placestats

South Asian ancestry vs. Unintended pregnancies ending in abortion

Holds up after adjusting for income

A country is not a person, and ancestry is not destiny. Countries where more people have ancestors from one part of the world also tend to differ on the other measure, and that is all. It does notfollow that people with that ancestry are the people with the different score. Making that leap is called the ecological fallacy: reading a pattern across places as a fact about individual people. Ancestry also goes very closely with region and with history, including colonisation and the Atlantic slave trade, and with national income. So much of what looks like ancestry here is really those. That is what the adjusted figures and the region-by-region view are for. The shares are one estimate, for around the year 2000, of where people’s ancestors lived in 1500, not how anyone identifies.

Across 129 countries, South Asian ancestry and Unintended pregnancies ending in abortion rise and fall together at r = +0.29 (on a scale from −1 to +1, where 0 means no link). It holds up almost unchanged when only countries with similar income (GDP per person) are compared (r = +0.29, q = 0.046, allowing for the many pairs tested). Caution: direction is not consistent across regions.

Link before adjusting (r)+0.286p = 0.001 · 129 countries
After adjusting for national income+0.290p = 0.001 · likely range [+0.12, +0.44]
p, allowing for regions0.005standard error 0.066 · 7 regions
Check for luck (q)0.046across 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.

South Asian ancestry · % of populationlink with income +0.17 · independent
Unintended pregnancies ending in abortion · % of unintended pregnancieslink with income +0.12 · independent

Neither of these is closely tied to national income, so adjusting for income is a real test here, not a formality.

