placestats

Gender score vs. Tax revenue

Holds up after adjusting for income

Across 146 countries, Gender score and Tax revenue rise and fall together at r = +0.36 (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 = +0.27, q = 0.041, allowing for the many pairs tested). Caution: direction is not consistent across regions.

Comparing countries across the middle half of tax revenue, 12% to 21.8% of GDP, gender score is better by 0.376 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.267 typical gaps.

Link before adjusting (r)+0.361p = 7.8e-6 · 146 countries
After adjusting for national income+0.268p = 0.001 · likely range [+0.11, +0.41]
p, allowing for regions0.020standard error 0.086 · 7 regions
Check for luck (q)0.041across 12,497 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
Tax revenue · % of GDPlink with income +0.33 · 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) →Tax revenue · % of GDP →-1.212.190.648%35.4%Afghanistan: -0.756 typical gaps (standard deviations), 9.9% of GDPAngola: 0.461 typical gaps (standard deviations), 7.59% of GDPAlbania: 1.27 typical gaps (standard deviations), 18% of GDPUnited Arab Emirates: 0.89 typical gaps (standard deviations), 0.648% of GDPArgentina: 1.08 typical gaps (standard deviations), 10.4% of GDPArmenia: 1.26 typical gaps (standard deviations), 22.4% of GDPAustralia: 1.57 typical gaps (standard deviations), 23.6% of GDPAustria: 1.17 typical gaps (standard deviations), 25.8% of GDPAzerbaijan: -0.374 typical gaps (standard deviations), 17.3% of GDPBurundi: 0.991 typical gaps (standard deviations), 12.7% of GDPBelgium: 1.45 typical gaps (standard deviations), 23% of GDPBurkina Faso: -0.093 typical gaps (standard deviations), 18.7% of GDPBangladesh: -0.782 typical gaps (standard deviations), 7.64% of GDPBulgaria: 0.273 typical gaps (standard deviations), 20.7% of GDPBahrain: 0.0565 typical gaps (standard deviations), 2.82% of GDPBahamas, The: 0.783 typical gaps (standard deviations), 17.3% of GDPBosnia and Herzegovina: 0.116 typical gaps (standard deviations), 20.2% of GDPBelarus: 0.153 typical gaps (standard deviations), 13% of GDPBelize: -0.821 typical gaps (standard deviations), 21.3% of GDPBrazil: 0.685 typical gaps (standard deviations), 15.4% of GDPBarbados: 1.16 typical gaps (standard deviations), 23.4% of GDPBhutan: -0.491 typical gaps (standard deviations), 12.3% of GDPBotswana: 0.25 typical gaps (standard deviations), 22.4% of GDPCentral African Republic: -0.215 typical gaps (standard deviations), 8.21% of GDPCanada: 1.38 typical gaps (standard deviations), 13.7% of GDPSwitzerland: 1.22 typical gaps (standard deviations), 9.45% of GDPChile: 1.72 typical gaps (standard deviations), 18.3% of GDPChina: -0.0629 typical gaps (standard deviations), 7.02% of GDPCote d'Ivoire: 0.00107 typical gaps (standard deviations), 13% of GDPCameroon: 0.739 typical gaps (standard deviations), 11.3% of GDPCongo, Dem. Rep.: -0.00209 typical gaps (standard deviations), 10.7% of GDPCongo, Rep.: -0.252 typical gaps (standard deviations), 6.51% of GDPColombia: 0.908 typical gaps (standard deviations), 15.2% of GDPCabo Verde: 1.2 typical gaps (standard deviations), 18.4% of GDPCosta Rica: 1.7 typical gaps (standard deviations), 13.5% of GDPCyprus: 0.593 typical gaps (standard deviations), 23.9% of GDPCzechia: 0.196 typical gaps (standard deviations), 18.6% of GDPGermany: 1.34 typical gaps (standard deviations), 10.9% of GDPDenmark: 1.1 typical gaps (standard deviations), 33.4% of GDPDominican Republic: 1.32 typical gaps (standard deviations), 14.7% of GDPEcuador: 1.34 typical gaps (standard deviations), 13.1% of GDPEgypt, Arab Rep.: -0.204 typical gaps (standard deviations), 12.5% of GDPSpain: 1.69 typical gaps (standard deviations), 14.9% of GDPEstonia: 1.7 typical gaps (standard deviations), 22.7% of GDPEthiopia: 1.4 typical gaps (standard deviations), 3.4% of GDPFinland: 2.04 typical gaps (standard deviations), 25.3% of GDPFiji: -0.199 typical gaps (standard deviations), 23.9% of GDPFrance: 1.36 typical gaps (standard deviations), 22.8% of GDPMicronesia, Fed. Sts.: -0.508 typical gaps (standard deviations), 7.03% of GDPGabon: 0.419 typical gaps (standard deviations), 9.49% of GDPUnited Kingdom: 1.23 typical gaps (standard deviations), 26.9% of GDPGeorgia: 0.641 typical gaps (standard deviations), 24.3% of GDPGhana: 0.134 typical gaps (standard deviations), 12.4% of GDPGuinea-Bissau: -0.221 typical gaps (standard deviations), 8.3% of GDPEquatorial Guinea: -0.151 typical gaps (standard deviations), 6.6% of GDPGreece: 0.861 typical gaps (standard deviations), 27.4% of GDPGuatemala: 0.823 typical gaps (standard deviations), 11.6% of GDPHonduras: 1.04 typical gaps (standard deviations), 15.1% of GDPCroatia: 0.51 typical gaps (standard deviations), 21.2% of GDPHungary: -0.00646 typical gaps (standard deviations), 22.7% of GDPIndia: -0.409 typical gaps (standard deviations), 6.93% of GDPIreland: 0.727 typical gaps (standard deviations), 17.9% of GDPIraq: 0.119 typical gaps (standard deviations), 1.34% of GDPIceland: 2.19 typical gaps (standard deviations), 23.2% of GDPIsrael: 0.453 typical gaps (standard deviations), 23.2% of GDPItaly: 0.707 typical gaps (standard deviations), 25.6% of GDPJamaica: 1.09 typical gaps (standard deviations), 23.7% of GDPJordan: -0.0146 typical gaps (standard