placestats

Education score vs. Mobile subscriptions

Holds up after adjusting for income

Across 190 countries, Education score and Mobile subscriptions rise and fall together at r = +0.42 (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 (q = 0.041, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a very weak positive link remains. Caution: direction is not consistent across regions.

Comparing countries across the middle half of mobile subscriptions, 94.1 to 133 per 100 people, education score is better by 0.2 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0349 typical gaps.

Link before adjusting (r)+0.420p = 1.5e-9 · 190 countries
After adjusting for national income+0.112p = 0.124 · likely range [-0.03, +0.25]
p, allowing for regions0.093standard error 0.056 · 7 regions
Check for luck (q)0.041across 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
Mobile subscriptions · per 100 peoplelink with income +0.58 · moderate

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

Every country as a dot

Education score · typical gaps (standard deviations) →Mobile subscriptions · per 100 people →-2.11.4420574Aruba: 0.697 typical gaps (standard deviations), 131 per 100 peopleAfghanistan: -0.557 typical gaps (standard deviations), 60.1 per 100 peopleAngola: -0.482 typical gaps (standard deviations), 69.7 per 100 peopleAlbania: 0.923 typical gaps (standard deviations), 89.1 per 100 peopleAndorra: 0.892 typical gaps (standard deviations), 156 per 100 peopleUnited Arab Emirates: 0.911 typical gaps (standard deviations), 203 per 100 peopleArgentina: 1.03 typical gaps (standard deviations), 140 per 100 peopleArmenia: 0.65 typical gaps (standard deviations), 135 per 100 peopleAntigua and Barbuda: 0.912 typical gaps (standard deviations), 201 per 100 peopleAustralia: 1.03 typical gaps (standard deviations), 113 per 100 peopleAustria: 1 typical gaps (standard deviations), 124 per 100 peopleAzerbaijan: 0.629 typical gaps (standard deviations), 110 per 100 peopleBurundi: -0.587 typical gaps (standard deviations), 63.2 per 100 peopleBelgium: 1.25 typical gaps (standard deviations), 104 per 100 peopleBenin: -0.586 typical gaps (standard deviations), 126 per 100 peopleBurkina Faso: -0.582 typical gaps (standard deviations), 117 per 100 peopleBangladesh: 0.335 typical gaps (standard deviations), 108 per 100 peopleBulgaria: 0.794 typical gaps (standard deviations), 118 per 100 peopleBahrain: 0.537 typical gaps (standard deviations), 160 per 100 peopleBahamas, The: 0.202 typical gaps (standard deviations), 96.7 per 100 peopleBosnia and Herzegovina: 0.607 typical gaps (standard deviations), 121 per 100 peopleBelarus: 0.722 typical gaps (standard deviations), 131 per 100 peopleBelize: 0.166 typical gaps (standard deviations), 67.2 per 100 peopleBermuda: 0.693 typical gaps (standard deviations), 96.7 per 100 peopleBolivia: 0.634 typical gaps (standard deviations), 98 per 100 peopleBrazil: 0.731 typical gaps (standard deviations), 102 per 100 peopleBarbados: 0.581 typical gaps (standard deviations), 115 per 100 peopleBrunei Darussalam: 0.499 typical gaps (standard deviations), 118 per 100 peopleBhutan: 0.277 typical gaps (standard deviations), 99.8 per 100 peopleBotswana: 0.469 typical gaps (standard deviations), 164 per 100 peopleCentral African Republic: -1.65 typical gaps (standard deviations), 38.8 per 100 peopleCanada: 1.06 typical gaps (standard deviations), 94.1 per 100 peopleSwitzerland: 0.92 typical gaps (standard deviations), 129 per 100 peopleChile: 0.979 typical gaps (standard deviations), 133 per 100 peopleChina: 0.674 typical gaps (standard deviations), 132 per 100 peopleCote d'Ivoire: -0.0434 typical gaps (standard deviations), 184 per 100 peopleCameroon: -0.125 typical gaps (standard deviations), 108 per 100 peopleCongo, Dem. Rep.: -0.251 typical gaps (standard deviations), 58.5 per 100 peopleCongo, Rep.: 0.119 typical gaps (standard deviations), 95.5 per 100 peopleColombia: 0.63 typical gaps (standard deviations), 174 per 100 peopleComoros: 0.00771 typical gaps (standard deviations), 110 per 100 peopleCabo Verde: -0.216 typical gaps (standard deviations), 112 per 100 peopleCosta Rica: 0.629 typical gaps (standard deviations), 136 per 100 peopleCyprus: 1.02 typical gaps (standard deviations), 156 per 100 peopleCzechia: 1.08 typical gaps (standard deviations), 127 per 100 peopleGermany: 0.938 typical gaps (standard deviations), 129 per 100 peopleDjibouti: -0.825 typical gaps (standard deviations), 48.5 per 100 peopleDominica: 0.668 typical gaps (standard deviations), 85.2 per 100 peopleDenmark: 0.941 typical gaps (standard deviations), 127 per 100 peopleDominican Republic: 0.651 typical gaps (standard deviations), 93.7 per 100 peopleAlgeria: 0.663 typical gaps (standard deviations), 115 per 100 peopleEcuador: 0.722 typical gaps (standard deviations), 102 per 100 peopleEgypt, Arab Rep.: 0.489 typical gaps (standard