Do Frequent prayer and Researchers go together across countries?

Across 68 countries the link before adjusting for anything is r = -0.63, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.71.

Does the link hold within countries over time, not just across countries?

There are not enough years of data within countries to check this over time.

Does Frequent prayer cause Researchers?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Frequent prayer vs. Researchers

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 68 countries, Frequent prayer and Researchers rise and fall together at r = -0.63 (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, which could easily be chance (q = 0.26, 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 frequent prayer, 28.3% to 82.7% of respondents, researchers is worse by 2,826 per million people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 1,032 per million people.

Link before adjusting (r)-0.627p = 1.1e-8 · 68 countries

After adjusting for national income-0.265p = 0.030 · likely range [-0.48, -0.03]

p, allowing for regions0.091standard error 0.132 · 7 regions

Check for luck (q)0.256across 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.

Frequent prayer · % of respondentslink with income -0.68 · high

Researchers · per million peoplelink with income +0.85 · proxy

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

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	-0.627	1.1e-8	68
By rank (Spearman), not adjusted	-0.710	1.2e-11	68
Standard (Pearson), adjusted for national income	-0.265	0.030	68
By rank (Spearman), adjusted for national income	-0.336	0.005	68

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 68 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: +0.361 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.324 (without SGP) to -0.216 (without PRI). 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: SGP (-0.059).

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.

	n	r
East Asia & Pacific	15	-0.06
Europe & Central Asia	23	-0.32

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.

	n	r
Poorest third	23	-0.13
Middle third	22	-0.06
Richest third	23	-0.60

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.

	n	r
Christians	37	-0.49
Muslims	16	-0.05

By last european coloniser

	n	r
Never a European colony	13	-0.01
United Kingdom	19	-0.53

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 = -0.246 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

	% of respondents	per million people	Years
Nigeria NGA	98.4%	22.1	2022, 2019
Trinidad and Tobago TTO	96.8%	493	2014, 2022
Egypt, Arab Rep. EGY	96.6%	1,120	2022, 2024
Ghana GHA	95.5%	87.5	2014, 2015
Indonesia IDN	95.3%	395	2022, 2020
Ethiopia ETH	94.9%	89	2022, 2017
Zimbabwe ZWE	94.2%	95.1	2022, 2012
Kenya KEN	93.3%	384	2022, 2024
Philippines PHL	92.4%	320	2022, 2020
West Bank and Gaza PSE	90.5%	584	2014, 2013
Pakistan PAK	89.8%	474	2022, 2023
Colombia COL	87.9%	90.2	2022, 2017
Jordan JOR	86.9%	576	2022, 2017
Puerto Rico (US) PRI	85.7%	332	2022, 2015
Iraq IRQ	84.1%	164	2022, 2021

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

Sources

Frequent prayer

Share praying at least several times a week

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)

Private practice. Attendance is public practice, and importance is belief. The three can differ: a country can pray often and attend rarely. Asked only from the 2014 wave on, so it covers about eighty countries. No "better" direction is set, for the same reason as attendance.

Researchers

Researchers in R&D (per million people)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

Researchers counted as full-time equivalents (two half-time researchers count as one), from national research surveys that many countries run only now and then. So coverage is thin outside the OECD.

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 Frequent prayer and Researchers

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