placestats

Infrastructure score vs. Religious attendance

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, Infrastructure score and Religious attendance rise and fall together at r = -0.73 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.43, which could easily be chance (q = 0.086, 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. Caution: direction is not consistent across regions.

Comparing countries across the middle half of religious attendance, 17.3% to 58.1% of respondents, infrastructure score is worse by 0.663 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.229 typical gaps.

Link before adjusting (r)-0.733p = 9.5e-19 · 104 countries
After adjusting for national income-0.434p = 4.6e-6 · likely range [-0.58, -0.26]
p, allowing for regions0.002standard error 0.084 · 7 regions
Check for luck (q)0.086across 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.

Infrastructure score · typical gaps (standard deviations)link with income +0.90 · proxy
Religious attendance · % of respondentslink with income -0.66 · 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

Infrastructure score · typical gaps (standard deviations) →Religious attendance · % of respondents →-0.4951.782.79%93%Albania: 1.14 typical gaps (standard deviations), 14.8% of respondentsAndorra: 1.78 typical gaps (standard deviations), 16% of respondentsArgentina: 1.25 typical gaps (standard deviations), 36% of respondentsArmenia: 1.09 typical gaps (standard deviations), 35.8% of respondentsAustralia: 1.5 typical gaps (standard deviations), 17.5% of respondentsAustria: 1.37 typical gaps (standard deviations), 26.8% of respondentsAzerbaijan: 1.19 typical gaps (standard deviations), 13.9% of respondentsBelgium: 1.64 typical gaps (standard deviations), 18.9% of respondentsBurkina Faso: -0.245 typical gaps (standard deviations), 76.9% of respondentsBangladesh: 0.649 typical gaps (standard deviations), 54.2% of respondentsBulgaria: 1.25 typical gaps (standard deviations), 17.3% of respondentsBosnia and Herzegovina: 1.29 typical gaps (standard deviations), 43.3% of respondentsBelarus: 1.48 typical gaps (standard deviations), 19.9% of respondentsBolivia: 0.886 typical gaps (standard deviations), 65% of respondentsBrazil: 1.18 typical gaps (standard deviations), 59.6% of respondentsCanada: 1.56 typical gaps (standard deviations), 16.4% of respondentsSwitzerland: 1.7 typical gaps (standard deviations), 16.2% of respondentsChile: 1.25 typical gaps (standard deviations), 31% of respondentsChina: 1.66 typical gaps (standard deviations), 2.79% of respondentsColombia: 1.01 typical gaps (standard deviations), 58.3% of respondentsCyprus: 1.47 typical gaps (standard deviations), 50.2% of respondentsCzechia: 1.48 typical gaps (standard deviations), 10% of respondentsGermany: 1.65 typical gaps (standard deviations), 19.2% of respondentsDenmark: 1.65 typical gaps (standard deviations), 6.48% of respondentsAlgeria: 0.932 typical gaps (standard deviations), 53.5% of respondentsEcuador: 0.989 typical gaps (standard deviations), 61.3% of respondentsEgypt, Arab Rep.: 0.864 typical gaps (standard deviations), 58.8% of respondentsSpain: 1.55 typical gaps (standard deviations), 22.8% of respondentsEstonia: 1.45 typical gaps (standard deviations), 8% of respondentsEthiopia: -0.309 typical gaps (standard deviations), 93% of respondentsFinland: 1.48 typical gaps (standard deviations), 11.1% of respondentsFrance: 1.7 typical gaps (standard deviations), 12.4% of respondentsUnited Kingdom: 1.6 typical gaps (standard deviations), 16.1% of respondentsGeorgia: 1.29 typical gaps (standard deviations), 33.7% of respondentsGhana: 0.59 typical gaps (standard deviations), 83.8% of respondentsGreece: 1.57 typical gaps (standard deviations), 44.5% of respondentsGuatemala: 0.576 typical gaps (standard deviations), 63.4% of respondentsHong Kong SAR, China: 1.57 typical gaps (standard deviations), 16.1% of respondentsCroatia: 1.28 typical gaps (standard deviations), 33.7% of respondentsHaiti: -0.312 typical gaps (standard deviations), 38.6% of respondentsHungary: 1.5 typical gaps (standard deviations), 17.3% of respondentsIndonesia: 0.742 typical gaps (standard deviations), 77.8% of respondentsIndia: 0.527 typical gaps (standard deviations), 62.7% of respondentsIreland: 1.43 typical gaps (standard deviations), 53.3% of respondentsIran, Islamic