placestats

Education score vs. Women's political empowerment

Holds up after adjusting for income

Across 165 countries, Education score and Women's political empowerment rise and fall together at r = +0.49 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.23 (q = 0.043, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak positive link remains.

Comparing countries across the middle half of women's political empowerment, 0.626 to 0.874 points, education score is better by 0.456 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.13 typical gaps.

Link before adjusting (r)+0.485p = 3.9e-11 · 165 countries
After adjusting for national income+0.234p = 0.003 · likely range [+0.08, +0.37]
p, allowing for regions0.091standard error 0.116 · 7 regions
Check for luck (q)0.043across 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
Women's political empowerment · index (0-1)link with income +0.54 · 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) →Women's political empowerment · index (0-1) →-2.11.440.0280.954Afghanistan: -0.557 typical gaps (standard deviations), 0.028 index (0-1)Angola: -0.482 typical gaps (standard deviations), 0.77 index (0-1)Albania: 0.923 typical gaps (standard deviations), 0.893 index (0-1)United Arab Emirates: 0.911 typical gaps (standard deviations), 0.614 index (0-1)Argentina: 1.03 typical gaps (standard deviations), 0.832 index (0-1)Armenia: 0.65 typical gaps (standard deviations), 0.875 index (0-1)Australia: 1.03 typical gaps (standard deviations), 0.906 index (0-1)Austria: 1 typical gaps (standard deviations), 0.905 index (0-1)Azerbaijan: 0.629 typical gaps (standard deviations), 0.649 index (0-1)Burundi: -0.587 typical gaps (standard deviations), 0.653 index (0-1)Belgium: 1.25 typical gaps (standard deviations), 0.952 index (0-1)Benin: -0.586 typical gaps (standard deviations), 0.882 index (0-1)Burkina Faso: -0.582 typical gaps (standard deviations), 0.602 index (0-1)Bangladesh: 0.335 typical gaps (standard deviations), 0.537 index (0-1)Bulgaria: 0.794 typical gaps (standard deviations), 0.907 index (0-1)Bahrain: 0.537 typical gaps (standard deviations), 0.568 index (0-1)Bosnia and Herzegovina: 0.607 typical gaps (standard deviations), 0.821 index (0-1)Belarus: 0.722 typical gaps (standard deviations), 0.657 index (0-1)Bolivia: 0.634 typical gaps (standard deviations), 0.84 index (0-1)Brazil: 0.731 typical gaps (standard deviations), 0.818 index (0-1)Barbados: 0.581 typical gaps (standard deviations), 0.895 index (0-1)Bhutan: 0.277 typical gaps (standard deviations), 0.675 index (0-1)Botswana: 0.469 typical gaps (standard deviations), 0.699 index (0-1)Central African Republic: -1.65 typical gaps (standard deviations), 0.556 index (0-1)Canada: 1.06 typical gaps (standard deviations), 0.872 index (0-1)Switzerland: 0.92 typical gaps (standard deviations), 0.93 index (0-1)Chile: 0.979 typical gaps (standard deviations), 0.913 index (0-1)China: 0.674 typical gaps (standard deviations), 0.587 index (0-1)Cote d'Ivoire: -0.0434 typical gaps (standard deviations), 0.696 index (0-1)Cameroon: -0.125 typical gaps (standard deviations), 0.76 index (0-1)Congo, Dem. Rep.: -0.251 typical gaps (standard deviations), 0.55 index (0-1)Congo, Rep.: 0.119 typical gaps (standard deviations), 0.466 index (0-1)Colombia: 0.63 typical gaps (standard deviations), 0.777 index (0-1)Comoros: 0.00771 typical gaps (standard deviations), 0.556 index (0-1)Cabo Verde: -0.216 typical gaps (standard deviations), 0.855 index (0-1)Costa Rica: 0.629 typical gaps (standard deviations), 0.907 index (0-1)Cyprus: 1.02 typical gaps (standard deviations), 0.856 index (0-1)Czechia: 1.08 typical gaps (standard deviations), 0.93 index (0-1)Germany: 0.938 typical gaps (standard deviations), 0.942 index (0-1)Djibouti: -0.825 typical gaps (standard deviations), 0.584 index (0-1)Denmark: 0.941 typical gaps (standard deviations), 0.954 index (0-1)Dominican Republic: 0.651 typical gaps (standard deviations), 0.825 index (0-1)Algeria: 0.663 typical gaps (standard deviations), 0.625 index (0-1)Ecuador: 0.722 typical gaps (standard deviations), 0.79 index (0-1)Egypt, Arab Rep.: 0.489 typical gaps (standard deviations), 0.547 index (0-1)Spain: 0.983 typical gaps (standard deviations), 0.943 index (0-1)Estonia: 0.841 typical gaps (standard deviations), 0.941 index (0-1)Ethiopia: -0.777 typical gaps (standard deviations), 0.691 index (0-1)Finland: 1.02 typical gaps (standard deviations), 0.932 index (0-1)Fiji: 0.887 typical gaps (standard deviations), 0.644 index (0-1)France: 0.907 typical gaps (standard deviations), 0.929 index (0-1)Gabon: -0.00264 typical gaps (standard deviations), 0.798 index (0-1)United Kingdom: 1.19 typical gaps (standard deviations), 0.838 index (0-1)Georgia: 0.697 typical gaps (standard deviations), 0.816 index (0-1)Ghana: 0.168 typical gaps (standard deviations), 0.767 index (0-1)Guinea: -0.598 typical gaps (standard deviations), 0.532 index (0-1)Gambia, The: -0.345 typical gaps (standard deviations), 0.813 index (0-1)Guinea-Bissau: -1.3 typical gaps (standard deviations), 0.502 index (0-1)Equatorial Guinea: -0.759 typical gaps (standard deviations), 0.449 index (0-1)Greece: 1.31 typical gaps (standard deviations), 0.896 index (0-1)Guatemala: 0.22 typical gaps (standard deviations), 0.722 index (0-1)Guyana: 0.39 typical gaps (standard deviations), 0.81 index (0-1)Hong Kong SAR, China: 1.34 typical gaps (standard deviations), 0.801 index (0-1)Honduras: 0.0516 typical gaps (standard deviations), 0.789 index (0-1)Croatia: 0.985 typical gaps (standard deviations), 0.913 index (0-1)Hungary: 0.83 typical gaps (standard deviations), 0.818 index (0-1)Indonesia: 0.591 typical gaps (standard deviations), 0.726 index (0-1)India: 0.605 typical gaps (standard deviations), 0.658 index (0-1)Ireland: 1.18 typical gaps (standard deviations), 0.927 index (0-1)Iran, Islamic Rep.: 0.667 typical gaps (standard deviations), 0.32 index (0-1)Iraq: -0.133 typical gaps (standard deviations), 0.555 index (0-1)Iceland: 1.26 typical gaps (standard deviations), 0.883 index (0-1)Israel: 0.804 typical gaps (standard deviations), 0.862 index (0-1)Italy: 0.94 typical gaps (standard deviations), 0.929 index (0-1)Jamaica: 0.446 typical gaps (standard deviations), 0.929 index (0-1)Jordan: 0.68 typical gaps (standard deviations), 0.669 index (0-1)Japan: 1.04 typical gaps (standard deviations), 0.844 index (0-1)Kazakhstan: 0.74 typical gaps (standard deviations), 0.682 index (0-1)Kenya: 0.0734 typical gaps (standard deviations), 0.804 index (0-1)Kyrgyz Republic: 0.764 typical gaps (standard deviations), 0.733 index (0-1)Cambodia: 0.174 typical gaps (standard deviations), 0.534 index (0-1)Korea, Rep.: 1.03 typical gaps (standard deviations), 0.878 index (0-1)Kuwait: 0.25 typical gaps (standard deviations), 0.357 index (0-1)Lao PDR: -0.35 typical gaps (standard deviations), 0.508 index (0-1)Lebanon: 0.088 typical gaps (standard deviations), 0.608 index (0-1)Liberia: -1.22 typical gaps (standard deviations), 0.823 index (0-1)Sri Lanka: 0.421 typical gaps (standard deviations), 0.742 index (0-1)Lesotho: -0.337 typical gaps (standard deviations), 0.809 index (0-1)Lithuania: 0.928 typical gaps (standard deviations), 0.926 index (0-1)Luxembourg: 0.66 typical gaps (standard deviations), 0.896 index (0-1)Latvia: 0.923 typical gaps (standard deviations), 0.95 index (0-1)Morocco: 0.345 typical gaps (standard deviations), 0.712 index (0-1)Moldova: 0.734 typical gaps (standard deviations), 0.898 index (0-1)Madagascar: -0.569 typical gaps (standard deviations), 0.704 index (0-1)Maldives: 0.427 typical gaps (standard deviations), 0.532 index (0-1)Mexico: 0.636 typical gaps (standard deviations), 0.801 index (0-1)North Macedonia: 0.658 typical gaps (standard deviations), 0.824 index (0-1)Mali: -1.24 typical gaps (standard deviations), 0.706 index (0-1)Malta: 0.945 typical gaps (standard deviations), 0.917 index (0-1)Myanmar: -0.0676 typical gaps (standard deviations), 0.33 index (0-1)Montenegro: 0.943 typical gaps (standard deviations), 0.866 index (0-1)Mongolia: 0.749 typical gaps (standard deviations), 0.806 index (0-1)Mozambique: -0.56 typical gaps (standard deviations), 0.725 index (0-1)Mauritania: -0.911 typical gaps (standard deviations), 0.541 index (0-1)Mauritius: 0.621 typical gaps (standard deviations), 0.814 index (0-1)Malawi: -0.546 typical gaps (standard