placestats

Basic water access vs. Education score

Fades after adjusting for income

Across 190 countries, Basic water access and Education score rise and fall together at r = +0.77 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.39, which could easily be chance (q = 0.058, 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 basic water access, 87.4% to 99.7% of population, education score is better by 0.45 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.208 typical gaps.

Link before adjusting (r)+0.771p = 1.1e-38 · 190 countries
After adjusting for national income+0.385p = 4.4e-8 · likely range [+0.26, +0.50]
p, allowing for regions0.007standard error 0.095 · 7 regions
Check for luck (q)0.058across 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.

Basic water access · % of populationlink with income +0.85 · proxy
Education score · typical gaps (standard deviations)link with income +0.81 · 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

Basic water access · % of population →Education score · typical gaps (standard deviations) →35.7%100%-2.11.44Aruba: 96.7% of population, 0.697 typical gaps (standard deviations)Afghanistan: 80.8% of population, -0.557 typical gaps (standard deviations)Angola: 68% of population, -0.482 typical gaps (standard deviations)Albania: 95.1% of population, 0.923 typical gaps (standard deviations)Andorra: 100% of population, 0.892 typical gaps (standard deviations)United Arab Emirates: 100% of population, 0.911 typical gaps (standard deviations)Argentina: 99% of population, 1.03 typical gaps (standard deviations)Armenia: 99.9% of population, 0.65 typical gaps (standard deviations)Antigua and Barbuda: 98.9% of population, 0.912 typical gaps (standard deviations)Australia: 100% of population, 1.03 typical gaps (standard deviations)Austria: 100% of population, 1 typical gaps (standard deviations)Azerbaijan: 97.7% of population, 0.629 typical gaps (standard deviations)Burundi: 65.9% of population, -0.587 typical gaps (standard deviations)Belgium: 100% of population, 1.25 typical gaps (standard deviations)Benin: 69.6% of population, -0.586 typical gaps (standard deviations)Burkina Faso: 50% of population, -0.582 typical gaps (standard deviations)Bangladesh: 98.8% of population, 0.335 typical gaps (standard deviations)Bulgaria: 99.2% of population, 0.794 typical gaps (standard deviations)Bahrain: 99.9% of population, 0.537 typical gaps (standard deviations)Bahamas, The: 98.9% of population, 0.202 typical gaps (standard deviations)Bosnia and Herzegovina: 96.1% of population, 0.607 typical gaps (standard deviations)Belarus: 99.9% of population, 0.722 typical gaps (standard deviations)Belize: 94.8% of population, 0.166 typical gaps (standard deviations)Bermuda: 100% of population, 0.693 typical gaps (standard deviations)Bolivia: 93.4% of population, 0.634 typical gaps (standard deviations)Brazil: 100% of population, 0.731 typical gaps (standard deviations)Barbados: 99.2% of population, 0.581 typical gaps (standard deviations)Brunei Darussalam: 100% of population, 0.499 typical gaps (standard deviations)Bhutan: 99.5% of population, 0.277 typical gaps (standard deviations)Botswana: 91.8% of population, 0.469 typical gaps (standard deviations)Central African Republic: 36.5% of population, -1.65 typical gaps (standard deviations)Canada: 98.3% of population, 1.06 typical gaps (standard deviations)Switzerland: 100% of population, 0.92 typical gaps (standard deviations)Chile: 99.1% of population, 0.979 typical gaps (standard deviations)China: 96.1% of population, 0.674 typical gaps (standard deviations)Cote d'Ivoire: 77.2% of population, -0.0434 typical gaps (standard deviations)Cameroon: 71.4% of population, -0.125 typical gaps (standard deviations)Congo, Dem. Rep.: 35.7% of population, -0.251 typical gaps (standard deviations)Congo, Rep.: 73.5% of population, 0.119 typical gaps (standard deviations)Colombia: 97.1% of population, 0.63 typical gaps (standard deviations)Comoros: 85.1% of population, 0.00771 typical gaps (standard deviations)Cabo Verde: 92.3% of population, -0.216 typical gaps (standard deviations)Costa Rica: 99.8% of population, 0.629 typical gaps (standard deviations)Cyprus: 99.7% of population, 1.02 typical gaps (standard deviations)Czechia: 98.7% of population, 1.08 typical gaps (standard deviations)Germany: 100% of population, 0.938 typical gaps (standard deviations)Djibouti: 79.7% of population, -0.825 typical gaps (standard deviations)Dominica: 97% of population, 0.668 typical gaps (standard deviations)Denmark: 100% of population, 0.941 typical gaps (standard deviations)Dominican Republic: 97.5% of population, 0.651 typical gaps (standard deviations)Algeria: 92.4% of population, 0.663 typical gaps (standard deviations)Ecuador: 92.1% of population, 0.722 typical gaps (standard deviations)Egypt, Arab Rep.: 97.3% of population, 0.489 typical gaps (standard deviations)Spain: 99.9% of population, 