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

South Asian ancestry · % of population →Unintended pregnancies ending in abortion · % of unintended pregnancies →0%100%19.7%86.1%Angola: 0% of population, 28.1% of unintended pregnanciesAlbania: 0% of population, 59.2% of unintended pregnanciesArgentina: 0% of population, 48.4% of unintended pregnanciesArmenia: 0% of population, 81.8% of unintended pregnanciesAustralia: 0.8% of population, 43.1% of unintended pregnanciesAzerbaijan: 0% of population, 77.6% of unintended pregnanciesBurundi: 0% of population, 26.2% of unintended pregnanciesBelgium: 0% of population, 37.1% of unintended pregnanciesBenin: 0% of population, 36.9% of unintended pregnanciesBurkina Faso: 0% of population, 40.6% of unintended pregnanciesBangladesh: 100% of population, 60.1% of unintended pregnanciesBulgaria: 0% of population, 35.7% of unintended pregnanciesBosnia and Herzegovina: 0% of population, 57.3% of unintended pregnanciesBelarus: 0% of population, 56% of unintended pregnanciesBelize: 3.5% of population, 45.2% of unintended pregnanciesBolivia: 0% of population, 36.3% of unintended pregnanciesBrazil: 0% of population, 48.4% of unintended pregnanciesBhutan: 100% of population, 67.9% of unintended pregnanciesBotswana: 0% of population, 32.2% of unintended pregnanciesCentral African Republic: 0% of population, 30% of unintended pregnanciesCanada: 3.12% of population, 36.8% of unintended pregnanciesSwitzerland: 0.5% of population, 25.5% of unintended pregnanciesChile: 0% of population, 52.5% of unintended pregnanciesChina: 0% of population, 77.9% of unintended pregnanciesCote d'Ivoire: 0% of population, 36.7% of unintended pregnanciesCameroon: 0% of population, 40.7% of unintended pregnanciesCongo, Dem. Rep.: 0% of population, 27.9% of unintended pregnanciesCongo, Rep.: 0% of population, 42.6% of unintended pregnanciesColombia: 0% of population, 43.2% of unintended pregnanciesComoros: 0% of population, 32.4% of unintended pregnanciesCabo Verde: 0% of population, 48.6% of unintended pregnanciesCosta Rica: 0% of population, 41% of unintended pregnanciesCzechia: 0% of population, 19.7% of unintended pregnanciesGermany: 0% of population, 27.1% of unintended pregnanciesDenmark: 0% of population, 38.9% of unintended pregnanciesDominican Republic: 0% of population, 44% of unintended pregnanciesEcuador: 0% of population, 35.1% of unintended pregnanciesSpain: 0% of population, 43.1% of unintended pregnanciesEstonia: 0% of population, 41.7% of unintended pregnanciesEthiopia: 0% of population, 31% of unintended pregnanciesFinland: 0% of population, 24.8% of unintended pregnanciesFrance: 0% of population, 49.5% of unintended pregnanciesGabon: 0% of population, 36.8% of unintended pregnanciesUnited Kingdom: 3.6% of population, 36.2% of unintended pregnanciesGeorgia: 0% of population, 86.1% of unintended pregnanciesGhana: 0% of population, 35.9% of unintended pregnanciesGuinea: 0% of population, 38.1% of unintended pregnanciesGambia, The: 0% of population, 31.1% of unintended pregnanciesGuinea-Bissau: 0% of population, 58.5% of unintended pregnanciesGreece: 0% of population, 45.8% of unintended pregnanciesGuatemala: 0% of population, 30.1% of unintended pregnanciesGuyana: 54% of population, 47.5% of unintended pregnanciesHong Kong SAR, China: 0% of population, 46.1% of unintended pregnanciesHonduras: 0% of population, 30.6% of unintended pregnanciesCroatia: 0% of population, 49.9% of unintended pregnanciesHaiti: 0% of population, 35.5% of unintended pregnanciesHungary: 0% of population, 49.4% of unintended pregnanciesIndonesia: 0% of population, 63% of unintended pregnanciesIndia: 100% of population, 77.1% of unintended pregnanciesItaly: 0% of population, 67.1% of unintended pregnanciesJamaica: 2.5% of population, 43.9% of unintended pregnanciesJapan: 0% of population, 30.1% of unintended pregnanciesKazakhstan: 0% of population, 82.1% of unintended pregnanciesKenya: 0% of population, 38% of unintended pregnanciesKyrgyz Republic: 0% of population, 79.2% of unintended pregnanciesCambodia: 0% of population, 65.3% of unintended pregnanciesKorea, Rep.: 0% of population, 61.9% of unintended pregnanciesLao PDR: 0% of population, 68.3% of unintended pregnanciesLiberia: 0% of population, 40.4% of unintended pregnanciesSri Lanka: 100% of population, 72.3% of unintended pregnanciesLesotho: 0% of population, 23.9% of unintended pregnanciesLithuania: 0% of population, 27.7% of unintended pregnanciesLatvia: 0% of population, 30.2% of unintended pregnanciesMoldova: 0% of population, 70.6% of unintended pregnanciesMadagascar: 0% of population, 62.8% of unintended pregnanciesMexico: 0% of population, 50.9% of unintended pregnanciesNorth Macedonia: 0% of population, 63.4% of unintended pregnanciesMali: 0% of population, 33.5% of unintended pregnanciesMyanmar: 2.5% of population, 74.2% of unintended pregnanciesMongolia: 0% of population, 65.7% of unintended pregnanciesMozambique: 0% of population, 46.7% of unintended pregnanciesMauritania: 0% of population, 25.4% of unintended pregnanciesMalawi: 0% of population, 27.4% of unintended pregnanciesNamibia: 0% of population, 27.9% of unintended pregnanciesNiger: 0% of population, 31.3% of unintended pregnanciesNigeria: 0% of population, 48.2% of unintended pregnanciesNicaragua: 0% of population, 27.5% of unintended pregnanciesNetherlands: 0.65% of population, 40.8% of unintended pregnanciesNorway: 0% of population, 31.7% of unintended pregnanciesNepal: 100% of population, 69.4% of unintended pregnanciesNew Zealand: 0.9% of population, 29.3% of unintended pregnanciesPakistan: 100% of population, 60.9% of unintended pregnanciesPanama: 4% of population, 41.3% of unintended pregnanciesPeru: 0% of population, 47.5% of unintended pregnanciesPhilippines: 0% of population, 50.5% of unintended pregnanciesPapua New Guinea: 0% of population, 46.3% of unintended pregnanciesPoland: 0% of population, 36.6% of unintended pregnanciesPuerto Rico (US): 0% of population, 58.3% of unintended pregnanciesPortugal: 0% of population, 32.4% of unintended pregnanciesParaguay: 0% of population, 51% of unintended pregnanciesRomania: 0% of population, 71.2% of unintended pregnanciesRussian Federation: 0% of population, 71.8% of unintended pregnanciesRwanda: 0% of population, 28.7% of unintended pregnanciesSenegal: 0% of population, 25.1% of unintended pregnanciesSingapore: 7.5% of population, 27.5% of unintended pregnanciesSierra Leone: 0% of population, 51.5% of unintended pregnanciesEl Salvador: 0% of population, 41.5% of unintended pregnanciesSomalia, Fed. Rep.: 0% of population, 29.2% of unintended pregnanciesSlovak Republic: 0% of population, 26.2% of unintended pregnanciesSlovenia: 0% of population, 24.4% of unintended pregnanciesSweden: 0% of population, 48.3% of unintended pregnanciesEswatini: 0.8% of population, 26.5% of unintended pregnanciesChad: 0% of population, 29.5% of unintended pregnanciesTogo: 0% of population, 32.2% of unintended pregnanciesThailand: 0% of population, 64.4% of unintended pregnanciesTajikistan: 0% of population, 71% of unintended pregnanciesTurkmenistan: 0% of population, 83.4% of unintended pregnanciesTimor-Leste: 0% of population, 67.8% of unintended pregnanciesTrinidad and Tobago: 45.4% of population, 58% of unintended pregnanciesTanzania: 0% of population, 35.9% of unintended pregnanciesUganda: 0% of population, 29.7% of unintended pregnanciesUkraine: 0% of population, 75.9% of unintended pregnanciesUruguay: 0% of population, 27.6% of unintended pregnanciesUnited States: 0.764% of population, 34.2% of unintended pregnanciesUzbekistan: 0% of population, 80.2% of unintended pregnanciesViet Nam: 0% of population, 74.5% of unintended pregnanciesSouth Africa: 2.9% of population, 36.6% of unintended pregnanciesZambia: 0% of population, 28.2% of unintended pregnanciesZimbabwe: 0% of population, 24.8% of unintended pregnanciesBGDBTNCZEGEOINDKAZLKALSONPLPAKSVNTKM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.2860.001129
By rank (Spearman), not adjusted+0.0880.321129
Standard (Pearson), adjusted for national income+0.2900.001129
By rank (Spearman), adjusted for national income+0.0700.433129

