placestats

Government effectiveness vs. Wellbeing score

Fades after adjusting for income

Across 107 countries, Government effectiveness and Wellbeing score rise and fall together at r = +0.74 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.35, which could easily be chance (q = 0.21, 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 effectiveness, -0.277 to 1.13 points, wellbeing score is better by 0.82 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.472 typical gaps.

Link before adjusting (r)+0.740p = 9.2e-20 · 107 countries
After adjusting for national income+0.346p = 0.000 · likely range [+0.17, +0.50]
p, allowing for regions0.062standard error 0.151 · 7 regions
Check for luck (q)0.206across 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.

Government effectiveness · index (approx. -2.5 to 2.5)link with income +0.85 · proxy
Wellbeing score · typical gaps (standard deviations)link with income +0.80 · high

Both of these rise and fall closely with national income, so a strong link between them before adjusting was close to certain. Read the adjusted figure, not the first one.

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

Every country as a dot

Government effectiveness · index (approx. -2.5 to 2.5) →Wellbeing score · typical gaps (standard deviations) →-2.022.28-1.981.89Albania: 0.312 index (approx. -2.5 to 2.5), -0.617 typical gaps (standard deviations)Andorra: 1.16 index (approx. -2.5 to 2.5), 0.512 typical gaps (standard deviations)Argentina: 0.184 index (approx. -2.5 to 2.5), 0.216 typical gaps (standard deviations)Armenia: -0.364 index (approx. -2.5 to 2.5), -0.118 typical gaps (standard deviations)Australia: 1.81 index (approx. -2.5 to 2.5), 1.25 typical gaps (standard deviations)Austria: 1.51 index (approx. -2.5 to 2.5), 1.26 typical gaps (standard deviations)Azerbaijan: 0.166 index (approx. -2.5 to 2.5), -0.0603 typical gaps (standard deviations)Belgium: 1.19 index (approx. -2.5 to 2.5), 0.818 typical gaps (standard deviations)Burkina Faso: -0.875 index (approx. -2.5 to 2.5), -0.476 typical gaps (standard deviations)Bangladesh: -0.619 index (approx. -2.5 to 2.5), -0.561 typical gaps (standard deviations)Bulgaria: 0.0428 index (approx. -2.5 to 2.5), -0.572 typical gaps (standard deviations)Bosnia and Herzegovina: -0.635 index (approx. -2.5 to 2.5), -0.087 typical gaps (standard deviations)Belarus: -0.702 index (approx. -2.5 to 2.5), 0.732 typical gaps (standard deviations)Bolivia: -0.61 index (approx. -2.5 to 2.5), -0.361 typical gaps (standard deviations)Brazil: -0.224 index (approx. -2.5 to 2.5), 0.123 typical gaps (standard deviations)Canada: 1.76 index (approx. -2.5 to 2.5), 1.06 typical gaps (standard deviations)Switzerland: 1.84 index (approx. -2.5 to 2.5), 1.55 typical gaps (standard deviations)Chile: 0.964 index (approx. -2.5 to 2.5), 0.19 typical gaps (standard deviations)China: 0.896 index (approx. -2.5 to 2.5), 0.913 typical gaps (standard deviations)Colombia: -0.111 index (approx. -2.5 to 2.5), -0.125 typical gaps (standard deviations)Cyprus: 0.87 index (approx. -2.5 to 2.5), -0.0176 typical gaps (standard deviations)Czechia: 1.18 index (approx. -2.5 to 2.5), 0.726 typical gaps (standard deviations)Germany: 1.55 index (approx. -2.5 to 2.5), 0.981 typical gaps (standard deviations)Denmark: 1.91 index (approx. -2.5 to 2.5), 1.89 typical gaps (standard deviations)Algeria: -0.251 index (approx. -2.5 to 2.5), -0.205 typical gaps (standard deviations)Ecuador: -0.22 index (approx. -2.5 to 2.5), -0.164 typical gaps (standard deviations)Egypt, Arab Rep.: -0.0971 index (approx. -2.5 to 2.5), -1.08 typical gaps (standard deviations)Spain: 1.11 index (approx. -2.5 to 2.5), 0.816 typical gaps (standard deviations)Estonia: 1.3 index (approx. -2.5 to 2.5), 0.622 typical gaps (standard deviations)Ethiopia: -0.646 index (approx. -2.5 to 2.5), -0.726 typical gaps (standard deviations)Finland: 1.82 index (approx. -2.5 to 2.5), 1.87 typical gaps (standard deviations)France: 1.2 index (approx. -2.5 to 2.5), 0.717 typical gaps (standard deviations)United Kingdom: 1.18 index (approx. -2.5 to 2.5), 1.07 typical gaps (standard deviations)Georgia: 0.456 index (approx. -2.5 to 2.5), -0.493 typical gaps (standard deviations)Ghana: 0.0596 index (approx. -2.5 to 2.5), -0.73 typical gaps (standard deviations)Greece: 0.191 index (approx. -2.5 to 2.5), -0.284 typical gaps (standard deviations)Guatemala: -0.909 index (approx. -2.5 to 2.5), 0.352 typical gaps (standard deviations)Hong Kong SAR, China: 1.64 index (approx. -2.5 to 2.5), 0.0778 typical gaps (standard deviations)Croatia: 0.71 index (approx. -2.5 to 2.5), 0.0883 typical gaps (standard deviations)Haiti: -2.02 index (approx. -2.5 to 2.5), -0.461 typical gaps (standard deviations)Hungary: 0.502 index (approx. -2.5 to 2.5), 0.266 typical gaps (standard deviations)Indonesia: 0.18 index (approx. -2.5 to 2.5), -0.192 typical gaps (standard deviations)India: 0.405 index (approx. -2.5 to 2.5), -0.736 typical gaps (standard deviations)Ireland: 1.61 index (approx. -2.5 to 2.5), 0.894 typical gaps (standard deviations)Iran, Islamic Rep.: -0.706 index (approx. -2.5 to 2.5), -0.684 typical gaps (standard deviations)Iraq: -1.1 index (approx. -2.5 to 2.5), -0.726 typical gaps (standard deviations)Iceland: 1.65 index (approx. -2.5 to 2.5), 1.78 typical gaps (standard deviations)Italy: 0.792 index (approx. -2.5 to 2.5), 0.438 