0.983 typical gaps (standard deviations)Estonia: 100% of population, 0.841 typical gaps (standard deviations)Ethiopia: 55.6% of population, -0.777 typical gaps (standard deviations)Finland: 100% of population, 1.02 typical gaps (standard deviations)Fiji: 95.6% of population, 0.887 typical gaps (standard deviations)France: 100% of population, 0.907 typical gaps (standard deviations)Micronesia, Fed. Sts.: 89.9% of population, -0.00832 typical gaps (standard deviations)Gabon: 88.6% of population, -0.00264 typical gaps (standard deviations)United Kingdom: 100% of population, 1.19 typical gaps (standard deviations)Georgia: 97.3% of population, 0.697 typical gaps (standard deviations)Ghana: 89.9% of population, 0.168 typical gaps (standard deviations)Guinea: 74.2% of population, -0.598 typical gaps (standard deviations)Gambia, The: 87.1% of population, -0.345 typical gaps (standard deviations)Guinea-Bissau: 61.8% of population, -1.3 typical gaps (standard deviations)Equatorial Guinea: 64.7% of population, -0.759 typical gaps (standard deviations)Greece: 100% of population, 1.31 typical gaps (standard deviations)Grenada: 95.6% of population, 0.804 typical gaps (standard deviations)Guatemala: 93.2% of population, 0.22 typical gaps (standard deviations)Guyana: 95.6% of population, 0.39 typical gaps (standard deviations)Hong Kong SAR, China: 100% of population, 1.34 typical gaps (standard deviations)Honduras: 95.7% of population, 0.0516 typical gaps (standard deviations)Croatia: 99.9% of population, 0.985 typical gaps (standard deviations)Hungary: 100% of population, 0.83 typical gaps (standard deviations)Indonesia: 89% of population, 0.591 typical gaps (standard deviations)India: 95.7% of population, 0.605 typical gaps (standard deviations)Ireland: 97.1% of population, 1.18 typical gaps (standard deviations)Iran, Islamic Rep.: 97.9% of population, 0.667 typical gaps (standard deviations)Iraq: 98.4% of population, -0.133 typical gaps (standard deviations)Iceland: 100% of population, 1.26 typical gaps (standard deviations)Israel: 100% of population, 0.804 typical gaps (standard deviations)Italy: 99.9% of population, 0.94 typical gaps (standard deviations)Jamaica: 87.6% of population, 0.446 typical gaps (standard deviations)Jordan: 99.3% of population, 0.68 typical gaps (standard deviations)Japan: 99.2% of population, 1.04 typical gaps (standard deviations)Kazakhstan: 97.8% of population, 0.74 typical gaps (standard deviations)Kenya: 65.6% of population, 0.0734 typical gaps (standard deviations)Kyrgyz Republic: 94.9% of population, 0.764 typical gaps (standard deviations)Cambodia: 82.8% of population, 0.174 typical gaps (standard deviations)Kiribati: 79.1% of population, 0.376 typical gaps (standard deviations)St. Kitts and Nevis: 98.6% of population, 1.13 typical gaps (standard deviations)Korea, Rep.: 100% of population, 1.03 typical gaps (standard deviations)Kuwait: 100% of population, 0.25 typical gaps (standard deviations)Lao PDR: 95.9% of population, -0.35 typical gaps (standard deviations)Lebanon: 92.6% of population, 0.088 typical gaps (standard deviations)Liberia: 78.8% of population, -1.22 typical gaps (standard deviations)St. Lucia: 98.1% of population, 0.462 typical gaps (standard deviations)Sri Lanka: 90.2% of population, 0.421 typical gaps (standard deviations)Lesotho: 78.1% of population, -0.337 typical gaps (standard deviations)Lithuania: 99.9% of population, 0.928 typical gaps (standard deviations)Luxembourg: 100% of population, 0.66 typical gaps (standard deviations)Latvia: 98.8% of population, 0.923 typical gaps (standard deviations)Macao SAR, China: 100% of population, 0.965 typical gaps (standard deviations)Morocco: 92.2% of population, 0.345 typical gaps (standard deviations)Moldova: 92.7% of population, 0.734 typical gaps (standard deviations)Madagascar: 55.3% of population, -0.569 typical gaps (standard deviations)Maldives: 99.8% of population, 0.427 typical gaps (standard deviations)Mexico: 99.5% of population, 0.636 typical gaps (standard deviations)Marshall Islands: 87.4% of population, 0.934 typical gaps (standard deviations)North Macedonia: 98.2% of population, 0.658 typical gaps (standard deviations)Mali: 86.4% of population, -1.24 typical gaps (standard deviations)Malta: 100% of population, 0.945 typical gaps (standard deviations)Myanmar: 85.7% of population, -0.0676 typical gaps (standard deviations)Montenegro: 99.3% of population, 0.943 typical gaps (standard deviations)Mongolia: 86.1% of population, 0.749 typical gaps (standard deviations)Mozambique: 66.6% of population, -0.56 typical gaps (standard deviations)Mauritania: 77.3% of population, -0.911 typical gaps (standard deviations)Mauritius: 99.9% of population, 0.621 typical gaps (standard deviations)Malawi: 73.1% of population, -0.546 typical gaps (standard deviations)Malaysia: 97.9% of population, 0.529 typical gaps (standard deviations)Namibia: 86.5% of population, 