placestats

Regime type vs. Women in a union

Holds up after adjusting for income

Across 167 countries, Regime type and Women in a union rise and fall together at r = -0.43 (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 = -0.30, q = 0.021, allowing for the many pairs tested).

Comparing countries across the middle half of women in a union, 52.1% to 63% of women aged 15-49, regime type is worse by 0.523 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.355 points.

Link before adjusting (r)-0.425p = 1.0e-8 · 167 countries
After adjusting for national income-0.303p = 7.1e-5 · likely range [-0.44, -0.16]
p, allowing for regions0.007standard error 0.076 · 7 regions
Check for luck (q)0.021across 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.

Regime type · regime category (0-3)link with income +0.49 · moderate
Women in a union · % of women aged 15-49link with income -0.44 · moderate

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

Regime type · regime category (0-3) →Women in a union · % of women aged 15-49 →0333.6%81.3%Afghanistan: 0 regime category (0-3), 65.9% of women aged 15-49Angola: 1 regime category (0-3), 57.2% of women aged 15-49Albania: 1 regime category (0-3), 65.7% of women aged 15-49United Arab Emirates: 0 regime category (0-3), 67.4% of women aged 15-49Argentina: 2 regime category (0-3), 49.7% of women aged 15-49Armenia: 2 regime category (0-3), 61.5% of women aged 15-49Australia: 3 regime category (0-3), 54% of women aged 15-49Austria: 3 regime category (0-3), 56.6% of women aged 15-49Azerbaijan: 1 regime category (0-3), 59.7% of women aged 15-49Burundi: 1 regime category (0-3), 58.2% of women aged 15-49Belgium: 3 regime category (0-3), 52.4% of women aged 15-49Benin: 1 regime category (0-3), 65.7% of women aged 15-49Burkina Faso: 0 regime category (0-3), 74.3% of women aged 15-49Bangladesh: 0 regime category (0-3), 80.6% of women aged 15-49Bulgaria: 2 regime category (0-3), 56.6% of women aged 15-49Bahrain: 0 regime category (0-3), 56.4% of women aged 15-49Bosnia and Herzegovina: 1 regime category (0-3), 65.3% of women aged 15-49Belarus: 0 regime category (0-3), 62.4% of women aged 15-49Bolivia: 2 regime category (0-3), 50.2% of women aged 15-49Brazil: 2 regime category (0-3), 56.9% of women aged 15-49Barbados: 3 regime category (0-3), 57.2% of women aged 15-49Bhutan: 2 regime category (0-3), 62% of women aged 15-49Botswana: 2 regime category (0-3), 41.5% of women aged 15-49Central African Republic: 1 regime category (0-3), 65.4% of women aged 15-49Canada: 2 regime category (0-3), 51.6% of women aged 15-49Switzerland: 3 regime category (0-3), 57.5% of women aged 15-49Chile: 3 regime category (0-3), 48.5% of women aged 15-49China: 0 regime category (0-3), 69.5% of women aged 15-49Cote d'Ivoire: 1 regime category (0-3), 62.7% of women aged 15-49Cameroon: 1 regime category (0-3), 58.1% of women aged 15-49Congo, Dem. Rep.: 1 regime category (0-3), 58.2% of women aged 15-49Congo, Rep.: 1 regime category (0-3), 55.3% of women aged 15-49Colombia: 2 regime category (0-3), 50.9% of women aged 15-49Comoros: 1 regime category (0-3), 63.1% of women aged 15-49Cabo Verde: 2 regime category (0-3), 45% of women aged 15-49Costa Rica: 3 regime category (0-3), 41.7% of women aged 15-49Cyprus: 3 regime category (0-3), 52.3% of women aged 15-49Czechia: 3 regime category (0-3), 53.7% of women aged 15-49Germany: 3 regime category (0-3), 54.4% of women aged 15-49Djibouti: 1 regime category (0-3), 46.5% of women aged 15-49Denmark: 3 regime category (0-3), 54.3% of women aged 15-49Dominican Republic: 2 regime category (0-3), 53.2% of women aged 15-49Algeria: 1 regime category (0-3), 55.5% of women aged 15-49Ecuador: 2 regime category (0-3), 45.3% of women aged 15-49Egypt, Arab Rep.: 1 regime category (0-3), 65.6% of women aged 