placestats

Equality score vs. Researchers

Fades after adjusting for income

Across 117 countries, Equality score and Researchers rise and fall together at r = +0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.02, which could easily be chance (q = 0.93, 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. Caution: sign flips when a single country is removed.

Comparing countries across the middle half of researchers, 138 to 2,855 per million people, equality score is better by 0.489 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0178 typical gaps.

Link before adjusting (r)+0.532p = 6.5e-10 · 117 countries
After adjusting for national income+0.020p = 0.831 · likely range [-0.16, +0.20]
p, allowing for regions0.871standard error 0.118 · 7 regions
Check for luck (q)0.932across 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.

Equality score · typical gaps (standard deviations)link with income +0.64 · high
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.

Every country as a dot

Equality score · typical gaps (standard deviations) →Researchers · per million people →-2.511.061.459,472Angola: -1.52 typical gaps (standard deviations), 18.9 per million peopleAlbania: 0.741 typical gaps (standard deviations), 429 per million peopleUnited Arab Emirates: 0.921 typical gaps (standard deviations), 2,607 per million peopleArgentina: -0.0236 typical gaps (standard deviations), 1,313 per million peopleArmenia: 0.843 typical gaps (standard deviations), 942 per million peopleAustralia: 0.473 typical gaps (standard deviations), 4,569 per million peopleAustria: 0.632 typical gaps (standard deviations), 6,634 per million peopleBurundi: -1.63 typical gaps (standard deviations), 22.5 per million peopleBelgium: 0.895 typical gaps (standard deviations), 5,990 per million peopleBurkina Faso: -0.796 typical gaps (standard deviations), 46.6 per million peopleBulgaria: 0.143 typical gaps (standard deviations), 2,401 per million peopleBosnia and Herzegovina: 0.689 typical gaps (standard deviations), 705 per million peopleBelarus: 1.04 typical gaps (standard deviations), 1,568 per million peopleBolivia: 0.0341 typical gaps (standard deviations), 63.1 per million peopleBrazil: -0.529 typical gaps (standard deviations), 903 per million peopleBhutan: 0.8 typical gaps (standard deviations), 236 per million peopleBotswana: -1.27 typical gaps (standard deviations), 180 per million peopleCanada: 0.62 typical gaps (standard deviations), 5,624 per million peopleSwitzerland: 0.491 typical gaps (standard deviations), 6,108 per million peopleChile: -0.0426 typical gaps (standard deviations), 707 per million peopleChina: 0.37 typical gaps (standard deviations), 2,107 per million peopleCongo, Dem. Rep.: -2.33 typical gaps (standard deviations), 10.1 per million peopleCongo, Rep.: -1.38 typical gaps (standard deviations), 127 per million peopleColombia: -0.906 typical gaps (standard deviations), 90.2 per million peopleCabo Verde: -0.374 typical gaps (standard deviations), 125 per million peopleCosta Rica: -0.196 typical gaps (standard deviations), 508 per million peopleCyprus: 0.611 typical gaps (standard deviations), 1,686 per million peopleCzechia: 0.961 typical gaps (standard deviations), 4,505 per million peopleGermany: 0.484 typical gaps (standard deviations), 5,926 per million peopleDenmark: 0.709 typical gaps (standard deviations), 8,838 per million peopleDominican Republic: 0.177 typical gaps (standard deviations), 20 per million peopleAlgeria: 0.852 typical gaps (standard deviations), 1,764 per million peopleEcuador: -0.286 typical gaps (standard deviations), 400 per million peopleEgypt, Arab Rep.: 0.764 typical gaps (standard deviations), 1,120 per million peopleSpain: 0.498 typical gaps (standard deviations), 3,654 per million peopleEstonia: 0.666 typical gaps (standard deviations), 4,800 per million peopleEthiopia: -0.344 typical gaps (standard deviations), 89 per million peopleFinland: 0.858 typical gaps (standard deviations), 8,315 per million peopleFrance: 0.608 typical gaps (standard deviations), 5,369 per million peopleUnited Kingdom: 0.566 typical gaps (standard deviations), 4,473 per million peopleGeorgia: 0.382 typical gaps (standard deviations), 1,896 per million peopleGhana: -1.07 typical gaps (standard deviations), 87.5 per million peopleGambia, The: -0.358 typical gaps (standard deviations), 50.9 per million peopleGreece: 0.503 typical gaps (standard deviations), 5,283 per million peopleGuatemala: -0.409 typical gaps (standard deviations), 1.45 per million peopleHonduras: -0.592 typical gaps (standard deviations), 188 per million peopleCroatia: 0.701 typical gaps (standard deviations), 2,440 per million peopleHungary: 0.656 typical gaps (standard deviations), 4,567 per million peopleIndonesia: 0.358 typical gaps (standard deviations), 395 per million peopleIndia: 0.836 typical gaps (standard deviations), 259 per million peopleIreland: 0.769 typical gaps (standard deviations), 5,753 per million peopleIran, Islamic