placestats

Gender Inequality Index vs. Unintended pregnancy rate

Fades after adjusting for income

Across 137 countries, Gender Inequality Index and Unintended pregnancy rate rise and fall together at r = +0.75 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.46, which could easily be chance (q = 0.11, 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. Caution: direction is not consistent across regions.

Comparing countries across the middle half of unintended pregnancy rate, 34.7 to 86.9 per 1,000 women aged 15-49, gender inequality index is worse by 0.257 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0992 points.

Link before adjusting (r)+0.754p = 1.9e-26 · 137 countries
After adjusting for national income+0.458p = 2.1e-8 · likely range [+0.31, +0.58]
p, allowing for regions0.008standard error 0.118 · 7 regions
Check for luck (q)0.110across 14,952 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 Inequality Index · index, 0 to 1link with income -0.89 · proxy
Unintended pregnancy rate · per 1,000 women aged 15-49link with income -0.69 · high

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

Gender Inequality Index · index, 0 to 1 →Unintended pregnancy rate · per 1,000 women aged 15-49 →0.0030.67710.9145Angola: 0.515 index, 0 to 1, 120 per 1,000 women aged 15-49Albania: 0.107 index, 0 to 1, 16 per 1,000 women aged 15-49Argentina: 0.264 index, 0 to 1, 69 per 1,000 women aged 15-49Armenia: 0.18 index, 0 to 1, 52.6 per 1,000 women aged 15-49Australia: 0.056 index, 0 to 1, 37.4 per 1,000 women aged 15-49Azerbaijan: 0.315 index, 0 to 1, 92.9 per 1,000 women aged 15-49Burundi: 0.501 index, 0 to 1, 98.1 per 1,000 women aged 15-49Belgium: 0.031 index, 0 to 1, 21 per 1,000 women aged 15-49Benin: 0.573 index, 0 to 1, 85.9 per 1,000 women aged 15-49Burkina Faso: 0.555 index, 0 to 1, 75.2 per 1,000 women aged 15-49Bangladesh: 0.487 index, 0 to 1, 58.9 per 1,000 women aged 15-49Bulgaria: 0.208 index, 0 to 1, 28.6 per 1,000 women aged 15-49Bosnia and Herzegovina: 0.157 index, 0 to 1, 20 per 1,000 women aged 15-49Belarus: 0.08 index, 0 to 1, 18.7 per 1,000 women aged 15-49Belize: 0.428 index, 0 to 1, 70.9 per 1,000 women aged 15-49Bolivia: 0.419 index, 0 to 1, 105 per 1,000 women aged 15-49Brazil: 0.39 index, 0 to 1, 67.2 per 1,000 women aged 15-49Barbados: 0.297 index, 0 to 1, 76.9 per 1,000 women aged 15-49Bhutan: 0.278 index, 0 to 1, 47.5 per 1,000 women aged 15-49Botswana: 0.49 index, 0 to 1, 96.8 per 1,000 women aged 15-49Canada: 0.052 index, 0 to 1, 31.5 per 1,000 women aged 15-49Switzerland: 0.01 index, 0 to 1, 20.3 per 1,000 women aged 15-49Chile: 0.102 index, 0 to 1, 70 per 1,000 women aged 15-49China: 0.132 index, 0 to 1, 64.8 per 1,000 women aged 15-49Cote d'Ivoire: 0.589 index, 0 to 1, 96.4 per 1,000 women aged 15-49Cameroon: 0.558 index, 0 to 1, 83.4 per 1,000 women aged 15-49Congo, Dem. Rep.: 0.604 index, 0 to 1, 117 per 1,000 women aged 15-49Congo, Rep.: 0.565 index, 0 to 1, 106 per 1,000 women aged 15-49Colombia: 0.393 index, 0 to 1, 62.7 per 1,000 women aged 15-49Comoros: 0.501 index, 0 to 1, 95.2 per 1,000 women aged 15-49Cabo Verde: 0.298 index, 0 to 1, 102 per 1,000 women aged 15-49Costa Rica: 0.217 index, 0 to 1, 51.8 per 1,000 women aged 15-49Czechia: 0.088 index, 0 to 1, 