placestats

Education score vs. Property rights

Fades after adjusting for income

Across 166 countries, Education score and Property rights rise and fall together at r = +0.51 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.21, which could easily be chance (q = 0.12, 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 property rights, 0.644 to 0.867 points, education score is better by 0.388 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.097 typical gaps.

Link before adjusting (r)+0.508p = 2.8e-12 · 166 countries
After adjusting for national income+0.207p = 0.008 · likely range [+0.06, +0.35]
p, allowing for regions0.073standard error 0.095 · 7 regions
Check for luck (q)0.119across 17,292 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
Property rights · index (0-1)link with income +0.57 · 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) →Property rights · index (0-1) →-2.11.440.1150.968Afghanistan: -0.557 typical gaps (standard deviations), 0.252 index (0-1)Angola: -0.482 typical gaps (standard deviations), 0.415 index (0-1)Albania: 0.923 typical gaps (standard deviations), 0.799 index (0-1)United Arab Emirates: 0.911 typical gaps (standard deviations), 0.319 index (0-1)Argentina: 1.03 typical gaps (standard deviations), 0.872 index (0-1)Armenia: 0.65 typical gaps (standard deviations), 0.753 index (0-1)Australia: 1.03 typical gaps (standard deviations), 0.892 index (0-1)Austria: 1 typical gaps (standard deviations), 0.858 index (0-1)Azerbaijan: 0.629 typical gaps (standard deviations), 0.639 index (0-1)Burundi: -0.587 typical gaps (standard deviations), 0.621 index (0-1)Belgium: 1.25 typical gaps (standard deviations), 0.954 index (0-1)Benin: -0.586 typical gaps (standard deviations), 0.901 index (0-1)Burkina Faso: -0.582 typical gaps (standard deviations), 0.645 index (0-1)Bangladesh: 0.335 typical gaps (standard deviations), 0.896 index (0-1)Bulgaria: 0.794 typical gaps (standard deviations), 0.905 index (0-1)Bahrain: 0.537 typical gaps (standard deviations), 0.792 index (0-1)Bosnia and Herzegovina: 0.607 typical gaps (standard deviations), 0.816 index (0-1)Belarus: 0.722 typical gaps (standard deviations), 0.71 index (0-1)Bolivia: 0.634 typical gaps (standard deviations), 0.674 index (0-1)Brazil: 0.731 typical gaps (standard deviations), 0.808 index (0-1)Barbados: 0.581 typical gaps (standard deviations), 0.844 index (0-1)Bhutan: 0.277 typical gaps (standard deviations), 0.682 index (0-1)Botswana: 0.469 typical gaps (standard deviations), 0.826 index (0-1)Central African Republic: -1.65 typical gaps (standard deviations), 0.322 index (0-1)Canada: 1.06 typical gaps (standard deviations), 0.784 index (0-1)Switzerland: 0.92 typical gaps (standard deviations), 0.931 index (0-1)Chile: 0.979 typical gaps (standard deviations), 0.929 index (0-1)China: 0.674 typical gaps (standard deviations), 0.427 index (0-1)Cote d'Ivoire: -0.0434 typical gaps (standard deviations), 0.689 index (0-1)Cameroon: -0.125 typical gaps (standard deviations), 0.803 index (0-1)Congo, Dem. Rep.: -0.251 typical gaps (standard deviations), 0.256 index (0-1)Congo, Rep.: 0.119 typical gaps (standard deviations), 0.533 index (0-1)Colombia: 0.63 typical gaps (standard deviations), 0.772 index (0-1)Comoros: 0.00771 typical gaps (standard deviations), 0.476 index (0-1)Cabo Verde: -0.216 typical gaps (standard deviations), 0.846 index (0-1)Costa Rica: 0.629 typical gaps (standard deviations), 0.925 index (0-1)Cyprus: 1.02 typical gaps (standard deviations), 0.789 index (0-1)Czechia: 1.08 typical gaps (standard deviations), 0.929 index (0-1)Germany: 0.938 typical gaps (standard deviations), 0.968 index (0-1)Djibouti: -0.825 typical gaps (standard deviations), 0.562 index (0-1)Denmark: 0.941 typical gaps (standard deviations), 0.941 index (0-1)Dominican Republic: 0.651 typical gaps (standard deviations), 0.816 index (0-1)Algeria: 0.663 typical gaps (standard deviations), 0.739 index (0-1)Ecuador: 0.722 typical gaps (standard deviations), 0.644 index (0-1)Egypt, Arab Rep.: 0.489 typical gaps (standard deviations), 0.642 index (0-1)Spain: 0.983 typical gaps (standard deviations), 0.954 index (0-1)Estonia: 0.841 typical gaps (standard deviations), 0.927 index (0-1)Ethiopia: -0.777 typical gaps (standard deviations), 0.493 index (0-1)Finland: 1.02 typical gaps (standard deviations), 0.824 index (0-1)Fiji: 0.887 typical gaps (standard deviations), 0.532 index (0-1)France: 0.907 typical gaps (standard deviations), 0.946 index (0-1)Gabon: -0.00264 typical gaps (standard deviations), 0.798 index (0-1)United Kingdom: 1.19 typical gaps (standard deviations), 0.858 index (0-1)Georgia: 0.697 typical gaps (standard deviations), 0.886 index (0-1)Ghana: 0.168 typical gaps (standard deviations), 0.847 index (0-1)Guinea: -0.598 typical gaps (standard deviations), 0.319 index (0-1)Gambia, The: -0.345 typical gaps (standard deviations), 0.814 index (0-1)Guinea-Bissau: -1.3 typical gaps (standard deviations), 0.514 index (0-1)Equatorial Guinea: -0.759 typical gaps (standard deviations), 0.413 index (0-1)Greece: 1.31 typical gaps (standard deviations), 0.899 index (0-1)Guatemala: 0.22 typical gaps (standard deviations), 0.627 index (0-1)Guyana: 0.39 typical gaps (standard deviations), 0.836 index (0-1)Hong Kong SAR, China: 1.34 typical gaps (standard deviations), 0.871 index (0-1)Honduras: 0.0516 typical gaps (standard deviations), 0.851 index (0-1)Croatia: 0.985 typical gaps (standard deviations), 0.955 index (0-1)Hungary: 0.83 typical gaps (standard deviations), 0.922 index (0-1)Indonesia: 0.591 typical gaps (standard deviations), 0.672 index (0-1)India: 0.605 typical gaps (standard deviations), 0.77 index (0-1)Ireland: 1.18 typical gaps (standard deviations), 0.919 index (0-1)Iran, Islamic Rep.: 0.667 typical gaps (standard deviations), 0.685 index (0-1)Iraq: -0.133 typical gaps (standard deviations), 0.648 index (0-1)Iceland: 1.26 typical gaps (standard deviations), 0.959 index (0-1)Israel: 0.804 typical gaps (standard deviations), 0.885 index (0-1)Italy: 0.94 typical gaps (standard deviations), 0.927 index (0-1)Jamaica: 0.446 typical gaps (standard deviations), 0.898 index (0-1)Jordan: 0.68 typical gaps (standard deviations), 0.808 index (0-1)Japan: 1.04 typical gaps (standard deviations), 0.924 index (0-1)Kazakhstan: 0.74 typical gaps (standard deviations), 0.763 index (0-1)Kenya: 0.0734 typical gaps (standard deviations), 0.706 index (0-1)Kyrgyz Republic: 0.764 typical gaps (standard deviations), 0.85 index (0-1)Cambodia: 0.174 typical gaps (standard deviations), 0.498 index (0-1)Korea, Rep.: 1.03 typical gaps (standard deviations), 0.829 index (0-1)Kuwait: 0.25 typical gaps (standard deviations), 0.609 index (0-1)Lao PDR: -0.35 typical gaps (standard deviations), 0.344 index (0-1)Lebanon: 0.088 typical gaps (standard deviations), 0.671 index (0-1)Liberia: -1.22 typical gaps (standard deviations), 0.745 index (0-1)Sri Lanka: 0.421 typical gaps (standard deviations), 0.807 index (0-1)Lesotho: -0.337 typical gaps (standard deviations), 0.712 index (0-1)Lithuania: 0.928 typical gaps (standard deviations), 0.934 index (0-1)Luxembourg: 0.66 typical gaps (standard deviations), 0.929 index (0-1)Latvia: 0.923 typical gaps (standard deviations), 0.91 index (0-1)Morocco: 0.345 typical gaps (standard deviations), 0.803 index (0-1)Moldova: 0.734 typical gaps (standard deviations), 0.92 index (0-1)Madagascar: -0.569 typical gaps (standard deviations), 0.832 index (0-1)Maldives: 0.427 typical gaps (standard deviations), 0.82 index (0-1)Mexico: 0.636 typical gaps (standard deviations), 0.846 index (0-1)North Macedonia: 0.658 typical gaps (standard deviations), 0.799 index (0-1)Mali: -1.24 typical gaps (standard deviations), 0.775 index (0-1)Malta: 0.945 typical gaps (standard deviations), 0.919 index (0-1)Myanmar: -0.0676 typical gaps (standard deviations), 0.288 index (0-1)Montenegro: 0.943 typical gaps (standard deviations), 0.91 index (0-1)Mongolia: 0.749 typical gaps (standard deviations), 0.803 index (0-1)Mozambique: -0.56 typical gaps (standard deviations), 0.476 index (0-1)Mauritania: -0.911 typical gaps (standard deviations), 0.638 index (0-1)Mauritius: 0.621 typical gaps (standard deviations), 0.83 index (0-1)Malawi: -0.546 typical gaps (standard deviations), 0.787 index (0-1)Malaysia: 0.529 typical gaps (standard deviations), 0.709 index (0-1)Namibia: 0.228 typical gaps (standard deviations), 0.846 index (0-1)Niger: -1.04 typical gaps (standard deviations), 0.687 index (0-1)Nigeria: -0.241 