placestats

Frequent prayer vs. Labour score

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 77 countries, Frequent prayer and Labour score rise and fall together at r = -0.57 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.15, which could easily be chance (q = 0.25, 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 frequent prayer, 29.6% to 82.9% of respondents, labour score is worse by 0.6 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.139 typical gaps.

Link before adjusting (r)-0.573p = 5.2e-8 · 77 countries
After adjusting for national income-0.154p = 0.184 · likely range [-0.37, +0.07]
p, allowing for regions0.085standard error 0.075 · 7 regions
Check for luck (q)0.246across 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
Labour score · typical gaps (standard deviations)link with income +0.79 · 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

Frequent prayer · % of respondents →Labour score · typical gaps (standard deviations) →0.838%98.4%-1.511.14Argentina: 54% of respondents, 0.34 typical gaps (standard deviations)Armenia: 71.7% of respondents, -0.302 typical gaps (standard deviations)Australia: 28.6% of respondents, 0.867 typical gaps (standard deviations)Azerbaijan: 17.9% of respondents, -0.0868 typical gaps (standard deviations)Bangladesh: 78% of respondents, -0.0147 typical gaps (standard deviations)Belarus: 27.4% of respondents, 0.978 typical gaps (standard deviations)Bolivia: 82.7% of respondents, -0.0485 typical gaps (standard deviations)Brazil: 77.4% of respondents, 0.16 typical gaps (standard deviations)Canada: 35.5% of respondents, 0.581 typical gaps (standard deviations)Chile: 37.4% of respondents, 0.344 typical gaps (standard deviations)China: 0.838% of respondents, 0.367 typical gaps (standard deviations)Colombia: 87.9% of respondents, -0.377 typical gaps (standard deviations)Cyprus: 69.4% of respondents, 0.812 typical gaps (standard deviations)Czechia: 13.8% of respondents, 0.889 typical gaps (standard deviations)Germany: 30.2% of respondents, 0.988 typical gaps (standard deviations)Algeria: 81.8% of respondents, -0.0627 typical gaps (standard deviations)Ecuador: 82.9% of respondents, -0.398 typical gaps (standard deviations)Egypt, Arab Rep.: 96.6% of respondents, 0.367 typical gaps (standard deviations)Spain: 20.2% of respondents, 0.483 typical gaps (standard deviations)Estonia: 12.4% of respondents, 0.558 typical gaps (standard deviations)Ethiopia: 94.9% of respondents, -0.481 typical gaps (standard deviations)United Kingdom: 24.1% of respondents, 0.635 typical gaps (standard deviations)Georgia: 60.6% of respondents, -0.418 typical gaps (standard deviations)Ghana: 95.5% of respondents, 0.00187 typical gaps (standard deviations)Greece: 66.4% of respondents, 0.426 typical gaps (standard deviations)Guatemala: 73.2% of respondents, 0.48 typical gaps (standard deviations)Hong Kong SAR, China: 18.6% of respondents, 1.11 typical gaps (standard deviations)Haiti: 95.4% of respondents, -1.28 typical gaps (standard deviations)Indonesia: 95.3% of respondents, 0.169 typical gaps (standard deviations)India: 68.4% of respondents, -0.486 typical gaps (standard deviations)Iran, Islamic Rep.: 80.9% of respondents, -0.0514 typical gaps (standard deviations)Iraq: 84.1% of respondents, -0.409 typical gaps (standard deviations)Jordan: 86.9% of respondents, -0.133 typical gaps (standard deviations)Japan: 25% of respondents, 1.03 typical gaps (standard deviations)Kazakhstan: 35.2% of respondents, 0.516 typical gaps (standard deviations)Kenya: 93.3% of respondents, -0.274 typical gaps (standard deviations)Kyrgyz Republic: 68.3% of respondents, 0.54 typical gaps (standard deviations)Korea, Rep.: 19% of respondents, 0.822 typical gaps (standard deviations)Lebanon: 80.9% of respondents, 0.107 typical gaps (standard deviations)Libya: 97.6% of respondents, -0.574 typical gaps (standard deviations)Macao SAR, China: 15.8% of respondents, 1.12 typical gaps (standard deviations)Morocco: 80.9% of respondents, -0.0339 typical gaps (standard deviations)Maldives: 93.5% of respondents, 0.686 typical gaps (standard deviations)Mexico: 71.9% of respondents, 0.513 typical gaps (standard deviations)Myanmar: 83.3% of respondents, -0.0589 typical gaps (standard deviations)Mongolia: 17.8% of respondents, 0.223 typical gaps (standard deviations)Malaysia: 79.7% of respondents, 0.654 typical gaps (standard deviations)Nigeria: 98.4% of respondents, -0.128 typical gaps (standard deviations)Nicaragua: 76.3% of respondents, 0.229 typical gaps (standard deviations)Netherlands: 23% of respondents, 0.804 typical gaps (standard deviations)New Zealand: 29.6% of respondents, 0.721 typical gaps (standard deviations)Pakistan: 89.8% of respondents, -0.988 typical gaps (standard deviations)Peru: 66.6% of respondents, -0.454 typical gaps (standard deviations)Philippines: 92.4% of respondents, 0.559 typical gaps (standard deviations)Poland: 63.2% of respondents, 0.842 typical gaps (standard deviations)Puerto Rico (US): 85.7% of respondents, 0.688 typical gaps (standard deviations)West Bank and Gaza: 90.5% of respondents, -0.578 typical gaps (standard deviations)Romania: 65.2% of respondents, 0.637 typical gaps (standard deviations)Russian Federation: 27% of respondents, 1.14 typical gaps (standard deviations)Rwanda: 73.7% of respondents, -0.108 typical gaps (standard deviations)Singapore: 53.1% of respondents, 1.05 typical gaps (standard deviations)Serbia: 28.7% of respondents, 0.409 typical gaps (standard deviations)Slovak Republic: 41.5% of respondents, 0.713 typical gaps (standard deviations)Slovenia: 22.5% of respondents, 0.914 typical gaps (standard deviations)Sweden: 18.3% of respondents, 0.531 typical gaps (standard deviations)Thailand: 41.4% of respondents, 0.466 typical gaps (standard deviations)Tajikistan: 67.6% of respondents, 0.278 typical gaps (standard deviations)Trinidad and Tobago: 96.8% of respondents, 0.701 typical gaps (standard deviations)Tunisia: 76.5% of respondents, -0.205 typical gaps (standard deviations)Turkiye: 71% of respondents, 0.224 typical gaps (standard deviations)Ukraine: 39% of respondents, 0.208 typical gaps (standard deviations)Uruguay: 40.6% of respondents, 0.298 typical gaps (standard deviations)United States: 62.3% of respondents, 0.862 typical gaps (standard deviations)Uzbekistan: 72.6% of respondents, 0.368 typical gaps (standard deviations)Viet Nam: 11.6% of respondents, 0.283 typical gaps (standard deviations)South Africa: 73.6% of respondents, -1.51 typical gaps (standard deviations)Zimbabwe: 94.2% of respondents, -0.673 typical gaps (standard deviations)AZECHNCZEHKGHTIMACMDVPAKPHLTTOZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5735.2e-877
By rank (Spearman), not adjusted-0.5803.3e-877
Standard (Pearson), adjusted for national income-0.1540.18477
By rank (Spearman), adjusted for national income-0.0830.47377