34.9 per 1,000 women aged 15-49Germany: 0.057 index, 0 to 1, 21.3 per 1,000 women aged 15-49Djibouti: 0.481 index, 0 to 1, 50.4 per 1,000 women aged 15-49Denmark: 0.003 index, 0 to 1, 29.8 per 1,000 women aged 15-49Dominican Republic: 0.417 index, 0 to 1, 83.7 per 1,000 women aged 15-49Ecuador: 0.358 index, 0 to 1, 79.7 per 1,000 women aged 15-49Spain: 0.043 index, 0 to 1, 20.3 per 1,000 women aged 15-49Estonia: 0.061 index, 0 to 1, 38.1 per 1,000 women aged 15-49Ethiopia: 0.497 index, 0 to 1, 79.5 per 1,000 women aged 15-49Finland: 0.021 index, 0 to 1, 32.6 per 1,000 women aged 15-49France: 0.034 index, 0 to 1, 29 per 1,000 women aged 15-49Gabon: 0.505 index, 0 to 1, 112 per 1,000 women aged 15-49United Kingdom: 0.083 index, 0 to 1, 36.2 per 1,000 women aged 15-49Georgia: 0.257 index, 0 to 1, 93.6 per 1,000 women aged 15-49Ghana: 0.514 index, 0 to 1, 102 per 1,000 women aged 15-49Guinea: 0.609 index, 0 to 1, 69.8 per 1,000 women aged 15-49Gambia, The: 0.578 index, 0 to 1, 53.7 per 1,000 women aged 15-49Guinea-Bissau: 0.632 index, 0 to 1, 99.5 per 1,000 women aged 15-49Greece: 0.103 index, 0 to 1, 33 per 1,000 women aged 15-49Guatemala: 0.48 index, 0 to 1, 62.8 per 1,000 women aged 15-49Guyana: 0.427 index, 0 to 1, 76.5 per 1,000 women aged 15-49Honduras: 0.437 index, 0 to 1, 67.8 per 1,000 women aged 15-49Croatia: 0.074 index, 0 to 1, 33.5 per 1,000 women aged 15-49Haiti: 0.618 index, 0 to 1, 107 per 1,000 women aged 15-49Hungary: 0.213 index, 0 to 1, 27.3 per 1,000 women aged 15-49Indonesia: 0.423 index, 0 to 1, 39.9 per 1,000 women aged 15-49India: 0.403 index, 0 to 1, 62.4 per 1,000 women aged 15-49Iceland: 0.024 index, 0 to 1, 32.6 per 1,000 women aged 15-49Italy: 0.043 index, 0 to 1, 28.3 per 1,000 women aged 15-49Jamaica: 0.358 index, 0 to 1, 86.9 per 1,000 women aged 15-49Japan: 0.059 index, 0 to 1, 21.4 per 1,000 women aged 15-49Kazakhstan: 0.182 index, 0 to 1, 57.5 per 1,000 women aged 15-49Kenya: 0.526 index, 0 to 1, 113 per 1,000 women aged 15-49Kyrgyz Republic: 0.34 index, 0 to 1, 39.7 per 1,000 women aged 15-49Cambodia: 0.506 index, 0 to 1, 69.2 per 1,000 women aged 15-49Korea, Rep.: 0.038 index, 0 to 1, 34.5 per 1,000 women aged 15-49Lao PDR: 0.475 index, 0 to 1, 50.7 per 1,000 women aged 15-49Liberia: 0.646 index, 0 to 1, 106 per 1,000 women aged 15-49St. Lucia: 0.327 index, 0 to 1, 68.7 per 1,000 women aged 15-49Sri Lanka: 0.367 index, 0 to 1, 38.9 per 1,000 women aged 15-49Lesotho: 0.534 index, 0 to 1, 98.9 per 1,000 women aged 15-49Lithuania: 0.07 index, 0 to 1, 25.6 per 1,000 women aged 15-49Latvia: 0.117 index, 0 to 1, 33.8 per 1,000 women aged 15-49Moldova: 0.146 index, 0 to 1, 26.2 per 1,000 women aged 15-49Madagascar: 0.584 index, 0 to 1, 94.8 per 1,000 women aged 15-49Maldives: 0.309 index, 0 to 1, 59.5 per 1,000 women aged 15-49Mexico: 0.358 index, 0 to 1, 60.3 per 1,000 women aged 15-49North Macedonia: 0.112 index, 0 to 1, 23.2 per 1,000 women aged 15-49Mali: 0.612 index, 0 to 1, 67.6 per 1,000 women aged 15-49Myanmar: 0.478 index, 0 to 1, 35.5 per 1,000 women aged 15-49Montenegro: 0.121 index, 0 to 1, 10.9 per 1,000 women aged 15-49Mongolia: 0.284 index, 0 to 1, 31.4 per 1,000 women aged 15-49Mozambique: 0.479 index, 0 to 1, 88.2 per 1,000 women aged 15-49Mauritania: 0.603 index, 0 