placestats

Abortion rate (estimate) vs. Redistribution

Fades after adjusting for income

Across 40 countries, Abortion rate (estimate) and Redistribution rise and fall together at r = -0.71 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.47, which could easily be chance (q = 0.076, 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), 8.12 to 19.4 per 1,000 women aged 15-49, redistribution is 8.2% reduction in the Gini lower at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 5.71% reduction in the Gini.

Link before adjusting (r)-0.711p = 2.8e-7 · 40 countries
After adjusting for national income-0.471p = 0.002 · likely range [-0.68, -0.18]
p, allowing for regions0.011standard error 0.120 · 6 regions
Check for luck (q)0.076across 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
Redistribution · % reduction in the Ginilink with income +0.50 · moderate

“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 →Redistribution · % reduction in the Gini →5.18492.67%48.3%Australia: 16.1 per 1,000 women aged 15-49, 27.7% reduction in the GiniBelgium: 7.78 per 1,000 women aged 15-49, 47.7% reduction in the GiniBulgaria: 10.2 per 1,000 women aged 15-49, 26% reduction in the GiniBrazil: 32.3 per 1,000 women aged 15-49, 13.8% reduction in the GiniCanada: 11.6 per 1,000 women aged 15-49, 29.3% reduction in the GiniSwitzerland: 5.18 per 1,000 women aged 15-49, 21.2% reduction in the GiniChile: 35.9 per 1,000 women aged 15-49, 8.68% reduction in the GiniChina: 49 per 1,000 women aged 15-49, 6.22% reduction in the GiniCosta Rica: 21.1 per 1,000 women aged 15-49, 12.6% reduction in the GiniCzechia: 6.86 per 1,000 women aged 15-49, 43.5% reduction in the GiniGermany: 5.77 per 1,000 women aged 15-49, 38.7% reduction in the GiniDenmark: 11.6 per 1,000 women aged 15-49, 37.5% reduction in the GiniSpain: 8.73 per 1,000 women aged 15-49, 34.4% reduction in the GiniEstonia: 15.9 per 1,000 women aged 15-49, 33.5% reduction in the GiniFinland: 8.09 per 1,000 women aged 15-49, 47.2% reduction in the GiniFrance: 14.4 per 1,000 women aged 15-49, 42.2% reduction in the GiniUnited Kingdom: 13.1 per 1,000 women aged 15-49, 29.7% reduction in the GiniGreece: 15 per 1,000 women aged 15-49, 36.9% reduction in the GiniCroatia: 16.5 per 1,000 women aged 15-49, 33.1% reduction in the GiniHungary: 13.6 per 1,000 women aged 15-49, 29.4% reduction in the GiniIndia: 48 per 1,000 women aged 15-49, 2.67% reduction in the GiniIceland: 12.7 per 1,000 women aged 15-49, 35.7% reduction in the GiniItaly: 18.9 per 1,000 women aged 15-49, 36.2% reduction in the GiniJapan: 6 per 1,000 women aged 15-49, 34.1% reduction in the GiniKorea, Rep.: 21 per 1,000 women aged 15-49, 18.2% reduction in the GiniLithuania: 7.15 per 1,000 women aged 15-49, 29.7% reduction in the GiniLatvia: 10.3 per 1,000 women aged 15-49, 28.1% reduction in the GiniMexico: 30.6 per 1,000 women aged 15-49, 3.77% reduction in the GiniNetherlands: 7.15 per 1,000 women aged 15-49, 34.2% reduction in the GiniNorway: 10.8 per 1,000 women aged 15-49, 39.5% reduction in the GiniNew Zealand: 12 per 1,000 women aged 15-49, 28.7% reduction in the GiniPoland: 10.5 per 1,000 women aged 15-49, 41.9% reduction in the GiniPortugal: 6.77 per 1,000 women aged 15-49, 36.5% reduction in the GiniRomania: 31 per 1,000 women aged 15-49, 38.1% reduction in the GiniRussian Federation: 45.7 per 1,000 women aged 15-49, 27% reduction in the GiniSlovak Republic: 7.15 per 1,000 women aged 15-49, 48.3% reduction in the GiniSlovenia: 8.13 per 1,000 women aged 15-49, 42.4% reduction in the GiniSweden: 17.3 per 1,000 women aged 15-49, 33.1% reduction in the GiniUnited States: 11.9 per 1,000 women aged 15-49, 22.1% reduction in the GiniSouth Africa: 29.7 per 1,000 women aged 15-49, 12.8% reduction in the GiniBELCHECHLCHNCRIFINFRAINDROURUSSVKUSA

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7112.8e-740
By rank (Spearman), not adjusted-0.5808.6e-540
Standard (Pearson), adjusted for national income-0.4710.00240
By rank (Spearman), adjusted for national income-0.4580.00340

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 40 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 -0.536 (without ROU) to -0.417 (without CHN). 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: ROU (-0.065).

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
Europe & Central Asia27-0.23

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 third14-0.50
Middle third13-0.40
Richest third13-0.52

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
Christians33-0.37

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 = -0.466 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
% reduction in the Gini		Years
China CHN496.22%2019, 2011
India IND482.67%2019, 2011
Russian Federation RUS45.727%2019, 2017
Chile CHL35.98.68%2019, 2022
Brazil BRA32.313.8%2019, 2022
Romania ROU3138.1%2019, 2023
Mexico MEX30.63.77%2019, 2022
South Africa ZAF29.712.8%2019, 2017
Costa Rica CRI21.112.6%2019, 2024
Korea, Rep. KOR2118.2%2019, 2022
Italy ITA18.936.2%2019, 2023
Sweden SWE17.333.1%2019, 2023
Croatia HRV16.533.1%2019, 2023
Australia AUS16.127.7%2019, 2020
Estonia EST15.933.5%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.
OECD databases as named on each indicator: PISA, Social Expenditure Database (SOCX), Income Distribution Database, Development Assistance Committee statistics, Family Database, Government at a Glance, Corporate Tax Statistics, employment and environment statistics

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.

Redistribution

Reduction in the Gini coefficient from taxes and transfers

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator · comparable from 2011 only

The Gini before taxes and payments minus the Gini after them, as a share of the first. It shows how much of the inequality produced by the market the government removes. It is built from the before-tax Gini above, so their pair is built from the same numbers and flagged. No "better" direction is set: a country may redistribute little because its market incomes are already equal.

How this was computed

Data used: most recent value in 2010-2025; values come from 2011–2024. 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 6 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 Redistribution

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

More links with Abortion rate (estimate) or Redistribution