placestats

Pre-trial detainee share vs. South Asian ancestry

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 141 countries, Pre-trial detainee share and South Asian ancestry 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 = +0.28, q = 0.0049, allowing for the many pairs tested). Caution: direction is not consistent across regions.

Link before adjusting (r)+0.289p = 0.001 · 141 countries
After adjusting for national income+0.277p = 0.001 · likely range [+0.12, +0.42]
p, allowing for regions0.176standard error 0.181 · 7 regions
Check for luck (q)0.005across 13,797 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.

Pre-trial detainee share · % of prisonerslink with income -0.27 · independent
South Asian ancestry · % of populationlink with income +0.17 · 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

Pre-trial detainee share · % of prisoners →South Asian ancestry · % of population →0.3%83.6%0%100%Afghanistan: 31.3% of prisoners, 87% of populationAngola: 48.5% of prisoners, 0% of populationAlbania: 62% of prisoners, 0% of populationUnited Arab Emirates: 38.2% of prisoners, 0% of populationArgentina: 38.8% of prisoners, 0% of populationArmenia: 52.2% of prisoners, 0% of populationAustralia: 39.9% of prisoners, 0.8% of populationAustria: 19.6% of prisoners, 0% of populationAzerbaijan: 20.7% of prisoners, 0% of populationBurundi: 53.1% of prisoners, 0% of populationBelgium: 23.6% of prisoners, 0% of populationBenin: 59.9% of prisoners, 0% of populationBurkina Faso: 29% of prisoners, 0% of populationBangladesh: 75.6% of prisoners, 100% of populationBulgaria: 8.79% of prisoners, 0% of populationBahrain: 13.8% of prisoners, 0% of populationBosnia and Herzegovina: 36.6% of prisoners, 0% of populationBelarus: 9.17% of prisoners, 0% of populationBelize: 34.7% of prisoners, 3.5% of populationBolivia: 58.7% of prisoners, 0% of populationBrazil: 27.3% of prisoners, 0% of populationBhutan: 14.2% of prisoners, 100% of populationBotswana: 22.5% of prisoners, 0% of populationCanada: 49.9% of prisoners, 3.12% of populationSwitzerland: 51.9% of prisoners, 0.5% of populationChile: 36.5% of prisoners, 0% of populationCote d'Ivoire: 33.7% of prisoners, 0% of populationCameroon: 55.5% of prisoners, 0% of populationCongo, Dem. Rep.: 70% of prisoners, 0% of populationColombia: 21.6% of prisoners, 0% of populationCabo Verde: 31.3% of prisoners, 0% of populationCosta Rica: 23.7% of prisoners, 0% of populationCyprus: 24.4% of prisoners, 0% of populationCzechia: 8.21% of prisoners, 0% of populationGermany: 27% of prisoners, 0% of populationDenmark: 23.9% of prisoners, 0% of populationDominican Republic: 62% of prisoners, 0% of populationAlgeria: 0.3% of prisoners, 0% of populationEcuador: 34.6% of prisoners, 0% of populationEgypt, Arab Rep.: 9.9% of prisoners, 0% of populationSpain: 17.7% of prisoners, 0% of populationEstonia: 20.2% of prisoners, 0% of populationFinland: 27.4% of prisoners, 0% of populationFiji: 19.9% of prisoners, 45% of populationFrance: 25.8% of prisoners, 0% of populationGabon: 70% of prisoners, 0% of populationGeorgia: 19.3% of prisoners, 0% of populationGhana: 10.5% of prisoners, 0% of populationGuinea: 55.5% of prisoners, 0% of populationGambia, The: 55.6% of prisoners, 0% of populationGuinea-Bissau: 55.5% of prisoners, 0% of populationGreece: 25.5% of prisoners, 0% of populationGuatemala: 28.3% of prisoners, 0% of populationGuyana: 38.7% of prisoners, 54% of populationHong Kong SAR, China: 40.6% of prisoners, 0% of populationHonduras: 49.2% of prisoners, 0% of populationCroatia: 42.4% of prisoners, 0% of populationHaiti: 83.6% of prisoners, 0% of populationHungary: 20.7% of prisoners, 0% of populationIndonesia: 21.3% of prisoners, 0% of populationIndia: 73.5% of prisoners, 100% of populationIreland: 19.3% of prisoners, 0% of populationIran, Islamic Rep.: 25.1% of prisoners, 2% of populationIsrael: 25.8% of prisoners, 0.723% of populationItaly: 16.9% of prisoners, 0% of populationJamaica: 39.2% of prisoners, 2.5% of populationJordan: 36.9% of prisoners, 0% of populationJapan: 15.5% of prisoners, 0% of populationKazakhstan: 20.3% of prisoners, 0% of populationKenya: 41% of prisoners, 0% of populationKyrgyz Republic: 25.6% of prisoners, 0% of populationCambodia: 26.6% of prisoners, 0% of populationKorea, Rep.: 35% of prisoners, 0% of populationKuwait: 14.6% of prisoners, 0% of populationLebanon: 43.4% of prisoners, 0% of populationLiberia: 60.5% of prisoners, 0% of populationLibya: 29% of prisoners, 0% of populationSri Lanka: 66.2% of prisoners, 100% of populationLesotho: 19.5% of prisoners, 0% of populationLithuania: 13% of prisoners, 0% of populationLuxembourg: 48.3% of prisoners, 0% of populationLatvia: 28.6% of prisoners, 0% of populationMorocco: 10.5% of prisoners, 0% of populationMoldova: 17% of prisoners, 0% of populationMadagascar: 39.3% of prisoners, 0% of populationMexico: 36.3% of prisoners, 0% of populationNorth Macedonia: 14% of prisoners, 0% of populationMalta: 38.2% of prisoners, 0% of populationMyanmar: 22% of prisoners, 2.5% of populationMongolia: 18.9% of prisoners, 0% of populationMozambique: 27.9% of prisoners, 0% of populationMauritania: 50% of prisoners, 0% of populationMauritius: 46.1% of prisoners, 68% of populationMalawi: 17.6% of prisoners, 0% of populationMalaysia: 32.6% of prisoners, 7.5% of populationNamibia: 3.16% of prisoners, 0% of populationNiger: 62% of prisoners, 0% of populationNigeria: 66.8% of prisoners, 0% of populationNicaragua: 14.3% of prisoners, 0% of populationNetherlands: 27.1% of prisoners, 0.65% of populationNorway: 22.4% of prisoners, 0% of populationNepal: 53% of prisoners, 100% of populationNew Zealand: 42.3% of prisoners, 0.9% of populationPakistan: 69% of prisoners, 100% of populationPanama: 35.2% of prisoners, 4% of populationPeru: 38% of prisoners, 0% of populationPhilippines: 65.2% of prisoners, 0% of populationPapua New Guinea: 42.9% of prisoners, 0% of populationPoland: 10.9% of prisoners, 0% of populationPuerto Rico (US): 18.8% of prisoners, 0% of populationPortugal: 21.7% of prisoners, 0% of populationParaguay: 63.9% of prisoners, 0% of populationQatar: 23.6% of prisoners, 20% of populationRomania: 9.22% of prisoners, 0% of populationRussian Federation: 15.4% of prisoners, 0% of populationRwanda: 9.01% of prisoners, 0% of populationSaudi Arabia: 20% of prisoners, 1.9% of populationSudan: 20.4% of prisoners, 0% of populationSenegal: 48% of prisoners, 0% of populationSingapore: 13.1% of prisoners, 7.5% of populationSierra Leone: 51.1% of prisoners, 0% of populationEl Salvador: 26% of prisoners, 0% of populationSlovak Republic: 13% of prisoners, 0% of populationSlovenia: 39.8% of prisoners, 0% of populationSweden: 27% of prisoners, 0% of populationEswatini: 24.5% of prisoners, 0.8% of populationTogo: 60% of prisoners, 0% of populationThailand: 8.94% of prisoners, 0% of populationTimor-Leste: 23.8% of prisoners, 0% of populationTrinidad and Tobago: 62.3% of prisoners, 45.4% of populationTunisia: 54.9% of prisoners, 0% of populationTurkiye: 14.4% of prisoners, 0% of populationTanzania: 31.4% of prisoners, 0% of populationUganda: 47.2% of prisoners, 0% of populationUkraine: 34.9% of prisoners, 0% of populationUruguay: 6.29% of prisoners, 0% of populationUnited States: 26.7% of prisoners, 0.764% of populationViet Nam: 11.5% of prisoners, 0% of populationSouth Africa: 38% of prisoners, 2.9% of populationZambia: 18.1% of prisoners, 0% of populationZimbabwe: 23.5% of prisoners, 0% of populationAFGBGDBTNCODDZAGABHTIINDNAMNPLPAKURY

