placestats

European ancestry vs. Hours worked (PWT)

Fades 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 125 countries, European ancestry and Hours worked (PWT) rise and fall together at r = -0.56 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) leaves r = -0.48, which could easily be chance (q = 0.074, allowing for the many pairs tested, and allowing for countries in the same region being alike). Caution: direction is not consistent across regions.

Comparing countries across the middle half of European ancestry, 0% to 99.7% of population, hours worked (PWT) is 378 hours per year lower at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 366 hours per year.

Link before adjusting (r)-0.559p = 1.2e-11 · 125 countries
After adjusting for national income-0.476p = 2.4e-8 · likely range [-0.60, -0.33]
p, allowing for regions0.011standard error 0.130 · 7 regions
Check for luck (q)0.074across 18,622 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
Hours worked (PWT) · hours per yearlink with income -0.40 · moderate

“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 →Hours worked (PWT) · hours per year →0%100%1,3352,658Angola: 2% of population, 2,115 hours per yearAlbania: 100% of population, 2,152 hours per yearUnited Arab Emirates: 0% of population, 2,514 hours per yearArgentina: 90% of population, 1,612 hours per yearArmenia: 100% of population, 1,976 hours per yearAustralia: 89.6% of population, 1,611 hours per yearAustria: 100% of population, 1,438 hours per yearAzerbaijan: 99% of population, 1,791 hours per yearBelgium: 98.4% of population, 1,593 hours per yearBurkina Faso: 0% of population, 2,356 hours per yearBangladesh: 0% of population, 2,383 hours per yearBulgaria: 100% of population, 1,659 hours per yearBahrain: 0% of population, 2,052 hours per yearBosnia and Herzegovina: 100% of population, 2,122 hours per yearBelarus: 100% of population, 1,875 hours per yearBolivia: 26.7% of population, 1,968 hours per yearBrazil: 74.4% of population, 1,994 hours per yearBotswana: 1.5% of population, 2,258 hours per yearCanada: 85.6% of population, 1,731 hours per yearSwitzerland: 99.5% of population, 1,529 hours per yearChile: 59.3% of population, 1,950 hours per yearChina: 0% of population, 2,328 hours per yearCote d'Ivoire: 0% of population, 2,103 hours per yearCameroon: 0% of population, 2,181 hours per yearCongo, Dem. Rep.: 0.46% of population, 1,841 hours per yearCongo, Rep.: 0% of population, 2,534 hours per yearColombia: 45.6% of population, 2,471 hours per yearCosta Rica: 60.4% of population, 2,177 hours per yearCyprus: 100% of population, 1,840 hours per yearCzechia: 100% of population, 1,774 hours per yearGermany: 100% of population, 1,335 hours per yearDenmark: 100% of population, 1,380 hours per yearDominican Republic: 52% of population, 2,072 hours per yearAlgeria: 0% of population, 2,232 hours per yearEcuador: 32% of population, 1,594 hours per yearEgypt, Arab Rep.: 0% of population, 2,369 hours per yearSpain: 99.7% of population, 1,638 hours per yearEstonia: 100% of population, 1,606 hours per yearEthiopia: 0% of population, 1,599 hours per yearFinland: 100% of population, 1,497 hours per yearFrance: 94.2% of population, 1,487 hours per yearGabon: 1% of population, 2,174 hours per yearUnited Kingdom: 94.6% of population, 1,523 hours per yearGeorgia: 100% of population, 1,932 hours per yearGhana: 0% of population, 1,656 hours per yearGreece: 100% of population, 1,893 hours per yearGuatemala: 22% of population, 2,151 hours per yearHong Kong SAR, China: 0% of population, 2,240 hours per yearCroatia: 100% of population, 1,974 hours per yearHungary: 100% of population, 1,668 hours per yearIndonesia: 0% of population, 1,958 hours per yearIndia: 0% of population, 2,383 hours per yearIreland: 100% of population, 1,654 hours per yearIran, Islamic Rep.: 0% of population, 2,246 hours per yearIraq: 5.6% of population, 1,583 hours per yearIsrael: 35.6% of population, 1,890 hours per yearItaly: 99.8% of population, 1,701 hours per yearJamaica: 8% of population, 1,963 hours per yearJordan: 2% of population, 2,483 hours per yearJapan: 0% of population, 1,654 hours per yearKazakhstan: 97.9% of population, 1,975 hours per yearKenya: 0% of population, 2,035 hours per yearKyrgyz Republic: 98% of population, 1,830 hours per yearCambodia: 0% of population, 2,389 hours per yearKorea, Rep.: 0% of population, 1,910 hours per yearKuwait: 0% of population, 2,318 hours per yearLebanon: 6.9% of population, 2,412 hours per yearSri Lanka: 0% of population, 2,053 hours per yearLithuania: 100% of population, 1,640 hours per yearLuxembourg: 99.2% of population, 1,452 hours per yearLatvia: 100% of population, 1,625 hours per yearMorocco: 8% of population, 2,174 hours per yearMoldova: 100% of population, 1,989 hours per yearMadagascar: 0.6% of population, 1,799 hours per yearMexico: 30% of population, 1,629 hours per yearNorth Macedonia: 100% of population, 1,949 hours per yearMali: 0% of population, 2,243 hours per yearMalta: 4.2% of population, 1,848 hours per yearMyanmar: 0% of population, 2,197 hours per yearMozambique: 0% of population, 1,507 hours per yearMauritius: 16% of population, 1,990 hours per yearMalawi: 0% of population, 1,596 hours per yearMalaysia: 0% of population, 2,308 hours per yearNamibia: 10.8% of population, 2,184 hours per yearNiger: 0% of population, 2,069 hours per yearNigeria: 0% of population, 2,062 hours per yearNetherlands: 96.6% of population, 1,439 hours per yearNorway: 100% of population, 1,412 hours per yearNew Zealand: 80.6% of population, 1,708 hours per yearOman: 0% of population, 2,266 hours per yearPakistan: 0% of population, 2,471 hours per yearPeru: 28% of population, 2,143 hours per yearPhilippines: 0% of population, 2,096 hours per yearPoland: 100% of population, 1,807 hours per yearPortugal: 98.4% of population, 1,735 hours per yearParaguay: 52.9% of population, 2,123 hours per yearQatar: 0.9% of population, 2,435 hours per yearRomania: 100% of population, 1,826 hours per yearRussian Federation: 100% of population, 2,087 hours per yearRwanda: 0% of population, 1,584 hours per yearSaudi Arabia: 0% of population, 2,125 hours per yearSudan: 0% of population, 2,658 hours per yearSenegal: 1.3% of population, 2,335 hours per yearSingapore: 0.5% of population, 2,283 hours per yearSlovak Republic: 100% of population, 1,621 hours per yearSlovenia: 100% of population, 1,588 hours per yearSweden: 99.3% of population, 1,432 hours per yearSyrian Arab Republic: 5.2% of population, 1,621 hours per yearChad: 0% of population, 1,585 hours per yearThailand: 0% of population, 2,177 hours per yearTajikistan: 100% of population, 2,134 hours per yearTurkmenistan: 100% of population, 2,179 hours per yearTrinidad and Tobago: 7.1% of population, 1,610 hours per yearTunisia: 0% of population, 2,288 hours per yearTurkiye: 99.3% of population, 1,747 hours per yearTanzania: 0% of population, 2,129 hours per yearUganda: 0% of population, 2,128 hours per yearUkraine: 100% of population, 2,045 hours per yearUruguay: 92% of population, 1,481 hours per yearUnited States: 76.2% of population, 1,789 hours per yearUzbekistan: 99% of population, 2,099 hours per yearViet Nam: 0% of population, 2,160 hours per yearSouth Africa: 18% of population, 2,181 hours per yearZambia: 1% of population, 2,200 hours per yearZimbabwe: 1% of population, 2,311 hours per yearALBARECOGDEUDNKMOZNORRWASDNTCDTJKTKM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5591.2e-11125
By rank (Spearman), not adjusted-0.5071.6e-9125
Standard (Pearson), adjusted for national income-0.4762.4e-8125
By rank (Spearman), adjusted for national income-0.3888.2e-6125

