placestats

Governance score vs. Government expense

Fades after adjusting for income

Across 138 countries, Governance score and Government expense 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) shrinks it to +0.40, which could easily be chance (q = 0.093, allowing for the many pairs tested, and allowing for countries in the same region being alike). So the first figure mostly shows richer countries differing from poorer ones, not a link between these two things.

Comparing countries across the middle half of government expense, 17.7% to 33.8% of GDP, governance score is better by 0.734 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.49 typical gaps.

Link before adjusting (r)+0.561p = 8.7e-13 · 138 countries
After adjusting for national income+0.404p = 9.7e-7 · likely range [+0.25, +0.54]
p, allowing for regions0.007standard error 0.101 · 7 regions
Check for luck (q)0.093across 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.

Governance score · typical gaps (standard deviations)link with income +0.53 · moderate
Government expense · % of GDPlink with income +0.44 · moderate

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

Every country as a dot

Governance score · typical gaps (standard deviations) →Government expense · % of GDP →-1.41.473.77%65.6%Afghanistan: -1.4 typical gaps (standard deviations), 39.2% of GDPAngola: -0.522 typical gaps (standard deviations), 14.6% of GDPAlbania: 0.323 typical gaps (standard deviations), 25.4% of GDPUnited Arab Emirates: -0.981 typical gaps (standard deviations), 3.77% of GDPArgentina: 0.815 typical gaps (standard deviations), 21% of GDPArmenia: 0.22 typical gaps (standard deviations), 20.9% of GDPAustralia: 1.38 typical gaps (standard deviations), 27.6% of GDPAustria: 1.33 typical gaps (standard deviations), 46% of GDPAzerbaijan: -1.33 typical gaps (standard deviations), 23% of GDPBurundi: -1.09 typical gaps (standard deviations), 12.3% of GDPBelgium: 1.41 typical gaps (standard deviations), 39.7% of GDPBurkina Faso: -0.784 typical gaps (standard deviations), 19.6% of GDPBangladesh: -0.359 typical gaps (standard deviations), 8.32% of GDPBulgaria: 0.74 typical gaps (standard deviations), 35.8% of GDPBahrain: -1.05 typical gaps (standard deviations), 26.1% of GDPBosnia and Herzegovina: 0.0899 typical gaps (standard deviations), 35.3% of GDPBelarus: -1.26 typical gaps (standard deviations), 28.5% of GDPBelize: 0.0883 typical gaps (standard deviations), 22.3% of GDPBrazil: 0.85 typical gaps (standard deviations), 32.4% of GDPBarbados: 1.24 typical gaps (standard deviations), 28.8% of GDPBhutan: 0.61 typical gaps (standard deviations), 24.1% of GDPBotswana: 0.872 typical gaps (standard deviations), 26.5% of GDPCentral African Republic: -1.02 typical gaps (standard deviations), 11.7% of GDPCanada: 1.36 typical gaps (standard deviations), 19.1% of GDPSwitzerland: 1.47 typical gaps (standard deviations), 16.1% of GDPChile: 1.32 typical gaps (standard deviations), 24.6% of GDPCote d'Ivoire: -0.116 typical gaps (standard deviations), 17.9% of GDPCameroon: -0.834 typical gaps (standard deviations), 12.4% of GDPCongo, Dem. Rep.: -0.824 typical gaps (standard deviations), 12.4% of GDPCongo, Rep.: -0.925 typical gaps (standard deviations), 16.1% of GDPColombia: 0.733 typical gaps (standard deviations), 33.3% of GDPCabo Verde: 1.15 typical gaps (standard deviations), 30.9% of GDPCosta Rica: 1.34 typical gaps (standard deviations), 28.6% of GDPCyprus: 1.1 typical gaps (standard deviations), 38.5% of GDPCzechia: 1.31 typical gaps (standard deviations), 34.4% of GDPGermany: 1.36 typical gaps (standard deviations), 31.2% of GDPDenmark: 1.47 typical gaps (standard deviations), 33.3% of GDPDominican Republic: 0.621 typical gaps (standard deviations), 18.1% of GDPEcuador: 0.447 typical gaps (standard deviations), 30.4% of GDPEgypt, Arab Rep.: -0.987 typical gaps (standard deviations), 30.2% of GDPSpain: 1.32 typical gaps (standard deviations), 34% of GDPEstonia: 1.41 typical gaps (standard deviations), 37.7% of GDPEthiopia: -0.741 typical gaps (standard deviations), 6.12% of GDPFinland: 1.38 typical gaps (standard deviations), 44.9% of GDPFiji: 0.57 typical gaps (standard deviations), 27.8% of GDPFrance: 1.24 typical gaps (standard deviations), 47.3% of GDPGabon: -0.529 typical gaps (standard deviations), 14.1% of GDPUnited Kingdom: 1.27 typical gaps (standard deviations), 41.2% of GDPGeorgia: 0.0179 typical gaps (standard deviations), 25.2% of GDPGhana: 0.845 typical gaps (standard deviations), 22.7% of GDPGuinea-Bissau: -0.465 typical gaps (standard deviations), 15.5% of GDPEquatorial Guinea: -1.32 typical gaps (standard deviations), 11% of GDPGreece: 0.914 typical gaps (standard deviations), 47.3% of GDPGuatemala: 0.291 typical gaps (standard deviations), 13.5% of GDPHonduras: 0.146 typical gaps (standard deviations), 24.4% of GDPCroatia: 0.865 typical gaps (standard deviations), 38.1% of GDPHungary: 0.225 typical gaps (standard deviations), 42.4% of GDPIndia: 0.146 typical gaps (standard deviations), 13.7% of GDPIreland: 1.41 typical gaps (standard deviations), 19.2% of