placestats

Education score vs. Extreme poverty rate

Fades after adjusting for income

Across 161 countries, Education score and Extreme poverty rate rise and fall together at r = -0.72 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.27, which could easily be chance (q = 0.46, 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: direction is not consistent across regions.

Comparing countries across the middle half of extreme poverty rate, 0.4% to 17.9% of population, education score is worse by 0.399 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.125 typical gaps.

Link before adjusting (r)-0.721p = 3.8e-27 · 161 countries
After adjusting for national income-0.266p = 0.001 · likely range [-0.40, -0.12]
p, allowing for regions0.157standard error 0.165 · 7 regions
Check for luck (q)0.461across 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
Extreme poverty rate · % of populationlink with income -0.83 · 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

Education score · typical gaps (standard deviations) →Extreme poverty rate · % of population →-1.651.310%85.3%Angola: -0.482 typical gaps (standard deviations), 39.3% of populationAlbania: 0.923 typical gaps (standard deviations), 0.3% of populationUnited Arab Emirates: 0.911 typical gaps (standard deviations), 0% of populationArgentina: 1.03 typical gaps (standard deviations), 1% of populationArmenia: 0.65 typical gaps (standard deviations), 0.8% of populationAustralia: 1.03 typical gaps (standard deviations), 0.9% of populationAustria: 1 typical gaps (standard deviations), 0.5% of populationBurundi: -0.587 typical gaps (standard deviations), 74.2% of populationBelgium: 1.25 typical gaps (standard deviations), 0.1% of populationBenin: -0.586 typical gaps (standard deviations), 27.2% of populationBurkina Faso: -0.582 typical gaps (standard deviations), 42.1% of populationBangladesh: 0.335 typical gaps (standard deviations), 5.9% of populationBulgaria: 0.794 typical gaps (standard deviations), 1% of populationBosnia and Herzegovina: 0.607 typical gaps (standard deviations), 0.3% of populationBelarus: 0.722 typical gaps (standard deviations), 0% of populationBelize: 0.166 typical gaps (standard deviations), 1% of populationBolivia: 0.634 typical gaps (standard deviations), 2.1% of populationBrazil: 0.731 typical gaps (standard deviations), 3% of populationBarbados: 0.581 typical gaps (standard deviations), 1.7% of populationBhutan: 0.277 typical gaps (standard deviations), 0% of populationBotswana: 0.469 typical gaps (standard deviations), 21.4% of populationCentral African Republic: -1.65 typical gaps (standard deviations), 71.6% of populationCanada: 1.06 typical gaps (standard deviations), 0.3% of populationSwitzerland: 0.92 typical gaps (standard deviations), 0.2% of populationChile: 0.979 typical gaps (standard deviations), 0.4% of populationChina: 0.674 typical gaps (standard deviations), 0% of populationCote d'Ivoire: -0.0434 typical gaps (standard deviations), 20.8% of populationCameroon: -0.125 typical gaps (standard deviations), 26.7% of populationCongo, Dem. Rep.: -0.251 typical gaps (standard deviations), 85.3% of populationCongo, Rep.: 0.119 typical gaps (standard deviations), 39.2% of populationColombia: 0.63 typical gaps (standard deviations), 8.5% of populationComoros: 0.00771 typical gaps (standard deviations), 5.1% of populationCabo Verde: -0.216 typical gaps (standard deviations), 14.6% of populationCosta Rica: 0.629 typical gaps (standard deviations), 0.8% of populationCyprus: 1.02 typical gaps (standard deviations), 0% of populationCzechia: 1.08 typical gaps (standard deviations), 0% of populationGermany: 0.938 typical gaps (standard deviations), 0.7% of populationDjibouti: -0.825 typical gaps (standard deviations), 25.4% of populationDenmark: 0.941 typical gaps (standard deviations), 0.4% of populationDominican Republic: 0.651 typical gaps (standard deviations), 0.8% of populationAlgeria: 0.663 typical gaps (standard deviations), 0% of populationEcuador: 0.722 typical gaps (standard deviations), 3.4% of populationEgypt, Arab Rep.: 0.489 typical gaps (standard deviations), 1.4% of populationSpain: 0.983 typical gaps (standard deviations), 0.8% of populationEstonia: 0.841 typical gaps (standard deviations), 0.3% of populationEthiopia: -0.777 typical gaps (standard deviations), 38.6% of populationFinland: 1.02 typical gaps (standard deviations), 0.2% of populationFiji: 0.887 typical gaps (standard deviations), 4.7% of populationFrance: 0.907 typical gaps (standard deviations), 0.1% of populationMicronesia, Fed. Sts.: -0.00832 typical gaps (standard deviations), 26.1% of populationGabon: -0.00264 typical gaps (standard deviations), 3.8% of populationUnited Kingdom: 1.19 typical gaps (standard deviations), 0.4% of populationGeorgia: 0.697 typical gaps (standard deviations), 4.2% of populationGhana: 0.168 typical gaps (standard deviations), 39% of populationGuinea: -0.598 typical gaps (standard deviations), 11.7% of populationGambia, The: -0.345 typical gaps (standard deviations), 22% of populationGuinea-Bissau: -1.3 typical gaps (standard deviations), 39.9% of populationEquatorial Guinea: -0.759 typical gaps (standard deviations), 8.8% of populationGreece: 1.31 typical gaps (standard deviations), 0.6% of populationGrenada: 0.804 typical gaps (standard deviations), 0.8% of populationGuatemala: 0.22 typical gaps (standard deviations), 9.7% of populationHonduras: 0.0516 typical gaps (standard deviations), 15.7% of populationCroatia: 0.985 typical gaps (standard deviations), 0.3% of populationHungary: 0.83 typical gaps (standard deviations), 0.9% of populationIndonesia: 0.591 typical gaps (standard deviations), 4% of populationIndia: 0.605 typical gaps (standard deviations), 5.3% of populationIreland: 1.18 typical gaps (standard deviations), 0.1% of populationIran, Islamic Rep.: 0.667 typical gaps (standard deviations), 2.5% of populationIraq: -0.133 typical gaps (standard deviations), 0.5% of populationIceland: 1.26 typical gaps (standard deviations), 0.1% of populationIsrael: 0.804 typical gaps (standard deviations), 0.4% of populationItaly: 0.94 typical gaps (standard deviations), 0.9% of populationJamaica: 0.446 typical gaps (standard deviations), 1.4% of