15-49Spain: 3 regime category (0-3), 50.2% of women aged 15-49Estonia: 3 regime category (0-3), 54.1% of women aged 15-49Ethiopia: 1 regime category (0-3), 66.3% of women aged 15-49Finland: 3 regime category (0-3), 57.1% of women aged 15-49Fiji: 2 regime category (0-3), 59.2% of women aged 15-49France: 3 regime category (0-3), 54.9% of women aged 15-49Gabon: 1 regime category (0-3), 48.5% of women aged 15-49United Kingdom: 2 regime category (0-3), 49.7% of women aged 15-49Georgia: 1 regime category (0-3), 65.3% of women aged 15-49Ghana: 2 regime category (0-3), 53.4% of women aged 15-49Guinea: 0 regime category (0-3), 70.9% of women aged 15-49Gambia, The: 2 regime category (0-3), 63% of women aged 15-49Guinea-Bissau: 0 regime category (0-3), 59% of women aged 15-49Equatorial Guinea: 1 regime category (0-3), 61.1% of women aged 15-49Greece: 2 regime category (0-3), 52.4% of women aged 15-49Guatemala: 2 regime category (0-3), 56.2% of women aged 15-49Guyana: 2 regime category (0-3), 59% of women aged 15-49Hong Kong SAR, China: 0 regime category (0-3), 49.8% of women aged 15-49Honduras: 2 regime category (0-3), 54.4% of women aged 15-49Croatia: 2 regime category (0-3), 50.1% of women aged 15-49Haiti: 0 regime category (0-3), 53.6% of women aged 15-49Hungary: 1 regime category (0-3), 52.3% of women aged 15-49Indonesia: 1 regime category (0-3), 70.3% of women aged 15-49India: 1 regime category (0-3), 72.3% of women aged 15-49Ireland: 3 regime category (0-3), 47.1% of women aged 15-49Iran, Islamic Rep.: 1 regime category (0-3), 69.6% of women aged 15-49Iraq: 1 regime category (0-3), 64.3% of women aged 15-49Iceland: 3 regime category (0-3), 48.5% of women aged 15-49Israel: 2 regime category (0-3), 50.4% of women aged 15-49Italy: 3 regime category (0-3), 51.8% of women aged 15-49Jamaica: 2 regime category (0-3), 35.2% of women aged 15-49Jordan: 0 regime category (0-3), 56% of women aged 15-49Japan: 3 regime category (0-3), 50% of women aged 15-49Kazakhstan: 1 regime category (0-3), 63.8% of women aged 15-49Kenya: 2 regime category (0-3), 53.2% of women aged 15-49Kyrgyz Republic: 1 regime category (0-3), 57.4% of women aged 15-49Cambodia: 1 regime category (0-3), 67.2% of women aged 15-49Korea, Rep.: 3 regime category (0-3), 49.8% of women aged 15-49Kuwait: 0 regime category (0-3), 59.5% of women aged 15-49Lao PDR: 0 regime category (0-3), 61.6% of women aged 15-49Lebanon: 1 regime category (0-3), 52.4% of women aged 15-49Liberia: 2 regime category (0-3), 51.4% of women aged 15-49Libya: 0 regime category (0-3), 43.8% of women aged 15-49Sri Lanka: 2 regime category (0-3), 66.3% of women aged 15-49Lesotho: 2 regime category (0-3), 46.9% of women aged 15-49Lithuania: 3 regime category (0-3), 51.8% of women aged 15-49Luxembourg: 3 regime category (0-3), 51.1% of women aged 15-49Latvia: 3 regime category (0-3), 49.6% of women aged 15-49Morocco: 0 regime category (0-3), 58% of women aged 15-49Moldova: 2 regime category (0-3), 68.7% of women aged 15-49Madagascar: 0 regime category (0-3), 62.3% of women aged 15-49Maldives: 2 regime category (0-3), 68.4% of women aged 15-49Mexico: 1 regime category (0-3), 53% of women aged 15-49North Macedonia: 2 regime category (0-3), 66.1% of women aged 15-49Mali: 0 regime category (0-3), 79.2% of women aged 15-49Malta: 2 regime category (0-3), 52% of women aged 15-49Myanmar: 0 regime category (0-3), 58% of women aged 15-49Montenegro: 2 regime category (0-3), 57.6% of women aged 15-49Mongolia: 2 regime category (0-3), 60.2% of women aged 15-49Mozambique: 1 regime category (0-3), 64.8% of women aged 15-49Mauritania: 1 regime category (0-3), 66.1% of women aged 15-49Mauritius: 2 regime category (0-3), 51.6% of women aged 15-49Malawi: 2 regime category (0-3), 62.1% of women aged 15-49Malaysia: 2 regime category (0-3), 57.1% of women