Abortion rate (estimate) vs. Productivity (TFP)
Fades after adjusting for income
Across 96 countries, Abortion rate (estimate) and Productivity (TFP) rise and fall together at r = -0.51 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.20, which could easily be chance (q = 0.21, 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 abortion rate (estimate), 12.7 to 33.2 per 1,000 women aged 15-49, productivity (TFP) is worse by 0.18 ratio to the US at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.046 ratio to the US.
Link before adjusting (r)-0.505p = 1.5e-7 · 96 countries
After adjusting for national income-0.197p = 0.056 · likely range [-0.38, +0.01]
p, allowing for regions0.068standard error 0.085 · 6 regions
Check for luck (q)0.214across 18,622 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
Productivity (TFP) · ratio to the US (US = 1)link with income +0.79 · 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 | -0.505 | 1.5e-7 | 96 |
| By rank (Spearman), not adjusted | -0.519 | 6.1e-8 | 96 |
| Standard (Pearson), adjusted for national income | -0.197 | 0.056 | 96 |
| By rank (Spearman), adjusted for national income | -0.040 | 0.699 | 96 |
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 96 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: +0.309 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.237 (without GAB) to -0.152 (without BRB). 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: BRB (+0.045).
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 | 12 | -0.32 | |
| Europe & Central Asia | 35 | -0.13 | |
| Latin America & Caribbean | 20 | -0.63 | |
| Sub-Saharan Africa | 25 | +0.14 |
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 | 32 | +0.01 | |
| Middle third | 32 | -0.06 | |
| Richest third | 32 | -0.18 |
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 | 70 | -0.19 | |
| Muslims | 12 | +0.21 |
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 | ||
|---|---|---|---|
| Spain | 17 | -0.47 | |
| United Kingdom | 21 | -0.31 |
By official ideology
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 | ||
|---|---|---|---|
| Nationalist | 12 | -0.20 | |
| No strong official ideology | 73 | -0.06 |
By party system
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 | ||
|---|---|---|---|
| A few parties banned | 12 | +0.00 | |
| No parties banned | 78 | -0.08 |
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 = -0.197 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.
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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 | ratio to the US (US = 1) | Years | |
|---|---|---|---|
| China CHN | 49 | 0.471 | 2019, 2023 |
| Trinidad and Tobago TTO | 48.3 | 0.638 | 2019, 2023 |
| India IND | 48 | 0.397 | 2019, 2023 |
| Kazakhstan KAZ | 47 | 0.591 | 2019, 2023 |
| Russian Federation RUS | 45.7 | 0.514 | 2019, 2023 |
| Sierra Leone SLE | 45.4 | 0.465 | 2019, 2023 |
| Barbados BRB | 44.6 | 0.324 | 2019, 2023 |
| Armenia ARM | 42.8 | 0.626 | 2019, 2023 |
| Kenya KEN | 42.7 | 0.49 | 2019, 2023 |
| Peru PER | 42.4 | 0.422 | 2019, 2023 |
| Gabon GAB | 40.6 | 0.843 | 2019, 2023 |
| Mozambique MOZ | 40.4 | 0.236 | 2019, 2023 |
| Jamaica JAM | 38.1 | 0.376 | 2019, 2023 |
| Bolivia BOL | 37.8 | 0.394 | 2019, 2023 |
| Tanzania TZA | 37.5 | 0.337 | 2019, 2023 |
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.
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table", American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt. Data: Penn World Table version 11.0, Groningen Growth and Development Centre, doi:10.34894/FABVLR.
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.
Productivity (TFP)
Total factor productivity level, compared with the US (Penn World Table)
Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)
Total factor productivity (TFP) is how much a country produces compared with what its workers, their schooling, and its machines and buildings would be expected to produce. It is the part of GDP that those inputs do not account for, such as better technology or better organisation. The US is set to 1 in every year, so 0.5 means a country gets about half as much output from the same inputs as the US. It is worked out from GDP, capital, the number of workers, their schooling and the labor share, so it is linked to each of them by arithmetic. It is left out of the economy score: being built from GDP, it would turn that score back into GDP per person.
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 6 World Bank regions. The q-value corrects for testing all 18,622 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Abortion rate (estimate) and Productivity (TFP)
This page shows that two measures go together. It does not show that Abortion rate (estimate) changes Productivity (TFP), or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Abortion rate (estimate) or Productivity (TFP)
- Abortion rate (estimate) vs Redistribution before -0.71 → after adjusting -0.47
- Abortion rate (estimate) vs Researchers before -0.53 → after adjusting -0.36
- Abortion rate (estimate) vs Women's age at first marriage (census-based) before -0.51 → after adjusting -0.34
- Inequality-adjusted HDI vs Productivity (TFP) before +0.70 → after adjusting -0.21
- African ancestry vs Productivity (TFP) before -0.50 → after adjusting +0.15
- Abortion rate (estimate) vs PISA science before -0.51 → after adjusting -0.14
- Liberal democracy vs Productivity (TFP) before +0.52 → after adjusting +0.14
- Productivity (TFP) vs Voice and accountability before +0.54 → after adjusting +0.12