placestats

Abortion rate (estimate) vs. Researchers

Fades after adjusting for income

Across 106 countries, Abortion rate (estimate) and Researchers rise and fall together at r = -0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.36, which could easily be chance (q = 0.059, 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: much weaker within regions than across them (largely a between-region difference).

Comparing countries across the middle half of abortion rate (estimate), 12.7 to 33.1 per 1,000 women aged 15-49, researchers is worse by 1,853 per million people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 941 per million people.

Link before adjusting (r)-0.533p = 4.1e-9 · 106 countries
After adjusting for national income-0.360p = 0.000 · likely range [-0.52, -0.18]
p, allowing for regions0.007standard error 0.091 · 7 regions
Check for luck (q)0.059across 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.

Abortion rate (estimate) · per 1,000 women aged 15-49link with income -0.50 · moderate
Researchers · per million peoplelink with income +0.85 · proxy

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

Every country as a dot

Abortion rate (estimate) · per 1,000 women aged 15-49 →Researchers · per million people →580.31.459,472Angola: 33.4 per 1,000 women aged 15-49, 18.9 per million peopleAlbania: 9.45 per 1,000 women aged 15-49, 429 per million peopleArgentina: 33.2 per 1,000 women aged 15-49, 1,313 per million peopleArmenia: 42.8 per 1,000 women aged 15-49, 942 per million peopleAustralia: 16.1 per 1,000 women aged 15-49, 4,569 per million peopleAzerbaijan: 71.9 per 1,000 women aged 15-49, 1,663 per million peopleBurundi: 25.6 per 1,000 women aged 15-49, 22.5 per million peopleBelgium: 7.78 per 1,000 women aged 15-49, 5,990 per million peopleBurkina Faso: 30.5 per 1,000 women aged 15-49, 46.6 per million peopleBulgaria: 10.2 per 1,000 women aged 15-49, 2,401 per million peopleBosnia and Herzegovina: 11 per 1,000 women aged 15-49, 705 per million peopleBelarus: 10.5 per 1,000 women aged 15-49, 1,568 per million peopleBolivia: 37.8 per 1,000 women aged 15-49, 63.1 per million peopleBrazil: 32.3 per 1,000 women aged 15-49, 903 per million peopleBhutan: 32.1 per 1,000 women aged 15-49, 236 per million peopleBotswana: 30.7 per 1,000 women aged 15-49, 180 per million peopleCanada: 11.6 per 1,000 women aged 15-49, 5,624 per million peopleSwitzerland: 5.18 per 1,000 women aged 15-49, 6,108 per million peopleChile: 35.9 per 1,000 women aged 15-49, 707 per million peopleChina: 49 per 1,000 women aged 15-49, 2,107 per million peopleCongo, Dem. Rep.: 32.7 per 1,000 women aged 15-49, 10.1 per million peopleCongo, Rep.: 44.4 per 1,000 women aged 15-49, 127 per million peopleColombia: 27.1 per 1,000 women aged 15-49, 90.2 per million peopleCabo Verde: 49.1 per 1,000 women aged 15-49, 125 per million peopleCosta Rica: 21.1 per 1,000 women aged 15-49, 508 per million peopleCzechia: 6.86 per 1,000 women aged 15-49, 4,505 per million peopleGermany: 5.77 per 1,000 women aged 15-49, 5,926 per million peopleDenmark: 11.6 per 1,000 women aged 15-49, 8,838 per million peopleDominican Republic: 36.6 per 1,000 women aged 15-49, 20 per million peopleEcuador: 27.9 per 1,000 women aged 15-49, 400 per million peopleSpain: 8.73 per 1,000 women aged 15-49, 3,654 per million peopleEstonia: 15.9 per 1,000 women aged 15-49, 4,800 per million peopleEthiopia: 24.4 per 1,000 women aged 15-49, 89 per million peopleFinland: 8.09 per 1,000 women aged 15-49, 8,315 per million peopleFrance: 14.4 per 1,000 women aged 15-49, 5,369 per million peopleUnited Kingdom: 13.1 per 1,000 women aged 15-49, 4,473 per million peopleGeorgia: 80.3 per 1,000 women aged 15-49, 1,896 per million peopleGhana: 36.6 per 1,000 women aged 15-49, 87.5 per million peopleGambia, The: 16.4 per 1,000 women aged 15-49, 50.9 per million peopleGreece: 15 per 1,000 women aged 15-49, 5,283 per million peopleGuatemala: 18.9 per 1,000 women aged 15-49, 1.45 per million peopleHong Kong SAR, China: 13 per 1,000 women aged 15-49, 5,874 per million peopleHonduras: 20.7 per 1,000 women aged 15-49, 188 per million peopleCroatia: 16.5 