populationJordan: 0.68 typical gaps (standard deviations), 0.1% of populationJapan: 1.04 typical gaps (standard deviations), 1.2% of populationKazakhstan: 0.74 typical gaps (standard deviations), 0% of populationKenya: 0.0734 typical gaps (standard deviations), 45.5% of populationKyrgyz Republic: 0.764 typical gaps (standard deviations), 2.2% of populationKiribati: 0.376 typical gaps (standard deviations), 0.9% of populationKorea, Rep.: 1.03 typical gaps (standard deviations), 0.1% of populationLao PDR: -0.35 typical gaps (standard deviations), 7.1% of populationLebanon: 0.088 typical gaps (standard deviations), 5.8% of populationLiberia: -1.22 typical gaps (standard deviations), 33.6% of populationSt. Lucia: 0.462 typical gaps (standard deviations), 0.1% of populationSri Lanka: 0.421 typical gaps (standard deviations), 2.7% of populationLesotho: -0.337 typical gaps (standard deviations), 41.9% of populationLithuania: 0.928 typical gaps (standard deviations), 1.1% of populationLuxembourg: 0.66 typical gaps (standard deviations), 0.1% of populationLatvia: 0.923 typical gaps (standard deviations), 0.4% of populationMorocco: 0.345 typical gaps (standard deviations), 3.7% of populationMoldova: 0.734 typical gaps (standard deviations), 0.1% of populationMadagascar: -0.569 typical gaps (standard deviations), 69.2% of populationMaldives: 0.427 typical gaps (standard deviations), 0% of populationMexico: 0.636 typical gaps (standard deviations), 1.6% of populationMarshall Islands: 0.934 typical gaps (standard deviations), 2.1% of populationNorth Macedonia: 0.658 typical gaps (standard deviations), 3.9% of populationMali: -1.24 typical gaps (standard deviations), 36.1% of populationMalta: 0.945 typical gaps (standard deviations), 0.4% of populationMyanmar: -0.0676 typical gaps (standard deviations), 10.3% of populationMontenegro: 0.943 typical gaps (standard deviations), 2.5% of populationMongolia: 0.749 typical gaps (standard deviations), 0.4% of populationMozambique: -0.56 typical gaps (standard deviations), 81.4% of populationMauritania: -0.911 typical gaps (standard deviations), 10.2% of populationMauritius: 0.621 typical gaps (standard deviations), 0.5% of populationMalawi: -0.546 typical gaps (standard deviations), 75.4% of populationMalaysia: 0.529 typical gaps (standard deviations), 0% of populationNamibia: 0.228 typical gaps (standard deviations), 22.9% of populationNiger: -1.04 typical gaps (standard deviations), 60.5% of populationNigeria: -0.241 typical gaps (standard deviations), 41.8% of populationNicaragua: 0.168 typical gaps (standard deviations), 6.6% of populationNetherlands: 1.27 typical gaps (standard deviations), 0.1% of populationNorway: 1 typical gaps (standard deviations), 0.2% of populationNepal: 0.452 typical gaps (standard deviations), 2.4% of populationNaoero: 0.496 typical gaps (standard deviations), 9.7% of populationPakistan: -0.556 typical gaps (standard deviations), 23% of populationPanama: 0.393 typical gaps (standard deviations), 3.1% of populationPeru: 0.773 typical gaps (standard deviations), 5.1% of populationPhilippines: 0.548 typical gaps (standard deviations), 5.3% of populationPoland: 0.952 typical gaps (standard deviations), 0.2% of populationPortugal: 1.02 typical gaps (standard deviations), 0.4% of populationParaguay: 0.452 typical gaps (standard deviations), 2.1% of populationWest Bank and Gaza: 0.564 typical gaps (standard deviations), 2.1% of populationQatar: 0.734 typical gaps (standard deviations), 0% of populationRomania: 0.584 typical gaps (standard deviations), 0.7% of populationRussian Federation: 0.85 typical gaps (standard deviations), 0.1% of populationRwanda: -0.127 typical gaps (standard deviations), 38.6% of populationSudan: -0.831 typical gaps (standard deviations), 10.1% of populationSenegal: -0.815 typical gaps (standard deviations), 17.9% of populationSolomon Islands: -0.0603 typical gaps (standard deviations), 40.3% of populationSierra Leone: -0.279 typical gaps (standard deviations), 41.5% of populationEl Salvador: 0.37 typical gaps (standard deviations), 4.6% of populationSerbia: 0.761 typical gaps (standard deviations), 1.1% of populationSao Tome and Principe: 0.172 typical gaps (standard deviations), 13% of populationSuriname: -0.157 typical gaps (standard deviations), 2.2% of populationSlovak Republic: 0.801 typical gaps (standard deviations), 0.5% of populationSlovenia: 1.18 typical gaps (standard deviations), 0% of populationSweden: 0.968 typical gaps (standard deviations), 0.8% of populationEswatini: 0.404 typical gaps (standard deviations), 44.5% of populationSeychelles: 0.782 typical gaps (standard deviations), 0.7% of populationSyrian Arab Republic: -0.332 typical gaps (standard deviations), 18% of populationChad: -1.2 typical gaps (standard deviations), 39.5% of populationTogo: 0.075 typical gaps (standard deviations), 34.7% of populationThailand: 0.764 typical gaps (standard deviations), 0% of populationTajikistan: 0.525 typical gaps (standard deviations), 6.1% of populationTimor-Leste: 0.12 typical gaps (standard deviations), 44% of populationTonga: 0.921 typical gaps (standard deviations), 0.4% of populationTunisia: 0.496 typical gaps (standard deviations), 0.7% of populationTurkiye: 1.03 typical gaps (standard deviations), 0.2% of populationTuvalu: 0.577 typical gaps (standard deviations), 9% of populationTanzania: -0.351 typical gaps (standard deviations), 51.3% of populationUganda: -0.832 typical gaps (standard deviations), 59.8% of populationUkraine: 0.784 typical gaps (standard deviations), 0% of populationUruguay: 0.9 typical gaps (standard deviations), 0.2% of populationUnited States: 1.11 typical gaps (standard deviations), 1.1% of populationUzbekistan: 0.848 typical gaps (standard deviations), 2.3% of populationViet Nam: 0.537 typical gaps (standard deviations), 1.6% of populationVanuatu: 0.198 typical gaps (standard deviations), 19.5% of populationSamoa: 0.515 typical gaps (standard deviations), 4.5% of populationSouth Africa: 0.665 typical gaps (standard deviations), 17.4% of populationZambia: -0.123 typical gaps (standard deviations), 71.7% of populationZimbabwe: 0.181 typical gaps (standard deviations), 49.2% of populationCAFCODGNQGRCISLMOZMRTNERNLDSDNZMB

