placestats

Infrastructure score vs. Researchers

Fades after adjusting for income

Across 131 countries, Infrastructure score and Researchers rise and fall together at r = +0.64 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.04, which could easily be chance (q = 0.86, 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, 139 to 3,181 per million people, infrastructure score is better by 0.518 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0169 typical gaps.

Link before adjusting (r)+0.638p = 2.6e-16 · 131 countries
After adjusting for national income+0.037p = 0.672 · likely range [-0.14, +0.21]
p, allowing for regions0.745standard error 0.110 · 7 regions
Check for luck (q)0.857across 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.

Infrastructure score · typical gaps (standard deviations)link with income +0.90 · 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.

Every country as a dot

Infrastructure score · typical gaps (standard deviations) →Researchers · per million people →-0.8351.721.459,472Angola: -0.152 typical gaps (standard deviations), 18.9 per million peopleAlbania: 1.14 typical gaps (standard deviations), 429 per million peopleUnited Arab Emirates: 1.61 typical gaps (standard deviations), 2,607 per million peopleArgentina: 1.25 typical gaps (standard deviations), 1,313 per million peopleArmenia: 1.09 typical gaps (standard deviations), 942 per million peopleAustralia: 1.5 typical gaps (standard deviations), 4,569 per million peopleAustria: 1.37 typical gaps (standard deviations), 6,634 per million peopleAzerbaijan: 1.19 typical gaps (standard deviations), 1,663 per million peopleBurundi: -0.835 typical gaps (standard deviations), 22.5 per million peopleBelgium: 1.64 typical gaps (standard deviations), 5,990 per million peopleBurkina Faso: -0.245 typical gaps (standard deviations), 46.6 per million peopleBulgaria: 1.25 typical gaps (standard deviations), 2,401 per million peopleBahrain: 1.17 typical gaps (standard deviations), 849 per million peopleBosnia and Herzegovina: 1.29 typical gaps (standard deviations), 705 per million peopleBelarus: 1.48 typical gaps (standard deviations), 1,568 per million peopleBolivia: 0.886 typical gaps (standard deviations), 63.1 per million peopleBrazil: 1.18 typical gaps (standard deviations), 903 per million peopleBrunei Darussalam: 1.22 typical gaps (standard deviations), 506 per million peopleBhutan: 0.847 typical gaps (standard deviations), 236 per million peopleBotswana: 0.5 typical gaps (standard deviations), 180 per million peopleCanada: 1.56 typical gaps (standard deviations), 5,624 per million peopleSwitzerland: 1.7 typical gaps (standard deviations), 6,108 per million peopleChile: 1.25 typical gaps (standard deviations), 707 per million peopleChina: 1.66 typical gaps (standard deviations), 2,107 per million peopleCongo, Dem. Rep.: -0.672 typical gaps (standard deviations), 10.1 per million peopleCongo, Rep.: 0.312 typical gaps (standard deviations), 127 per million peopleColombia: 1.01 typical gaps (standard deviations), 90.2 per million peopleCabo Verde: 0.806 typical gaps (standard deviations), 125 per million peopleCosta Rica: 1.19 typical gaps (standard deviations), 508 per million peopleCyprus: 1.47 typical gaps (standard deviations), 1,686 per million peopleCzechia: 1.48 typical gaps (standard deviations), 4,505 per million peopleGermany: 1.65 typical gaps (standard deviations), 5,926 per million peopleDenmark: 1.65 typical gaps (standard deviations), 8,838 per million peopleDominican Republic: 0.956 typical gaps (standard deviations), 20 per million peopleAlgeria: 0.932 typical gaps (standard deviations), 1,764 per million peopleEcuador: 0.989 typical gaps (standard deviations), 400 per million peopleEgypt, Arab Rep.: 0.864 typical gaps (standard deviations), 1,120 per million peopleSpain: 1.55 typical gaps (standard deviations), 3,654 per million peopleEstonia: 1.45 typical gaps (standard deviations), 4,800 per million peopleEthiopia: -0.309 typical gaps (standard deviations), 89 per million peopleFinland: 1.48 typical gaps (standard deviations), 8,315 per million peopleFrance: 1.7 typical gaps (standard deviations), 5,369 per million peopleUnited Kingdom: 1.6 typical gaps (standard deviations), 4,473 per million peopleGeorgia: 1.29 typical gaps (standard deviations), 1,896 per million peopleGhana: 0.59 typical gaps (standard deviations), 87.5 per million peopleGambia, The: 0.215 typical gaps (standard deviations), 50.9 per million peopleGreece: 1.57 typical gaps (standard deviations), 5,283 per million peopleGuatemala: 0.576 typical gaps (standard deviations), 1.45 per million peopleHong Kong SAR, China: 1.57 typical gaps (standard deviations), 5,874 per million peopleHonduras: 0.417 typical gaps (standard deviations), 188 per million peopleCroatia: 1.28 typical gaps (standard deviations), 2,440 per million peopleHungary: 1.5 typical gaps (standard deviations), 4,567 per million peopleIndonesia: 0.742 typical gaps (standard deviations), 395 per million peopleIndia: 0.527 typical gaps (standard deviations), 259 per million peopleIreland: 1.43 typical gaps (standard deviations), 5,753 per million peopleIran, Islamic Rep.: 0.97 typical gaps (standard deviations), 2,240 per million peopleIraq: 1.04 typical gaps (standard deviations), 164 per million peopleIceland: 1.53 typical gaps (standard deviations), 5,953 per million peopleItaly: 1.36 typical gaps (standard deviations), 2,855 