placestats

European ancestry vs. Informal employment

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 119 countries, European ancestry and Informal employment rise and fall together at r = -0.83 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.53 (q = 0.033, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate negative link remains.

Comparing countries across the middle half of european ancestry, 0% to 99.3% of population, informal employment is better by 66% of employment at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 29.1% of employment.

Link before adjusting (r)-0.829p = 2.5e-31 · 119 countries
After adjusting for national income-0.535p = 4.4e-10 · likely range [-0.65, -0.39]
p, allowing for regions0.002standard error 0.095 · 6 regions
Check for luck (q)0.033across 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.

European ancestry · % of populationlink with income +0.57 · moderate
Informal employment · % of employmentlink with income -0.88 · proxy

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

Every country as a dot

European ancestry · % of population →Informal employment · % of employment →0%100%0.63%98.5%Afghanistan: 12% of population, 86.1% of employmentAngola: 2% of population, 95.3% of employmentArgentina: 90% of population, 51.6% of employmentArmenia: 100% of population, 76.1% of employmentAustria: 100% of population, 1.32% of employmentBurundi: 0% of population, 98% of employmentBelgium: 98.4% of population, 1.37% of employmentBenin: 0% of population, 96.3% of employmentBurkina Faso: 0% of population, 93.8% of employmentBangladesh: 0% of population, 84% of employmentBulgaria: 100% of population, 1.2% of employmentBosnia and Herzegovina: 100% of population, 16.6% of employmentBolivia: 26.7% of population, 83.9% of employmentBrazil: 74.4% of population, 35.6% of employmentBotswana: 1.5% of population, 75.6% of employmentSwitzerland: 99.5% of population, 1.48% of employmentChile: 59.3% of population, 26.4% of employmentCote d'Ivoire: 0% of population, 92.1% of employmentCameroon: 0% of population, 87.1% of employmentCongo, Dem. Rep.: 0.46% of population, 96.3% of employmentColombia: 45.6% of population, 56% of employmentComoros: 0% of population, 88.8% of employmentCabo Verde: 41.4% of population, 60.6% of employmentCosta Rica: 60.4% of population, 36.1% of employmentCyprus: 100% of population, 1.68% of employmentCzechia: 100% of population, 2.26% of employmentGermany: 100% of population, 2.49% of employmentDenmark: 100% of population, 2.82% of employmentDominican Republic: 52% of population, 54.7% of employmentEcuador: 32% of population, 68.5% of employmentEgypt, Arab Rep.: 0% of population, 68.6% of employmentSpain: 99.7% of population, 2.2% of employmentEstonia: 100% of population, 2.4% of employmentEthiopia: 0% of population, 77.7% of employmentFinland: 100% of population, 2.19% of employmentFiji: 2.2% of population, 45.7% of employmentFrance: 94.2% of population, 3.15% of employmentUnited Kingdom: 94.6% of population, 6.51% of employmentGeorgia: 100% of population, 51.5% of employmentGhana: 0% of population, 78% of employmentGambia, The: 0.5% of population, 83.9% of employmentGuinea-Bissau: 3% of population, 94.8% of employmentGreece: 100% of population, 3.58% of employmentGuatemala: 22% of population, 75.9% of employmentGuyana: 1.3% of population, 51% of employmentHonduras: 46% of population, 82.6% of employmentCroatia: 100% of population, 1.17% of employmentHaiti: 2.5% of population, 91.6% of employmentHungary: 100% of population, 1.31% of employmentIndonesia: 0% of population, 81% of employmentIndia: 0% of population, 88.1% of employmentIreland: 100% of population, 1.9% of employmentIraq: 5.6% of population, 67.6% of employmentItaly: 99.8% of population, 3.42% of employmentJamaica: 8% of population, 54.6% of employmentJordan: 2% of population, 55.1% of employmentKenya: 0% of population, 86.5% of employmentKyrgyz Republic: 98% of population, 57.5% of employmentCambodia: 0% of population, 89.4% of employmentKorea, Rep.: 0% of population, 29.1% of employmentLao PDR: 0% of population, 90.5% of employmentLebanon: 6.9% of population, 55.5% of employmentLiberia: 0% of population, 89.3% of employmentSri Lanka: 0% of population, 67.6% of employmentLesotho: 0% of population, 89.1% of employmentLithuania: 100% of population, 3.2% of employmentLuxembourg: 99.2% of population, 1.93% of employmentLatvia: 100% of population, 2.17% of employmentMoldova: 100% of population, 52.2% of employmentMadagascar: 0.6% of population, 96.1% of employmentMexico: 30% of population, 56.4% of employmentNorth Macedonia: 100% of population, 11.1% of employmentMali: 0% of population, 94.4% of employmentMalta: 4.2% of population, 0.63% of employmentMyanmar: 0% of population, 80.7% of employmentMongolia: 1.8% of population, 27.1% of employmentMozambique: 0% of population, 95.5% of employmentMauritania: 0% of population, 89.3% of employmentMauritius: 16% of population, 23.4% of employmentMalawi: 0% of population, 92.7% of employmentNamibia: 10.8% of population, 55.6% of employmentNiger: 0% of population, 98.5% of employmentNigeria: 0% of population, 93.1% of employmentNicaragua: 51% of population, 81.8% of employmentNetherlands: 96.6% of population, 2.63% of employmentNorway: 100% of population, 1.43% of employmentNepal: 0% of population, 81.5% of employmentPakistan: 0% of population, 88.7% of employmentPanama: 45.2% of population, 58.7% of employmentPeru: 28% of population, 70.5% of employmentPoland: 100% of population, 8.62% of employmentPortugal: 98.4% of population, 4.73% of employmentParaguay: 52.9% of population, 64.5% of employmentRomania: 100% of population, 2.21% of employmentRussian Federation: 100% of population, 19.3% of employmentRwanda: 0% of population, 84.3% of employmentSudan: 0% of population, 94.2% of employmentSenegal: 1.3% of population, 89.2% of employmentSierra Leone: 0% of population, 93.1% of employmentEl Salvador: 50% of population, 64% of employmentSomalia, Fed. Rep.: 0% of population, 81.9% of employmentSlovak Republic: 100% of population, 4.37% of employmentSlovenia: 100% of population, 0.7% of employmentSweden: 99.3% of population, 2.83% of employmentEswatini: 2.8% of population, 53.3% of employmentChad: 0% of population, 96.9% of employmentTogo: 0% of population, 92.3% of employmentThailand: 0% of population, 63.2% of employmentTajikistan: 100% of population, 63.6% of employmentTimor-Leste: 0% of population, 80.6% of employmentTunisia: 0% of population, 36.9% of employmentTurkiye: 99.3% of population, 27.7% of employmentTanzania: 0% of population, 90.8% of employmentUganda: 0% of population, 94.5% of employmentUruguay: 92% of population, 28.5% of employmentViet Nam: 0% of population, 67% of employmentSouth Africa: 18% of population, 34.8% of employmentZambia: 1% of population, 82.7% of employmentZimbabwe: 1% of population, 85% of employmentARMBDIBGRHRVKGZKORMLTMNGNERSVNTCDTJK

