Do Pupil-teacher ratio and Slum share of urban population go together across countries?

Across 169 countries the link before adjusting for anything is r = +0.71, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.75.

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

Comparing each country only with itself across 2000-2018, and setting aside what happened worldwide in each year, the link is +0.182 (123 countries, 994 country-years). Using year-to-year changes, it is —.

Does Pupil-teacher ratio 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.

Pupil-teacher ratio vs. Slum share of urban population

Fades after adjusting for income

Across 169 countries, Pupil-teacher ratio and Slum share of urban population rise and fall together at r = +0.71 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.30, which could easily be chance (q = 0.12, 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 pupil-teacher ratio, 14.2 to 30.1 pupils per teacher, slum share of urban population is worse by 21.8% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 10.4% of urban population.

Link before adjusting (r)+0.708p = 5.0e-27 · 169 countries

After adjusting for national income+0.301p = 7.6e-5 · likely range [+0.16, +0.43]

p, allowing for regions0.024standard error 0.100 · 7 regions

Check for luck (q)0.119across 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.

Pupil-teacher ratio · pupils per teacherlink with income -0.83 · 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	+0.708	5.0e-27	169
By rank (Spearman), not adjusted	+0.749	1.1e-31	169
Standard (Pearson), adjusted for national income	+0.301	7.6e-5	169
By rank (Spearman), adjusted for national income	+0.282	0.000	169

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 169 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: +0.408 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.276 (without SLB) to +0.323 (without NRU). 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: SLB (-0.024).

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	25	-0.01
Europe & Central Asia	47	+0.03
Latin America & Caribbean	27	+0.09
Middle East, North Africa, Afghanistan & Pakistan	18	+0.73
Sub-Saharan Africa	44	+0.07

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	57	+0.36
Middle third	56	+0.09
Richest third	56	+0.28

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	99	+0.33
Muslims	43	+0.51

By last european coloniser

	n	r
France	23	+0.11
Never a European colony	20	+0.47
Spain	18	-0.16
United Kingdom	50	+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 = +0.343 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 19years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

The link shows up within countries over time (r = +0.182) but mostly disappears in year-to-year changes (r = —). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)+0.301for comparison

Within countries+0.441each country vs itself

…and vs the same year elsewhere+0.182q = 0.002 · 123 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.

994 country-years (one country in one year), 123 countries, 2000–2018, about 8 years each on average. Share of the variation that happens within countries over time: 5% for Pupil-teacher ratio, 7% 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): +0.182. 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 data

	pupils per teacher	% of urban population	Years
Central African Republic CAF	83.4	68.9%	2016, 2022
Rwanda RWA	59.5	38.3%	2018, 2022
Malawi MWI	58.7	38%	2018, 2022
Chad TCD	56.9	82%	2016, 2022
Mozambique MOZ	55.3	55%	2018, 2022
Ethiopia ETH	55.1	64.3%	2011, 2022
Guinea-Bissau GNB	51.9	59%	2010, 2022
Tanzania TZA	50.6	70.1%	2018, 2022
Angola AGO	50	62.7%	2015, 2022
Afghanistan AFG	48.8	71.6%	2018, 2022
Guinea GIN	47.1	44%	2016, 2022
Cameroon CMR	44.8	32.7%	2018, 2022
Congo, Rep. COG	44.4	75.3%	2012, 2022
Pakistan PAK	44.1	56%	2018, 2022
Uganda UGA	42.7	52.7%	2017, 2022

World Bank, World Development Indicators
World Bank, World Development Indicators

Sources

Pupil-teacher ratio

Pupil-teacher ratio, primary

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

The World Bank's copy stops at 2019.

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 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 Pupil-teacher ratio and Slum share of urban population

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