deviations), 97.1 per 100 peopleSpain: 0.983 typical gaps (standard deviations), 130 per 100 peopleEstonia: 0.841 typical gaps (standard deviations), 151 per 100 peopleEthiopia: -0.777 typical gaps (standard deviations), 65.1 per 100 peopleFinland: 1.02 typical gaps (standard deviations), 126 per 100 peopleFiji: 0.887 typical gaps (standard deviations), 574 per 100 peopleFrance: 0.907 typical gaps (standard deviations), 117 per 100 peopleMicronesia, Fed. Sts.: -0.00832 typical gaps (standard deviations), 20 per 100 peopleGabon: -0.00264 typical gaps (standard deviations), 125 per 100 peopleUnited Kingdom: 1.19 typical gaps (standard deviations), 122 per 100 peopleGeorgia: 0.697 typical gaps (standard deviations), 161 per 100 peopleGhana: 0.168 typical gaps (standard deviations), 114 per 100 peopleGuinea: -0.598 typical gaps (standard deviations), 97.9 per 100 peopleGambia, The: -0.345 typical gaps (standard deviations), 126 per 100 peopleGuinea-Bissau: -1.3 typical gaps (standard deviations), 147 per 100 peopleEquatorial Guinea: -0.759 typical gaps (standard deviations), 49.5 per 100 peopleGreece: 1.31 typical gaps (standard deviations), 114 per 100 peopleGrenada: 0.804 typical gaps (standard deviations), 95.8 per 100 peopleGuatemala: 0.22 typical gaps (standard deviations), 113 per 100 peopleGuyana: 0.39 typical gaps (standard deviations), 113 per 100 peopleHong Kong SAR, China: 1.34 typical gaps (standard deviations), 365 per 100 peopleHonduras: 0.0516 typical gaps (standard deviations), 70.9 per 100 peopleCroatia: 0.985 typical gaps (standard deviations), 122 per 100 peopleHungary: 0.83 typical gaps (standard deviations), 105 per 100 peopleIndonesia: 0.591 typical gaps (standard deviations), 123 per 100 peopleIndia: 0.605 typical gaps (standard deviations), 79.4 per 100 peopleIreland: 1.18 typical gaps (standard deviations), 113 per 100 peopleIran, Islamic Rep.: 0.667 typical gaps (standard deviations), 174 per 100 peopleIraq: -0.133 typical gaps (standard deviations), 100 per 100 peopleIceland: 1.26 typical gaps (standard deviations), 121 per 100 peopleIsrael: 0.804 typical gaps (standard deviations), 177 per 100 peopleItaly: 0.94 typical gaps (standard deviations), 133 per 100 peopleJamaica: 0.446 typical gaps (standard deviations), 118 per 100 peopleJordan: 0.68 typical gaps (standard deviations), 69.6 per 100 peopleJapan: 1.04 typical gaps (standard deviations), 178 per 100 peopleKazakhstan: 0.74 typical gaps (standard deviations), 127 per 100 peopleKenya: 0.0734 typical gaps (standard deviations), 126 per 100 peopleKyrgyz Republic: 0.764 typical gaps (standard deviations), 107 per 100 peopleCambodia: 0.174 typical gaps (standard deviations), 116 per 100 peopleKiribati: 0.376 typical gaps (standard deviations), 53.5 per 100 peopleSt. Kitts and Nevis: 1.13 typical gaps (standard deviations), 119 per 100 peopleKorea, Rep.: 1.03 typical gaps (standard deviations), 173 per 100 peopleKuwait: 0.25 typical gaps (standard deviations), 168 per 100 peopleLao PDR: -0.35 typical gaps (standard deviations), 64.8 per 100 peopleLebanon: 0.088 typical gaps (standard deviations), 74 per 100 peopleLiberia: -1.22 typical gaps (standard deviations), 32.1 per 100 peopleSt. Lucia: 0.462 typical gaps (standard deviations), 98.7 per 100 peopleSri Lanka: 0.421 typical gaps (standard deviations), 133 per 100 peopleLesotho: -0.337 typical gaps (standard deviations), 70.3 per 100 peopleLithuania: 0.928 typical gaps (standard deviations), 139 per 100 peopleLuxembourg: 0.66 typical gaps (standard deviations), 144 per 100 peopleLatvia: 0.923 typical gaps (standard deviations), 121 per 100 peopleMacao SAR, China: 0.965 typical gaps (standard deviations), 201 per 100 peopleMorocco: 0.345 typical gaps (standard deviations), 153 per 100 peopleMoldova: 0.734 typical gaps (standard deviations), 120 per 100 peopleMadagascar: -0.569 typical gaps (standard deviations), 75.5 per 100 peopleMaldives: 0.427 typical gaps (standard deviations), 148 per 100 peopleMexico: 0.636 typical gaps (standard deviations), 116 per 100 peopleMarshall Islands: 0.934 typical gaps (standard deviations), 39.7 per 100 peopleNorth Macedonia: 0.658 typical gaps (standard deviations), 108 per 100 peopleMali: -1.24 typical gaps (standard deviations), 112 per 100 peopleMalta: 0.945 typical gaps (standard deviations), 142 per 100 peopleMyanmar: -0.0676 typical gaps (standard deviations), 114 per 100 peopleMontenegro: 0.943 typical gaps (standard deviations), 222 per 100 peopleMongolia: 0.749 typical gaps (standard deviations), 141 per 100 peopleMozambique: -0.56 typical gaps (standard deviations), 49.5 per 100 peopleMauritania: -0.911 typical gaps (standard deviations), 92.1 per 100 peopleMauritius: 0.621 typical gaps (standard deviations), 173 per 100 peopleMalawi: -0.546 typical gaps (standard deviations), 69.3 per 100 peopleMalaysia: 0.529 typical gaps (standard deviations), 140 per 100 