placestats

Governance score vs. Researchers

Fades after adjusting for income

Across 127 countries, Governance score and Researchers rise and fall together at r = +0.54 (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 = 0.36, 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 researchers, 138 to 3,181 per million people, governance score is better by 0.58 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.398 typical gaps.

Link before adjusting (r)+0.536p = 8.3e-11 · 127 countries
After adjusting for national income+0.303p = 0.001 · likely range [+0.14, +0.45]
p, allowing for regions0.132standard error 0.174 · 7 regions
Check for luck (q)0.360across 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.

Governance score · typical gaps (standard deviations)link with income +0.53 · moderate
Researchers · per million peoplelink with income +0.85 · proxy

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

Governance score · typical gaps (standard deviations) →Researchers · per million people →-1.391.471.459,472Angola: -0.522 typical gaps (standard deviations), 18.9 per million peopleAlbania: 0.323 typical gaps (standard deviations), 429 per million peopleUnited Arab Emirates: -0.981 typical gaps (standard deviations), 2,607 per million peopleArgentina: 0.815 typical gaps (standard deviations), 1,313 per million peopleArmenia: 0.22 typical gaps (standard deviations), 942 per million peopleAustralia: 1.38 typical gaps (standard deviations), 4,569 per million peopleAustria: 1.33 typical gaps (standard deviations), 6,634 per million peopleAzerbaijan: -1.33 typical gaps (standard deviations), 1,663 per million peopleBurundi: -1.09 typical gaps (standard deviations), 22.5 per million peopleBelgium: 1.41 typical gaps (standard deviations), 5,990 per million peopleBurkina Faso: -0.784 typical gaps (standard deviations), 46.6 per million peopleBulgaria: 0.74 typical gaps (standard deviations), 2,401 per million peopleBahrain: -1.05 typical gaps (standard deviations), 849 per million peopleBosnia and Herzegovina: 0.0899 typical gaps (standard deviations), 705 per million peopleBelarus: -1.26 typical gaps (standard deviations), 1,568 per million peopleBolivia: 0.552 typical gaps (standard deviations), 63.1 per million peopleBrazil: 0.85 typical gaps (standard deviations), 903 per million peopleBhutan: 0.61 typical gaps (standard deviations), 236 per million peopleBotswana: 0.872 typical gaps (standard deviations), 180 per million peopleCanada: 1.36 typical gaps (standard deviations), 5,624 per million peopleSwitzerland: 1.47 typical gaps (standard deviations), 6,108 per million peopleChile: 1.32 typical gaps (standard deviations), 707 per million peopleChina: -1.2 typical gaps (standard deviations), 2,107 per million peopleCongo, Dem. Rep.: -0.824 typical gaps (standard deviations), 10.1 per million peopleCongo, Rep.: -0.925 typical gaps (standard deviations), 127 per million peopleColombia: 0.733 typical gaps (standard deviations), 90.2 per million peopleCabo Verde: 1.15 typical gaps (standard deviations), 125 per million peopleCosta Rica: 1.34 typical gaps (standard deviations), 508 per million peopleCyprus: 1.1 typical gaps (standard deviations), 1,686 per million peopleCzechia: 1.31 typical gaps (standard deviations), 4,505 per million peopleGermany: 1.36 typical gaps (standard deviations), 5,926 per million peopleDenmark: 1.47 typical gaps (standard deviations), 8,838 per million peopleDominican Republic: 0.621 typical gaps (standard deviations), 20 per million peopleAlgeria: -0.695 typical gaps (standard deviations), 1,764 per million peopleEcuador: 0.447 typical gaps (standard deviations), 400 per million peopleEgypt, Arab Rep.: -0.987 typical gaps (standard deviations), 1,120 per million peopleSpain: 1.32 typical gaps (standard deviations), 3,654 per million peopleEstonia: 1.41 typical gaps (standard deviations), 4,800 per million peopleEthiopia: -0.741 typical gaps (standard deviations), 89 per million peopleFinland: 1.38 typical gaps (standard deviations), 8,315 per million peopleFrance: 1.24 typical gaps (standard deviations), 5,369 per million peopleUnited Kingdom: 1.27 typical gaps (standard deviations), 4,473 per million peopleGeorgia: 0.0179 typical gaps (standard deviations), 1,896 per million peopleGhana: 0.845 typical gaps (standard deviations), 87.5 per million peopleGambia, The: 0.362 typical gaps (standard deviations), 50.9 per million peopleGreece: 0.914 typical gaps (standard deviations), 5,283 per million peopleGuatemala: 0.291 typical gaps (standard deviations), 1.45 per million peopleHong Kong SAR, China: -0.536 typical gaps (standard deviations), 5,874 per million peopleHonduras: 0.146 typical gaps (standard deviations), 188 per million peopleCroatia: 0.865 typical gaps (standard deviations), 2,440 per million peopleHungary: 0.225 typical gaps (standard deviations), 4,567 per million peopleIndonesia: 0.125 typical gaps (standard deviations), 395 per million peopleIndia: 0.146 typical gaps (standard deviations), 259 per million peopleIreland: 1.41 typical gaps (standard deviations), 5,753 per million peopleIran, Islamic Rep.: -1.26 typical gaps (standard deviations), 2,240 per million peopleIraq: -0.488 typical gaps (standard deviations), 164 per million peopleIceland: 1.32 typical gaps (standard deviations), 