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7213.8e-27161
By rank (Spearman), not adjusted-0.7803.8e-34161
Standard (Pearson), adjusted for national income-0.2660.001161
By rank (Spearman), adjusted for national income-0.2390.002161

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 161 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: +0.455 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.301 (without COD) to -0.244 (without CAF). 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: COD (-0.034).

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 & Pacific23-0.34
Europe & Central Asia46-0.16
Latin America & Caribbean23-0.18
Middle East, North Africa, Afghanistan & Pakistan16-0.84
Sub-Saharan Africa45+0.30

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 third54-0.15
Middle third53-0.23
Richest third54-0.21

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
Christians102-0.25
Muslims39-0.56

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
France23+0.01
Never a European colony16-0.89
Spain20-0.32
United Kingdom48-0.32

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 = -0.391 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)

% of population
Years
Greece GRC1.310.6%2023
Netherlands NLD1.270.1%2023, 2021
Iceland ISL1.260.1%2023, 2019
Belgium BEL1.250.1%2023
United Kingdom GBR1.190.4%2023, 2021
Ireland IRL1.180.1%2022, 2023
Slovenia SVN1.180%2023
United States USA1.111.1%2022, 2024
Czechia CZE1.080%2023
Canada CAN1.060.3%2023, 2022
Japan JPN1.041.2%2023, 2020
Argentina ARG1.031%2023, 2024
Australia AUS1.030.9%2024, 2020
Korea, Rep. KOR1.030.1%2024, 2021
Turkiye TUR1.030.2%2023

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.

Extreme poverty rate

Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population)

World Bank · CC BY 4.0 · updated quarterly

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 Extreme poverty rate

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

More links with Education score or Extreme poverty rate