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.2890.001141
By rank (Spearman), not adjusted+0.1850.028141
Standard (Pearson), adjusted for national income+0.2770.001141
By rank (Spearman), adjusted for national income+0.2340.005141

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: +0.013 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.228 (without BGD) to +0.331 (without BTN). 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: BTN (+0.055).

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.

nr
East Asia & Pacific17-0.18
Europe & Central Asia41+0.25
Latin America & Caribbean23+0.45
Middle East, North Africa, Afghanistan & Pakistan17+0.41
Sub-Saharan Africa36+0.10

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 third47+0.38
Middle third47+0.17
Richest third47+0.10

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
Christians87+0.13
Muslims35+0.32

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 colony15+0.06
Spain18+0.03
United Kingdom44+0.53

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 = +0.277 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 prisoners
% of population		Years
Haiti HTI83.6%0%2024, 2000
Bangladesh BGD75.6%100%2022, 2000
India IND73.5%100%2023, 2000
Congo, Dem. Rep. COD70%0%2022, 2000
Gabon GAB70%0%2024, 2000
Pakistan PAK69%100%2024, 2000
Nigeria NGA66.8%0%2024, 2000
Sri Lanka LKA66.2%100%2024, 2000
Philippines PHL65.2%0%2022, 2000
Paraguay PRY63.9%0%2024, 2000
Trinidad and Tobago TTO62.3%45.4%2020, 2000
Albania ALB62%0%2024, 2000
Dominican Republic DOM62%0%2024, 2000
Niger NER62%0%2024, 2000
Liberia LBR60.5%0%2021, 2000

United Nations Global SDG Indicators Database; series compiled by the International Labour Organization (8.3.1) and the UN Office on Drugs and Crime (16.3.2)
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).

Sources

Pre-trial detainee share

Unsentenced detainees as a share of the prison population

United Nations Office on Drugs and Crime · via Our World in Data · CC BY 3.0 IGO · updated annual

Prisoners who have not been sentenced, as a share of everyone held. It measures how long justice takes, not how much crime there is. A country can imprison few people and hold most of them for years without trial. The site has no prison population rate, because the only series that compares countries is a private collection, not UN data.

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.

How this was computed

Data used: most recent value in 2010-2025; values come from 2000–2024. 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 13,797 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Pre-trial detainee share and South Asian ancestry

This page shows that two measures go together. It does not show that Pre-trial detainee share changes South Asian ancestry, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Pre-trial detainee share or South Asian ancestry