per million peopleJordan: 0.776 typical gaps (standard deviations), 576 per million peopleJapan: 1.45 typical gaps (standard deviations), 5,609 per million peopleKazakhstan: 1.09 typical gaps (standard deviations), 758 per million peopleKenya: 0.22 typical gaps (standard deviations), 384 per million peopleCambodia: 0.689 typical gaps (standard deviations), 30.4 per million peopleKorea, Rep.: 1.72 typical gaps (standard deviations), 9,472 per million peopleKuwait: 0.878 typical gaps (standard deviations), 145 per million peopleSri Lanka: 0.669 typical gaps (standard deviations), 94.5 per million peopleLesotho: -0.0187 typical gaps (standard deviations), 32.2 per million peopleLithuania: 1.29 typical gaps (standard deviations), 3,704 per million peopleLuxembourg: 1.59 typical gaps (standard deviations), 5,359 per million peopleLatvia: 1.28 typical gaps (standard deviations), 2,191 per million peopleMacao SAR, China: 1.33 typical gaps (standard deviations), 4,634 per million peopleMorocco: 0.928 typical gaps (standard deviations), 1,083 per million peopleMoldova: 1.23 typical gaps (standard deviations), 650 per million peopleMadagascar: -0.434 typical gaps (standard deviations), 32.9 per million peopleMexico: 1.13 typical gaps (standard deviations), 438 per million peopleNorth Macedonia: 1.36 typical gaps (standard deviations), 888 per million peopleMali: -0.495 typical gaps (standard deviations), 26 per million peopleMalta: 1.63 typical gaps (standard deviations), 2,355 per million peopleMyanmar: 0.298 typical gaps (standard deviations), 28 per million peopleMontenegro: 1.37 typical gaps (standard deviations), 764 per million peopleMongolia: 1.03 typical gaps (standard deviations), 535 per million peopleMozambique: -0.592 typical gaps (standard deviations), 44.4 per million peopleMauritius: 1.17 typical gaps (standard deviations), 689 per million peopleMalawi: -0.672 typical gaps (standard deviations), 25.2 per million peopleMalaysia: 1.09 typical gaps (standard deviations), 1,218 per million peopleNamibia: 0.203 typical gaps (standard deviations), 225 per million peopleNiger: -0.7 typical gaps (standard deviations), 26.9 per million peopleNigeria: -0.0861 typical gaps (standard deviations), 22.1 per million peopleNetherlands: 1.63 typical gaps (standard deviations), 6,563 per million peopleNorway: 1.68 typical gaps (standard deviations), 7,451 per million peopleNew Zealand: 1.51 typical gaps (standard deviations), 5,435 per million peopleOman: 1.03 typical gaps (standard deviations), 739 per million peoplePakistan: 0.386 typical gaps (standard deviations), 474 per million peoplePanama: 0.927 typical gaps (standard deviations), 250 per million peoplePhilippines: 0.696 typical gaps (standard deviations), 320 per million peoplePapua New Guinea: -0.611 typical gaps (standard deviations), 33.2 per million peoplePoland: 1.24 typical gaps (standard deviations), 3,675 per million peoplePuerto Rico (US): 1.14 typical gaps (standard deviations), 332 per million peoplePortugal: 1.6 typical gaps (standard deviations), 5,983 per million peopleParaguay: 0.928 typical gaps (standard deviations), 139 per million peopleWest Bank and Gaza: 0.774 typical gaps (standard deviations), 584 per million peopleQatar: 1.06 typical gaps (standard deviations), 948 per million peopleRomania: 1.45 typical gaps (standard deviations), 1,082 per million peopleRussian Federation: 1.32 typical gaps (standard deviations), 2,639 per million peopleRwanda: 0.0662 typical gaps (standard deviations), 133 per million peopleSaudi Arabia: 1.67 typical gaps (standard deviations), 1,235 per million peopleSenegal: 0.482 typical gaps (standard deviations), 572 per million peopleSingapore: 1.33 typical gaps (standard deviations), 8,782 per million peopleEl Salvador: 0.822 typical gaps (standard deviations), 55.5 per million peopleSerbia: 1.38 typical gaps (standard deviations), 2,688 per million peopleSlovak Republic: 1.4 typical gaps (standard deviations), 3,507 per million peopleSlovenia: 1.42 typical gaps (standard deviations), 5,616 per million peopleSweden: 1.57 typical gaps (standard deviations), 8,911 per million peopleEswatini: 0.534 typical gaps (standard deviations), 138 per million peopleSyrian Arab Republic: 0.27 typical gaps (standard deviations), 140 per million peopleChad: -0.72 typical gaps (standard deviations), 56.7 per million peopleTogo: -0.0274 typical gaps (standard deviations), 57.8 per million peopleThailand: 1.06 typical gaps (standard deviations), 1,592 per million peopleTrinidad and Tobago: 1.22 typical gaps (standard deviations), 493 per million peopleTunisia: 0.956 typical gaps (standard deviations), 1,668 per million peopleTurkiye: 1.19 typical gaps (standard deviations), 2,645 per million peopleTanzania: -0.0741 typical gaps (standard deviations), 19.3 per million peopleUganda: -0.319 typical gaps (standard deviations), 81.3 per million peopleUkraine: 0.97 typical gaps (standard deviations), 586 per million peopleUruguay: 1.41 typical gaps (standard deviations), 938 per million peopleUnited States: 1.53 typical gaps (standard deviations), 4,937 per million peopleUzbekistan: 1.42 typical gaps (standard deviations), 640 per million peopleViet Nam: 1.17 typical gaps (standard deviations), 836 per million peopleSouth Africa: 0.709 typical gaps (standard deviations), 444 per million peopleZimbabwe: 0.0863 typical gaps (standard deviations), 95.1 per million peopleBDIDNKKORMNENERSAUSGPSWETCDUZB