aged 15-49Namibia: 2 regime category (0-3), 33.7% of women aged 15-49Niger: 0 regime category (0-3), 81.3% of women aged 15-49Nigeria: 2 regime category (0-3), 67.6% of women aged 15-49Nicaragua: 1 regime category (0-3), 56% of women aged 15-49Netherlands: 3 regime category (0-3), 53.5% of women aged 15-49Norway: 3 regime category (0-3), 57.4% of women aged 15-49Nepal: 2 regime category (0-3), 75.1% of women aged 15-49New Zealand: 3 regime category (0-3), 57.7% of women aged 15-49Oman: 0 regime category (0-3), 55.8% of women aged 15-49Pakistan: 1 regime category (0-3), 63.4% of women aged 15-49Panama: 2 regime category (0-3), 53.2% of women aged 15-49Peru: 2 regime category (0-3), 52.7% of women aged 15-49Philippines: 1 regime category (0-3), 55.6% of women aged 15-49Papua New Guinea: 1 regime category (0-3), 66.9% of women aged 15-49Poland: 2 regime category (0-3), 58.6% of women aged 15-49Portugal: 2 regime category (0-3), 50.6% of women aged 15-49Paraguay: 2 regime category (0-3), 54.8% of women aged 15-49Qatar: 0 regime category (0-3), 64.6% of women aged 15-49Romania: 2 regime category (0-3), 56% of women aged 15-49Russian Federation: 1 regime category (0-3), 57.6% of women aged 15-49Rwanda: 1 regime category (0-3), 49.9% of women aged 15-49Saudi Arabia: 0 regime category (0-3), 55.3% of women aged 15-49Sudan: 0 regime category (0-3), 62.6% of women aged 15-49Senegal: 2 regime category (0-3), 65.5% of women aged 15-49Singapore: 1 regime category (0-3), 43.8% of women aged 15-49Solomon Islands: 2 regime category (0-3), 64.5% of women aged 15-49Sierra Leone: 1 regime category (0-3), 61.5% of women aged 15-49El Salvador: 1 regime category (0-3), 50.7% of women aged 15-49Somalia, Fed. Rep.: 0 regime category (0-3), 62.4% of women aged 15-49Serbia: 1 regime category (0-3), 54.3% of women aged 15-49Sao Tome and Principe: 2 regime category (0-3), 53.1% of women aged 15-49Suriname: 2 regime category (0-3), 52.2% of women aged 15-49Slovak Republic: 2 regime category (0-3), 49.2% of women aged 15-49Slovenia: 2 regime category (0-3), 47.2% of women aged 15-49Sweden: 3 regime category (0-3), 53.6% of women aged 15-49Eswatini: 0 regime category (0-3), 36.4% of women aged 15-49Seychelles: 3 regime category (0-3), 44.6% of women aged 15-49Chad: 1 regime category (0-3), 72.6% of women aged 15-49Togo: 1 regime category (0-3), 63.7% of women aged 15-49Thailand: 1 regime category (0-3), 52.4% of women aged 15-49Tajikistan: 1 regime category (0-3), 68% of women aged 15-49Turkmenistan: 1 regime category (0-3), 65% of women aged 15-49Timor-Leste: 2 regime category (0-3), 56.4% of women aged 15-49Trinidad and Tobago: 3 regime category (0-3), 42.7% of women aged 15-49Tunisia: 1 regime category (0-3), 52.5% of women aged 15-49Turkiye: 1 regime category (0-3), 59.6% of women aged 15-49Tanzania: 1 regime category (0-3), 59.2% of women aged 15-49Uganda: 1 regime category (0-3), 57.8% of women aged 15-49Ukraine: 1 regime category (0-3), 61.9% of women aged 15-49Uruguay: 3 regime category (0-3), 53.6% of women aged 15-49United States: 2 regime category (0-3), 52.1% of women aged 15-49Uzbekistan: 1 regime category (0-3), 74.7% of women aged 15-49Viet Nam: 0 regime category (0-3), 72.8% of women aged 15-49Vanuatu: 2 regime category (0-3), 68.6% of women aged 15-49Kosovo: 2 regime category (0-3), 61.6% of women aged 15-49South Africa: 2 regime category (0-3), 33.6% of women aged 15-49Zambia: 1 regime category (0-3), 55.7% of women aged 15-49Zimbabwe: 1 regime category (0-3), 62.2% of women aged 15-49BGDCRIHKGLBYMDAMLINAMNERNPLSWZVUTZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.4251.0e-8167
By rank (Spearman), not adjusted-0.4584.8e-10167
Standard (Pearson), adjusted for national income-0.3037.1e-5167
By rank (Spearman), adjusted for national income-0.3242.0e-5167