Rep.: 0.97 typical gaps (standard deviations), 43.2% of respondentsIraq: 1.04 typical gaps (standard deviations), 43.7% of respondentsIceland: 1.53 typical gaps (standard deviations), 8.48% of respondentsItaly: 1.36 typical gaps (standard deviations), 40.7% of respondentsJordan: 0.776 typical gaps (standard deviations), 56.4% of respondentsJapan: 1.45 typical gaps (standard deviations), 12.3% of respondentsKazakhstan: 1.09 typical gaps (standard deviations), 32.8% of respondentsKenya: 0.22 typical gaps (standard deviations), 84.6% of respondentsKyrgyz Republic: 0.943 typical gaps (standard deviations), 39.2% of respondentsKorea, Rep.: 1.72 typical gaps (standard deviations), 22.8% of respondentsLebanon: 0.731 typical gaps (standard deviations), 53.3% of respondentsLibya: 0.512 typical gaps (standard deviations), 54% of respondentsLithuania: 1.29 typical gaps (standard deviations), 30% of respondentsLuxembourg: 1.59 typical gaps (standard deviations), 22.7% of respondentsLatvia: 1.28 typical gaps (standard deviations), 12.8% of respondentsMacao SAR, China: 1.33 typical gaps (standard deviations), 9.5% of respondentsMorocco: 0.928 typical gaps (standard deviations), 58.1% of respondentsMoldova: 1.23 typical gaps (standard deviations), 27.8% of respondentsMaldives: 1.11 typical gaps (standard deviations), 64.8% of respondentsMexico: 1.13 typical gaps (standard deviations), 60.6% of respondentsNorth Macedonia: 1.36 typical gaps (standard deviations), 40.1% of respondentsMali: -0.495 typical gaps (standard deviations), 73.9% of respondentsMalta: 1.63 typical gaps (standard deviations), 81.5% of respondentsMyanmar: 0.298 typical gaps (standard deviations), 43.5% of respondentsMontenegro: 1.37 typical gaps (standard deviations), 36.7% of respondentsMongolia: 1.03 typical gaps (standard deviations), 14.1% of respondentsMalaysia: 1.09 typical gaps (standard deviations), 60.3% of respondentsNigeria: -0.0861 typical gaps (standard deviations), 90.3% of respondentsNicaragua: 0.574 typical gaps (standard deviations), 54.2% of respondentsNetherlands: 1.63 typical gaps (standard deviations), 14.3% of respondentsNorway: 1.68 typical gaps (standard deviations), 11.8% of respondentsNew Zealand: 1.51 typical gaps (standard deviations), 16.8% of respondentsPakistan: 0.386 typical gaps (standard deviations), 60.9% of respondentsPeru: 0.907 typical gaps (standard deviations), 57.1% of respondentsPhilippines: 0.696 typical gaps (standard deviations), 81.8% of respondentsPoland: 1.24 typical gaps (standard deviations), 64.3% of respondentsPuerto Rico (US): 1.14 typical gaps (standard deviations), 53.5% of respondentsPortugal: 1.6 typical gaps (standard deviations), 29.7% of respondentsWest Bank and Gaza: 0.774 typical gaps (standard deviations), 58.5% of respondentsRomania: 1.45 typical gaps (standard deviations), 46% of respondentsRussian Federation: 1.32 typical gaps (standard deviations), 16.3% of respondentsRwanda: 0.0662 typical gaps (standard deviations), 78.2% of respondentsSingapore: 1.33 typical gaps (standard deviations), 42.6% of respondentsSerbia: 1.38 typical gaps (standard deviations), 20.1% of respondentsSlovak Republic: 1.4 typical gaps (standard deviations), 36.1% of respondentsSlovenia: 1.42 typical gaps (standard deviations), 23.8% of respondentsSweden: 1.57 typical gaps (standard deviations), 9.65% of respondentsThailand: 1.06 typical gaps (standard deviations), 38.1% of respondentsTajikistan: 0.389 typical gaps (standard deviations), 40.7% of respondentsTrinidad and Tobago: 1.22 typical gaps (standard deviations), 57.8% of respondentsTunisia: 0.956 typical gaps (standard deviations), 43.5% of respondentsTurkiye: 1.19 typical gaps (standard deviations), 39.5% of respondentsUkraine: 0.97 typical gaps (standard deviations), 29.1% of respondentsUruguay: 1.41 typical gaps (standard deviations), 13.3% of respondentsUnited States: 1.53 typical gaps (standard deviations), 39% of respondentsUzbekistan: 1.42 typical gaps (standard deviations), 41.7% of respondentsViet Nam: 1.17 typical gaps (standard deviations), 17.2% of respondentsSouth Africa: 0.709 typical gaps (standard deviations), 69% of respondentsZambia: -0.161 typical gaps (standard deviations), 80.9% of respondentsZimbabwe: 0.0863 typical gaps (standard deviations), 84.8% of respondentsCHNDNKETHFRAHTIIRLMLIMLTMMRNGAPOL