0.228 typical gaps (standard deviations)Niger: 53.4% of population, -1.04 typical gaps (standard deviations)Nigeria: 82.2% of population, -0.241 typical gaps (standard deviations)Netherlands: 100% of population, 1.27 typical gaps (standard deviations)Norway: 100% of population, 1 typical gaps (standard deviations)Nepal: 93.6% of population, 0.452 typical gaps (standard deviations)Naoero: 97.8% of population, 0.496 typical gaps (standard deviations)New Zealand: 100% of population, 1.44 typical gaps (standard deviations)Oman: 92.7% of population, 0.293 typical gaps (standard deviations)Pakistan: 90.7% of population, -0.556 typical gaps (standard deviations)Panama: 95.3% of population, 0.393 typical gaps (standard deviations)Peru: 95.6% of population, 0.773 typical gaps (standard deviations)Philippines: 95.9% of population, 0.548 typical gaps (standard deviations)Palau: 99.5% of population, 0.84 typical gaps (standard deviations)Papua New Guinea: 53.3% of population, -0.297 typical gaps (standard deviations)Poland: 90.1% of population, 0.952 typical gaps (standard deviations)Puerto Rico (US): 100% of population, 0.956 typical gaps (standard deviations)Portugal: 99.3% of population, 1.02 typical gaps (standard deviations)Paraguay: 99.6% of population, 0.452 typical gaps (standard deviations)West Bank and Gaza: 98.8% of population, 0.564 typical gaps (standard deviations)Qatar: 100% of population, 0.734 typical gaps (standard deviations)Romania: 100% of population, 0.584 typical gaps (standard deviations)Russian Federation: 96.9% of population, 0.85 typical gaps (standard deviations)Rwanda: 61.4% of population, -0.127 typical gaps (standard deviations)Saudi Arabia: 99% of population, 0.8 typical gaps (standard deviations)Sudan: 64.9% of population, -0.831 typical gaps (standard deviations)Senegal: 88% of population, -0.815 typical gaps (standard deviations)Singapore: 100% of population, 1.26 typical gaps (standard deviations)Solomon Islands: 71.6% of population, -0.0603 typical gaps (standard deviations)Sierra Leone: 68.1% of population, -0.279 typical gaps (standard deviations)El Salvador: 91.6% of population, 0.37 typical gaps (standard deviations)San Marino: 100% of population, 0.61 typical gaps (standard deviations)Somalia, Fed. Rep.: 75.3% of population, -2.1 typical gaps (standard deviations)Serbia: 95.4% of population, 0.761 typical gaps (standard deviations)Sao Tome and Principe: 78% of population, 0.172 typical gaps (standard deviations)Suriname: 98% of population, -0.157 typical gaps (standard deviations)Slovak Republic: 99.8% of population, 0.801 typical gaps (standard deviations)Slovenia: 99.6% of population, 1.18 typical gaps (standard deviations)Sweden: 99.8% of population, 0.968 typical gaps (standard deviations)Eswatini: 80.4% of population, 0.404 typical gaps (standard deviations)Sint Maarten (Dutch part): 99.3% of population, 0.541 typical gaps (standard deviations)Seychelles: 97.7% of population, 0.782 typical gaps (standard deviations)Syrian Arab Republic: 94.1% of population, -0.332 typical gaps (standard deviations)Turks and Caicos Islands: 98.8% of population, 0.306 typical gaps (standard deviations)Chad: 52.2% of population, -1.2 typical gaps (standard deviations)Togo: 64.8% of population, 0.075 typical gaps (standard deviations)Thailand: 100% of population, 0.764 typical gaps (standard deviations)Tajikistan: 89.2% of population, 0.525 typical gaps (standard deviations)Turkmenistan: 99.9% of population, 0.598 typical gaps (standard deviations)Timor-Leste: 87.3% of population, 0.12 typical gaps (standard deviations)Tonga: 98.9% of population, 0.921 typical gaps (standard deviations)Trinidad and Tobago: 99.3% of population, 0.581 typical gaps (standard deviations)Tunisia: 96.7% of population, 0.496 typical gaps (standard deviations)Turkiye: 96% of population, 1.03 typical gaps (standard deviations)Tuvalu: 98.7% of population, 0.577 typical gaps (standard deviations)Tanzania: 64.6% of population, -0.351 typical gaps (standard deviations)Uganda: 63% of population, -0.832 typical gaps (standard deviations)Ukraine: 93.5% of population, 0.784 typical gaps (standard deviations)Uruguay: 99.6% of population, 0.9 typical gaps (standard deviations)United States: 100% of population, 1.11 typical gaps (standard deviations)Uzbekistan: 97.3% of population, 0.848 typical gaps (standard deviations)St. Vincent and the Grenadines: 97.7% of population, 0.91 typical gaps (standard deviations)Viet Nam: 98.8% of population, 0.537 typical gaps (standard deviations)Vanuatu: 87.1% of population, 0.198 typical gaps (standard deviations)Samoa: 99.4% of population, 0.515 typical gaps (standard deviations)South Africa: 90.2% of population, 0.665 typical gaps (standard deviations)Zambia: 73.4% of population, -0.123 typical gaps (standard deviations)Zimbabwe: 67.2% of population, 0.181 typical gaps (standard deviations)BFACAFCODGRCHKGMLINZLPNGSENSOMTCD

