0%	2023, 2022
Japan JPN	3.66	2%	2023, 2022
Slovenia SVN	3.66	0.527%	2023, 2022
Australia AUS	3.61	0.035%	2023, 2022
Finland FIN	3.6	0%	2023, 2022

Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table", American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt. Data: Penn World Table version 11.0, Groningen Growth and Development Centre, doi:10.34894/FABVLR.
World Bank, World Development Indicators

Sources

Human capital (PWT)

Human capital index, from years of schooling (Penn World Table)

Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)

"Human capital" means the skills and knowledge workers bring to their jobs. This index measures it from schooling alone. It starts from how many years adults spent in school, then counts the early years for more than the later ones, following studies of how much higher pay tends to be with each extra year. A score of 1 means no schooling; the US is about 3.8. It uses the same schooling figures as mean years of schooling, so the two are close by construction. For that reason it is left out of the education score, which already counts years of schooling.

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 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 18,622 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Human capital (PWT) and Slum share of urban population

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