placestats

Governance score vs. Religion very important

Fades after adjusting for income

A country is not a person. Countries where more people are counted as belonging to this group also tend to differ on the other measure, and that is all. It does not follow that the people in the group 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. It is the most common misuse of this kind of data. Religion also varies strongly with region, history and national income, so much of what looks religious here is really those. That is what the adjusted figures are for. The religion shares are estimates for two years only, and the survey questions are asked in rounds that can be ten years apart.

Across 104 countries, Governance score and Religion very important rise and fall together at r = -0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.24, which could easily be chance (q = 0.45, 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 religion very important, 15.5% to 69.8% of respondents, governance score is worse by 0.827 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.381 typical gaps.

Link before adjusting (r)-0.531p = 6.8e-9 · 104 countries
After adjusting for national income-0.242p = 0.014 · likely range [-0.42, -0.05]
p, allowing for regions0.343standard error 0.236 · 7 regions
Check for luck (q)0.450across 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
Religion very important · % of respondentslink with income -0.66 · high

“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) →Religion very important · % of respondents →-1.391.473.25%98.9%Albania: 0.323 typical gaps (standard deviations), 24.4% of respondentsAndorra: 0.558 typical gaps (standard deviations), 9.46% of respondentsArgentina: 0.815 typical gaps (standard deviations), 24.3% of respondentsArmenia: 0.22 typical gaps (standard deviations), 52.7% of respondentsAustralia: 1.38 typical gaps (standard deviations), 13.8% of respondentsAustria: 1.33 typical gaps (standard deviations), 13.9% of respondentsAzerbaijan: -1.33 typical gaps (standard deviations), 15.9% of respondentsBelgium: 1.41 typical gaps (standard deviations), 13.2% of respondentsBurkina Faso: -0.784 typical gaps (standard deviations), 82.9% of respondentsBangladesh: -0.359 typical gaps (standard deviations), 94.2% of respondentsBulgaria: 0.74 typical gaps (standard deviations), 21.9% of respondentsBosnia and Herzegovina: 0.0899 typical gaps (standard deviations), 39.8% of respondentsBelarus: -1.26 typical gaps (standard deviations), 19.7% of respondentsBolivia: 0.552 typical gaps (standard deviations), 58.4% of respondentsBrazil: 0.85 typical gaps (standard deviations), 45.1% of respondentsCanada: 1.36 typical gaps (standard deviations), 15.5% of respondentsSwitzerland: 1.47 typical gaps (standard deviations), 11.3% of respondentsChile: 1.32 typical gaps (standard deviations), 20.7% of respondentsChina: -1.2 typical gaps (standard deviations), 3.25% of respondentsColombia: 0.733 typical gaps (standard deviations), 58.4% of respondentsCyprus: 1.1 typical gaps (standard deviations), 50.5% of respondentsCzechia: 1.31 typical gaps (standard deviations), 7.67% of respondentsGermany: 1.36 typical gaps (standard deviations), 13.5% of respondentsDenmark: 1.47 typical gaps (standard deviations), 5.73% of respondentsAlgeria: -0.695 typical gaps (standard deviations), 90.7% of respondentsEcuador: 0.447 typical gaps (standard deviations), 53.6% of respondentsEgypt, Arab Rep.: -0.987 typical gaps (standard deviations), 97.3% of respondentsSpain: 1.32 typical gaps (standard deviations), 17.2% of respondentsEstonia: 1.41 typical gaps (standard deviations), 6.54% of respondentsEthiopia: -0.741 typical gaps (standard deviations), 92.8% of respondentsFinland: 1.38 typical gaps (standard deviations), 11% of respondentsFrance: 1.24 typical gaps (standard deviations), 14.3% of respondentsUnited Kingdom: 1.27 typical gaps (standard deviations), 15.6% of respondentsGeorgia: 0.0179 typical gaps (standard deviations), 69.6% of respondentsGhana: 0.845 typical gaps (standard deviations), 91.1% of respondentsGreece: 0.914 typical gaps (standard deviations), 54.8% of respondentsGuatemala: 0.291 typical gaps (standard deviations), 51.5% of respondentsHong Kong SAR, China: -0.536 typical gaps (standard deviations), 10.7% of respondentsCroatia: 0.865 typical gaps (standard deviations), 20.6% of respondentsHaiti: -0.66 typical gaps (standard deviations), 28% of respondentsHungary: 0.225 typical gaps (standard deviations), 16.1% of respondentsIndonesia: 0.125 typical