typical gaps (standard deviations), 0.742 index (0-1)Nicaragua: 0.168 typical gaps (standard deviations), 0.495 index (0-1)Netherlands: 1.27 typical gaps (standard deviations), 0.851 index (0-1)Norway: 1 typical gaps (standard deviations), 0.934 index (0-1)Nepal: 0.452 typical gaps (standard deviations), 0.723 index (0-1)New Zealand: 1.44 typical gaps (standard deviations), 0.935 index (0-1)Oman: 0.293 typical gaps (standard deviations), 0.604 index (0-1)Pakistan: -0.556 typical gaps (standard deviations), 0.763 index (0-1)Panama: 0.393 typical gaps (standard deviations), 0.812 index (0-1)Peru: 0.773 typical gaps (standard deviations), 0.8 index (0-1)Philippines: 0.548 typical gaps (standard deviations), 0.792 index (0-1)Papua New Guinea: -0.297 typical gaps (standard deviations), 0.581 index (0-1)Poland: 0.952 typical gaps (standard deviations), 0.93 index (0-1)Portugal: 1.02 typical gaps (standard deviations), 0.659 index (0-1)Paraguay: 0.452 typical gaps (standard deviations), 0.748 index (0-1)West Bank and Gaza: 0.564 typical gaps (standard deviations), 0.646 index (0-1)Qatar: 0.734 typical gaps (standard deviations), 0.115 index (0-1)Romania: 0.584 typical gaps (standard deviations), 0.9 index (0-1)Russian Federation: 0.85 typical gaps (standard deviations), 0.762 index (0-1)Rwanda: -0.127 typical gaps (standard deviations), 0.172 index (0-1)Saudi Arabia: 0.8 typical gaps (standard deviations), 0.517 index (0-1)Sudan: -0.831 typical gaps (standard deviations), 0.145 index (0-1)Senegal: -0.815 typical gaps (standard deviations), 0.813 index (0-1)Singapore: 1.26 typical gaps (standard deviations), 0.715 index (0-1)Solomon Islands: -0.0603 typical gaps (standard deviations), 0.772 index (0-1)Sierra Leone: -0.279 typical gaps (standard deviations), 0.673 index (0-1)El Salvador: 0.37 typical gaps (standard deviations), 0.717 index (0-1)Somalia, Fed. Rep.: -2.1 typical gaps (standard deviations), 0.252 index (0-1)Serbia: 0.761 typical gaps (standard deviations), 0.896 index (0-1)Sao Tome and Principe: 0.172 typical gaps (standard deviations), 0.804 index (0-1)Suriname: -0.157 typical gaps (standard deviations), 0.859 index (0-1)Slovak Republic: 0.801 typical gaps (standard deviations), 0.929 index (0-1)Slovenia: 1.18 typical gaps (standard deviations), 0.888 index (0-1)Sweden: 0.968 typical gaps (standard deviations), 0.933 index (0-1)Eswatini: 0.404 typical gaps (standard deviations), 0.156 index (0-1)Seychelles: 0.782 typical gaps (standard deviations), 0.74 index (0-1)Syrian Arab Republic: -0.332 typical gaps (standard deviations), 0.468 index (0-1)Chad: -1.2 typical gaps (standard deviations), 0.779 index (0-1)Togo: 0.075 typical gaps (standard deviations), 0.698 index (0-1)Thailand: 0.764 typical gaps (standard deviations), 0.717 index (0-1)Tajikistan: 0.525 typical gaps (standard deviations), 0.559 index (0-1)Turkmenistan: 0.598 typical gaps (standard deviations), 0.163 index (0-1)Timor-Leste: 0.12 typical gaps (standard deviations), 0.65 index (0-1)Trinidad and Tobago: 0.581 typical gaps (standard deviations), 0.882 index (0-1)Tunisia: 0.496 typical gaps (standard deviations), 0.87 index (0-1)Turkiye: 1.03 typical gaps (standard deviations), 0.629 index (0-1)Tanzania: -0.351 typical gaps (standard deviations), 0.657 index (0-1)Uganda: -0.832 typical gaps (standard deviations), 0.451 index (0-1)Ukraine: 0.784 typical gaps (standard deviations), 0.79 index (0-1)Uruguay: 0.9 typical gaps (standard deviations), 0.695 index (0-1)United States: 1.11 typical gaps (standard deviations), 0.844 index (0-1)Uzbekistan: 0.848 typical gaps (standard deviations), 0.623 index (0-1)Viet Nam: 0.537 typical gaps (standard deviations), 0.71 index (0-1)Vanuatu: 0.198 typical gaps (standard deviations), 0.725 index (0-1)South Africa: 0.665 typical gaps (standard deviations), 0.858 index (0-1)Zambia: -0.123 typical gaps (standard deviations), 0.633 index (0-1)Zimbabwe: 0.181 typical gaps (standard deviations), 0.544 index (0-1)BELCAFISLLBRMLINZLQATSDNSOMSWZTCDTKM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5082.8e-12166
By rank (Spearman), not adjusted+0.5812.4e-16166
Standard (Pearson), adjusted for national income+0.2070.008166
By rank (Spearman), adjusted for national income+0.2140.006166