deviations), 0.755 index (0-1)Malaysia: 0.529 typical gaps (standard deviations), 0.772 index (0-1)Namibia: 0.228 typical gaps (standard deviations), 0.855 index (0-1)Niger: -1.04 typical gaps (standard deviations), 0.709 index (0-1)Nigeria: -0.241 typical gaps (standard deviations), 0.732 index (0-1)Nicaragua: 0.168 typical gaps (standard deviations), 0.531 index (0-1)Netherlands: 1.27 typical gaps (standard deviations), 0.903 index (0-1)Norway: 1 typical gaps (standard deviations), 0.926 index (0-1)Nepal: 0.452 typical gaps (standard deviations), 0.792 index (0-1)New Zealand: 1.44 typical gaps (standard deviations), 0.937 index (0-1)Oman: 0.293 typical gaps (standard deviations), 0.357 index (0-1)Pakistan: -0.556 typical gaps (standard deviations), 0.524 index (0-1)Panama: 0.393 typical gaps (standard deviations), 0.742 index (0-1)Peru: 0.773 typical gaps (standard deviations), 0.775 index (0-1)Philippines: 0.548 typical gaps (standard deviations), 0.81 index (0-1)Papua New Guinea: -0.297 typical gaps (standard deviations), 0.48 index (0-1)Poland: 0.952 typical gaps (standard deviations), 0.878 index (0-1)Portugal: 1.02 typical gaps (standard deviations), 0.811 index (0-1)Paraguay: 0.452 typical gaps (standard deviations), 0.669 index (0-1)Qatar: 0.734 typical gaps (standard deviations), 0.388 index (0-1)Romania: 0.584 typical gaps (standard deviations), 0.872 index (0-1)Russian Federation: 0.85 typical gaps (standard deviations), 0.546 index (0-1)Rwanda: -0.127 typical gaps (standard deviations), 0.626 index (0-1)Saudi Arabia: 0.8 typical gaps (standard deviations), 0.347 index (0-1)Sudan: -0.831 typical gaps (standard deviations), 0.219 index (0-1)Senegal: -0.815 typical gaps (standard deviations), 0.855 index (0-1)Singapore: 1.26 typical gaps (standard deviations), 0.785 index (0-1)Solomon Islands: -0.0603 typical gaps (standard deviations), 0.675 index (0-1)Sierra Leone: -0.279 typical gaps (standard deviations), 0.801 index (0-1)El Salvador: 0.37 typical gaps (standard deviations), 0.587 index (0-1)Somalia, Fed. Rep.: -2.1 typical gaps (standard deviations), 0.337 index (0-1)Serbia: 0.761 typical gaps (standard deviations), 0.864 index (0-1)Sao Tome and Principe: 0.172 typical gaps (standard deviations), 0.843 index (0-1)Suriname: -0.157 typical gaps (standard deviations), 0.877 index (0-1)Slovak Republic: 0.801 typical gaps (standard deviations), 0.878 index (0-1)Slovenia: 1.18 typical gaps (standard deviations), 0.94 index (0-1)Sweden: 0.968 typical gaps (standard deviations), 0.923 index (0-1)Eswatini: 0.404 typical gaps (standard deviations), 0.391 index (0-1)Seychelles: 0.782 typical gaps (standard deviations), 0.908 index (0-1)Syrian Arab Republic: -0.332 typical gaps (standard deviations), 0.29 index (0-1)Chad: -1.2 typical gaps (standard deviations), 0.625 index (0-1)Togo: 0.075 typical gaps (standard deviations), 0.692 index (0-1)Thailand: 0.764 typical gaps (standard deviations), 0.688 index (0-1)Tajikistan: 0.525 typical gaps (standard deviations), 0.431 index (0-1)Turkmenistan: 0.598 typical gaps (standard deviations), 0.437 index (0-1)Timor-Leste: 0.12 typical gaps (standard deviations), 0.768 index (0-1)Trinidad and Tobago: 0.581 typical gaps (standard deviations), 0.874 index (0-1)Tunisia: 0.496 typical gaps (standard deviations), 0.765 index (0-1)Turkiye: 1.03 typical gaps (standard deviations), 0.558 index (0-1)Tanzania: -0.351 typical gaps (standard deviations), 0.834 index (0-1)Uganda: -0.832 typical gaps (standard deviations), 0.636 index (0-1)Ukraine: 0.784 typical gaps (standard deviations), 0.888 index (0-1)Uruguay: 0.9 typical gaps (standard deviations), 0.875 index (0-1)United States: 1.11 typical gaps (standard deviations), 0.829 index (0-1)Uzbekistan: 0.848 typical gaps (standard deviations), 0.643 index (0-1)Viet Nam: 0.537 typical gaps (standard deviations), 0.808 index (0-1)Vanuatu: 0.198 typical gaps (standard deviations), 0.715 index (0-1)South Africa: 0.665 typical gaps (standard deviations), 0.83 index (0-1)Zambia: -0.123 typical gaps (standard deviations), 0.791 index (0-1)Zimbabwe: 0.181 typical gaps (standard deviations), 0.727 index (0-1)AFGBELBENIRNLBRNZLSAUSDNSENSOMSVN

