Across 142 countries, Abortion rate (estimate) and Labour score rise and fall together at r = -0.41 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.19 (q = 0.041, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a very weak negative link remains. Caution: direction is not consistent across regions.
Comparing countries across the middle half of abortion rate (estimate), 15.3 to 36.4 per 1,000 women aged 15-49, labour score is worse by 0.396 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.143 typical gaps.
Link before adjusting (r)-0.415p = 2.9e-7 · 142 countries
After adjusting for national income-0.187p = 0.026 · likely range [-0.34, -0.02]
p, allowing for regions0.004standard error 0.041 · 7 regions
Check for luck (q)0.041across 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
Labour score · typical gaps (standard deviations)link with income +0.80 · high
“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.
Every country as a dot
best-fit line line after adjusting
Four ways to measure the link
Method
r
p
Countries
Standard (Pearson), not adjusted
-0.415
2.9e-7
142
By rank (Spearman), not adjusted
-0.490
6.1e-10
142
Standard (Pearson), adjusted for national income
-0.187
0.026
142
By rank (Spearman), adjusted for national income
-0.191
0.023
142
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 142 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: +0.228 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.217 (without DJI) to -0.166 (without GEO). 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: DJI (-0.030).
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.
n
r
East Asia & Pacific
21
-0.22
Europe & Central Asia
43
-0.33
Latin America & Caribbean
26
-0.11
Sub-Saharan Africa
42
+0.13
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.
n
r
Poorest third
48
+0.04
Middle third
47
-0.08
Richest third
47
-0.24
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.
n
r
Christians
96
-0.28
Muslims
27
+0.10
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.
n
r
France
21
+0.18
Never a European colony
16
-0.66
Spain
17
-0.17
United Kingdom
38
+0.00
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 = -0.173 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 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
Georgia GEO
80.3
-0.19
2019, 2025
Azerbaijan AZE
71.9
-0.0868
2019, 2025
Viet Nam VNM
64
0.283
2019, 2025
Madagascar MDG
59.9
-0.463
2019, 2025
Guinea-Bissau GNB
58.5
-0.255
2019, 2025
Cabo Verde CPV
49.1
-0.252
2019, 2025
China CHN
49
0.367
2019, 2025
Trinidad and Tobago TTO
48.3
0.701
2019, 2025
India IND
48
-0.49
2019, 2025
Kazakhstan KAZ
47
0.516
2019, 2025
Russian Federation RUS
45.7
1.13
2019, 2025
Sierra Leone SLE
45.4
-0.503
2019, 2025
Cambodia KHM
45
0.533
2019, 2025
Barbados BRB
44.6
0.485
2019, 2025
Congo, Rep. COG
44.4
-1.69
2019, 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. un_sdg; wdi
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.
PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel
The average of 4 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: Informal employment, Vulnerable employment, Youth not in work or study, Unemployment.
How this was computed
Data used: most recent value in 2010-2025; values come from 2019–2025. 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 Abortion rate (estimate) and Labour score
This page shows that two measures go together. It does not show that Abortion rate (estimate) changes Labour score, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Abortion rate (estimate) or Labour score