per 1,000 women aged 15-49, 2,440 per million peopleHungary: 13.6 per 1,000 women aged 15-49, 4,567 per million peopleIndonesia: 25 per 1,000 women aged 15-49, 395 per million peopleIndia: 48 per 1,000 women aged 15-49, 259 per million peopleIceland: 12.7 per 1,000 women aged 15-49, 5,953 per million peopleItaly: 18.9 per 1,000 women aged 15-49, 2,855 per million peopleJapan: 6 per 1,000 women aged 15-49, 5,609 per million peopleKazakhstan: 47 per 1,000 women aged 15-49, 758 per million peopleKenya: 42.7 per 1,000 women aged 15-49, 384 per million peopleCambodia: 45 per 1,000 women aged 15-49, 30.4 per million peopleKorea, Rep.: 21 per 1,000 women aged 15-49, 9,472 per million peopleSri Lanka: 27.9 per 1,000 women aged 15-49, 94.5 per million peopleLesotho: 23.5 per 1,000 women aged 15-49, 32.2 per million peopleLithuania: 7.15 per 1,000 women aged 15-49, 3,704 per million peopleLatvia: 10.3 per 1,000 women aged 15-49, 2,191 per million peopleMoldova: 18.2 per 1,000 women aged 15-49, 650 per million peopleMadagascar: 59.9 per 1,000 women aged 15-49, 32.9 per million peopleMexico: 30.6 per 1,000 women aged 15-49, 438 per million peopleNorth Macedonia: 14.3 per 1,000 women aged 15-49, 888 per million peopleMali: 22.5 per 1,000 women aged 15-49, 26 per million peopleMyanmar: 26 per 1,000 women aged 15-49, 28 per million peopleMontenegro: 5.89 per 1,000 women aged 15-49, 764 per million peopleMongolia: 21 per 1,000 women aged 15-49, 535 per million peopleMozambique: 40.4 per 1,000 women aged 15-49, 44.4 per million peopleMalawi: 31.4 per 1,000 women aged 15-49, 25.2 per million peopleNamibia: 29.2 per 1,000 women aged 15-49, 225 per million peopleNiger: 15.2 per 1,000 women aged 15-49, 26.9 per million peopleNigeria: 32.7 per 1,000 women aged 15-49, 22.1 per million peopleNetherlands: 7.15 per 1,000 women aged 15-49, 6,563 per million peopleNorway: 10.8 per 1,000 women aged 15-49, 7,451 per million peopleNew Zealand: 12 per 1,000 women aged 15-49, 5,435 per million peoplePakistan: 43.1 per 1,000 women aged 15-49, 474 per million peoplePanama: 29.6 per 1,000 women aged 15-49, 250 per million peoplePhilippines: 36 per 1,000 women aged 15-49, 320 per million peoplePapua New Guinea: 36.4 per 1,000 women aged 15-49, 33.2 per million peoplePoland: 10.5 per 1,000 women aged 15-49, 3,675 per million peoplePuerto Rico (US): 31.5 per 1,000 women aged 15-49, 332 per million peoplePortugal: 6.77 per 1,000 women aged 15-49, 5,983 per million peopleParaguay: 34 per 1,000 women aged 15-49, 139 per million peopleRomania: 31 per 1,000 women aged 15-49, 1,082 per million peopleRussian Federation: 45.7 per 1,000 women aged 15-49, 2,639 per million peopleRwanda: 27.8 per 1,000 women aged 15-49, 133 per million peopleSenegal: 15.3 per 1,000 women aged 15-49, 572 per million peopleSingapore: 5 per 1,000 women aged 15-49, 8,782 per million peopleEl Salvador: 24 per 1,000 women aged 15-49, 55.5 per million peopleSerbia: 12.7 per 1,000 women aged 15-49, 2,688 per million peopleSlovak Republic: 7.15 per 1,000 women aged 15-49, 3,507 per million peopleSlovenia: 8.13 per 1,000 women aged 15-49, 5,616 per million peopleSweden: 17.3 per 1,000 women aged 15-49, 8,911 per million peopleEswatini: 29.3 per 1,000 women aged 15-49, 138 per million peopleChad: 17.4 per 1,000 women aged 15-49, 56.7 per million peopleTogo: 32.1 per 1,000 women aged 15-49, 57.8 per million peopleThailand: 25 per 1,000 women aged 15-49, 1,592 per million peopleTrinidad and Tobago: 48.3 per 1,000 women aged 15-49, 493 per million peopleTanzania: 37.5 per 1,000 women aged 15-49, 19.3 per million peopleUganda: 43 per 1,000 women aged 15-49, 81.3 per million peopleUkraine: 20.5 per 1,000 women aged 15-49, 586 per million peopleUruguay: 11.4 per 1,000 women aged 15-49, 938 per million peopleUnited States: 11.9 per 1,000 women aged 15-49, 4,937 per million peopleUzbekistan: 22.7 per 1,000 women aged 15-49, 640 per million peopleViet Nam: 64 per 1,000 women aged 15-49, 836 per million peopleSouth Africa: 29.7 per 1,000 women aged 15-49, 444 per million peopleZimbabwe: 18.2 per 1,000 women aged 15-49, 95.1 per million peopleALBAZEBIHDNKFINGEOKORMDGMNESGP

