placestats

Gender score vs. Hours worked (PWT)

Holds up after adjusting for income

Across 126 countries, Gender score and Hours worked (PWT) rise and fall together at r = -0.58 (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 = -0.53, q = 0.021, allowing for the many pairs tested).

Comparing countries across the middle half of hours worked (PWT), 1,644 to 2,177 hours per year, gender score is worse by 0.709 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.661 typical gaps.

Link before adjusting (r)-0.577p = 1.6e-12 · 126 countries
After adjusting for national income-0.527p = 2.7e-10 · likely range [-0.64, -0.39]
p, allowing for regions0.000standard error 0.060 · 7 regions
Check for luck (q)0.021across 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.

Gender score · typical gaps (standard deviations)link with income +0.37 · 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

Gender score · typical gaps (standard deviations) →Hours worked (PWT) · hours per year →-1.222.191,3352,658Angola: 0.459 typical gaps (standard deviations), 2,115 hours per yearAlbania: 1.27 typical gaps (standard deviations), 2,152 hours per yearUnited Arab Emirates: 0.417 typical gaps (standard deviations), 2,514 hours per yearArgentina: 1.08 typical gaps (standard deviations), 1,612 hours per yearArmenia: 1.26 typical gaps (standard deviations), 1,976 hours per yearAustralia: 1.46 typical gaps (standard deviations), 1,611 hours per yearAustria: 1.17 typical gaps (standard deviations), 1,438 hours per yearAzerbaijan: -0.374 typical gaps (standard deviations), 1,791 hours per yearBelgium: 1.44 typical gaps (standard deviations), 1,593 hours per yearBurkina Faso: -0.0943 typical gaps (standard deviations), 2,356 hours per yearBangladesh: -0.785 typical gaps (standard deviations), 2,383 hours per yearBulgaria: 0.272 typical gaps (standard deviations), 1,659 hours per yearBahrain: 0.0565 typical gaps (standard deviations), 2,052 hours per yearBosnia and Herzegovina: 0.442 typical gaps (standard deviations), 2,122 hours per yearBelarus: 0.867 typical gaps (standard deviations), 1,875 hours per yearBolivia: 1.1 typical gaps (standard deviations), 1,968 hours per yearBrazil: 0.684 typical gaps (standard deviations), 1,994 hours per yearBotswana: 0.25 typical gaps (standard deviations), 2,258 hours per yearCanada: 1.38 typical gaps (standard deviations), 1,731 hours per yearSwitzerland: 1.22 typical gaps (standard deviations), 1,529 hours per yearChile: 1.72 typical gaps (standard deviations), 1,950 hours per yearChina: -0.0629 typical gaps (standard deviations), 2,328 hours per yearCote d'Ivoire: 0.00107 typical gaps (standard deviations), 2,103 hours per yearCameroon: 0.739 typical gaps (standard deviations), 2,181 hours per yearCongo, Dem. Rep.: -0.00209 typical gaps (standard deviations), 1,841 hours per yearCongo, Rep.: -0.252 typical gaps (standard deviations), 2,534 hours per yearColombia: 0.908 typical gaps (standard deviations), 2,471 hours per yearCosta Rica: 1.7 typical gaps (standard deviations), 2,177 hours per yearCyprus: 0.592 typical gaps (standard deviations), 1,840 hours per yearCzechia: 0.195 typical gaps (standard deviations), 1,774 hours per yearGermany: 1.34 typical gaps (standard deviations), 1,335 hours per yearDenmark: 1.1 typical gaps (standard deviations), 1,380 hours per yearDominican Republic: 1.32 typical gaps (standard deviations), 2,072 hours per yearAlgeria: -0.399 typical gaps (standard deviations), 2,232 hours per yearEcuador: 1.34 typical gaps (standard deviations), 1,594 hours per yearEgypt, Arab Rep.: -0.205 typical gaps (standard deviations), 2,369 hours per yearSpain: 1.69 typical gaps (standard deviations), 1,638 hours per yearEstonia: 1.7 typical gaps (standard deviations), 1,606 hours per yearEthiopia: 1.4 typical gaps (standard deviations), 1,599 hours per yearFinland: 2.03 typical gaps (standard deviations), 1,497 hours per yearFrance: 1.36 typical gaps (standard deviations), 1,487 hours per yearGabon: 0.418 typical gaps (standard deviations), 2,174 hours per yearUnited Kingdom: 1.23 typical gaps (standard deviations), 1,523 hours per yearGeorgia: 0.577 typical gaps (standard deviations), 1,932 hours per yearGhana: 0.209 typical gaps (standard deviations), 1,656 hours per yearGreece: 0.86 typical gaps (standard deviations), 1,893 hours per yearGuatemala: 0.488 typical gaps (standard deviations), 2,151 hours per yearCroatia: 0.51 typical gaps (standard deviations), 1,974 hours per yearHungary: -0.00668 typical gaps (standard deviations), 1,668 hours per yearIndonesia: 0.0942 typical gaps (standard deviations), 1,958 hours per yearIndia: -0.411 typical gaps (standard deviations), 2,383 hours per yearIreland: 0.727 typical gaps (standard deviations), 1,654 hours per yearIran, Islamic Rep.: -0.962 typical gaps (standard deviations), 2,246 hours per yearIraq: 0.119 typical gaps (standard deviations), 1,583 hours per yearIceland: 2.19 typical gaps (standard deviations), 1,452 hours per yearIsrael: 0.452 typical gaps (standard deviations), 1,890 hours per yearItaly: 0.706 typical gaps (standard deviations), 1,701 hours per yearJamaica: 1.09 typical gaps (standard deviations), 1,963 