placestats

Frequent prayer vs. Physicians

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 75 countries, Frequent prayer and Physicians rise and fall together at r = -0.60 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.23, which could easily be chance (q = 0.060, 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, 29.9% to 82.8% of respondents, physicians is worse by 1.75 per 1,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.587 per 1,000 people.

Link before adjusting (r)-0.602p = 1.1e-8 · 75 countries
After adjusting for national income-0.227p = 0.052 · likely range [-0.43, +0.00]
p, allowing for regions0.007standard error 0.057 · 7 regions
Check for luck (q)0.060across 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
Physicians · per 1,000 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

Frequent prayer · % of respondents →Physicians · per 1,000 people →0.838%98.4%0.096.58Andorra: 27.5% of respondents, 5.07 per 1,000 peopleArgentina: 54% of respondents, 5.11 per 1,000 peopleArmenia: 71.7% of respondents, 3.36 per 1,000 peopleAustralia: 28.6% of respondents, 4.09 per 1,000 peopleAzerbaijan: 17.9% of respondents, 3.19 per 1,000 peopleBangladesh: 78% of respondents, 0.722 per 1,000 peopleBelarus: 27.4% of respondents, 4.72 per 1,000 peopleBolivia: 82.7% of respondents, 1.28 per 1,000 peopleBrazil: 77.4% of respondents, 2.36 per 1,000 peopleCanada: 35.5% of respondents, 2.82 per 1,000 peopleChile: 37.4% of respondents, 3.33 per 1,000 peopleChina: 0.838% of respondents, 3.11 per 1,000 peopleColombia: 87.9% of respondents, 2.54 per 1,000 peopleCyprus: 69.4% of respondents, 3.56 per 1,000 peopleCzechia: 13.8% of respondents, 4.35 per 1,000 peopleGermany: 30.2% of respondents, 4.53 per 1,000 peopleAlgeria: 81.8% of respondents, 1.66 per 1,000 peopleEcuador: 82.9% of respondents, 2.31 per 1,000 peopleEgypt, Arab Rep.: 96.6% of respondents, 0.671 per 1,000 peopleSpain: 20.2% of respondents, 4.29 per 1,000 peopleEstonia: 12.4% of respondents, 3.47 per 1,000 peopleEthiopia: 94.9% of respondents, 0.143 per 1,000 peopleUnited Kingdom: 24.1% of respondents, 3.3 per 1,000 peopleGeorgia: 60.6% of respondents, 5.64 per 1,000 peopleGhana: 95.5% of respondents, 0.266 per 1,000 peopleGreece: 66.4% of respondents, 6.58 per 1,000 peopleGuatemala: 73.2% of respondents, 1.28 per 1,000 peopleHaiti: 95.4% of respondents, 0.292 per 1,000 peopleIndonesia: 95.3% of respondents, 0.524 per 1,000 peopleIndia: 68.4% of respondents, 0.723 per 1,000 peopleIran, Islamic Rep.: 80.9% of respondents, 1.81 per 1,000 peopleIraq: 84.1% of respondents, 1.02 per 1,000 peopleJordan: 86.9% of respondents, 2.85 per 1,000 peopleJapan: 25% of respondents, 2.65 per 1,000 peopleKazakhstan: 35.2% of respondents, 3.75 per 1,000 peopleKenya: 93.3% of respondents, 0.289 per 1,000 peopleKyrgyz Republic: 68.3% of respondents, 1.57 per 1,000 peopleKorea, Rep.: 19% of respondents, 2.61 per 1,000 peopleLebanon: 80.9% of respondents, 2.68 per 1,000 peopleLibya: 97.6% of respondents, 2.04 per 1,000 peopleMorocco: 80.9% of respondents, 0.74 per 1,000 peopleMaldives: 93.5% of respondents, 2.23 per 1,000 peopleMexico: 71.9% of respondents, 2.59 per 1,000 peopleMyanmar: 83.3% of respondents, 0.757 per 1,000 peopleMongolia: 17.8% of respondents, 4.13 per 1,000 peopleMalaysia: 79.7% of respondents, 2.34 per 1,000 peopleNigeria: 98.4% of respondents, 0.38 per 1,000 peopleNicaragua: 76.3% of respondents, 0.682 per 1,000 peopleNetherlands: 23% of respondents, 3.88 per 1,000 peopleNew Zealand: 29.6% of respondents, 3.61 per 1,000 peoplePakistan: 89.8% of respondents, 1.16 per 1,000 peoplePeru: 66.6% of respondents, 1.69 per 1,000 peoplePhilippines: 92.4% of respondents, 0.792 per 1,000 peoplePoland: 63.2% of respondents, 4.03 per 1,000 peopleWest Bank and Gaza: 90.5% of respondents, 2.17 per 1,000 peopleRomania: 65.2% of respondents, 3.63 per 1,000 peopleRussian Federation: 27% of respondents, 5.11 per 1,000 peopleRwanda: 73.7% of respondents, 0.09 per 1,000 peopleSingapore: 53.1% of respondents, 2.83 per 1,000 peopleSerbia: 28.7% of respondents, 3.1 per 1,000 peopleSlovak Republic: 41.5% of respondents, 3.7 per 1,000 peopleSlovenia: 22.5% of respondents, 3.37 per 1,000 peopleSweden: 18.3% of respondents, 4.41 per 1,000 peopleThailand: 41.4% of respondents, 0.541 per 1,000 peopleTajikistan: 67.6% of respondents, 1.87 per 1,000 peopleTrinidad and Tobago: 96.8% of respondents, 4.16 per 1,000 peopleTunisia: 76.5% of respondents, 1.31 per 1,000 peopleTurkiye: 71% of respondents, 2.24 per 1,000 peopleUkraine: 39% of respondents, 3.53 per 1,000 peopleUruguay: 40.6% of respondents, 4.67 per 1,000 peopleUnited States: 62.3% of respondents, 3.68 per 1,000 peopleUzbekistan: 72.6% of respondents, 2.81 per 1,000 peopleViet Nam: 11.6% of respondents, 1.11 per 1,000 peopleSouth Africa: 73.6% of respondents, 0.794 per 1,000 peopleZimbabwe: 94.2% of respondents, 0.136 per 1,000 peopleANDCHNCZEETHGEOGRCNGARUSTHATTOVNMZWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6021.1e-875
By rank (Spearman), not adjusted-0.6601.2e-1075
Standard (Pearson), adjusted for national income-0.2270.05275
By rank (Spearman), adjusted for national income-0.2680.02175