deviations), 15.5% of GDPKazakhstan: 0.296 typical gaps (standard deviations), 11.9% of GDPKenya: 0.693 typical gaps (standard deviations), 14.1% of GDPKyrgyz Republic: 0.129 typical gaps (standard deviations), 19.6% of GDPCambodia: -0.47 typical gaps (standard deviations), 12.2% of GDPKiribati: -0.559 typical gaps (standard deviations), 17.5% of GDPKorea, Rep.: 0.412 typical gaps (standard deviations), 13.3% of GDPLao PDR: -0.093 typical gaps (standard deviations), 12.1% of GDPLebanon: -0.687 typical gaps (standard deviations), 11.1% of GDPSt. Lucia: -0.14 typical gaps (standard deviations), 18.2% of GDPSri Lanka: -0.262 typical gaps (standard deviations), 9.88% of GDPLesotho: 0.261 typical gaps (standard deviations), 35.4% of GDPLithuania: 0.944 typical gaps (standard deviations), 21.9% of GDPLuxembourg: 0.927 typical gaps (standard deviations), 28.6% of GDPLatvia: 1.32 typical gaps (standard deviations), 17.6% of GDPMorocco: 0.514 typical gaps (standard deviations), 20.7% of GDPMoldova: 1.45 typical gaps (standard deviations), 18.6% of GDPMadagascar: 0.668 typical gaps (standard deviations), 10.9% of GDPMaldives: -0.746 typical gaps (standard deviations), 19.5% of GDPMexico: 1.33 typical gaps (standard deviations), 14.8% of GDPMarshall Islands: -0.769 typical gaps (standard deviations), 17.2% of GDPNorth Macedonia: 1.08 typical gaps (standard deviations), 18.8% of GDPMali: 0.147 typical gaps (standard deviations), 12% of GDPMalta: 0.55 typical gaps (standard deviations), 24.1% of GDPMyanmar: 0.308 typical gaps (standard deviations), 6.02% of GDPMongolia: 0.547 typical gaps (standard deviations), 16.9% of GDPMozambique: 0.926 typical gaps (standard deviations), 21.8% of GDPMauritius: 0.0843 typical gaps (standard deviations), 20.5% of GDPMalawi: 0.728 typical gaps (standard deviations), 15.6% of GDPMalaysia: -0.0868 typical gaps (standard deviations), 12.4% of GDPNamibia: 1.41 typical gaps (standard deviations), 33.9% of GDPNicaragua: 1.98 typical gaps (standard deviations), 20.1% of GDPNetherlands: 1.19 typical gaps (standard deviations), 25.3% of GDPNorway: 1.53 typical gaps (standard deviations), 24.5% of GDPNepal: 0.416 typical gaps (standard deviations), 13.9% of GDPNaoero: 0.259 typical gaps (standard deviations), 21.4% of GDPNew Zealand: 1.62 typical gaps (standard deviations), 29.5% of GDPPanama: 0.873 typical gaps (standard deviations), 7.18% of GDPPeru: 0.933 typical gaps (standard deviations), 15.9% of GDPPhilippines: 0.494 typical gaps (standard deviations), 14.4% of GDPPalau: -0.545 typical gaps (standard deviations), 18.1% of GDPPapua New Guinea: -1.01 typical gaps (standard deviations), 15.5% of GDPPoland: 1.13 typical gaps (standard deviations), 18% of GDPPortugal: 1.05 typical gaps (standard deviations), 22.3% of GDPParaguay: 0.39 typical gaps (standard deviations), 11.3% of GDPRomania: 0.285 typical gaps (standard deviations), 16.2% of GDPRussian Federation: 0.137 typical gaps (standard deviations), 10.9% of GDPRwanda: 1.02 typical gaps (standard deviations), 13.1% of GDPSaudi Arabia: -0.552 typical gaps (standard deviations), 8.09% of GDPSudan: -1.21 typical gaps (standard deviations), 7.39% of GDPSenegal: 0.477 typical gaps (standard deviations), 19.5% of GDPSingapore: 1.04 typical gaps (standard deviations), 13.6% of GDPSolomon Islands: -0.473 typical gaps (standard deviations), 23.3% of GDPEl Salvador: 0.618 typical gaps (standard deviations), 21.8% of GDPSomalia, Fed. Rep.: -0.615 typical gaps (standard deviations), 2.24% of GDPSerbia: 1.01 typical gaps (standard deviations), 23.9% of GDPSlovak Republic: 0.374 typical gaps (standard deviations), 19.1% of GDPSlovenia: 1.26 typical gaps (standard deviations), 21% of GDPSweden: 1.79 typical gaps (standard deviations), 27.2% of GDPEswatini: -0.0447 typical gaps (standard deviations), 24.3% of GDPTogo: 0.48 typical gaps (standard deviations), 13.9% of GDPThailand: 0.332 typical gaps (standard deviations), 15.2% of GDPTajikistan: -0.106 typical gaps (standard deviations), 10.7% of GDPTimor-Leste: 0.73 typical gaps (standard deviations), 21.6% of GDPTonga: -0.456 typical gaps (standard deviations), 20.5% of GDPTrinidad and Tobago: 1.21 typical gaps (standard deviations), 16.8% of GDPTunisia: 0.319 typical gaps (standard deviations), 20.1% of GDPTurkiye: -0.502 typical gaps (standard deviations), 17.6% of GDPTanzania: 0.828 typical gaps (standard deviations), 12.7% of GDPUganda: 0.849 typical gaps (standard deviations), 12.6% of GDPUkraine: 0.643 typical gaps (standard deviations), 21% of GDPUruguay: 0.63 typical gaps (standard deviations), 17.9% of GDPUnited States: 1.06 typical gaps (standard deviations), 10.8% of GDPUzbekistan: 0.228 typical gaps (standard deviations), 11% of GDPVanuatu: 0.107 typical gaps (standard deviations), 16.5% of GDPSamoa: 0.528 typical gaps (standard deviations), 24% of GDPSouth Africa: 1.45 typical gaps (standard deviations), 25.9% of GDPZambia: 0.211 typical gaps (standard deviations), 18.8% of GDPZimbabwe: 0.576 typical gaps (standard deviations), 7.21% of GDPAREBGDBLZCRIETHFINLSONAMNZLSDNSLBSOM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.3617.8e-6146
By rank (Spearman), not adjusted+0.3704.4e-6146
Standard (Pearson), adjusted for national income+0.2680.001146
By rank (Spearman), adjusted for national income+0.2650.001146

