Do Redistribution and Unintended pregnancy rate go together across countries?

Across 40 countries the link before adjusting for anything is r = -0.73, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.56.

Does the link hold within countries over time, not just across countries?

There are not enough years of data within countries to check this over time.

Does Redistribution cause Unintended pregnancy rate?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Redistribution vs. Unintended pregnancy rate

Fades after adjusting for income

Across 40 countries, Redistribution and Unintended pregnancy rate rise and fall together at r = -0.73 (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: correlation moves sharply once outliers are trimmed.

Comparing countries across the middle half of unintended pregnancy rate, 28.1 to 38.8 per 1,000 women aged 15-49, redistribution is 6.01% reduction in the Gini lower at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 4.49% reduction in the Gini.

Link before adjusting (r)-0.728p = 1.0e-7 · 40 countries

After adjusting for national income-0.472p = 0.002 · likely range [-0.69, -0.18]

p, allowing for regions0.003standard error 0.090 · 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.

Redistribution · % reduction in the Ginilink with income +0.50 · moderate

Unintended pregnancy rate · per 1,000 women aged 15-49link with income -0.69 · 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	-0.728	1.0e-7	40
By rank (Spearman), not adjusted	-0.558	0.000	40
Standard (Pearson), adjusted for national income	-0.472	0.002	40
By rank (Spearman), adjusted for national income	-0.430	0.006	40

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: +0.256 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.507 (without CHE) to -0.418 (without CHL). 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: CHL (+0.054).

Fragile: this link may not hold up.

correlation moves sharply once outliers are trimmed
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.

	n	r
Europe & Central Asia	27	-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	14	-0.53
Middle third	13	-0.36
Richest third	13	-0.25

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	33	-0.50

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 = -0.307 after dropping 7 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

	% reduction in the Gini	per 1,000 women aged 15-49	Years
Slovak Republic SVK	48.3%	27.3	2023, 2019
Belgium BEL	47.7%	21	2023, 2019
Finland FIN	47.2%	32.6	2023, 2019
Czechia CZE	43.5%	34.9	2023, 2019
Slovenia SVN	42.4%	33.2	2023, 2019
France FRA	42.2%	29	2023, 2019
Poland POL	41.9%	28.7	2023, 2019
Norway NOR	39.5%	34.2	2022, 2019
Germany DEU	38.7%	21.3	2022, 2019
Romania ROU	38.1%	44.4	2023, 2019
Denmark DNK	37.5%	29.8	2022, 2019
Greece GRC	36.9%	33	2023, 2019
Portugal PRT	36.5%	20.9	2023, 2019
Italy ITA	36.2%	28.3	2023, 2019
Iceland ISL	35.7%	32.6	2019

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
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.

Sources

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.

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.

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 Redistribution and Unintended pregnancy rate

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