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7339.5e-19104
By rank (Spearman), not adjusted-0.7291.8e-18104
Standard (Pearson), adjusted for national income-0.4344.6e-6104
By rank (Spearman), adjusted for national income-0.4267.4e-6104

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: +0.299 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.484 (without HTI) to -0.404 (without IRL). 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: HTI (-0.050).

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 & Pacific15-0.63
Europe & Central Asia48-0.28
Latin America & Caribbean14+0.04
Middle East, North Africa, Afghanistan & Pakistan12+0.11

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 third35-0.51
Middle third34-0.41
Richest third35-0.44

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
Christians62-0.31
Muslims26-0.54

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 colony15-0.60
Spain12-0.33
United Kingdom23-0.55

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 = -0.479 after dropping 6 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
Andorra AND1.7816%2024, 2022
Korea, Rep. KOR1.7222.8%2024, 2022
Switzerland CHE1.716.2%2024, 2022
France FRA1.712.4%2024, 2022
Norway NOR1.6811.8%2024, 2022
China CHN1.662.79%2024, 2022
Denmark DNK1.656.48%2024, 2022
Germany DEU1.6519.2%2024, 2022
Belgium BEL1.6418.9%2024, 2010
Malta MLT1.6381.5%2024, 2010
Netherlands NLD1.6314.3%2024, 2022
Portugal PRT1.629.7%2024, 2022
United Kingdom GBR1.616.1%2024, 2022
Luxembourg LUX1.5922.7%2024, 2010
Greece GRC1.5744.5%2024, 2022

wdi
Haerpfer, C. et al., World Values Survey; EVS, European Values Study

Sources

Infrastructure score

Infrastructure score

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

The average of 5 measures from 1 publisher. 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: Electricity access, Slum share of urban population, Mobile subscriptions, Fixed broadband, Internet users.

Religious attendance

Share attending religious services frequently

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 share who attend religious services at least once a month. This measures taking part, not belief or belonging to a religion. Those are three different things, and they can differ sharply. A country can be almost entirely religious by belonging and yet rarely attend anything. No "better" direction is set, and none could be defended. Attendance describes how people live. It does not show whether a country is doing well, and calling either end the good one would be a claim this site has no basis for. The survey runs in waves, not every year, so data for each country are sparse and uneven.

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 Infrastructure score and Religious attendance

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

More links with Infrastructure score or Religious attendance