peopleNamibia: 0.228 typical gaps (standard deviations), 85.2 per 100 peopleNiger: -1.04 typical gaps (standard deviations), 60.6 per 100 peopleNigeria: -0.241 typical gaps (standard deviations), 70.8 per 100 peopleNicaragua: 0.168 typical gaps (standard deviations), 106 per 100 peopleNetherlands: 1.27 typical gaps (standard deviations), 129 per 100 peopleNorway: 1 typical gaps (standard deviations), 109 per 100 peopleNepal: 0.452 typical gaps (standard deviations), 99.8 per 100 peopleNaoero: 0.496 typical gaps (standard deviations), 87.2 per 100 peopleNew Zealand: 1.44 typical gaps (standard deviations), 130 per 100 peopleOman: 0.293 typical gaps (standard deviations), 121 per 100 peoplePakistan: -0.556 typical gaps (standard deviations), 76.9 per 100 peoplePanama: 0.393 typical gaps (standard deviations), 157 per 100 peoplePeru: 0.773 typical gaps (standard deviations), 125 per 100 peoplePhilippines: 0.548 typical gaps (standard deviations), 115 per 100 peoplePalau: 0.84 typical gaps (standard deviations), 135 per 100 peoplePapua New Guinea: -0.297 typical gaps (standard deviations), 38.8 per 100 peoplePoland: 0.952 typical gaps (standard deviations), 138 per 100 peoplePuerto Rico (US): 0.956 typical gaps (standard deviations), 126 per 100 peoplePortugal: 1.02 typical gaps (standard deviations), 124 per 100 peopleParaguay: 0.452 typical gaps (standard deviations), 127 per 100 peopleWest Bank and Gaza: 0.564 typical gaps (standard deviations), 76.7 per 100 peopleQatar: 0.734 typical gaps (standard deviations), 154 per 100 peopleRomania: 0.584 typical gaps (standard deviations), 120 per 100 peopleRussian Federation: 0.85 typical gaps (standard deviations), 186 per 100 peopleRwanda: -0.127 typical gaps (standard deviations), 93.2 per 100 peopleSaudi Arabia: 0.8 typical gaps (standard deviations), 160 per 100 peopleSudan: -0.831 typical gaps (standard deviations), 70.2 per 100 peopleSenegal: -0.815 typical gaps (standard deviations), 124 per 100 peopleSingapore: 1.26 typical gaps (standard deviations), 171 per 100 peopleSolomon Islands: -0.0603 typical gaps (standard deviations), 62.1 per 100 peopleSierra Leone: -0.279 typical gaps (standard deviations), 108 per 100 peopleEl Salvador: 0.37 typical gaps (standard deviations), 177 per 100 peopleSan Marino: 0.61 typical gaps (standard deviations), 120 per 100 peopleSomalia, Fed. Rep.: -2.1 typical gaps (standard deviations), 54 per 100 peopleSerbia: 0.761 typical gaps (standard deviations), 124 per 100 peopleSao Tome and Principe: 0.172 typical gaps (standard deviations), 62.4 per 100 peopleSuriname: -0.157 typical gaps (standard deviations), 142 per 100 peopleSlovak Republic: 0.801 typical gaps (standard deviations), 140 per 100 peopleSlovenia: 1.18 typical gaps (standard deviations), 131 per 100 peopleSweden: 0.968 typical gaps (standard deviations), 141 per 100 peopleEswatini: 0.404 typical gaps (standard deviations), 140 per 100 peopleSint Maarten (Dutch part): 0.541 typical gaps (standard deviations), 205 per 100 peopleSeychelles: 0.782 typical gaps (standard deviations), 126 per 100 peopleSyrian Arab Republic: -0.332 typical gaps (standard deviations), 71.2 per 100 peopleChad: -1.2 typical gaps (standard deviations), 72.9 per 100 peopleTogo: 0.075 typical gaps (standard deviations), 80.8 per 100 peopleThailand: 0.764 typical gaps (standard deviations), 161 per 100 peopleTajikistan: 0.525 typical gaps (standard deviations), 76.2 per 100 peopleTurkmenistan: 0.598 typical gaps (standard deviations), 88.2 per 100 peopleTimor-Leste: 0.12 typical gaps (standard deviations), 116 per 100 peopleTonga: 0.921 typical gaps (standard deviations), 61.7 per 100 peopleTrinidad and Tobago: 0.581 typical gaps (standard deviations), 119 per 100 peopleTunisia: 0.496 typical gaps (standard deviations), 118 per 100 peopleTurkiye: 1.03 typical gaps (standard deviations), 108 per 100 peopleTuvalu: 0.577 typical gaps (standard deviations), 98.9 per 100 peopleTanzania: -0.351 typical gaps (standard deviations), 127 per 100 peopleUganda: -0.832 typical gaps (standard deviations), 83.2 per 100 peopleUkraine: 0.784 typical gaps (standard deviations), 133 per 100 peopleUruguay: 0.9 typical gaps (standard deviations), 146 per 100 peopleUnited States: 1.11 typical gaps (standard deviations), 113 per 100 peopleUzbekistan: 0.848 typical gaps (standard deviations), 110 per 100 peopleSt. Vincent and the Grenadines: 0.91 typical gaps (standard deviations), 108 per 100 peopleViet Nam: 0.537 typical gaps (standard deviations), 128 per 100 peopleVanuatu: 0.198 typical gaps (standard deviations), 89.3 per 100 peopleSamoa: 0.515 typical gaps (standard deviations), 62.4 per 100 peopleSouth Africa: 0.665 typical gaps (standard deviations), 179 per 100 peopleZambia: -0.123 typical gaps (standard deviations), 109 per 100 peopleZimbabwe: 0.181 typical gaps (standard deviations), 94.2 per 100 peopleBELCAFFJIGNBHKGLBRMHLMNESOMTON