5,953 per million peopleItaly: 1 typical gaps (standard deviations), 2,855 per million peopleJordan: -0.271 typical gaps (standard deviations), 576 per million peopleJapan: 1.28 typical gaps (standard deviations), 5,609 per million peopleKazakhstan: -0.796 typical gaps (standard deviations), 758 per million peopleKenya: 0.217 typical gaps (standard deviations), 384 per million peopleCambodia: -1.01 typical gaps (standard deviations), 30.4 per million peopleKorea, Rep.: 1.05 typical gaps (standard deviations), 9,472 per million peopleKuwait: -0.696 typical gaps (standard deviations), 145 per million peopleSri Lanka: 0.602 typical gaps (standard deviations), 94.5 per million peopleLesotho: 0.671 typical gaps (standard deviations), 32.2 per million peopleLithuania: 1.14 typical gaps (standard deviations), 3,704 per million peopleLuxembourg: 1.38 typical gaps (standard deviations), 5,359 per million peopleLatvia: 1.22 typical gaps (standard deviations), 2,191 per million peopleMorocco: -0.351 typical gaps (standard deviations), 1,083 per million peopleMoldova: 0.5 typical gaps (standard deviations), 650 per million peopleMadagascar: -0.193 typical gaps (standard deviations), 32.9 per million peopleMexico: 0.134 typical gaps (standard deviations), 438 per million peopleNorth Macedonia: 0.352 typical gaps (standard deviations), 888 per million peopleMali: -0.753 typical gaps (standard deviations), 26 per million peopleMalta: 1.12 typical gaps (standard deviations), 2,355 per million peopleMyanmar: -1.39 typical gaps (standard deviations), 28 per million peopleMontenegro: 0.55 typical gaps (standard deviations), 764 per million peopleMongolia: 0.716 typical gaps (standard deviations), 535 per million peopleMozambique: -0.298 typical gaps (standard deviations), 44.4 per million peopleMauritius: 1.07 typical gaps (standard deviations), 689 per million peopleMalawi: 0.576 typical gaps (standard deviations), 25.2 per million peopleMalaysia: 0.169 typical gaps (standard deviations), 1,218 per million peopleNamibia: 0.69 typical gaps (standard deviations), 225 per million peopleNiger: -0.548 typical gaps (standard deviations), 26.9 per million peopleNigeria: -0.0132 typical gaps (standard deviations), 22.1 per million peopleNetherlands: 1.32 typical gaps (standard deviations), 6,563 per million peopleNorway: 1.43 typical gaps (standard deviations), 7,451 per million peopleNew Zealand: 1.32 typical gaps (standard deviations), 5,435 per million peopleOman: -0.665 typical gaps (standard deviations), 739 per million peoplePakistan: -0.437 typical gaps (standard deviations), 474 per million peoplePanama: 0.84 typical gaps (standard deviations), 250 per million peoplePhilippines: 0.0585 typical gaps (standard deviations), 320 per million peoplePapua New Guinea: 0.227 typical gaps (standard deviations), 33.2 per million peoplePoland: 0.996 typical gaps (standard deviations), 3,675 per million peoplePortugal: 1.26 typical gaps (standard deviations), 5,983 per million peopleParaguay: 0.349 typical gaps (standard deviations), 139 per million peopleQatar: -0.908 typical gaps (standard deviations), 948 per million peopleRomania: 0.749 typical gaps (standard deviations), 1,082 per million peopleRussian Federation: -1.14 typical gaps (standard deviations), 2,639 per million peopleRwanda: -0.868 typical gaps (standard deviations), 133 per million peopleSaudi Arabia: -1.14 typical gaps (standard deviations), 1,235 per million peopleSenegal: 0.683 typical gaps (standard deviations), 572 per million peopleSingapore: 0.0449 typical gaps (standard deviations), 8,782 per million peopleEl Salvador: -0.352 typical gaps (standard deviations), 55.5 per million peopleSerbia: -0.0738 typical gaps (standard deviations), 2,688 per million peopleSlovak Republic: 1.02 typical gaps (standard deviations), 3,507 per million peopleSlovenia: 1.19 typical gaps (standard deviations), 5,616 per million peopleSweden: 1.42 typical gaps (standard deviations), 8,911 per million peopleEswatini: -1.05 typical gaps (standard deviations), 138 per million peopleSyrian Arab Republic: -1.05 typical gaps (standard deviations), 140 per million peopleChad: -0.999 typical gaps (standard deviations), 56.7 per million peopleTogo: -0.371 typical gaps (standard deviations), 57.8 per million peopleThailand: -0.383 typical gaps (standard deviations), 1,592 per million peopleTrinidad and Tobago: 1.03 typical gaps (standard deviations), 493 per million peopleTunisia: -0.257 typical gaps (standard deviations), 1,668 per million peopleTurkiye: -0.691 typical gaps (standard deviations), 2,645 per million peopleTanzania: -0.315 typical gaps (standard deviations), 19.3 per million peopleUganda: -0.646 typical gaps (standard deviations), 81.3 per million peopleUkraine: -0.0525 typical gaps (standard deviations), 586 per million peopleUruguay: 1.39 typical gaps (standard deviations), 938 per million peopleUnited States: 0.998 typical gaps (standard deviations), 4,937 per million peopleUzbekistan: -1.04 typical gaps (standard deviations), 640 per million peopleViet Nam: -0.933 typical gaps (standard deviations), 836 per million peopleSouth Africa: 0.883 typical gaps (standard deviations), 444 per million peopleZimbabwe: -0.609 typical gaps (standard deviations), 95.1 per million peopleBDICHLCPVCRIDNKHKGKORMMRSGPSWESYR