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.8292.5e-31119
By rank (Spearman), not adjusted-0.8043.4e-28119
Standard (Pearson), adjusted for national income-0.5354.4e-10119
By rank (Spearman), adjusted for national income-0.4522.8e-7119

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 119 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: +0.294 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.582 (without ARM) to -0.496 (without GUY). 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: ARM (-0.047).

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 Asia38-0.02
Latin America & Caribbean20-0.53
Sub-Saharan Africa39-0.50

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 third40-0.58
Middle third39-0.24
Richest third40-0.59

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
Christians78-0.48
Muslims27-0.61

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
France18-0.39
Never a European colony12+0.07
Spain16-0.53
United Kingdom30-0.63

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 = -0.535 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 employment		Years
Tajikistan TJK100%63.6%2000, 2016
Slovenia SVN100%0.7%2000, 2024
Slovak Republic SVK100%4.37%2000, 2024
Russian Federation RUS100%19.3%2000, 2024
Romania ROU100%2.21%2000, 2024
Poland POL100%8.62%2000, 2024
Norway NOR100%1.43%2000, 2024
North Macedonia MKD100%11.1%2000, 2024
Moldova MDA100%52.2%2000, 2024
Latvia LVA100%2.17%2000, 2024
Lithuania LTU100%3.2%2000, 2024
Ireland IRL100%1.9%2000, 2024
Hungary HUN100%1.31%2000, 2020
Croatia HRV100%1.17%2000, 2024
Greece GRC100%3.58%2000, 2024

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).
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)

Sources

European ancestry

European and Central 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 Europe or Central Asia (the World Bank's Europe & Central Asia region, which includes Turkey, the Caucasus and Central Asia). 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.

Informal employment

Share of employment that is informal

International Labour Organization · via Our World in Data · CC BY 3.0 IGO · updated annual

Workers with no contract, no social protection or no registered employer, as the ILO (the UN's labour agency) makes them comparable across national labour force surveys. The World Bank's copy of this series was retired, so it comes from the UN's database for the Sustainable Development Goals.

How this was computed

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

This page shows that two measures go together. It does not show that European ancestry changes Informal employment, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with European ancestry or Informal employment