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.4201.5e-9190
By rank (Spearman), not adjusted+0.5021.7e-13190
Standard (Pearson), adjusted for national income+0.1120.124190
By rank (Spearman), adjusted for national income+0.0210.772190

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 190 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: +0.308 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.053 (without FJI) to +0.133 (without GNB). 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: FJI (-0.060).

Fragile: this link may not hold up.
  • 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.

nr
East Asia & Pacific31+0.25
Europe & Central Asia50-0.08
Latin America & Caribbean33+0.08
Middle East, North Africa, Afghanistan & Pakistan21+0.34
Sub-Saharan Africa46+0.22

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 third64-0.09
Middle third63+0.26
Richest third63+0.08

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
Christians109+0.21
Muslims48-0.04

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
France24+0.26
Never a European colony21+0.31
Spain20+0.37
United Kingdom62+0.16

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 = +0.051 after dropping 14 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 100 people		Years
New Zealand NZL1.441302023, 2024
Hong Kong SAR, China HKG1.343652024
Greece GRC1.311142023, 2024
Netherlands NLD1.271292023, 2024
Iceland ISL1.261212023, 2024
Singapore SGP1.261712023, 2024
Belgium BEL1.251042023, 2024
United Kingdom GBR1.191222023, 2024
Ireland IRL1.181132022, 2024
Slovenia SVN1.181312023, 2024
St. Kitts and Nevis KNA1.131192016, 2023
United States USA1.111132022, 2024
Czechia CZE1.081272023, 2024
Canada CAN1.0694.12023, 2024
Japan JPN1.041782023

uis; undp_hdr; wdi
World Bank, World Development Indicators

Sources

Education score

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.

Mobile subscriptions

Mobile cellular subscriptions (per 100 people)

International Telecommunication Union · CC BY 4.0 · updated quarterly

Counts SIM card subscriptions, not people. People with more than one SIM card push several countries well above 100 per 100 people.

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 Mobile subscriptions

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

More links with Education score or Mobile subscriptions