typical gaps (standard deviations)Jordan: 0.216 index (approx. -2.5 to 2.5), -0.666 typical gaps (standard deviations)Japan: 2.09 index (approx. -2.5 to 2.5), 0.552 typical gaps (standard deviations)Kazakhstan: 0.147 index (approx. -2.5 to 2.5), 0.304 typical gaps (standard deviations)Kenya: -0.298 index (approx. -2.5 to 2.5), -0.678 typical gaps (standard deviations)Kyrgyz Republic: -0.889 index (approx. -2.5 to 2.5), 0.225 typical gaps (standard deviations)Korea, Rep.: 1.49 index (approx. -2.5 to 2.5), 0.475 typical gaps (standard deviations)Kuwait: 0.328 index (approx. -2.5 to 2.5), 0.607 typical gaps (standard deviations)Lebanon: -1.13 index (approx. -2.5 to 2.5), -1.54 typical gaps (standard deviations)Libya: -1.42 index (approx. -2.5 to 2.5), -0.158 typical gaps (standard deviations)Lithuania: 1.2 index (approx. -2.5 to 2.5), 0.51 typical gaps (standard deviations)Luxembourg: 2.04 index (approx. -2.5 to 2.5), 0.652 typical gaps (standard deviations)Latvia: 0.743 index (approx. -2.5 to 2.5), 0.212 typical gaps (standard deviations)Macao SAR, China: 1.12 index (approx. -2.5 to 2.5), 0.875 typical gaps (standard deviations)Morocco: 0.176 index (approx. -2.5 to 2.5), -0.382 typical gaps (standard deviations)Moldova: 0.258 index (approx. -2.5 to 2.5), -1.29 typical gaps (standard deviations)Maldives: -0.221 index (approx. -2.5 to 2.5), 0.0845 typical gaps (standard deviations)Mexico: -0.22 index (approx. -2.5 to 2.5), 0.335 typical gaps (standard deviations)North Macedonia: -0.0323 index (approx. -2.5 to 2.5), -0.171 typical gaps (standard deviations)Mali: -1.29 index (approx. -2.5 to 2.5), -0.328 typical gaps (standard deviations)Malta: 0.622 index (approx. -2.5 to 2.5), 0.15 typical gaps (standard deviations)Myanmar: -1.56 index (approx. -2.5 to 2.5), -0.547 typical gaps (standard deviations)Montenegro: 0.366 index (approx. -2.5 to 2.5), 0.239 typical gaps (standard deviations)Mongolia: -0.389 index (approx. -2.5 to 2.5), 0.000193 typical gaps (standard deviations)Malaysia: 0.915 index (approx. -2.5 to 2.5), 0.215 typical gaps (standard deviations)Nigeria: -1.01 index (approx. -2.5 to 2.5), -0.611 typical gaps (standard deviations)Nicaragua: -1.05 index (approx. -2.5 to 2.5), 0.0736 typical gaps (standard deviations)Netherlands: 1.75 index (approx. -2.5 to 2.5), 1.54 typical gaps (standard deviations)Norway: 1.84 index (approx. -2.5 to 2.5), 1.82 typical gaps (standard deviations)New Zealand: 1.85 index (approx. -2.5 to 2.5), 1.46 typical gaps (standard deviations)Pakistan: -0.6 index (approx. -2.5 to 2.5), -0.299 typical gaps (standard deviations)Peru: -0.2 index (approx. -2.5 to 2.5), -0.348 typical gaps (standard deviations)Philippines: 0.145 index (approx. -2.5 to 2.5), -0.119 typical gaps (standard deviations)Poland: 0.673 index (approx. -2.5 to 2.5), 0.505 typical gaps (standard deviations)Puerto Rico (US): -0.255 index (approx. -2.5 to 2.5), 0.077 typical gaps (standard deviations)Portugal: 0.947 index (approx. -2.5 to 2.5), 0.195 typical gaps (standard deviations)West Bank and Gaza: -1.1 index (approx. -2.5 to 2.5), -0.644 typical gaps (standard deviations)Qatar: 1.13 index (approx. -2.5 to 2.5), 0.756 typical gaps (standard deviations)Romania: 0.418 index (approx. -2.5 to 2.5), 0.197 typical gaps (standard deviations)Russian Federation: -0.322 index (approx. -2.5 to 2.5), -0.00878 typical gaps (standard deviations)Rwanda: 0.349 index (approx. -2.5 to 2.5), -0.867 typical gaps (standard deviations)Singapore: 2.28 index (approx. -2.5 to 2.5), 0.779 typical gaps (standard deviations)Serbia: 0.102 index (approx. -2.5 to 2.5), 0.105 typical gaps (standard deviations)Slovak Republic: 0.658 index (approx. -2.5 to 2.5), 0.318 typical gaps (standard deviations)Slovenia: 1.09 index (approx. -2.5 to 2.5), 0.55 typical gaps (standard deviations)Sweden: 1.77 index (approx. -2.5 to 2.5), 1.7 typical gaps (standard deviations)Thailand: 0.254 index (approx. -2.5 to 2.5), 0.256 typical gaps (standard deviations)Tajikistan: -0.601 index (approx. -2.5 to 2.5), 0.104 typical gaps (standard deviations)Trinidad and Tobago: 0.111 index (approx. -2.5 to 2.5), 0.0637 typical gaps (standard deviations)Tunisia: -0.239 index (approx. -2.5 to 2.5), -0.73 typical gaps (standard deviations)Turkiye: -0.0707 index (approx. -2.5 to 2.5), -0.508 typical gaps (standard deviations)Ukraine: -0.599 index (approx. -2.5 to 2.5), -0.244 typical gaps (standard deviations)Uruguay: 0.69 index (approx. -2.5 to 2.5), 0.449 typical gaps (standard deviations)United States: 1.36 index (approx. -2.5 to 2.5), 0.943 typical gaps (standard deviations)Uzbekistan: -0.144 index (approx. -2.5 to 2.5), 0.617 typical gaps (standard deviations)Viet Nam: -0.0923 index (approx. -2.5 to 2.5), 0.469 typical gaps (standard deviations)Kosovo: -0.0613 index (approx. -2.5 to 2.5), -0.288 typical gaps (standard deviations)South Africa: -0.226 index (approx. -2.5 to 2.5), -0.515 typical gaps (standard deviations)Zambia: -0.533 index (approx. -2.5 to 2.5), -1.67 typical gaps (standard deviations)Zimbabwe: -0.988 index (approx. -2.5 to 2.5), -1.98 typical gaps (standard deviations)BLRDNKFINGTMHKGHTILBNMDANORZMBZWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7409.2e-20107
By rank (Spearman), not adjusted+0.7391.0e-19107
Standard (Pearson), adjusted for national income+0.3460.000107
By rank (Spearman), adjusted for national income+0.1790.067107