to 1, 67.6 per 1,000 women aged 15-49Malawi: 0.581 index, 0 to 1, 115 per 1,000 women aged 15-49Namibia: 0.448 index, 0 to 1, 104 per 1,000 women aged 15-49Niger: 0.591 index, 0 to 1, 49.2 per 1,000 women aged 15-49Nigeria: 0.677 index, 0 to 1, 68.2 per 1,000 women aged 15-49Nicaragua: 0.408 index, 0 to 1, 56.3 per 1,000 women aged 15-49Netherlands: 0.013 index, 0 to 1, 17.5 per 1,000 women aged 15-49Norway: 0.004 index, 0 to 1, 34.2 per 1,000 women aged 15-49Nepal: 0.487 index, 0 to 1, 59.7 per 1,000 women aged 15-49New Zealand: 0.082 index, 0 to 1, 40.9 per 1,000 women aged 15-49Pakistan: 0.536 index, 0 to 1, 70.9 per 1,000 women aged 15-49Panama: 0.374 index, 0 to 1, 73.1 per 1,000 women aged 15-49Peru: 0.34 index, 0 to 1, 89.5 per 1,000 women aged 15-49Philippines: 0.351 index, 0 to 1, 70.8 per 1,000 women aged 15-49Papua New Guinea: 0.584 index, 0 to 1, 79.8 per 1,000 women aged 15-49Poland: 0.081 index, 0 to 1, 28.7 per 1,000 women aged 15-49Portugal: 0.076 index, 0 to 1, 20.9 per 1,000 women aged 15-49Paraguay: 0.412 index, 0 to 1, 67.4 per 1,000 women aged 15-49Romania: 0.227 index, 0 to 1, 44.4 per 1,000 women aged 15-49Russian Federation: 0.169 index, 0 to 1, 64.2 per 1,000 women aged 15-49Rwanda: 0.394 index, 0 to 1, 97.6 per 1,000 women aged 15-49Senegal: 0.49 index, 0 to 1, 61.3 per 1,000 women aged 15-49Singapore: 0.031 index, 0 to 1, 18.8 per 1,000 women aged 15-49Solomon Islands: 0.478 index, 0 to 1, 114 per 1,000 women aged 15-49Sierra Leone: 0.566 index, 0 to 1, 88.2 per 1,000 women aged 15-49El Salvador: 0.362 index, 0 to 1, 57.6 per 1,000 women aged 15-49Somalia, Fed. Rep.: 0.675 index, 0 to 1, 99.6 per 1,000 women aged 15-49Serbia: 0.117 index, 0 to 1, 21 per 1,000 women aged 15-49Sao Tome and Principe: 0.492 index, 0 to 1, 130 per 1,000 women aged 15-49Suriname: 0.391 index, 0 to 1, 66.9 per 1,000 women aged 15-49Slovak Republic: 0.176 index, 0 to 1, 27.3 per 1,000 women aged 15-49Slovenia: 0.042 index, 0 to 1, 33.2 per 1,000 women aged 15-49Sweden: 0.007 index, 0 to 1, 35.8 per 1,000 women aged 15-49Eswatini: 0.484 index, 0 to 1, 111 per 1,000 women aged 15-49Chad: 0.67 index, 0 to 1, 59.6 per 1,000 women aged 15-49Togo: 0.564 index, 0 to 1, 100 per 1,000 women aged 15-49Thailand: 0.288 index, 0 to 1, 38.3 per 1,000 women aged 15-49Tajikistan: 0.258 index, 0 to 1, 36.9 per 1,000 women aged 15-49Timor-Leste: 0.394 index, 0 to 1, 49.6 per 1,000 women aged 15-49Tonga: 0.444 index, 0 to 1, 49.4 per 1,000 women aged 15-49Trinidad and Tobago: 0.262 index, 0 to 1, 83.9 per 1,000 women aged 15-49Tanzania: 0.504 index, 0 to 1, 105 per 1,000 women aged 15-49Uganda: 0.524 index, 0 to 1, 145 per 1,000 women aged 15-49Uruguay: 0.218 index, 0 to 1, 41.5 per 1,000 women aged 15-49United States: 0.169 index, 0 to 1, 34.7 per 1,000 women aged 15-49Uzbekistan: 0.291 index, 0 to 1, 28.6 per 1,000 women aged 15-49Viet Nam: 0.299 index, 0 to 1, 85.5 per 1,000 women aged 15-49Vanuatu: 0.556 index, 0 to 1, 86.9 per 1,000 women aged 15-49Samoa: 0.416 index, 0 to 1, 66.4 per 1,000 women aged 15-49South Africa: 0.388 index, 0 to 1, 81.5 per 1,000 women aged 15-49Zambia: 0.524 index, 0 to 1, 123 per 1,000 women aged 15-49Zimbabwe: 0.519 index, 0 to 1, 73.9 per 1,000 women aged 15-49CHECODCPVMMRNERNLDSGPSTPTCDUGA