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.4853.9e-11165
By rank (Spearman), not adjusted+0.5944.4e-17165
Standard (Pearson), adjusted for national income+0.2340.003165
By rank (Spearman), adjusted for national income+0.3009.4e-5165

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 165 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: +0.251 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.208 (without KWT) to +0.256 (without TJK). 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: KWT (-0.026).

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 & Pacific21+0.57
Europe & Central Asia48+0.18
Latin America & Caribbean22+0.25
Middle East, North Africa, Afghanistan & Pakistan20+0.32
Sub-Saharan Africa46+0.29

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 third55+0.15
Middle third55+0.18
Richest third55+0.52

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
Christians98+0.13
Muslims46+0.20

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
France24-0.03
Never a European colony21+0.10
Spain19+0.74
United Kingdom48+0.45

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 = +0.216 after dropping 4 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.

Loading…

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)

index (0-1)
Years
New Zealand NZL1.440.9372023, 2025
Hong Kong SAR, China HKG1.340.8012024, 2025
Greece GRC1.310.8962023, 2025
Netherlands NLD1.270.9032023, 2025
Iceland ISL1.260.8832023, 2025
Singapore SGP1.260.7852023, 2025
Belgium BEL1.250.9522023, 2025
United Kingdom GBR1.190.8382023, 2025
Ireland IRL1.180.9272022, 2025
Slovenia SVN1.180.942023, 2025
United States USA1.110.8292022, 2025
Czechia CZE1.080.932023, 2025
Canada CAN1.060.8722023, 2025
Japan JPN1.040.8442023, 2025
Argentina ARG1.030.8322023, 2025

uis; undp_hdr; wdi
Coppedge, M. et al., V-Dem Dataset, Varieties of Democracy (V-Dem) Project

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.

Women's political empowerment

Women's political empowerment index

V-Dem Institute, University of Gothenburg · via Our World in Data · CC BY 4.0 · updated annual

Women's civil liberties, their part in civic groups, and their political representation. It is built from its own set of questions, not from the parts of the democracy indices, so it is not tagged as measuring the same thing. Still, read its links with them carefully. Whether women take part in politics is partly built into whether real political participation exists at all. So a strong link with general democracy measures is closer to definitional (true by how the terms are defined) than to a finding about the world.

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 Women's political empowerment

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

More links with Education score or Women's political empowerment