placestats

Religious attendance vs. Secondary enrollment

Holds up 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 101 countries, Religious attendance and Secondary enrollment rise and fall together at r = -0.64 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.27 (q = 0.0029, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak negative link remains.

Comparing countries across the middle half of religious attendance, 17.3% to 58.3% of respondents, secondary enrollment is worse by 24% gross at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 8.84% gross.

Link before adjusting (r)-0.641p = 5.0e-13 · 101 countries
After adjusting for national income-0.268p = 0.007 · likely range [-0.44, -0.08]
p, allowing for regions3.9e-5standard error 0.025 · 7 regions
Check for luck (q)0.003across 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.

Religious attendance · % of respondentslink with income -0.66 · high
Secondary enrollment · % grosslink with income +0.71 · 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

Religious attendance · % of respondents →Secondary enrollment · % gross →2.79%93%30%143%Albania: 14.8% of respondents, 108% grossAndorra: 16% of respondents, 102% grossArgentina: 36% of respondents, 106% grossArmenia: 35.8% of respondents, 90.9% grossAustralia: 17.5% of respondents, 132% grossAustria: 26.8% of respondents, 101% grossAzerbaijan: 13.9% of respondents, 91.1% grossBelgium: 18.9% of respondents, 143% grossBurkina Faso: 76.9% of respondents, 30% grossBangladesh: 54.2% of respondents, 64.3% grossBulgaria: 17.3% of respondents, 94.8% grossBosnia and Herzegovina: 43.3% of respondents, 87.9% grossBelarus: 19.9% of respondents, 94.8% grossBolivia: 65% of respondents, 90.5% grossBrazil: 59.6% of respondents, 104% grossCanada: 16.4% of respondents, 104% grossSwitzerland: 16.2% of respondents, 101% grossChile: 31% of respondents, 106% grossChina: 2.79% of respondents, 92.3% grossColombia: 58.3% of respondents, 102% grossCyprus: 50.2% of respondents, 107% grossCzechia: 10% of respondents, 97.6% grossGermany: 19.2% of respondents, 100% grossDenmark: 6.48% of respondents, 126% grossAlgeria: 53.5% of respondents, 105% grossEcuador: 61.3% of respondents, 92.8% grossEgypt, Arab Rep.: 58.8% of respondents, 84.8% grossSpain: 22.8% of respondents, 119% grossEstonia: 8% of respondents, 109% grossEthiopia: 93% of respondents, 33.9% grossFinland: 11.1% of respondents, 142% grossFrance: 12.4% of respondents, 104% grossUnited Kingdom: 16.1% of respondents, 112% grossGeorgia: 33.7% of respondents, 101% grossGhana: 83.8% of respondents, 75.7% grossGreece: 44.5% of respondents, 105% grossGuatemala: 63.4% of respondents, 49.6% grossHong Kong SAR, China: 16.1% of respondents, 107% grossCroatia: 33.7% of respondents, 105% grossHungary: 17.3% of respondents, 107% grossIndonesia: 77.8% of respondents, 97.2% grossIndia: 62.7% of respondents, 79.6% grossIreland: 53.3% of respondents, 131% grossIran, Islamic Rep.: 43.2% of respondents, 85.1% grossIceland: 8.48% of respondents, 109% grossItaly: 40.7% of respondents, 101% grossJordan: 56.4% of respondents, 91.8% grossJapan: 12.3% of respondents, 102% grossKazakhstan: 32.8% of respondents, 97.6% grossKenya: 84.6% of respondents, 84.3% grossKyrgyz Republic: 39.2% of respondents, 91.7% grossKorea, Rep.: 22.8% of respondents, 96.3% grossLebanon: 53.3% of respondents, 63.8% grossLithuania: 30% of respondents, 105% grossLuxembourg: 22.7% of respondents, 109% grossLatvia: 12.8% of respondents, 101% grossMacao SAR, China: 9.5% of respondents, 94.7% grossMorocco: 58.1% of respondents, 90% grossMoldova: 27.8% of respondents, 99.3% grossMaldives: 64.8% of respondents, 79.4% grossMexico: 60.6% of respondents, 103% grossNorth Macedonia: 40.1% of respondents, 90.1% grossMali: 73.9% of respondents, 38.9% grossMalta: 81.5% of respondents, 103% grossMyanmar: 43.5% of respondents, 77% grossMontenegro: 36.7% of respondents, 93.9% grossMongolia: 14.1% of respondents, 96.8% grossMalaysia: 60.3% of respondents, 80.3% grossNigeria: 90.3% of respondents, 46.9% grossNicaragua: 54.2% of respondents, 70.2% grossNetherlands: 14.3% of respondents, 138% grossNorway: 11.8% of respondents, 116% grossNew Zealand: 16.8% of respondents, 114% grossPakistan: 60.9% of respondents, 48.3% grossPeru: 57.1% of respondents, 108% grossPhilippines: 81.8% of respondents, 85.3% grossPoland: 64.3% of respondents, 108% grossPuerto Rico (US): 53.5% of respondents, 81.5% grossPortugal: 29.7% of respondents, 120% grossWest Bank and Gaza: 58.5% of respondents, 89.1% grossRomania: 46% of respondents, 81.6% grossRussian Federation: 16.3% of respondents, 92.5% grossRwanda: 78.2% of respondents, 48.8% grossSingapore: 42.6% of respondents, 103% grossSerbia: 20.1% of respondents, 89.8% grossSlovak Republic: 36.1% of respondents, 93.2% grossSlovenia: 23.8% of respondents, 108% grossSweden: 9.65% of respondents, 132% grossThailand: 38.1% of respondents, 92.1% grossTajikistan: 40.7% of respondents, 86.9% grossTrinidad and Tobago: 57.8% of respondents, 95.7% grossTunisia: 43.5% of respondents, 90.3% grossTurkiye: 39.5% of respondents, 118% grossUkraine: 29.1% of respondents, 86.2% grossUruguay: 13.3% of respondents, 119% grossUnited States: 39% of respondents, 97.5% grossUzbekistan: 41.7% of respondents, 98.1% grossViet Nam: 17.2% of respondents, 93.4% grossSouth Africa: 69% of respondents, 104% grossZambia: 80.9% of respondents, 56.8% grossZimbabwe: 84.8% of respondents, 52.4% grossBELBFACHNETHFINIDNIRLMACMLTNGANLD