hours per yearJordan: -0.0146 typical gaps (standard deviations), 2,483 hours per yearJapan: -0.021 typical gaps (standard deviations), 1,654 hours per yearKazakhstan: 0.296 typical gaps (standard deviations), 1,975 hours per yearKenya: 0.693 typical gaps (standard deviations), 2,035 hours per yearKyrgyz Republic: 0.129 typical gaps (standard deviations), 1,830 hours per yearCambodia: -0.451 typical gaps (standard deviations), 2,389 hours per yearKorea, Rep.: 0.412 typical gaps (standard deviations), 1,910 hours per yearKuwait: -1.02 typical gaps (standard deviations), 2,318 hours per yearLebanon: -0.687 typical gaps (standard deviations), 2,412 hours per yearSri Lanka: -0.263 typical gaps (standard deviations), 2,053 hours per yearLithuania: 0.944 typical gaps (standard deviations), 1,640 hours per yearLuxembourg: 0.927 typical gaps (standard deviations), 1,452 hours per yearLatvia: 1.32 typical gaps (standard deviations), 1,625 hours per yearMorocco: 0.514 typical gaps (standard deviations), 2,174 hours per yearMoldova: 1.45 typical gaps (standard deviations), 1,989 hours per yearMadagascar: 0.668 typical gaps (standard deviations), 1,799 hours per yearMexico: 1.33 typical gaps (standard deviations), 1,629 hours per yearNorth Macedonia: 1.08 typical gaps (standard deviations), 1,949 hours per yearMali: 0.147 typical gaps (standard deviations), 2,243 hours per yearMalta: 0.55 typical gaps (standard deviations), 1,848 hours per yearMyanmar: 0.307 typical gaps (standard deviations), 2,197 hours per yearMozambique: 0.926 typical gaps (standard deviations), 1,507 hours per yearMauritius: 0.0838 typical gaps (standard deviations), 1,990 hours per yearMalawi: 0.728 typical gaps (standard deviations), 1,596 hours per yearMalaysia: -0.0868 typical gaps (standard deviations), 2,308 hours per yearNamibia: 1.41 typical gaps (standard deviations), 2,184 hours per yearNiger: 0.397 typical gaps (standard deviations), 2,069 hours per yearNigeria: -0.214 typical gaps (standard deviations), 2,062 hours per yearNetherlands: 1.19 typical gaps (standard deviations), 1,439 hours per yearNorway: 1.53 typical gaps (standard deviations), 1,412 hours per yearNew Zealand: 1.62 typical gaps (standard deviations), 1,708 hours per yearOman: -0.95 typical gaps (standard deviations), 2,266 hours per yearPakistan: -0.691 typical gaps (standard deviations), 2,471 hours per yearPeru: 0.932 typical gaps (standard deviations), 2,143 hours per yearPhilippines: 0.579 typical gaps (standard deviations), 2,096 hours per yearPoland: 1.13 typical gaps (standard deviations), 1,807 hours per yearPortugal: 1.05 typical gaps (standard deviations), 1,735 hours per yearParaguay: 0.39 typical gaps (standard deviations), 2,123 hours per yearQatar: -0.892 typical gaps (standard deviations), 2,435 hours per yearRomania: 0.285 typical gaps (standard deviations), 1,826 hours per yearRussian Federation: 0.137 typical gaps (standard deviations), 2,087 hours per yearRwanda: 1.02 typical gaps (standard deviations), 1,584 hours per yearSaudi Arabia: -0.552 typical gaps (standard deviations), 2,125 hours per yearSudan: -1.22 typical gaps (standard deviations), 2,658 hours per yearSenegal: 1.03 typical gaps (standard deviations), 2,335 hours per yearSingapore: 1.04 typical gaps (standard deviations), 2,283 hours per yearSerbia: 1.01 typical gaps (standard deviations), 1,995 hours per yearSlovak Republic: 0.374 typical gaps (standard deviations), 1,621 hours per yearSlovenia: 1.26 typical gaps (standard deviations), 1,588 hours per yearSweden: 1.79 typical gaps (standard deviations), 1,432 hours per yearSyrian Arab Republic: -0.767 typical gaps (standard deviations), 1,621 hours per yearChad: 0.144 typical gaps (standard deviations), 1,585 hours per yearThailand: 0.331 typical gaps (standard deviations), 2,177 hours per yearTajikistan: -0.106 typical gaps (standard deviations), 2,134 hours per yearTurkmenistan: -0.394 typical gaps (standard deviations), 2,179 hours per yearTrinidad and Tobago: 1.21 typical gaps (standard deviations), 1,610 hours per yearTunisia: 0.319 typical gaps (standard deviations), 2,288 hours per yearTurkiye: -0.26 typical gaps (standard deviations), 1,747 hours per yearTanzania: 0.804 typical gaps (standard deviations), 2,129 hours per yearUganda: 0.849 typical gaps (standard deviations), 2,128 hours per yearUkraine: 0.643 typical gaps (standard deviations), 2,045 hours per yearUruguay: 1.12 typical gaps (standard deviations), 1,481 hours per yearUnited States: 1.06 typical gaps (standard deviations), 1,789 hours per yearUzbekistan: 0.228 typical gaps (standard deviations), 2,099 hours per yearViet Nam: 0.0894 typical gaps (standard deviations), 2,160 hours per yearSouth Africa: 1.45 typical gaps (standard deviations), 2,181 hours per yearZambia: 0.21 typical gaps (standard deviations), 2,200 hours per yearZimbabwe: 0.6 typical gaps (standard deviations), 2,311 hours per yearAZECOLCRIFINISLPAKQATSDNSWESYRTURZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5771.6e-12126
By rank (Spearman), not adjusted-0.5581.1e-11126
Standard (Pearson), adjusted for national income-0.5272.7e-10126
By rank (Spearman), adjusted for national income-0.4851.0e-8126