Rep.: 0.311 typical gaps (standard deviations), 2,240 per million peopleIraq: 0.713 typical gaps (standard deviations), 164 per million peopleIceland: 0.895 typical gaps (standard deviations), 5,953 per million peopleItaly: 0.444 typical gaps (standard deviations), 2,855 per million peopleJordan: 0.499 typical gaps (standard deviations), 576 per million peopleJapan: 0.551 typical gaps (standard deviations), 5,609 per million peopleKazakhstan: 0.76 typical gaps (standard deviations), 758 per million peopleKenya: -0.947 typical gaps (standard deviations), 384 per million peopleKorea, Rep.: 0.545 typical gaps (standard deviations), 9,472 per million peopleSri Lanka: 0.202 typical gaps (standard deviations), 94.5 per million peopleLesotho: -1.22 typical gaps (standard deviations), 32.2 per million peopleLithuania: 0.341 typical gaps (standard deviations), 3,704 per million peopleLuxembourg: 0.505 typical gaps (standard deviations), 5,359 per million peopleLatvia: 0.474 typical gaps (standard deviations), 2,191 per million peopleMorocco: 0.0733 typical gaps (standard deviations), 1,083 per million peopleMoldova: 0.895 typical gaps (standard deviations), 650 per million peopleMadagascar: -1.46 typical gaps (standard deviations), 32.9 per million peopleMexico: -0.0506 typical gaps (standard deviations), 438 per million peopleNorth Macedonia: 0.413 typical gaps (standard deviations), 888 per million peopleMali: -0.544 typical gaps (standard deviations), 26 per million peopleMalta: 0.6 typical gaps (standard deviations), 2,355 per million peopleMyanmar: 0.408 typical gaps (standard deviations), 28 per million peopleMontenegro: 0.403 typical gaps (standard deviations), 764 per million peopleMongolia: 0.623 typical gaps (standard deviations), 535 per million peopleMozambique: -2.51 typical gaps (standard deviations), 44.4 per million peopleMauritius: 0.311 typical gaps (standard deviations), 689 per million peopleMalawi: -1.72 typical gaps (standard deviations), 25.2 per million peopleMalaysia: 0.0997 typical gaps (standard deviations), 1,218 per million peopleNamibia: -1.55 typical gaps (standard deviations), 225 per million peopleNiger: -1.01 typical gaps (standard deviations), 26.9 per million peopleNigeria: -0.587 typical gaps (standard deviations), 22.1 per million peopleNetherlands: 0.958 typical gaps (standard deviations), 6,563 per million peopleNorway: 0.91 typical gaps (standard deviations), 7,451 per million peoplePakistan: -0.0796 typical gaps (standard deviations), 474 per million peoplePanama: -0.497 typical gaps (standard deviations), 250 per million peoplePhilippines: 0.0435 typical gaps (standard deviations), 320 per million peoplePoland: 0.795 typical gaps (standard deviations), 3,675 per million peoplePortugal: 0.48 typical gaps (standard deviations), 5,983 per million peopleParaguay: -0.155 typical gaps (standard deviations), 139 per million peopleWest Bank and Gaza: 0.292 typical gaps (standard deviations), 584 per million peopleQatar: 0.421 typical gaps (standard deviations), 948 per million peopleRomania: 0.708 typical gaps (standard deviations), 1,082 per million peopleRussian Federation: 0.539 typical gaps (standard deviations), 2,639 per million peopleRwanda: -0.82 typical gaps (standard deviations), 133 per million peopleSenegal: -0.103 typical gaps (standard deviations), 572 per million peopleEl Salvador: 0.0329 typical gaps (standard deviations), 55.5 per million peopleSerbia: 0.525 typical gaps (standard deviations), 2,688 per million peopleSlovak Republic: 1.06 typical gaps (standard deviations), 3,507 per million peopleSlovenia: 1.02 typical gaps (standard deviations), 5,616 per million peopleSweden: 0.734 typical gaps (standard deviations), 8,911 per million peopleEswatini: -1.85 typical gaps (standard deviations), 138 per million peopleSyrian Arab Republic: 0.457 typical gaps (standard deviations), 140 per million peopleChad: -0.729 typical gaps (standard deviations), 56.7 per million peopleTogo: -0.634 typical gaps (standard deviations), 57.8 per million peopleThailand: 0.525 typical gaps (standard deviations), 1,592 per million peopleTunisia: 0.484 typical gaps (standard deviations), 1,668 per million peopleTurkiye: -0.0777 typical gaps (standard deviations), 2,645 per million peopleTanzania: -1.21 typical gaps (standard deviations), 19.3 per million peopleUganda: -1.56 typical gaps (standard deviations), 81.3 per million peopleUkraine: 0.967 typical gaps (standard deviations), 586 per million peopleUruguay: 0.135 typical gaps (standard deviations), 938 per million peopleUnited States: 0.00824 typical gaps (standard deviations), 4,937 per million peopleUzbekistan: 0.5 typical gaps (standard deviations), 640 per million peopleViet Nam: 0.323 typical gaps (standard deviations), 836 per million peopleSouth Africa: -1.12 typical gaps (standard deviations), 444 per million peopleZimbabwe: -1.72 typical gaps (standard deviations), 95.1 per million peopleBTNCODDNKINDKORMOZSWESWZUKR

