placestats

Equality score vs. Unintended pregnancy rate

Holds up after adjusting for income

Across 131 countries, Equality score and Unintended pregnancy rate rise and fall together at r = -0.74 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.50 (q = 0.0025, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate negative link remains. Caution: direction is not consistent across regions.

Comparing countries across the middle half of unintended pregnancy rate, 34.4 to 86.9 per 1,000 women aged 15-49, equality score is worse by 1.04 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.687 typical gaps.

Link before adjusting (r)-0.740p = 5.2e-24 · 131 countries
After adjusting for national income-0.500p = 1.3e-9 · likely range [-0.62, -0.36]
p, allowing for regions0.000standard error 0.061 · 7 regions
Check for luck (q)0.003across 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.

Equality score · typical gaps (standard deviations)link with income +0.64 · high
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

Equality score · typical gaps (standard deviations) →Unintended pregnancy rate · per 1,000 women aged 15-49 →-2.511.0610.9145Angola: -1.52 typical gaps (standard deviations), 120 per 1,000 women aged 15-49Albania: 0.74 typical gaps (standard deviations), 16 per 1,000 women aged 15-49Argentina: -0.0243 typical gaps (standard deviations), 69 per 1,000 women aged 15-49Armenia: 0.842 typical gaps (standard deviations), 52.6 per 1,000 women aged 15-49Australia: 0.472 typical gaps (standard deviations), 37.4 per 1,000 women aged 15-49Burundi: -1.63 typical gaps (standard deviations), 98.1 per 1,000 women aged 15-49Belgium: 0.895 typical gaps (standard deviations), 21 per 1,000 women aged 15-49Benin: -0.24 typical gaps (standard deviations), 85.9 per 1,000 women aged 15-49Burkina Faso: -0.796 typical gaps (standard deviations), 75.2 per 1,000 women aged 15-49Bangladesh: 0.51 typical gaps (standard deviations), 58.9 per 1,000 women aged 15-49Bulgaria: 0.142 typical gaps (standard deviations), 28.6 per 1,000 women aged 15-49Bosnia and Herzegovina: 0.688 typical gaps (standard deviations), 20 per 1,000 women aged 15-49Belarus: 1.03 typical gaps (standard deviations), 18.7 per 1,000 women aged 15-49Belize: 0.119 typical gaps (standard deviations), 70.9 per 1,000 women aged 15-49Bolivia: 0.0335 typical gaps (standard deviations), 105 per 1,000 women aged 15-49Brazil: -0.529 typical gaps (standard deviations), 67.2 per 1,000 women aged 15-49Barbados: 0.434 typical gaps (standard deviations), 76.9 per 1,000 women aged 15-49Bhutan: 0.799 typical gaps (standard deviations), 47.5 per 1,000 women aged 15-49Botswana: -1.27 typical gaps (standard deviations), 96.8 per 1,000 women aged 15-49Central African Republic: -1.88 typical gaps (standard deviations), 90.2 per 1,000 women aged 15-49Canada: 0.62 typical gaps (standard deviations), 31.5 per 1,000 women aged 15-49Switzerland: 0.51 typical gaps (standard deviations), 20.3 per 1,000 women aged 15-49Chile: -0.0433 typical gaps (standard deviations), 70 per 1,000 women aged 15-49China: 0.369 typical gaps (standard deviations), 64.8 per 1,000 women aged 15-49Cote d'Ivoire: -0.127 typical gaps (standard deviations), 96.4 per 1,000 women aged 15-49Cameroon: -0.675 typical gaps (standard deviations), 83.4 per 1,000 women aged 15-49Congo, Dem. Rep.: -2.33 typical gaps (standard deviations), 117 per 1,000 women aged 15-49Congo, Rep.: -1.38 typical gaps (standard deviations), 106 per 1,000 women aged 15-49Colombia: -0.906 typical gaps (standard deviations), 62.7 per 1,000 women aged 15-49Comoros: 0.565 typical gaps (standard deviations), 95.2 per 1,000 women aged 15-49Cabo Verde: -0.375 typical gaps (standard deviations), 102 per 1,000 women aged 15-49Costa Rica: -0.197 typical gaps (standard deviations), 51.8 per 1,000 women aged 15-49Czechia: 0.96 typical gaps (standard deviations), 34.9 per 1,000 women aged 15-49Germany: 0.483 typical gaps (standard deviations), 21.3 per 1,000 women aged 15-49Djibouti: -0.607 typical gaps (standard deviations), 50.4 per 1,000 women aged 15-49Denmark: 0.709 typical gaps (standard deviations), 29.8 per 1,000 women aged 15-49Dominican Republic: 0.176 typical gaps (standard deviations), 83.7 per 1,000 women aged 15-49Ecuador: -0.287 typical gaps (standard deviations), 79.7 per 1,000 women aged 15-49Spain: 0.498 typical gaps (standard deviations), 20.3 per 1,000 women aged 15-49Estonia: 0.665 typical gaps (standard deviations), 38.1 per 1,000 women aged 15-49Ethiopia: -0.344 typical gaps (standard deviations), 79.5 per 1,000 women aged 15-49Finland: 0.857 typical gaps (standard deviations), 32.6 per 1,000 women aged 15-49France: 0.607 typical gaps (standard deviations), 29 per 1,000 women aged 15-49Gabon: 0.156 typical gaps (standard deviations), 112 per 1,000 women aged 15-49United Kingdom: 0.565 typical gaps (standard deviations), 36.2 per 1,000 women aged 15-49Georgia: 0.381 typical gaps (standard deviations), 93.6 per 1,000 women aged 15-49Ghana: -1.07 typical gaps (standard deviations), 102 per 1,000 women aged 15-49Guinea: 0.435 typical gaps (standard deviations), 69.8 per 1,000 women aged 15-49Gambia, The: -0.359 typical gaps (standard deviations), 53.7 per 1,000 women aged 15-49Guinea-Bissau: -0.51 typical gaps (standard deviations), 99.5 per 1,000 women aged 15-49Greece: 0.503 typical gaps (standard deviations), 33 per 1,000 women aged 15-49Guatemala: -0.409 typical gaps (standard deviations), 62.8 per 1,000 women aged 15-49Honduras: -0.592 typical gaps (standard deviations), 67.8 per 1,000 women aged 15-49Croatia: 0.7 typical gaps (standard deviations), 33.5 per 1,000 women aged 15-49Haiti: -0.965 typical gaps (standard deviations), 107 per 1,000 women aged 15-49Hungary: 0.656 typical gaps (standard deviations), 27.3 per 1,000 women aged 15-49Indonesia: 0.365 typical gaps (standard deviations), 39.9 per 1,000 women aged 15-49India: 1.05 typical gaps (standard deviations), 62.4 per 1,000 women aged 15-49Iceland: 0.895 typical gaps (standard deviations), 32.6 per 1,000 women aged 15-49Italy: 0.443 typical gaps (standard deviations), 28.3 per 1,000 women aged 15-49Jamaica: 0.109 typical gaps (standard deviations), 86.9 per 1,000 women aged 15-49Japan: 0.55 typical gaps (standard deviations), 21.4 per 1,000 women aged 15-49Kazakhstan: 0.759 typical gaps (standard deviations), 57.5 per 1,000 women aged 15-49Kenya: -0.947 typical gaps (standard deviations), 113 per 1,000 women aged 15-49Kyrgyz Republic: 0.8 typical gaps (standard deviations), 39.7 per 1,000 women aged 15-49Korea, Rep.: 0.544 typical gaps (standard deviations), 34.5 per 1,000 women aged 15-49Lao PDR: 0.261 typical gaps (standard deviations), 50.7 per 1,000 women aged 15-49Liberia: -0.456 typical gaps (standard deviations), 106 per 1,000 women aged 15-49St. Lucia: -0.0757 typical gaps (standard deviations), 68.7 per 1,000 women aged 15-49Sri Lanka: 0.202 typical gaps (standard deviations), 38.9 per 1,000 women aged 15-49Lesotho: -1.22 typical gaps (standard deviations), 98.9 per 1,000 women aged 15-49Lithuania: 0.341 typical gaps (standard deviations), 25.6 per 1,000 women aged 15-49Latvia: 0.473 typical gaps (standard deviations), 33.8 per 1,000 women aged 15-49Moldova: 0.897 typical gaps (standard deviations), 26.2 per 1,000 women aged 15-49Madagascar: -1.46 typical gaps (standard deviations), 94.8 per 1,000 women aged 15-49Maldives: 0.754 typical gaps (standard deviations), 59.5 per 1,000 women aged 15-49Mexico: -0.0512 typical gaps (standard deviations), 60.3 per 1,000 women aged 15-49North Macedonia: 0.412 typical gaps (standard deviations), 23.2 per 1,000 women aged 15-49Mali: -0.544 typical gaps (standard deviations), 67.6 per 1,000 women aged 15-49Myanmar: 0.408 typical gaps (standard deviations), 35.5 per 1,000 women aged 15-49Montenegro: 0.402 typical gaps (standard deviations), 10.9 per 1,000 women aged 15-49Mongolia: 0.623 typical gaps (standard deviations), 31.4 per 1,000 women aged 15-49Mozambique: -2.51 typical gaps (standard deviations), 88.2 per 1,000 women aged 15-49Mauritania: 0.336 typical gaps (standard deviations), 67.6 per 1,000 women aged 15-49Malawi: -1.72 typical gaps (standard deviations), 115 per 1,000 women aged 