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6415.0e-13101
By rank (Spearman), not adjusted-0.5936.3e-11101
Standard (Pearson), adjusted for national income-0.2680.007101
By rank (Spearman), adjusted for national income-0.2150.031101

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 101 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: +0.373 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.290 (without IRL) to -0.240 (without NGA). 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: NGA (+0.028).

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 & Pacific15-0.25
Europe & Central Asia48-0.15
Latin America & Caribbean13-0.35

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 third34-0.27
Middle third33-0.25
Richest third34-0.26

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
Christians61-0.17
Muslims24-0.49

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 colony15-0.41
Spain12-0.06
United Kingdom21-0.43

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 = -0.155 after dropping 12 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


% of respondents
% gross		Years
Ethiopia ETH93%33.9%2022, 2015
Nigeria NGA90.3%46.9%2022, 2023
Zimbabwe ZWE84.8%52.4%2022, 2013
Kenya KEN84.6%84.3%2022, 2023
Ghana GHA83.8%75.7%2014, 2024
Philippines PHL81.8%85.3%2022, 2024
Malta MLT81.5%103%2010, 2023
Zambia ZMB80.9%56.8%2010, 2024
Rwanda RWA78.2%48.8%2014, 2024
Indonesia IDN77.8%97.2%2022, 2025
Burkina Faso BFA76.9%30%2010, 2024
Mali MLI73.9%38.9%2010, 2023
South Africa ZAF69%104%2014, 2023
Bolivia BOL65%90.5%2022, 2024
Maldives MDV64.8%79.4%2022, 2023

Haerpfer, C. et al., World Values Survey; EVS, European Values Study
World Bank, World Development Indicators

Sources

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.

Secondary enrollment

School enrollment, secondary (% gross)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

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 Religious attendance and Secondary enrollment

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

More links with Religious attendance or Secondary enrollment