Across 191 countries, Education score and Migrant share of population rise and fall together at r = +0.34 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) flips the link to -0.27 (q = 0.031, allowing for the many pairs tested). The first figure pointed the other way only as richer and poorer countries differ. Caution: correlation moves sharply once outliers are trimmed.
Comparing countries across the middle half of migrant share of population, 1.55% to 14.2% of population, education score is better by 0.172 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 0.0969 typical gaps the other way.
Link before adjusting (r)+0.337p = 1.9e-6 · 191 countries
After adjusting for national income-0.266p = 0.000 · likely range [-0.39, -0.13]
p, allowing for regions0.044standard error 0.105 · 7 regions
Check for luck (q)0.031across 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.
Education score · typical gaps (standard deviations)link with income +0.81 · proxy
Migrant share of population · % of populationlink with income +0.73 · 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
+0.337
1.9e-6
191
By rank (Spearman), not adjusted
+0.550
1.8e-16
191
Standard (Pearson), adjusted for national income
-0.266
0.000
191
By rank (Spearman), adjusted for national income
-0.097
0.185
191
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 191 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: +0.071 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.293 (without JOR) to -0.244 (without KWT). 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: JOR (-0.026).
Fragile: this link may not hold up.
correlation moves sharply 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.
n
r
East Asia & Pacific
31
+0.06
Europe & Central Asia
50
-0.08
Latin America & Caribbean
34
-0.19
Middle East, North Africa, Afghanistan & Pakistan
21
-0.20
Sub-Saharan Africa
46
-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.
n
r
Poorest third
64
-0.05
Middle third
63
-0.02
Richest third
64
-0.37
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
109
-0.22
Muslims
48
-0.34
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
-0.28
Never a European colony
21
-0.48
Spain
20
-0.64
United Kingdom
62
-0.19
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 = -0.108 after dropping 22 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
typical gaps (standard deviations)
% of population
Years
New Zealand NZL
1.44
28.2%
2023, 2024
Hong Kong SAR, China HKG
1.34
41.3%
2024
Greece GRC
1.31
14.2%
2023, 2024
Netherlands NLD
1.27
16.2%
2023, 2024
Iceland ISL
1.26
25.1%
2023, 2024
Singapore SGP
1.26
48.7%
2023, 2024
Belgium BEL
1.25
20%
2023, 2024
United Kingdom GBR
1.19
17.1%
2023, 2024
Ireland IRL
1.18
23.1%
2022, 2024
Slovenia SVN
1.18
14.9%
2023, 2024
St. Kitts and Nevis KNA
1.13
17%
2016, 2024
United States USA
1.11
15.2%
2022, 2024
Czechia CZE
1.08
9.5%
2023, 2024
Canada CAN
1.06
22.2%
2023, 2024
Japan JPN
1.04
2.8%
2023, 2024
uis; undp_hdr; wdi World Bank, World Development Indicators
PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel
The average of 10 measures from 3 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: Primary completion, Expected schooling, Mean years of schooling, Adult literacy, Pupil-teacher ratio, Primary enrollment, Out of school, primary, Secondary enrollment, Tertiary enrollment, Researchers.
UN Population Division, International Migrant Stock · CC BY 4.0 · updated quarterly
People living outside the country they were born in, as a share of the population. It counts people at one moment, while the net migration already on the site counts moves over a year. Published every five years until 2020, and for 2024.
How this was computed
Data used: most recent value in 2010-2025; values come from 2010–2025. 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 Education score and Migrant share of population
This page shows that two measures go together. It does not show that Education score changes Migrant share of population, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Education score or Migrant share of population