placestats

Unintended pregnancy rate vs. Wellbeing score

Fades after adjusting for income

Across 85 countries, Unintended pregnancy rate and Wellbeing score rise and fall together at r = -0.54 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.11, which could easily be chance (q = 0.50, 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, 28.6 to 67.6 per 1,000 women aged 15-49, wellbeing score is worse by 0.598 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.112 typical gaps.

Link before adjusting (r)-0.541p = 9.3e-8 · 85 countries
After adjusting for national income-0.113p = 0.304 · likely range [-0.32, +0.10]
p, allowing for regions0.298standard error 0.100 · 7 regions
Check for luck (q)0.498across 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.

Unintended pregnancy rate · per 1,000 women aged 15-49link with income -0.69 · high
Wellbeing score · typical gaps (standard deviations)link with income +0.80 · 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

Unintended pregnancy rate · per 1,000 women aged 15-49 →Wellbeing score · typical gaps (standard deviations) →10.9123-1.981.89Albania: 16 per 1,000 women aged 15-49, -0.617 typical gaps (standard deviations)Argentina: 69 per 1,000 women aged 15-49, 0.216 typical gaps (standard deviations)Armenia: 52.6 per 1,000 women aged 15-49, -0.118 typical gaps (standard deviations)Australia: 37.4 per 1,000 women aged 15-49, 1.25 typical gaps (standard deviations)Azerbaijan: 92.9 per 1,000 women aged 15-49, -0.0603 typical gaps (standard deviations)Belgium: 21 per 1,000 women aged 15-49, 0.818 typical gaps (standard deviations)Burkina Faso: 75.2 per 1,000 women aged 15-49, -0.476 typical gaps (standard deviations)Bangladesh: 58.9 per 1,000 women aged 15-49, -0.561 typical gaps (standard deviations)Bulgaria: 28.6 per 1,000 women aged 15-49, -0.572 typical gaps (standard deviations)Bosnia and Herzegovina: 20 per 1,000 women aged 15-49, -0.087 typical gaps (standard deviations)Belarus: 18.7 per 1,000 women aged 15-49, 0.732 typical gaps (standard deviations)Bolivia: 105 per 1,000 women aged 15-49, -0.361 typical gaps (standard deviations)Brazil: 67.2 per 1,000 women aged 15-49, 0.123 typical gaps (standard deviations)Canada: 31.5 per 1,000 women aged 15-49, 1.06 typical gaps (standard deviations)Switzerland: 20.3 per 1,000 women aged 15-49, 1.55 typical gaps (standard deviations)Chile: 70 per 1,000 women aged 15-49, 0.19 typical gaps (standard deviations)China: 64.8 per 1,000 women aged 15-49, 0.913 typical gaps (standard deviations)Colombia: 62.7 per 1,000 women aged 15-49, -0.125 typical gaps (standard deviations)Czechia: 34.9 per 1,000 women aged 15-49, 0.726 typical gaps (standard deviations)Germany: 21.3 per 1,000 women aged 15-49, 0.981 typical gaps (standard deviations)Denmark: 29.8 per 1,000 women aged 15-49, 1.89 typical gaps (standard deviations)Ecuador: 79.7 per 1,000 women aged 15-49, -0.164 typical gaps (standard deviations)Spain: 20.3 per 1,000 women aged 15-49, 0.816 typical gaps (standard deviations)Estonia: 38.1 per 1,000 women aged 15-49, 0.622 typical gaps (standard deviations)Ethiopia: 79.5 per 1,000 women aged 15-49, -0.726 typical gaps (standard deviations)Finland: 32.6 per 1,000 women aged 15-49, 1.87 typical gaps (standard deviations)France: 29 per 1,000 women aged 15-49, 0.717 typical gaps (standard deviations)United Kingdom: 36.2 per 1,000 women aged 15-49, 1.07 typical gaps (standard deviations)Georgia: 93.6 per 1,000 women aged 15-49, -0.493 typical gaps (standard deviations)Ghana: 102 per 1,000 women aged 15-49, -0.73 typical gaps (standard deviations)Greece: 33 per 1,000 women aged 15-49, -0.284 typical gaps (standard deviations)Guatemala: 62.8 per 1,000 women aged 15-49, 0.352 typical gaps (standard deviations)Hong Kong SAR, China: 27.8 per 1,000 women aged 15-49, 0.0778 typical gaps (standard deviations)Croatia: 33.5 per 1,000 women aged 15-49, 0.0883 typical gaps (standard deviations)Haiti: 107 per 1,000 women aged 15-49, -0.461 typical gaps (standard deviations)Hungary: 27.3 per 1,000 women aged 15-49, 0.266 typical gaps (standard deviations)Indonesia: 39.9 per 1,000 women aged 15-49, -0.192 typical gaps (standard deviations)India: 62.4 per 1,000 women aged 15-49, -0.736 