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5368.3e-11127
By rank (Spearman), not adjusted+0.5223.0e-10127
Standard (Pearson), adjusted for national income+0.3030.001127
By rank (Spearman), adjusted for national income+0.1100.221127

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 127 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: +0.233 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.273 (without QAT) to +0.334 (without SGP). 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: SGP (+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.

nr
East Asia & Pacific16+0.17
Europe & Central Asia45+0.30
Latin America & Caribbean16+0.32
Middle East, North Africa, Afghanistan & Pakistan16+0.20
Sub-Saharan Africa29+0.31

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 third43-0.04
Middle third42-0.35
Richest third42+0.60

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
Christians79+0.22
Muslims30-0.23

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
France13+0.51
Never a European colony15+0.55
Spain16+0.03
United Kingdom35+0.31

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 = +0.309 after dropping 5 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
Denmark DNK1.478,8382025, 2023
Switzerland CHE1.476,1082025, 2023
Norway NOR1.437,4512025, 2023
Sweden SWE1.428,9112025, 2023
Ireland IRL1.415,7532025, 2023
Belgium BEL1.415,9902025, 2023
Estonia EST1.414,8002025, 2023
Uruguay URY1.399382025, 2024
Finland FIN1.388,3152025, 2023
Australia AUS1.384,5692025, 2010
Luxembourg LUX1.385,3592025, 2023
Germany DEU1.365,9262025, 2023
Canada CAN1.365,6242025, 2022
Costa Rica CRI1.345082025, 2023
Austria AUT1.336,6342025, 2023

fh; un_sdg; vdem; wdi; wgi
World Bank, World Development Indicators

Sources

Governance score

Governance score

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

The average of 19 measures from 5 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: Civil liberties (FH), Political rights (FH), Pre-trial detainee share, Civil society participation, Political corruption, Electoral democracy, Freedom of association, Freedom of expression, Free and fair elections, Liberal democracy, Participatory democracy, Political civil liberties, Birth registration, Control of corruption, Government effectiveness, Political stability, Rule of law, Regulatory quality, Voice and accountability.

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 Governance score and Researchers

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

More links with Governance score or Researchers