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 129 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: -0.004 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.249 (without IND) to +0.301 (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: IND (-0.041).

Fragile: this link may not hold up.
  • direction is not consistent across regions
  • 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
East Asia & Pacific17-0.23
Europe & Central Asia40-0.06
Latin America & Caribbean23+0.11
Sub-Saharan Africa41-0.01

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 third43+0.32
Middle third43+0.38
Richest third43+0.17

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
Christians85+0.07
Muslims25+0.16

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.

nr
Never a European colony16+0.12
Spain17-0.17
United Kingdom32+0.76

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 = +0.290 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


% of population
% of unintended pregnancies		Years
Pakistan PAK100%60.9%2000, 2019
Nepal NPL100%69.4%2000, 2019
Sri Lanka LKA100%72.3%2000, 2019
India IND100%77.1%2000, 2019
Bhutan BTN100%67.9%2000, 2019
Bangladesh BGD100%60.1%2000, 2019
Guyana GUY54%47.5%2000, 2019
Trinidad and Tobago TTO45.4%58%2000, 2019
Singapore SGP7.5%27.5%2000, 2019
Panama PAN4%41.3%2000, 2019
United Kingdom GBR3.6%36.2%2000, 2019
Belize BLZ3.5%45.2%2000, 2019
Canada CAN3.12%36.8%2000, 2019
South Africa ZAF2.9%36.6%2000, 2019
Myanmar MMR2.5%74.2%2000, 2019

Putterman, L. and Weil, D. N. (2010), 'Post-1500 Population Flows and the Long-Run Determinants of Economic Growth and Inequality', Quarterly Journal of Economics 125(4); World Migration Matrix version 1.1 (2009).
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

South Asian ancestry

South Asian ancestry (share of the population)

Louis Putterman and David N. Weil, Brown University · No licence stated; used with citation · updated one edition (version 1.1, 2009)

The estimated share of today's population whose ancestors in the year 1500 lived in South Asia (India, Pakistan, Bangladesh, Afghanistan, Sri Lanka, Nepal, Bhutan and the Maldives). This is the closest thing to race shares that can be compared across countries: national censuses each use their own race and ethnic categories, and most countries record none. It is about where people's ancestors lived, not how anyone identifies, and it is one estimate for around the year 2000, built from history, migration records and genetic studies. Every country's six ancestry shares add up to 100%, so they are linked to each other by arithmetic. No "better" direction is set.

Unintended pregnancies ending in abortion

Share of unintended pregnancies ending in abortion (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)

Of the unintended pregnancies in the abortion rate's model, the share that ended in abortion, averaged over 2015-2019 (Guttmacher Institute). The rest ended in a birth or a miscarriage. Like the other two, it is an estimate with a range. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2000–2019. 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 South Asian ancestry and Unintended pregnancies ending in abortion

This page shows that two measures go together. It does not show that South Asian ancestry changes Unintended pregnancies ending in abortion, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with South Asian ancestry or Unintended pregnancies ending in abortion