15-49Namibia: -1.55 typical gaps (standard deviations), 104 per 1,000 women aged 15-49Niger: -1.01 typical gaps (standard deviations), 49.2 per 1,000 women aged 15-49Nigeria: -0.587 typical gaps (standard deviations), 68.2 per 1,000 women aged 15-49Nicaragua: -0.387 typical gaps (standard deviations), 56.3 per 1,000 women aged 15-49Netherlands: 0.958 typical gaps (standard deviations), 17.5 per 1,000 women aged 15-49Norway: 0.909 typical gaps (standard deviations), 34.2 per 1,000 women aged 15-49Nepal: 0.652 typical gaps (standard deviations), 59.7 per 1,000 women aged 15-49Pakistan: -0.0801 typical gaps (standard deviations), 70.9 per 1,000 women aged 15-49Panama: -0.497 typical gaps (standard deviations), 73.1 per 1,000 women aged 15-49Peru: 0.00216 typical gaps (standard deviations), 89.5 per 1,000 women aged 15-49Philippines: 0.0429 typical gaps (standard deviations), 70.8 per 1,000 women aged 15-49Poland: 0.794 typical gaps (standard deviations), 28.7 per 1,000 women aged 15-49Portugal: 0.479 typical gaps (standard deviations), 20.9 per 1,000 women aged 15-49Paraguay: -0.156 typical gaps (standard deviations), 67.4 per 1,000 women aged 15-49Romania: 0.707 typical gaps (standard deviations), 44.4 per 1,000 women aged 15-49Russian Federation: 0.539 typical gaps (standard deviations), 64.2 per 1,000 women aged 15-49Rwanda: -0.821 typical gaps (standard deviations), 97.6 per 1,000 women aged 15-49Senegal: -0.104 typical gaps (standard deviations), 61.3 per 1,000 women aged 15-49Solomon Islands: -0.732 typical gaps (standard deviations), 114 per 1,000 women aged 15-49Sierra Leone: -0.683 typical gaps (standard deviations), 88.2 per 1,000 women aged 15-49El Salvador: 0.0323 typical gaps (standard deviations), 57.6 per 1,000 women aged 15-49Serbia: 0.524 typical gaps (standard deviations), 21 per 1,000 women aged 15-49Sao Tome and Principe: -0.236 typical gaps (standard deviations), 130 per 1,000 women aged 15-49Suriname: 0.129 typical gaps (standard deviations), 66.9 per 1,000 women aged 15-49Slovak Republic: 1.06 typical gaps (standard deviations), 27.3 per 1,000 women aged 15-49Slovenia: 1.02 typical gaps (standard deviations), 33.2 per 1,000 women aged 15-49Sweden: 0.733 typical gaps (standard deviations), 35.8 per 1,000 women aged 15-49Eswatini: -1.85 typical gaps (standard deviations), 111 per 1,000 women aged 15-49Chad: -0.729 typical gaps (standard deviations), 59.6 per 1,000 women aged 15-49Togo: -0.634 typical gaps (standard deviations), 100 per 1,000 women aged 15-49Thailand: 0.524 typical gaps (standard deviations), 38.3 per 1,000 women aged 15-49Tajikistan: 0.206 typical gaps (standard deviations), 36.9 per 1,000 women aged 15-49Timor-Leste: -0.346 typical gaps (standard deviations), 49.6 per 1,000 women aged 15-49Tonga: 0.87 typical gaps (standard deviations), 49.4 per 1,000 women aged 15-49Tanzania: -1.21 typical gaps (standard deviations), 105 per 1,000 women aged 15-49Uganda: -1.16 typical gaps (standard deviations), 145 per 1,000 women aged 15-49Ukraine: 0.966 typical gaps (standard deviations), 27.1 per 1,000 women aged 15-49Uruguay: 0.134 typical gaps (standard deviations), 41.5 per 1,000 women aged 15-49United States: 0.00761 typical gaps (standard deviations), 34.7 per 1,000 women aged 15-49Uzbekistan: 0.499 typical gaps (standard deviations), 28.6 per 1,000 women aged 15-49Viet Nam: 0.322 typical gaps (standard deviations), 85.5 per 1,000 women aged 15-49Vanuatu: 0.079 typical gaps (standard deviations), 86.9 per 1,000 women aged 15-49Samoa: 0.098 typical gaps (standard deviations), 66.4 per 1,000 women aged 15-49South Africa: -1.12 typical gaps (standard deviations), 81.5 per 1,000 women aged 15-49Zambia: -2.37 typical gaps (standard deviations), 123 per 1,000 women aged 15-49Zimbabwe: -1.72 typical gaps (standard deviations), 73.9 per 1,000 women aged 15-49BELBLRCODCOMGABMOZNERNLDSTPUGAZMBZWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7405.2e-24131
By rank (Spearman), not adjusted-0.7615.7e-26131
Standard (Pearson), adjusted for national income-0.5001.3e-9131
By rank (Spearman), adjusted for national income-0.5318.2e-11131