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 146 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: +0.092 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.247 (without NAM) to +0.298 (without ETH). 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: ETH (+0.029).

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 & Pacific24+0.39
Europe & Central Asia46+0.25
Latin America & Caribbean22-0.12
Middle East, North Africa, Afghanistan & Pakistan12+0.30
Sub-Saharan Africa34+0.32

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 third49+0.27
Middle third48+0.17
Richest third49+0.37

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
Christians97+0.07
Muslims28+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
France14+0.27
Never a European colony16+0.18
Spain19+0.52
United Kingdom47+0.31

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 = +0.268 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)
% of GDP		Years
Iceland ISL2.1923.2%2024, 2023
Finland FIN2.0425.3%2024
Nicaragua NIC1.9820.1%2024
Sweden SWE1.7927.2%2024
Chile CHL1.7218.3%2024
Costa Rica CRI1.713.5%2025, 2024
Estonia EST1.722.7%2024
Spain ESP1.6914.9%2024
New Zealand NZL1.6229.5%2024
Australia AUS1.5723.6%2024, 2022
Norway NOR1.5324.5%2024
South Africa ZAF1.4525.9%2024
Moldova MDA1.4518.6%2024, 2023
Belgium BEL1.4523%2024
Namibia NAM1.4133.9%2024

vdem; wb_gender; wdi
World Bank, World Development Indicators

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.

Tax revenue

Tax revenue (% of GDP)

World Bank · CC BY 4.0 · updated quarterly

How this was computed

Data used: most recent value in 2010-2025; values come from 2012–2025. 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 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Gender score and Tax revenue

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

More links with Gender score or Tax revenue