gaps (standard deviations), 98.1% of respondentsIndia: 0.146 typical gaps (standard deviations), 64.2% of respondentsIreland: 1.41 typical gaps (standard deviations), 27.7% of respondentsIran, Islamic Rep.: -1.26 typical gaps (standard deviations), 70.5% of respondentsIraq: -0.488 typical gaps (standard deviations), 87.6% of respondentsIceland: 1.32 typical gaps (standard deviations), 13.2% of respondentsItaly: 1 typical gaps (standard deviations), 24.9% of respondentsJordan: -0.271 typical gaps (standard deviations), 95.4% of respondentsJapan: 1.28 typical gaps (standard deviations), 4.58% of respondentsKazakhstan: -0.796 typical gaps (standard deviations), 28.7% of respondentsKenya: 0.217 typical gaps (standard deviations), 86.7% of respondentsKyrgyz Republic: -0.725 typical gaps (standard deviations), 56% of respondentsKorea, Rep.: 1.05 typical gaps (standard deviations), 10.3% of respondentsKuwait: -0.696 typical gaps (standard deviations), 86.5% of respondentsLebanon: -0.112 typical gaps (standard deviations), 64.1% of respondentsLibya: -0.885 typical gaps (standard deviations), 97.9% of respondentsLithuania: 1.14 typical gaps (standard deviations), 12.2% of respondentsLuxembourg: 1.38 typical gaps (standard deviations), 16.8% of respondentsLatvia: 1.22 typical gaps (standard deviations), 8.84% of respondentsMorocco: -0.351 typical gaps (standard deviations), 89% of respondentsMoldova: 0.5 typical gaps (standard deviations), 32.9% of respondentsMaldives: 0.0802 typical gaps (standard deviations), 95.7% of respondentsMexico: 0.134 typical gaps (standard deviations), 50.5% of respondentsNorth Macedonia: 0.352 typical gaps (standard deviations), 53.7% of respondentsMali: -0.753 typical gaps (standard deviations), 88.2% of respondentsMalta: 1.12 typical gaps (standard deviations), 58.7% of respondentsMyanmar: -1.39 typical gaps (standard deviations), 81% of respondentsMontenegro: 0.55 typical gaps (standard deviations), 63.1% of respondentsMongolia: 0.716 typical gaps (standard deviations), 8.76% of respondentsMalaysia: 0.169 typical gaps (standard deviations), 72.4% of respondentsNigeria: -0.0132 typical gaps (standard deviations), 93.4% of respondentsNicaragua: -1.24 typical gaps (standard deviations), 60% of respondentsNetherlands: 1.32 typical gaps (standard deviations), 11.9% of respondentsNorway: 1.43 typical gaps (standard deviations), 11.2% of respondentsNew Zealand: 1.32 typical gaps (standard deviations), 14.5% of respondentsPakistan: -0.437 typical gaps (standard deviations), 89.6% of respondentsPeru: 0.522 typical gaps (standard deviations), 44.6% of respondentsPhilippines: 0.0585 typical gaps (standard deviations), 88.9% of respondentsPoland: 0.996 typical gaps (standard deviations), 37.3% of respondentsPortugal: 1.26 typical gaps (standard deviations), 20.1% of respondentsQatar: -0.908 typical gaps (standard deviations), 98.9% of respondentsRomania: 0.749 typical gaps (standard deviations), 48.2% of respondentsRussian Federation: -1.14 typical gaps (standard deviations), 17.4% of respondentsRwanda: -0.868 typical gaps (standard deviations), 40.3% of respondentsSingapore: 0.0449 typical gaps (standard deviations), 34% of respondentsSerbia: -0.0738 typical gaps (standard deviations), 25.3% of respondentsSlovak Republic: 1.02 typical gaps (standard deviations), 22.2% of respondentsSlovenia: 1.19 typical gaps (standard deviations), 11.5% of respondentsSweden: 1.42 typical gaps (standard deviations), 9.48% of respondentsThailand: -0.383 typical gaps (standard deviations), 41.8% of respondentsTajikistan: -1.34 typical gaps (standard deviations), 63.1% of respondentsTrinidad and Tobago: 1.03 typical gaps (standard deviations), 78.5% of respondentsTunisia: -0.257 typical gaps (standard deviations), 90.6% of respondentsTurkiye: -0.691 typical gaps (standard deviations), 60% of respondentsUkraine: -0.0525 typical gaps (standard deviations), 19.5% of respondentsUruguay: 1.39 typical gaps (standard deviations), 19.5% of respondentsUnited States: 0.998 typical gaps (standard deviations), 37.1% of respondentsUzbekistan: -1.04 typical gaps (standard deviations), 45.1% of respondentsViet Nam: -0.933 typical gaps (standard deviations), 12.3% of respondentsKosovo: 0.371 typical gaps (standard deviations), 47.1% of respondentsSouth Africa: 0.883 typical gaps (standard deviations), 55.8% of respondentsZambia: 0.212 typical gaps (standard deviations), 75.7% of respondentsZimbabwe: -0.609 typical gaps (standard deviations), 87.7% of respondentsAZEBLRCHNDNKEGYESTGHAIDNMMRQATSWETTO