typical gaps (standard deviations)Iceland: 32.6 per 1,000 women aged 15-49, 1.78 typical gaps (standard deviations)Italy: 28.3 per 1,000 women aged 15-49, 0.438 typical gaps (standard deviations)Japan: 21.4 per 1,000 women aged 15-49, 0.552 typical gaps (standard deviations)Kazakhstan: 57.5 per 1,000 women aged 15-49, 0.304 typical gaps (standard deviations)Kenya: 113 per 1,000 women aged 15-49, -0.678 typical gaps (standard deviations)Kyrgyz Republic: 39.7 per 1,000 women aged 15-49, 0.225 typical gaps (standard deviations)Korea, Rep.: 34.5 per 1,000 women aged 15-49, 0.475 typical gaps (standard deviations)Lithuania: 25.6 per 1,000 women aged 15-49, 0.51 typical gaps (standard deviations)Latvia: 33.8 per 1,000 women aged 15-49, 0.212 typical gaps (standard deviations)Moldova: 26.2 per 1,000 women aged 15-49, -1.29 typical gaps (standard deviations)Maldives: 59.5 per 1,000 women aged 15-49, 0.0845 typical gaps (standard deviations)Mexico: 60.3 per 1,000 women aged 15-49, 0.335 typical gaps (standard deviations)North Macedonia: 23.2 per 1,000 women aged 15-49, -0.171 typical gaps (standard deviations)Mali: 67.6 per 1,000 women aged 15-49, -0.328 typical gaps (standard deviations)Myanmar: 35.5 per 1,000 women aged 15-49, -0.547 typical gaps (standard deviations)Montenegro: 10.9 per 1,000 women aged 15-49, 0.239 typical gaps (standard deviations)Mongolia: 31.4 per 1,000 women aged 15-49, 0.000193 typical gaps (standard deviations)Nigeria: 68.2 per 1,000 women aged 15-49, -0.611 typical gaps (standard deviations)Nicaragua: 56.3 per 1,000 women aged 15-49, 0.0736 typical gaps (standard deviations)Netherlands: 17.5 per 1,000 women aged 15-49, 1.54 typical gaps (standard deviations)Norway: 34.2 per 1,000 women aged 15-49, 1.82 typical gaps (standard deviations)New Zealand: 40.9 per 1,000 women aged 15-49, 1.46 typical gaps (standard deviations)Pakistan: 70.9 per 1,000 women aged 15-49, -0.299 typical gaps (standard deviations)Peru: 89.5 per 1,000 women aged 15-49, -0.348 typical gaps (standard deviations)Philippines: 70.8 per 1,000 women aged 15-49, -0.119 typical gaps (standard deviations)Poland: 28.7 per 1,000 women aged 15-49, 0.505 typical gaps (standard deviations)Puerto Rico (US): 54.3 per 1,000 women aged 15-49, 0.077 typical gaps (standard deviations)Portugal: 20.9 per 1,000 women aged 15-49, 0.195 typical gaps (standard deviations)Romania: 44.4 per 1,000 women aged 15-49, 0.197 typical gaps (standard deviations)Russian Federation: 64.2 per 1,000 women aged 15-49, -0.00878 typical gaps (standard deviations)Rwanda: 97.6 per 1,000 women aged 15-49, -0.867 typical gaps (standard deviations)Singapore: 18.8 per 1,000 women aged 15-49, 0.779 typical gaps (standard deviations)Serbia: 21 per 1,000 women aged 15-49, 0.105 typical gaps (standard deviations)Slovak Republic: 27.3 per 1,000 women aged 15-49, 0.318 typical gaps (standard deviations)Slovenia: 33.2 per 1,000 women aged 15-49, 0.55 typical gaps (standard deviations)Sweden: 35.8 per 1,000 women aged 15-49, 1.7 typical gaps (standard deviations)Thailand: 38.3 per 1,000 women aged 15-49, 0.256 typical gaps (standard deviations)Tajikistan: 36.9 per 1,000 women aged 15-49, 0.104 typical gaps (standard deviations)Trinidad and Tobago: 83.9 per 1,000 women aged 15-49, 0.0637 typical gaps (standard deviations)Ukraine: 27.1 per 1,000 women aged 15-49, -0.244 typical gaps (standard deviations)Uruguay: 41.5 per 1,000 women aged 15-49, 0.449 typical gaps (standard deviations)United States: 34.7 per 1,000 women aged 15-49, 0.943 typical gaps (standard deviations)Uzbekistan: 28.6 per 1,000 women aged 15-49, 0.617 typical gaps (standard deviations)Viet Nam: 85.5 per 1,000 women aged 15-49, 0.469 typical gaps (standard deviations)South Africa: 81.5 per 1,000 women aged 15-49, -0.515 typical gaps (standard deviations)Zambia: 123 per 1,000 women aged 15-49, -1.67 typical gaps (standard deviations)Zimbabwe: 73.9 per 1,000 women aged 15-49, -1.98 typical gaps (standard deviations)ALBCHEDNKFINKENMDANLDNORVNMZMBZWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5419.3e-885
By rank (Spearman), not adjusted-0.5048.6e-785
Standard (Pearson), adjusted for national income-0.1130.30485
By rank (Spearman), adjusted for national income-0.0210.84785