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5326.5e-10117
By rank (Spearman), not adjusted+0.7197.1e-20117
Standard (Pearson), adjusted for national income+0.0200.831117
By rank (Spearman), adjusted for national income+0.3340.000117

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 117 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: +0.512 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.005 (without PAN) to +0.044 (without MOZ). 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: PAN (-0.025).

Fragile: this link may not hold up.
  • sign flips when a single country is removed

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.

nr
Europe & Central Asia44+0.20
Latin America & Caribbean15+0.09
Middle East, North Africa, Afghanistan & Pakistan13+0.12
Sub-Saharan Africa29+0.33

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.

nr
Poorest third39+0.33
Middle third39+0.33
Richest third39+0.18

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.

nr
Christians77+0.08
Muslims26-0.01

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.

nr
France12+0.34
Never a European colony12-0.35
Spain16+0.36
United Kingdom29-0.01

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 = +0.075 after dropping 7 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
Slovak Republic SVK1.063,5072023
Belarus BLR1.041,5682020, 2024
Slovenia SVN1.025,6162023
Ukraine UKR0.9675862020, 2022
Czechia CZE0.9614,5052023
Netherlands NLD0.9586,5632021, 2023
United Arab Emirates ARE0.9212,6072018, 2021
Norway NOR0.917,4512023
Moldova MDA0.8956502023, 2024
Iceland ISL0.8955,9532019, 2023
Belgium BEL0.8955,9902023
Finland FIN0.8588,3152023
Algeria DZA0.8521,7642011, 2024
Armenia ARM0.8439422024
India IND0.8362592022, 2020

wdi
World Bank, World Development Indicators

Sources

Equality score

Equality score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 2 measures from 1 publisher. 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: Extreme poverty rate, Gini index.

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 Equality score and Researchers

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

More links with Equality score or Researchers