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7541.9e-26137
By rank (Spearman), not adjusted+0.7613.8e-27137
Standard (Pearson), adjusted for national income+0.4582.1e-8137
By rank (Spearman), adjusted for national income+0.4494.1e-8137

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 137 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: +0.297 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.429 (without GUY) to +0.477 (without UGA). 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: GUY (-0.029).

Fragile: this link may not hold up.
  • direction is not consistent across regions

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 & Pacific20-0.20
Europe & Central Asia41+0.62
Latin America & Caribbean25+0.28
Sub-Saharan Africa41-0.25

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 third46+0.19
Middle third45+0.54
Richest third46+0.66

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
Christians93+0.62
Muslims26+0.53

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
France20+0.14
Never a European colony15+0.55
Spain17+0.23
United Kingdom38+0.23

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 = +0.458 after dropping 0 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


index, 0 to 1

per 1,000 women aged 15-49
Years
Nigeria NGA0.67768.22023, 2019
Somalia, Fed. Rep. SOM0.67599.62023, 2019
Chad TCD0.6759.62023, 2019
Liberia LBR0.6461062023, 2019
Guinea-Bissau GNB0.63299.52023, 2019
Haiti HTI0.6181072023, 2019
Mali MLI0.61267.62023, 2019
Guinea GIN0.60969.82023, 2019
Congo, Dem. Rep. COD0.6041172023, 2019
Mauritania MRT0.60367.62023, 2019
Niger NER0.59149.22023, 2019
Cote d'Ivoire CIV0.58996.42023, 2019
Papua New Guinea PNG0.58479.82023, 2019
Madagascar MDG0.58494.82023, 2019
Malawi MWI0.5811152023, 2019

United Nations Development Programme, Human Development Report 2025
Bearak, J. M., Popinchalk, A., Beavin, C. et al. (2022), 'Country-specific estimates of unintended pregnancy and abortion incidence: a global comparative analysis of levels in 2015-2019', BMJ Global Health 7(3), e007151. Guttmacher Institute. Supplementary appendix, tables A3-A5.

Sources

Gender Inequality Index

Gender Inequality Index

United Nations Development Programme, Human Development Report Office · via Our World in Data · CC BY 3.0 IGO · updated annual

Built from deaths in childbirth, births to teenagers, seats in parliament, secondary schooling, and the share of women and men in the workforce. Five of those inputs are on the site and flagged. It is kept out of the gender score, for the same reason the HDI is kept out of the economy score.

Unintended pregnancy rate

Unintended pregnancies per 1,000 women aged 15-49 (model estimate, 2015-2019)

Guttmacher Institute (Bearak et al. 2022, BMJ Global Health) · CC BY-NC 4.0 · updated one edition (2015-2019 estimates, published 2022)

The Guttmacher Institute's estimate of pregnancies that were not wanted then or not wanted at all, per 1,000 women aged 15 to 49, averaged over 2015-2019. Whether a pregnancy was intended comes from what women say in surveys, so it is an estimate built from those surveys and a statistical model. Each figure has a range: Uganda's is 145, but it could be from 123 to 169.

How this was computed

Data used: most recent value in 2010-2025; values come from 2019–2023. 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 14,952 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Gender Inequality Index and Unintended pregnancy rate

This page shows that two measures go together. It does not show that Gender Inequality Index changes Unintended pregnancy rate, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Gender Inequality Index or Unintended pregnancy rate