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7711.1e-38190
By rank (Spearman), not adjusted+0.7632.0e-37190
Standard (Pearson), adjusted for national income+0.3854.4e-8190
By rank (Spearman), adjusted for national income+0.2290.002190

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: +0.386 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.357 (without GNQ) to +0.426 (without COD). 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 (+0.041).

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+0.33
Europe & Central Asia50-0.03
Latin America & Caribbean33+0.17
Middle East, North Africa, Afghanistan & Pakistan21+0.64
Sub-Saharan Africa46+0.04

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+0.26
Middle third63+0.42
Richest third63+0.24

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
Christians108+0.45
Muslims48+0.44

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+0.00
Never a European colony21+0.58
Spain19+0.82
United Kingdom62+0.29

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 = +0.349 after dropping 24 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


% of population
typical gaps (standard deviations)		Years
Thailand THA100%0.7642024, 2025
Singapore SGP100%1.262024, 2023
Puerto Rico (US) PRI100%0.9562024, 2016
New Zealand NZL100%1.442024, 2023
Norway NOR100%12024
Netherlands NLD100%1.272024, 2023
Malta MLT100%0.9452024, 2023
Macao SAR, China MAC100%0.9652024
Luxembourg LUX100%0.662024
Kuwait KWT100%0.252024, 2020
Korea, Rep. KOR100%1.032024
Iceland ISL100%1.262024, 2023
Hungary HUN100%0.832024
Hong Kong SAR, China HKG100%1.342024
Greece GRC100%1.312024, 2023

World Bank, World Development Indicators
uis; undp_hdr; wdi

Sources

Basic water access

People using at least basic drinking water services (% of population)

World Bank · CC BY 4.0 · updated quarterly

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.

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 Basic water access and Education score

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

More links with Basic water access or Education score