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 85 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: +0.427 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.166 (without MDA) to -0.077 (without TJK). 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: MDA (-0.052).

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 & Pacific13+0.52
Europe & Central Asia42-0.13
Latin America & Caribbean14-0.75

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 third29-0.27
Middle third28+0.02
Richest third28+0.16

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
Christians57+0.03
Muslims14-0.17

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
Never a European colony13-0.60
United Kingdom17-0.27

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 = -0.082 after dropping 2 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


per 1,000 women aged 15-49

typical gaps (standard deviations)
Years
Zambia ZMB123-1.672019, 2010
Kenya KEN113-0.6782019, 2022
Haiti HTI107-0.4612019, 2014
Bolivia BOL105-0.3612019, 2022
Ghana GHA102-0.732019, 2014
Rwanda RWA97.6-0.8672019, 2014
Georgia GEO93.6-0.4932019, 2022
Azerbaijan AZE92.9-0.06032019, 2022
Peru PER89.5-0.3482019, 2022
Viet Nam VNM85.50.4692019, 2022
Trinidad and Tobago TTO83.90.06372019, 2014
South Africa ZAF81.5-0.5152019, 2014
Ecuador ECU79.7-0.1642019, 2022
Ethiopia ETH79.5-0.7262019, 2022
Burkina Faso BFA75.2-0.4762019, 2010

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.
ivs; whr

Sources

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.

Wellbeing score

Wellbeing score

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

The average of 3 measures from 2 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: Self-reported happiness, Interpersonal trust, Life satisfaction.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2022. 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 Unintended pregnancy rate and Wellbeing score

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

More links with Unintended pregnancy rate or Wellbeing score