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5334.1e-9106
By rank (Spearman), not adjusted-0.6001.0e-11106
Standard (Pearson), adjusted for national income-0.3600.000106
By rank (Spearman), adjusted for national income-0.2010.040106

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 106 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: +0.173 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.384 (without MDG) to -0.343 (without KAZ). 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: MDG (-0.024).

Fragile: this link may not hold up.
  • much weaker within regions than across them (largely a between-region difference)

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 & Pacific15-0.23
Europe & Central Asia40-0.13
Latin America & Caribbean17-0.13
Sub-Saharan Africa28-0.16

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 third36-0.10
Middle third35+0.16
Richest third35-0.30

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
Christians75-0.38
Muslims14+0.61

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
Spain15-0.13
United Kingdom25-0.61

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 = -0.427 after dropping 4 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
per million people		Years
Georgia GEO80.31,8962019, 2024
Azerbaijan AZE71.91,6632019, 2024
Viet Nam VNM648362019, 2023
Madagascar MDG59.932.92019, 2018
Cabo Verde CPV49.11252019, 2014
China CHN492,1072019, 2023
Trinidad and Tobago TTO48.34932019, 2022
India IND482592019, 2020
Kazakhstan KAZ477582019, 2024
Russian Federation RUS45.72,6392019, 2024
Cambodia KHM4530.42019, 2015
Congo, Rep. COG44.41272019, 2022
Pakistan PAK43.14742019, 2023
Uganda UGA4381.32019, 2023
Armenia ARM42.89422019, 2024

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.
World Bank, World Development Indicators

Sources

Abortion rate (estimate)

Abortions 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 all abortions, legal or not, per 1,000 women aged 15 to 49 (women of childbearing age), averaged over 2015-2019. Where a country has good counts, the estimate stays close to them. Where it does not, including most countries where abortion is banned, it is worked out by a statistical model from surveys, births, birth control use and similar countries. Only 75 of the 166 countries with any data had counts reliable enough to use directly, so for many countries this is an informed estimate with a wide range: Georgia's is 80, but it could be anywhere from 46 to 133. The official count of legal abortions is shown separately. No "better" direction is set.

Researchers

Researchers in R&D (per million people)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

Researchers counted as full-time equivalents (two half-time researchers count as one), from national research surveys that many countries run only now and then. So coverage is thin outside the OECD.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2024. 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 Abortion rate (estimate) and Researchers

This page shows that two measures go together. It does not show that Abortion rate (estimate) changes Researchers, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Abortion rate (estimate) or Researchers