GDPIraq: -0.488 typical gaps (standard deviations), 27.6% of GDPIceland: 1.32 typical gaps (standard deviations), 32% of GDPIsrael: 0.783 typical gaps (standard deviations), 37.3% of GDPItaly: 1 typical gaps (standard deviations), 47.3% of GDPJamaica: 1.01 typical gaps (standard deviations), 27.5% of GDPJordan: -0.271 typical gaps (standard deviations), 25.9% of GDPJapan: 1.28 typical gaps (standard deviations), 20.1% of GDPKazakhstan: -0.796 typical gaps (standard deviations), 17.4% of GDPKenya: 0.217 typical gaps (standard deviations), 27.8% of GDPKyrgyz Republic: -0.725 typical gaps (standard deviations), 23.7% of GDPCambodia: -1.01 typical gaps (standard deviations), 12.9% of GDPKorea, Rep.: 1.05 typical gaps (standard deviations), 28.3% of GDPLao PDR: -1.14 typical gaps (standard deviations), 10.3% of GDPLebanon: -0.112 typical gaps (standard deviations), 6.3% of GDPSt. Lucia: 0.552 typical gaps (standard deviations), 17.6% of GDPSri Lanka: 0.602 typical gaps (standard deviations), 18% of GDPLesotho: 0.671 typical gaps (standard deviations), 40.2% of GDPLithuania: 1.14 typical gaps (standard deviations), 33.9% of GDPLuxembourg: 1.38 typical gaps (standard deviations), 42.2% of GDPLatvia: 1.22 typical gaps (standard deviations), 34.2% of GDPMorocco: -0.351 typical gaps (standard deviations), 26.6% of GDPMoldova: 0.5 typical gaps (standard deviations), 34.5% of GDPMadagascar: -0.193 typical gaps (standard deviations), 11% of GDPMaldives: 0.0802 typical gaps (standard deviations), 29.5% of GDPMexico: 0.134 typical gaps (standard deviations), 22.5% of GDPNorth Macedonia: 0.352 typical gaps (standard deviations), 33.4% of GDPMali: -0.753 typical gaps (standard deviations), 12.2% of GDPMalta: 1.12 typical gaps (standard deviations), 33.7% of GDPMyanmar: -1.39 typical gaps (standard deviations), 13.8% of GDPMongolia: 0.716 typical gaps (standard deviations), 31.7% of GDPMozambique: -0.298 typical gaps (standard deviations), 26% of GDPMauritius: 1.07 typical gaps (standard deviations), 29.1% of GDPMalawi: 0.576 typical gaps (standard deviations), 20.2% of GDPMalaysia: 0.169 typical gaps (standard deviations), 17% of GDPNamibia: 0.69 typical gaps (standard deviations), 36.2% of GDPNicaragua: -1.24 typical gaps (standard deviations), 16.3% of GDPNetherlands: 1.32 typical gaps (standard deviations), 40% of GDPNorway: 1.43 typical gaps (standard deviations), 34.5% of GDPNepal: 0.441 typical gaps (standard deviations), 19.4% of GDPNew Zealand: 1.32 typical gaps (standard deviations), 35.5% of GDPPanama: 0.84 typical gaps (standard deviations), 17.1% of GDPPeru: 0.522 typical gaps (standard deviations), 22.8% of GDPPhilippines: 0.0585 typical gaps (standard deviations), 16.9% of GDPPapua New Guinea: 0.227 typical gaps (standard deviations), 20.3% of GDPPoland: 0.996 typical gaps (standard deviations), 39.1% of GDPPortugal: 1.26 typical gaps (standard deviations), 37.2% of GDPParaguay: 0.349 typical gaps (standard deviations), 18.9% of GDPRomania: 0.749 typical gaps (standard deviations), 37.3% of GDPRussian Federation: -1.14 typical gaps (standard deviations), 30% of GDPRwanda: -0.868 typical gaps (standard deviations), 18% of GDPSaudi Arabia: -1.14 typical gaps (standard deviations), 24.2% of GDPSudan: -1.22 typical gaps (standard deviations), 9.73% of GDPSenegal: 0.683 typical gaps (standard deviations), 23.6% of GDPSingapore: 0.0449 typical gaps (standard deviations), 17.1% of GDPSolomon Islands: 0.697 typical gaps (standard deviations), 28.9% of GDPEl Salvador: -0.352 typical gaps (standard deviations), 27.9% of GDPSomalia, Fed. Rep.: -1.01 typical gaps (standard deviations), 6.35% of GDPSerbia: -0.0738 typical gaps (standard deviations), 35.5% of GDPSlovak Republic: 1.02 typical gaps (standard deviations), 39.8% of GDPSlovenia: 1.19 typical gaps (standard deviations), 42.9% of GDPSweden: 1.42 typical gaps (standard deviations), 32.5% of GDPEswatini: -1.05 typical gaps (standard deviations), 24.6% of GDPTogo: -0.371 typical gaps (standard deviations), 16% of GDPThailand: -0.383 typical gaps (standard deviations), 20.7% of GDPTajikistan: -1.34 typical gaps (standard deviations), 11% of GDPTimor-Leste: 0.779 typical gaps (standard deviations), 52.5% of GDPTrinidad and Tobago: 1.03 typical gaps (standard deviations), 33.3% of GDPTunisia: -0.257 typical gaps (standard deviations), 33.8% of GDPTurkiye: -0.691 typical gaps (standard deviations), 33% of GDPTanzania: -0.315 typical gaps (standard deviations), 16.3% of GDPUganda: -0.646 typical gaps (standard deviations), 16.6% of GDPUkraine: -0.0525 typical gaps (standard deviations), 65.6% of GDPUruguay: 1.39 typical gaps (standard deviations), 36.5% of GDPUnited States: 0.998 typical gaps (standard deviations), 24.4% of GDPUzbekistan: -1.04 typical gaps (standard deviations), 22.3% of GDPVanuatu: 0.885 typical gaps (standard deviations), 26.6% of GDPSouth Africa: 0.883 typical gaps (standard deviations), 33.4% of GDPZambia: 0.212 typical gaps (standard deviations), 25.1% of GDPZimbabwe: -0.609 typical gaps (standard deviations), 11% of GDPAFGAREAUTCANCHEFRAIRLRUSSOMTJKUKR