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: +0.122 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.341 (without SWZ) to -0.284 (without ARE). 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: SWZ (-0.038).

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 & Pacific21-0.26
Europe & Central Asia49-0.20
Latin America & Caribbean23-0.36
Middle East, North Africa, Afghanistan & Pakistan20-0.20
Sub-Saharan Africa46-0.35

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 third56-0.31
Middle third55-0.31
Richest third56-0.41

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
Christians99-0.15
Muslims47-0.02

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
France24-0.34
Never a European colony21-0.34
Spain19-0.49
United Kingdom49-0.26

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 = -0.268 after dropping 5 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 35years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Within countries over time, there is no link that is clearly more than chance (r = -0.033, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose regime type moved did not tend to see their women in a union move with it.

Across countries (adjusted)-0.303for comparison
Within countries-0.195each country vs itself
…and vs the same year elsewhere-0.033q = 0.669 · 175 countries
Year-on-year changes+0.002q = 0.942

5,334 country-years (one country in one year), 175 countries, 1990–2024, about 31 years each on average. Share of the variation that happens within countries over time: 10% for Regime type, 17% for Women in a union. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.

Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.

With one measure years behind the other

0.10−0.1-100+10years Women in a union trails Regime type →lag -10: r = 0.006 (n = 4,492)lag -9: r = 0.003 (n = 4,650)lag -8: r = -0.003 (n = 4,805)lag -7: r = -0.005 (n = 4,952)lag -6: r = -0.004 (n = 5,076)lag -5: r = -0.011 (n = 5,183)lag -4: r = -0.013 (n = 5,270)lag -3: r = -0.016 (n = 5,323)lag -2: r = -0.025 (n = 5,340)lag -1: r = -0.029 (n = 5,339)lag +0: r = -0.033 (n = 5,334)lag +1: r = -0.033 (n = 5,159)lag +2: r = -0.036 (n = 4,984)lag +3: r = -0.040 (n = 4,809)lag +4: r = -0.048 (n = 4,634)lag +5: r = -0.056 (n = 4,459)lag +6: r = -0.067 (n = 4,285)lag +7: r = -0.077 (n = 4,110)lag +8: r = -0.084 (n = 3,936)lag +9: r = -0.089 (n = 3,762)lag +10: r = -0.091 (n = 3,587)

Same year (lag 0): -0.033. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.

This curve peaks at the edge of the window (lag +10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.

The data


regime category (0-3)

% of women aged 15-49
Years
Uruguay URY353.6%2025, 2013
Trinidad and Tobago TTO342.7%2025, 2022
Seychelles SYC344.6%2025, 2022
Sweden SWE353.6%2025, 2023
New Zealand NZL357.7%2025, 2018
Norway NOR357.4%2025, 2021
Netherlands NLD353.5%2025, 2023
Latvia LVA349.6%2025, 2021
Luxembourg LUX351.1%2025, 2022
Lithuania LTU351.8%2025, 2024
Korea, Rep. KOR349.8%2025, 2015
Japan JPN350%2025, 2020
Italy ITA351.8%2025, 2023
Iceland ISL348.5%2025, 2023
Ireland IRL347.1%2025, 2022

Coppedge, M. et al., V-Dem Dataset, Varieties of Democracy (V-Dem) Project
United Nations, Department of Economic and Social Affairs, Population Division: World Population Prospects 2024 and Estimates and Projections of Women of Reproductive Age Who Are Married or in a Union

Sources

Regime type

Regime type, Regimes of the World classification

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

Four ranked categories, not a scale. It is the only such measure on this site. A closed autocracy holds no multiparty elections for its leader. An electoral autocracy holds them, but they are neither free nor fair. An electoral democracy clears that bar. A liberal democracy also has independent courts and protected personal freedoms. The four steps are not equal distances apart. So read the rank-based link, which uses only the order, rather than the ordinary one. Both are shown. The categories are set by cut-off points on the V-Dem indices already on this site. That is why its pairs with them are flagged as built from the same numbers. It is also why it is kept out of the governance score: a grouping of those indices averaged back in beside them would count each twice. Our World in Data extends the categories back to territories before independence. This site starts at 1960, and its single-year snapshot at 2010, so that extension reaches nothing shown here.

Women in a union

Share of women aged 15 to 49 who are married or in a union

United Nations Department of Economic and Social Affairs, Population Division · via Our World in Data · CC BY 3.0 IGO · updated biennial

A snapshot of how many women are in a union at one time, not a count of new unions each year. It counts couples living together as a union, which the OECD marriage rate does not. So the two can disagree without either being wrong, and their pair is worth reading for exactly that. The UN estimates it with a statistical model from survey and census data, for over two hundred countries. No "better" direction is set.

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 Regime type and Women in a union

This page shows that two measures go together. It does not show that Regime type changes Women in a union, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Regime type or Women in a union