Do Refugees originating and Unintended pregnancy rate go together across countries?

Across 141 countries the link before adjusting for anything is r = +0.03, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.26.

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 Refugees originating 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.

Refugees originating vs. Unintended pregnancy rate

Holds up after adjusting for income

Across 141 countries, Refugees originating and Unintended pregnancy rate rise and fall together at r = +0.03 (on a scale from −1 to +1, where 0 means no link). There is almost no link to explain.

Comparing countries across the middle half of unintended pregnancy rate, 34.5 to 86.9 per 1,000 women aged 15-49, refugees originating is worse by 89.4 per 100,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 625 per 100,000 people the other way.

Link before adjusting (r)+0.032p = 0.709 · 141 countries

After adjusting for national income-0.168p = 0.047 · likely range [-0.32, -0.00]

p, allowing for regions0.000standard error 0.024 · 7 regions

Check for luck (q)0.026across 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.

Refugees originating · per 100,000 peoplelink with income -0.49 · 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	+0.032	0.709	141
By rank (Spearman), not adjusted	+0.259	0.002	141
Standard (Pearson), adjusted for national income	-0.168	0.047	141
By rank (Spearman), adjusted for national income	-0.136	0.109	141

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 141 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: -0.136 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.180 (without NER) to -0.117 (without UKR). 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: UKR (+0.051).

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	-0.49
Europe & Central Asia	42	-0.03
Latin America & Caribbean	26	-0.19
Sub-Saharan Africa	42	-0.00

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	47	-0.13
Middle third	47	-0.24
Richest third	47	+0.14

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	95	-0.27
Muslims	27	+0.22

By last european coloniser

	n	r
France	21	+0.18
Never a European colony	16	+0.62
Spain	17	-0.46
United Kingdom	38	-0.32

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 = -0.216 after dropping 23 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 100,000 people	per 1,000 women aged 15-49	Years
Ukraine UKR	13,524	27.1	2024, 2019
Central African Republic CAF	13,009	90.2	2024, 2019
Somalia, Fed. Rep. SOM	4,703	99.6	2024, 2019
Myanmar MMR	2,427	35.5	2024, 2019
Burundi BDI	2,295	98.1	2024, 2019
Rwanda RWA	1,747	97.6	2024, 2019
Azerbaijan AZE	1,482	92.9	2024, 2019
Mali MLI	1,446	67.6	2024, 2019
Haiti HTI	1,259	107	2024, 2019
El Salvador SLV	1,132	57.6	2024, 2019
Congo, Dem. Rep. COD	970	117	2024, 2019
Bhutan BTN	939	47.5	2024, 2019
Honduras HND	933	67.8	2024, 2019
Bosnia and Herzegovina BIH	848	20	2024, 2019
Albania ALB	784	16	2024, 2019

UNHCR Refugee Data Finder; rate per population from Our World in Data
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

Refugees originating

Refugees originating from the country, per 100,000 people

United Nations High Commissioner for Refugees · via Our World in Data · CC BY 4.0 · updated annual

People recognised as refugees abroad, compared with the population of the country they left. It is zero for most of the world and in the thousands for a dozen countries at war. So it is put on a log scale (which squeezes very large values together) wherever it is used for adjusting, and its links lean on those few countries. The "leave one out" check shows by how much. It has a "better" direction and sits under governance. But it is an outcome of governance, not a measure of it, so it is kept out of the governance score.

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 2019–2024. 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 Refugees originating and Unintended pregnancy rate

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