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 125 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: +0.084 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.493 (without JPN) to -0.453 (without QAT). 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: QAT (+0.022).

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 & Pacific14-0.64
Europe & Central Asia45+0.29
Latin America & Caribbean15+0.06
Middle East, North Africa, Afghanistan & Pakistan18-0.40
Sub-Saharan Africa28-0.09

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 third42-0.15
Middle third41-0.37
Richest third42-0.75

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
Christians72-0.26
Muslims36-0.37

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
France16-0.19
Never a European colony17-0.15
Spain14-0.25
United Kingdom35-0.54

By official ideology

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
Nationalist21-0.42
No strong official ideology84-0.44

By party system

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
A few parties banned18-0.12
No parties banned91-0.45

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 = -0.476 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.

Loading…

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
hours per year		Years
Ukraine UKR100%2,0452000, 2023
Turkmenistan TKM100%2,1792000, 2023
Tajikistan TJK100%2,1342000, 2023
Slovenia SVN100%1,5882000, 2023
Slovak Republic SVK100%1,6212000, 2023
Russian Federation RUS100%2,0872000, 2023
Romania ROU100%1,8262000, 2023
Poland POL100%1,8072000, 2023
Norway NOR100%1,4122000, 2023
North Macedonia MKD100%1,9492000, 2023
Moldova MDA100%1,9892000, 2023
Latvia LVA100%1,6252000, 2023
Lithuania LTU100%1,6402000, 2023
Ireland IRL100%1,6542000, 2023
Hungary HUN100%1,6682000, 2023

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).
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table", American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt. Data: Penn World Table version 11.0, Groningen Growth and Development Centre, doi:10.34894/FABVLR.

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.

Hours worked (PWT)

Average annual hours worked per person engaged (Penn World Table)

Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)

Hours actually worked in a year, averaged over everyone with a job, including the self-employed and part-timers. It measures the same thing as the OECD hours measure, and for countries in both the two mostly agree. This one is kept because it reaches much further: about 130 countries each year from 2010 to 2023, where the OECD series has 38 countries and stops in 2016. Before 2000 it covers mainly rich countries. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2000–2023. 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 18,622 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore European ancestry and Hours worked (PWT)

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

More links with European ancestry or Hours worked (PWT)