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5316.8e-9104
By rank (Spearman), not adjusted-0.5819.8e-11104
Standard (Pearson), adjusted for national income-0.2420.014104
By rank (Spearman), adjusted for national income-0.2570.009104

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 104 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: +0.288 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.289 (without CHN) to -0.153 (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 (+0.090).

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+0.01
Europe & Central Asia49-0.07
Latin America & Caribbean13-0.35
Middle East, North Africa, Afghanistan & Pakistan13-0.57

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 third35+0.06
Middle third34-0.05
Richest third35-0.68

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
Christians61+0.06
Muslims28+0.06

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 colony15-0.13
Spain12+0.08
United Kingdom25-0.70

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 = -0.242 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 respondents		Years
Denmark DNK1.475.73%2025, 2022
Switzerland CHE1.4711.3%2025, 2022
Norway NOR1.4311.2%2025, 2022
Sweden SWE1.429.48%2025, 2022
Ireland IRL1.4127.7%2025, 2010
Belgium BEL1.4113.2%2025, 2010
Estonia EST1.416.54%2025, 2022
Uruguay URY1.3919.5%2025, 2022
Finland FIN1.3811%2025, 2022
Australia AUS1.3813.8%2025, 2022
Luxembourg LUX1.3816.8%2025, 2010
Germany DEU1.3613.5%2025, 2022
Canada CAN1.3615.5%2025, 2022
Austria AUT1.3313.9%2025, 2022
Netherlands NLD1.3211.9%2025, 2022

fh; un_sdg; vdem; wdi; wgi
Haerpfer, C. et al., World Values Survey; EVS, European Values Study

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.

Religion very important

Share saying religion is very important in their life

World Values Survey Association and European Values Study · via Our World in Data · Free for non-commercial use with attribution · updated irregular (survey waves, roughly every 5 years)

The strongest of four answers. The others are "rather", "not very" and "not at all" important. Thinking religion is important is different from attending services, and the gap between them is the interesting part. The United States reports high importance while attendance falls. Several Catholic countries show the reverse. No "better" direction is set, for the same reason as attendance. The survey runs in waves, not every year.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2025. 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 Religion very important

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

More links with Governance score or Religion very important