placestats

Gender score vs. Property rights

Holds up after adjusting for income

Across 167 countries, Gender score and Property rights rise and fall together at r = +0.51 (on a scale from −1 to +1, where 0 means no link). It holds up almost unchanged when only countries with similar income (GDP per person) are compared (r = +0.42, q = 0.038, allowing for the many pairs tested).

Comparing countries across the middle half of property rights, 0.638 to 0.859 points, gender score is better by 0.407 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.361 typical gaps.

Link before adjusting (r)+0.506p = 3.1e-12 · 167 countries
After adjusting for national income+0.417p = 2.3e-8 · likely range [+0.28, +0.54]
p, allowing for regions0.004standard error 0.093 · 7 regions
Check for luck (q)0.038across 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.

Gender score · typical gaps (standard deviations)link with income +0.37 · moderate
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

Gender score · typical gaps (standard deviations) →Property rights · index (0-1) →-1.222.190.1150.968Afghanistan: -0.758 typical gaps (standard deviations), 0.252 index (0-1)Angola: 0.459 typical gaps (standard deviations), 0.415 index (0-1)Albania: 1.27 typical gaps (standard deviations), 0.799 index (0-1)United Arab Emirates: 0.417 typical gaps (standard deviations), 0.319 index (0-1)Argentina: 1.08 typical gaps (standard deviations), 0.872 index (0-1)Armenia: 1.26 typical gaps (standard deviations), 0.753 index (0-1)Australia: 1.46 typical gaps (standard deviations), 0.892 index (0-1)Austria: 1.17 typical gaps (standard deviations), 0.858 index (0-1)Azerbaijan: -0.374 typical gaps (standard deviations), 0.639 index (0-1)Burundi: 0.991 typical gaps (standard deviations), 0.621 index (0-1)Belgium: 1.44 typical gaps (standard deviations), 0.954 index (0-1)Benin: 0.759 typical gaps (standard deviations), 0.901 index (0-1)Burkina Faso: -0.0943 typical gaps (standard deviations), 0.645 index (0-1)Bangladesh: -0.785 typical gaps (standard deviations), 0.896 index (0-1)Bulgaria: 0.272 typical gaps (standard deviations), 0.905 index (0-1)Bahrain: 0.0565 typical gaps (standard deviations), 0.792 index (0-1)Bosnia and Herzegovina: 0.442 typical gaps (standard deviations), 0.816 index (0-1)Belarus: 0.867 typical gaps (standard deviations), 0.71 index (0-1)Bolivia: 1.1 typical gaps (standard deviations), 0.674 index (0-1)Brazil: 0.684 typical gaps (standard deviations), 0.808 index (0-1)Barbados: 1.16 typical gaps (standard deviations), 0.844 index (0-1)Bhutan: -0.492 typical gaps (standard deviations), 0.682 index (0-1)Botswana: 0.25 typical gaps (standard deviations), 0.826 index (0-1)Central African Republic: -0.215 typical gaps (standard deviations), 0.322 index (0-1)Canada: 1.38 typical gaps (standard deviations), 0.784 index (0-1)Switzerland: 1.22 typical gaps (standard deviations), 0.931 index (0-1)Chile: 1.72 typical gaps (standard deviations), 0.929 index (0-1)China: -0.0629 typical gaps (standard deviations), 0.427 index (0-1)Cote d'Ivoire: 0.00107 typical gaps (standard deviations), 0.689 index (0-1)Cameroon: 0.739 typical gaps (standard deviations), 0.803 index (0-1)Congo, Dem. Rep.: -0.00209 typical gaps (standard deviations), 0.256 index (0-1)Congo, Rep.: -0.252 typical gaps (standard deviations), 0.533 index (0-1)Colombia: 0.908 typical gaps (standard deviations), 0.772 index (0-1)Comoros: -0.257 typical gaps (standard deviations), 0.476 index (0-1)Cabo Verde: 1.2 typical gaps (standard deviations), 0.846 index (0-1)Costa Rica: 1.7 typical gaps (standard deviations), 0.925 index (0-1)Cyprus: 0.592 typical gaps (standard deviations), 0.789 index (0-1)Czechia: 0.195 typical gaps (standard deviations), 0.929 index (0-1)Germany: 1.34 typical gaps (standard deviations), 0.968 index (0-1)Djibouti: 0.259 typical gaps (standard deviations), 0.562 index (0-1)Denmark: 1.1 typical gaps (standard deviations), 0.941 index (0-1)Dominican Republic: 1.32 typical gaps (standard deviations), 0.816 index (0-1)Algeria: -0.399 typical gaps (standard deviations), 0.739 index (0-1)Ecuador: 1.34 typical gaps (standard deviations), 0.644 index (0-1)Egypt, Arab Rep.: -0.205 typical gaps (standard deviations), 0.642 index (0-1)Spain: 1.69 typical gaps (standard deviations), 0.954 index (0-1)Estonia: 1.7 typical gaps (standard deviations), 0.927 index (0-1)Ethiopia: 1.4 typical gaps (standard deviations), 0.493 index (0-1)Finland: 2.03 typical gaps (standard deviations), 0.824 index (0-1)Fiji: -0.199 typical gaps (standard deviations), 0.532 index (0-1)France: 1.36 typical gaps (standard deviations), 0.946 index (0-1)Gabon: 0.418 typical gaps (standard deviations), 0.798 index (0-1)United Kingdom: 1.23 typical gaps (standard deviations), 0.858 index (0-1)Georgia: 0.577 typical gaps (standard deviations), 0.886 index (0-1)Ghana: 0.209 typical gaps (standard deviations), 0.847 index (0-1)Guinea: 0.289 typical gaps (standard deviations), 0.319 index (0-1)Gambia, The: 0.215 typical gaps (standard deviations), 0.814 index (0-1)Guinea-Bissau: -0.221 typical gaps (standard deviations), 0.514 index (0-1)Equatorial Guinea: -0.151 typical gaps (standard deviations), 0.413 index (0-1)Greece: 0.86 typical gaps (standard deviations), 0.899 index (0-1)Guatemala: 0.488 typical gaps (standard deviations), 0.627 index (0-1)Guyana: 1.22 typical gaps (standard deviations), 0.836 index (0-1)Honduras: 1.04 typical gaps (standard deviations), 0.851 index (0-1)Croatia: 0.51 typical gaps (standard deviations), 0.955 index (0-1)Haiti: -0.419 typical gaps (standard deviations), 0.635 index (0-1)Hungary: -0.00668 typical gaps (standard deviations), 0.922 index (0-1)Indonesia: 0.0942 typical gaps (standard deviations), 0.672 index (0-1)India: -0.411 typical gaps (standard deviations), 0.77 index (0-1)Ireland: 0.727 typical gaps (standard deviations), 0.919 index (0-1)Iran, Islamic Rep.: -0.962 typical gaps (standard deviations), 0.685 index (0-1)Iraq: 0.119 typical gaps (standard deviations), 0.648 index (0-1)Iceland: 2.19 typical gaps (standard deviations), 0.959 index (0-1)Israel: 0.452 typical gaps (standard deviations), 0.885 index (0-1)Italy: 0.706 typical gaps (standard deviations), 0.927 index (0-1)Jamaica: 1.09 typical gaps (standard deviations), 0.898 index (0-1)Jordan: -0.0146 typical gaps (standard deviations), 0.808 index (0-1)Japan: -0.021 typical gaps (standard deviations), 0.924 index (0-1)Kazakhstan: 0.296 typical gaps (standard deviations), 0.763 index (0-1)Kenya: 0.693 typical gaps (standard deviations), 0.706 index (0-1)Kyrgyz Republic: 0.129 typical gaps (standard deviations), 0.85 index (0-1)Cambodia: -0.451 typical gaps (standard deviations), 0.498 index (0-1)Korea, Rep.: 0.412 typical gaps (standard deviations), 0.829 index (0-1)Kuwait: -1.02 typical gaps (standard deviations), 0.609 index (0-1)Lao PDR: -0.093 typical gaps (standard deviations), 0.344 index (0-1)Lebanon: -0.687 typical gaps (standard deviations), 0.671 index (0-1)Liberia: 0.525 typical gaps (standard deviations), 0.745 index (0-1)Libya: -0.0625 typical gaps (standard deviations), 0.558 index (0-1)Sri Lanka: -0.263 typical gaps (standard deviations), 0.807 index (0-1)Lesotho: 0.26 typical gaps (standard deviations), 0.712 index (0-1)Lithuania: 0.944 typical gaps (standard deviations), 0.934 index (0-1)Luxembourg: 0.927 typical gaps (standard deviations), 0.929 index (0-1)Latvia: 1.32 typical gaps (standard deviations), 0.91 index (0-1)Morocco: 0.514 typical gaps (standard deviations), 0.803 index (0-1)Moldova: 1.45 typical gaps (standard deviations), 0.92 index (0-1)Madagascar: 0.668 typical gaps (standard deviations), 0.832 index (0-1)Maldives: -0.746 typical gaps (standard deviations), 0.82 index (0-1)Mexico: 1.33 typical gaps (standard deviations), 0.846 index (0-1)North Macedonia: 1.08 typical gaps (standard deviations), 0.799 index (0-1)Mali: 0.147 typical gaps (standard deviations), 0.775 index (0-1)Malta: 0.55 typical gaps (standard deviations), 0.919 index (0-1)Myanmar: 0.307 typical gaps (standard deviations), 0.288 index (0-1)Montenegro: 0.895 typical gaps (standard deviations), 0.91 index (0-1)Mongolia: 0.915 typical gaps (standard deviations), 0.803 index (0-1)Mozambique: 0.926 typical gaps (standard deviations), 0.476 index (0-1)Mauritania: 0.103 typical gaps (standard deviations), 0.638 index (0-1)Mauritius: 0.0838 typical gaps (standard deviations), 0.83 index (0-1)Malawi: 0.728 typical gaps (standard deviations), 0.787 index (0-1)Malaysia: -0.0868 typical gaps (standard deviations), 0.709 index (0-1)Namibia: 1.41 typical gaps (standard deviations), 0.846 index (0-1)Niger: 0.397 typical gaps (standard deviations), 0.687 index (0-1)Nigeria: -0.214 typical gaps (standard deviations), 0.742 index (0-1)Nicaragua: 1.98 typical gaps (standard deviations), 0.495 index (0-1)Netherlands: 1.19 typical gaps (standard deviations), 0.851 index (0-1)Norway: 1.53 typical gaps (standard deviations), 0.934 index (0-1)Nepal: 0.416 typical gaps (standard deviations), 0.723 index (0-1)New Zealand: 1.62 typical gaps (standard deviations), 0.935 index (0-1)Oman: -0.95 typical gaps (standard deviations), 0.604 index (0-1)Pakistan: -0.691 typical gaps (standard deviations), 0.763 index (0-1)Panama: 0.873 typical gaps (standard deviations), 0.812 index (0-1)Peru: 0.932 typical gaps (standard deviations), 0.8 index (0-1)Philippines: 0.579 typical gaps (standard deviations), 0.792 index (0-1)Papua New Guinea: -1.01 typical gaps (standard deviations), 0.581 index (0-1)Poland: 1.13 typical gaps (standard deviations), 0.93 index (0-1)Portugal: 1.05 typical gaps (standard deviations), 0.659 index (0-1)Paraguay: 0.39 typical gaps (standard deviations), 0.748 index (0-1)Qatar: -0.892 typical gaps (standard deviations), 0.115 index (0-1)Romania: 0.285 typical gaps (standard deviations), 0.9 index (0-1)Russian Federation: 0.137 typical gaps (standard deviations), 0.762 index (0-1)Rwanda: 1.02 typical gaps (standard deviations), 0.172 index (0-1)Saudi Arabia: -0.552 typical gaps (standard deviations), 0.517 index (0-1)Sudan: -1.22 typical gaps (standard deviations), 0.145 index (0-1)Senegal: 1.03 typical gaps (standard deviations), 0.813 index (0-1)Singapore: 1.04 typical gaps (standard deviations), 0.715 index (0-1)Solomon Islands: -0.473 typical gaps (standard deviations), 0.772 index (0-1)Sierra Leone: 0.991 typical gaps (standard deviations), 0.673 index (0-1)El Salvador: 0.592 typical gaps (standard deviations), 0.717 index (0-1)Somalia, Fed. Rep.: -0.615 typical gaps (standard deviations), 0.252 index (0-1)Serbia: 1.01 typical gaps (standard deviations), 0.896 index (0-1)Sao Tome and Principe: 0.8 typical gaps (standard deviations), 0.804 index (0-1)Suriname: 1.04 typical gaps (standard deviations), 0.859 index (0-1)Slovak Republic: 0.374 typical gaps (standard deviations), 0.929 index (0-1)Slovenia: 1.26 typical gaps (standard deviations), 0.888 index (0-1)Sweden: 1.79 typical gaps (standard deviations), 0.933 index (0-1)Eswatini: -0.0447 typical gaps (standard deviations), 0.156 index (0-1)Seychelles: 1.17 typical gaps (standard deviations), 0.74 index (0-1)Syrian Arab Republic: -0.767 typical gaps (standard deviations), 0.468 index (0-1)Chad: 0.144 typical gaps (standard deviations), 0.779 index (0-1)Togo: 0.48 typical gaps (standard deviations), 0.698 index (0-1)Thailand: 0.331 typical gaps (standard deviations), 0.717 index (0-1)Tajikistan: -0.106 typical gaps (standard deviations), 0.559 index (0-1)Turkmenistan: -0.394 typical gaps (standard deviations), 0.163 index (0-1)Timor-Leste: 0.73 typical gaps (standard deviations), 0.65 index (0-1)Trinidad and Tobago: 1.21 typical gaps (standard deviations), 0.882 index (0-1)Tunisia: 0.319 typical gaps (standard deviations), 0.87 index (0-1)Turkiye: -0.26 typical gaps (standard deviations), 0.629 index (0-1)Tanzania: 0.804 typical gaps (standard deviations), 0.657 index (0-1)Uganda: 0.849 typical gaps (standard deviations), 0.451 index (0-1)Ukraine: 0.643 typical gaps (standard deviations), 0.79 index (0-1)Uruguay: 1.12 typical gaps (standard deviations), 0.695 index (0-1)United States: 1.06 typical gaps (standard deviations), 0.844 index (0-1)Uzbekistan: 0.228 typical gaps (standard deviations), 0.623 index (0-1)Viet Nam: 0.0894 typical gaps (standard deviations), 0.71 index (0-1)Vanuatu: 0.107 typical gaps (standard deviations), 0.725 index (0-1)Kosovo: 0.374 typical gaps (standard deviations), 0.518 index (0-1)South Africa: 1.45 typical gaps (standard deviations), 0.858 index (0-1)Zambia: 0.21 typical gaps (standard deviations), 0.633 index (0-1)Zimbabwe: 0.6 typical gaps (standard deviations), 0.544 index (0-1)AFGBGDESPETHISLMDVNICPAKQATRWASDNSWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5063.1e-12167
By rank (Spearman), not adjusted+0.5539.4e-15167
Standard (Pearson), adjusted for national income+0.4172.3e-8167
By rank (Spearman), adjusted for national income+0.4481.4e-9167