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 107 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: +0.394 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.324 (without FIN) to +0.376 (without BLR). 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: BLR (+0.030).

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 & Pacific15+0.37
Europe & Central Asia49+0.54
Latin America & Caribbean14-0.04
Middle East, North Africa, Afghanistan & Pakistan14+0.24

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 third36-0.05
Middle third35+0.12
Richest third36+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
Christians62+0.35
Muslims29+0.26

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
Never a European colony15+0.47
Spain12-0.43
United Kingdom25+0.21

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 = +0.353 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.

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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


index (approx. -2.5 to 2.5)

typical gaps (standard deviations)
Years
Singapore SGP2.280.7792024, 2022
Japan JPN2.090.5522024, 2022
Luxembourg LUX2.040.6522024, 2010
Denmark DNK1.911.892024, 2022
New Zealand NZL1.851.462024, 2022
Norway NOR1.841.822024, 2022
Switzerland CHE1.841.552024, 2022
Finland FIN1.821.872024, 2022
Australia AUS1.811.252024, 2022
Sweden SWE1.771.72024, 2022
Canada CAN1.761.062024, 2022
Netherlands NLD1.751.542024, 2022
Iceland ISL1.651.782024, 2022
Hong Kong SAR, China HKG1.640.07782024, 2022
Ireland IRL1.610.8942024, 2010

Kaufmann, D. and A. Kraay, Worldwide Governance Indicators, World Bank
ivs; whr

Sources

Government effectiveness

Government Effectiveness (estimate)

World Bank · CC BY 4.0 · updated annual

Wellbeing score

Wellbeing score

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

The average of 3 measures from 2 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: Self-reported happiness, Interpersonal trust, Life satisfaction.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2024. 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 Government effectiveness and Wellbeing score

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

More links with Government effectiveness or Wellbeing score