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6382.6e-16131
By rank (Spearman), not adjusted+0.8931.4e-46131
Standard (Pearson), adjusted for national income+0.0370.672131
By rank (Spearman), adjusted for national income+0.5711.3e-12131

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: +0.600 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.009 (without QAT) to +0.082 (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: QAT (-0.047).

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
East Asia & Pacific18+0.12
Europe & Central Asia45+0.48
Latin America & Caribbean17+0.65
Middle East, North Africa, Afghanistan & Pakistan17+0.57
Sub-Saharan Africa29+0.42

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 third44+0.40
Middle third43+0.43
Richest third44+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.

nr
Christians80-0.09
Muslims32+0.37

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
France13-0.18
Never a European colony15+0.14
Spain16+0.64
United Kingdom36+0.12

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 = +0.163 after dropping 12 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
Korea, Rep. KOR1.729,4722024, 2023
Switzerland CHE1.76,1082024, 2023
France FRA1.75,3692024, 2023
Norway NOR1.687,4512024, 2023
Saudi Arabia SAU1.671,2352024
China CHN1.662,1072024, 2023
Denmark DNK1.658,8382024, 2023
Germany DEU1.655,9262024, 2023
Belgium BEL1.645,9902024, 2023
Malta MLT1.632,3552024, 2023
Netherlands NLD1.636,5632024, 2023
United Arab Emirates ARE1.612,6072024, 2021
Portugal PRT1.65,9832024, 2023
United Kingdom GBR1.64,4732024, 2017
Luxembourg LUX1.595,3592024, 2023

wdi
World Bank, World Development Indicators

Sources

Infrastructure score

Infrastructure score

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

The average of 5 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: Electricity access, Slum share of urban population, Mobile subscriptions, Fixed broadband, Internet users.

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 2010–2024. 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 Infrastructure score and Researchers

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

More links with Infrastructure score or Researchers