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 167 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: +0.089 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.374 (without QAT) to +0.446 (without RWA). 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 (-0.042).

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 & Pacific20+0.32
Europe & Central Asia49+0.40
Latin America & Caribbean23-0.01
Middle East, North Africa, Afghanistan & Pakistan21+0.46
Sub-Saharan Africa46+0.41

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+0.22
Middle third55+0.49
Richest third56+0.59

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
Christians99+0.21
Muslims48+0.40

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
France25+0.59
Never a European colony21+0.48
Spain19+0.27
United Kingdom49+0.40

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
Nationalist24+0.40
No strong official ideology116+0.38

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 banned26+0.48
No parties banned121+0.26

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 = +0.365 after dropping 9 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)

index (0-1)
Years
Iceland ISL2.190.9592024, 2025
Finland FIN2.030.8242024, 2025
Nicaragua NIC1.980.4952024, 2025
Sweden SWE1.790.9332024, 2025
Chile CHL1.720.9292024, 2025
Costa Rica CRI1.70.9252025
Estonia EST1.70.9272024, 2025
Spain ESP1.690.9542024, 2025
New Zealand NZL1.620.9352024, 2025
Norway NOR1.530.9342024, 2025
Australia AUS1.460.8922025
South Africa ZAF1.450.8582024, 2025
Moldova MDA1.450.922024, 2025
Belgium BEL1.440.9542024, 2025
Namibia NAM1.410.8462024, 2025

vdem; wb_gender; 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

Gender score

Gender score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 5 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: Women's political empowerment, Women ministers, Women in parliament, Women in management, Married by 18.

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 2018–2025. 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 Gender score and Property rights

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

More links with Gender score or Property rights