Do Education score and Researchers go together across countries?

Across 131 countries the link before adjusting for anything is r = +0.63, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.86.

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

There are not enough years of data within countries to check this over time.

Does Education score cause Researchers?

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.

Education score vs. Researchers

Fades after adjusting for income

Across 131 countries, Education score and Researchers 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 +0.11, which could easily be chance (q = 0.68, 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. Researchers is one of the measures Education 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 researchers, 139 to 3,181 per million people, education score is better by 0.456 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0532 typical gaps.

Link before adjusting (r)+0.633p = 4.9e-16 · 131 countries

After adjusting for national income+0.108p = 0.222 · likely range [-0.07, +0.27]

p, allowing for regions0.471standard error 0.140 · 7 regions

Check for luck (q)0.679across 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

Researchers · per million peoplelink with income +0.85 · proxy

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. Researchers is one of the measures Education 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	+0.633	4.9e-16	131
By rank (Spearman), not adjusted	+0.864	2.7e-40	131
Standard (Pearson), adjusted for national income	+0.108	0.222	131
By rank (Spearman), adjusted for national income	+0.523	1.7e-10	131

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 131 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: +0.525 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.077 (without BRN) to +0.126 (without TCD). 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: BRN (-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	18	+0.44
Europe & Central Asia	45	+0.35
Latin America & Caribbean	17	+0.57
Middle East, North Africa, Afghanistan & Pakistan	17	+0.63
Sub-Saharan Africa	29	+0.03

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	44	+0.20
Middle third	43	+0.50
Richest third	44	+0.59

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	80	-0.03
Muslims	32	+0.44

By last european coloniser

	n	r
France	13	-0.08
Never a European colony	15	+0.14
Spain	16	+0.48
United Kingdom	36	+0.39

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 = +0.247 after dropping 10 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)	per million people	Years
New Zealand NZL	1.44	5,435	2023
Hong Kong SAR, China HKG	1.34	5,874	2024
Greece GRC	1.31	5,283	2023
Netherlands NLD	1.27	6,563	2023
Iceland ISL	1.26	5,953	2023
Singapore SGP	1.26	8,782	2023, 2022
Belgium BEL	1.25	5,990	2023
United Kingdom GBR	1.19	4,473	2023, 2017
Ireland IRL	1.18	5,753	2022, 2023
Slovenia SVN	1.18	5,616	2023
United States USA	1.11	4,937	2022
Czechia CZE	1.08	4,505	2023
Canada CAN	1.06	5,624	2023, 2022
Japan JPN	1.04	5,609	2023
Argentina ARG	1.03	1,313	2023

uis; undp_hdr; wdi
World Bank, World Development Indicators

Sources

Education score

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.

Researchers

Researchers in R&D (per million people)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

Researchers counted as full-time equivalents (two half-time researchers count as one), from national research surveys that many countries run only now and then. So coverage is thin outside the OECD.

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 Researchers

This page shows that two measures go together. It does not show that Education score changes Researchers, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.