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 131 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: +0.240 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.525 (without MOZ) to -0.479 (without SWZ). 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: MOZ (-0.025).

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 & Pacific16-0.52
Europe & Central Asia41-0.06
Latin America & Caribbean23+0.19
Sub-Saharan Africa41-0.39

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 third44-0.45
Middle third43-0.67
Richest third44-0.37

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
Christians92-0.43
Muslims24-0.03

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
France20-0.30
Never a European colony14-0.47
Spain17+0.22
United Kingdom32-0.69

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 = -0.509 after dropping 3 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)

per 1,000 women aged 15-49
Years
Slovak Republic SVK1.0627.32023, 2019
India IND1.0562.42023, 2019
Belarus BLR1.0318.72020, 2019
Slovenia SVN1.0233.22023, 2019
Ukraine UKR0.96627.12020, 2019
Czechia CZE0.9634.92023, 2019
Netherlands NLD0.95817.52021, 2019
Norway NOR0.90934.22023, 2019
Moldova MDA0.89726.22024, 2019
Iceland ISL0.89532.62019
Belgium BEL0.895212023, 2019
Tonga TON0.8749.42021, 2019
Finland FIN0.85732.62023, 2019
Armenia ARM0.84252.62024, 2019
Kyrgyz Republic KGZ0.839.72024, 2019

wdi
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

Equality score

Equality score

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

The average of 2 measures from 1 publisher. 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: Extreme poverty rate, Gini index.

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 2011–2025. 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 14,952 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Equality score and Unintended pregnancy rate

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

More links with Equality score or Unintended pregnancy rate