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5618.7e-13138
By rank (Spearman), not adjusted+0.5912.3e-14138
Standard (Pearson), adjusted for national income+0.4049.7e-7138
By rank (Spearman), adjusted for national income+0.4059.1e-7138

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 138 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: +0.156 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.368 (without ARE) to +0.445 (without UKR). 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: UKR (+0.041).

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+0.74
Europe & Central Asia46+0.34
Latin America & Caribbean21+0.51
Middle East, North Africa, Afghanistan & Pakistan12+0.29
Sub-Saharan Africa34+0.74

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 third46+0.55
Middle third46+0.14
Richest third46+0.48

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
Christians92+0.34
Muslims28+0.11

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+0.38
Never a European colony16+0.02
Spain18+0.56
United Kingdom42+0.55

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 = +0.445 after dropping 1 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


typical gaps (standard deviations)
% of GDP		Years
Denmark DNK1.4733.3%2025, 2023
Switzerland CHE1.4716.1%2025, 2023
Norway NOR1.4334.5%2025, 2023
Sweden SWE1.4232.5%2025, 2022
Ireland IRL1.4119.2%2025, 2022
Belgium BEL1.4139.7%2025, 2022
Estonia EST1.4137.7%2025, 2023
Uruguay URY1.3936.5%2025, 2023
Finland FIN1.3844.9%2025, 2023
Australia AUS1.3827.6%2025, 2022
Luxembourg LUX1.3842.2%2025, 2023
Germany DEU1.3631.2%2025, 2022
Canada CAN1.3619.1%2025, 2023
Costa Rica CRI1.3428.6%2025, 2023
Austria AUT1.3346%2025, 2023

fh; un_sdg; vdem; wdi; wgi
World Bank, World Development Indicators

Sources

Governance score

Governance score

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

The average of 19 measures from 5 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: Civil liberties (FH), Political rights (FH), Pre-trial detainee share, Civil society participation, Political corruption, Electoral democracy, Freedom of association, Freedom of expression, Free and fair elections, Liberal democracy, Participatory democracy, Political civil liberties, Birth registration, Control of corruption, Government effectiveness, Political stability, Rule of law, Regulatory quality, Voice and accountability.

Government expense

Government expense (% of GDP)

World Bank · CC BY 4.0 · updated quarterly

Total central government spending, as a share of GDP: buying, payments to people, subsidies and interest together. From the IMF's Government Finance Statistics. It is the fuller measure of government size, but it covers fewer countries. It counts central government only, so a country with large state and local budgets looks low. About forty fewer countries report it than report government consumption. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2011–2025. 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 Governance score and Government expense

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

More links with Governance score or Government expense