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 75 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: +0.375 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.287 (without GRC) to -0.186 (without SGP). 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: GRC (-0.060).

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 & Pacific13-0.50
Europe & Central Asia26+0.05
Latin America & Caribbean13-0.12

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 third25-0.24
Middle third25-0.17
Richest third25-0.15

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
Christians39-0.08
Muslims22-0.56

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 colony15-0.12
Spain12-0.07
United Kingdom22-0.03

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 = -0.227 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


% of respondents
per 1,000 people		Years
Nigeria NGA98.4%0.382022, 2023
Libya LBY97.6%2.042022, 2017
Trinidad and Tobago TTO96.8%4.162014, 2021
Egypt, Arab Rep. EGY96.6%0.6712022, 2020
Ghana GHA95.5%0.2662014, 2023
Haiti HTI95.4%0.2922014, 2022
Indonesia IDN95.3%0.5242022, 2023
Ethiopia ETH94.9%0.1432022, 2023
Zimbabwe ZWE94.2%0.1362022, 2023
Maldives MDV93.5%2.232022, 2019
Kenya KEN93.3%0.2892022, 2023
Philippines PHL92.4%0.7922022, 2021
West Bank and Gaza PSE90.5%2.172014, 2018
Pakistan PAK89.8%1.162022, 2021
Colombia COL87.9%2.542022, 2023

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.

Physicians

Physicians (per 1,000 people)

World Health Organization · CC BY 4.0 · updated quarterly

Reported irregularly, so the latest value in the time window used is from a different year for different countries. Hospital beds, already on the site, have no "better" direction, since more beds is not plainly better. More doctors is closer to plainly better, so a direction is set on that basis.

How this was computed

Data used: most recent value in 2010-2025; values come from 2014–2023. 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 Physicians

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

More links with Frequent prayer or Physicians