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 126 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: +0.050 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.558 (without SYR) to -0.503 (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: SYR (-0.031).

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 & Pacific13-0.46
Europe & Central Asia47-0.34
Latin America & Caribbean15-0.15
Middle East, North Africa, Afghanistan & Pakistan18-0.26
Sub-Saharan Africa28-0.45

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 third42-0.44
Middle third42-0.38
Richest third42-0.71

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
Christians74-0.35
Muslims36-0.21

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+0.07
Never a European colony17-0.48
Spain14-0.15
United Kingdom35-0.59

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
Nationalist20-0.40
No strong official ideology85-0.49

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 banned18-0.81
No parties banned93-0.36

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 = -0.527 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


typical gaps (standard deviations)
hours per year		Years
Iceland ISL2.191,4522024, 2023
Finland FIN2.031,4972024, 2023
Sweden SWE1.791,4322024, 2023
Chile CHL1.721,9502024, 2023
Costa Rica CRI1.72,1772025, 2023
Estonia EST1.71,6062024, 2023
Spain ESP1.691,6382024, 2023
New Zealand NZL1.621,7082024, 2023
Norway NOR1.531,4122024, 2023
Australia AUS1.461,6112025, 2023
South Africa ZAF1.452,1812024, 2023
Moldova MDA1.451,9892024, 2023
Belgium BEL1.441,5932024, 2023
Namibia NAM1.412,1842024, 2023
Ethiopia ETH1.41,5992024, 2023

vdem; wb_gender; wdi
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

Gender score

Gender score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 5 measures from 3 publishers. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Women's political empowerment, Women ministers, Women in parliament, Women in management, Married by 18.

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 2020–2025. 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 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 Gender score and Hours worked (PWT)

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

More links with Gender score or Hours worked (PWT)