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 166 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: +0.301 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.174 (without QAT) to +0.226 (without MLI). 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 (-0.033).

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+0.14
Latin America & Caribbean22+0.04
Middle East, North Africa, Afghanistan & Pakistan21+0.31
Sub-Saharan Africa46+0.12

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 third56+0.16
Middle third55+0.11
Richest third55+0.34

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+0.15
Muslims47+0.23

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+0.07
Never a European colony21+0.18
Spain19+0.56
United Kingdom48+0.38

By official ideology

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
Nationalist25+0.18
No strong official ideology114+0.25

By party system

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
A few parties banned25+0.00
No parties banned120+0.20

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 = +0.201 after dropping 10 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 NZL1.440.9352023, 2025
Hong Kong SAR, China HKG1.340.8712024, 2025
Greece GRC1.310.8992023, 2025
Netherlands NLD1.270.8512023, 2025
Iceland ISL1.260.9592023, 2025
Singapore SGP1.260.7152023, 2025
Belgium BEL1.250.9542023, 2025
United Kingdom GBR1.190.8582023, 2025
Ireland IRL1.180.9192022, 2025
Slovenia SVN1.180.8882023, 2025
United States USA1.110.8442022, 2025
Czechia CZE1.080.9292023, 2025
Canada CAN1.060.7842023, 2025
Japan JPN1.040.9242023, 2025
Argentina ARG1.030.8722023, 2025

uis; undp_hdr; wdi
Coppedge, M. et al., V-Dem Dataset v16, Varieties of Democracy (V-Dem) Project; Lindberg, S. I. et al., Varieties of Party Identity and Organization (V-Party) Dataset. Read from the V-Dem Institute's vdemdata package.

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.

Property rights

Property rights index

V-Dem Institute, University of Gothenburg · CC BY-SA 4.0 · updated annual (V-Dem); V-Party per election, to 2019

How far people have the right to own, buy, inherit and sell private property, including land, from 0 (virtually no one) to 1 (virtually everyone). It combines V-Dem's expert ratings for men and for women. Limits can come from the law, from the state failing to enforce it, or from custom and religion. It is about the right to property, not how much property people actually own.

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 17,292 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Education score and Property rights

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

More links with Education score or Property rights