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 77 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: +0.419 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.185 (without AZE) to -0.127 (without LBY). 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: AZE (-0.031).

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+0.19
Europe & Central Asia25-0.17
Latin America & Caribbean14+0.05

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 third26-0.30
Middle third25-0.07
Richest third26-0.23

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
Christians40-0.11
Muslims22-0.09

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 colony15-0.33
Spain12-0.27
United Kingdom22+0.17

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 = -0.177 after dropping 2 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

typical gaps (standard deviations)
Years
Nigeria NGA98.4%-0.1282022, 2025
Libya LBY97.6%-0.5742022, 2025
Trinidad and Tobago TTO96.8%0.7012014, 2025
Egypt, Arab Rep. EGY96.6%0.3672022, 2025
Ghana GHA95.5%0.001872014, 2025
Haiti HTI95.4%-1.282014, 2025
Indonesia IDN95.3%0.1692022, 2025
Ethiopia ETH94.9%-0.4812022, 2025
Zimbabwe ZWE94.2%-0.6732022, 2025
Maldives MDV93.5%0.6862022, 2025
Kenya KEN93.3%-0.2742022, 2025
Philippines PHL92.4%0.5592022, 2025
West Bank and Gaza PSE90.5%-0.5782014, 2025
Pakistan PAK89.8%-0.9882022, 2025
Colombia COL87.9%-0.3772022, 2025

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

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.

Labour score

Labour score

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

The average of 4 measures from 2 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: Informal employment, Vulnerable employment, Youth not in work or study, Unemployment.

How this was computed

Data used: most recent value in 2010-2025; values come from 2014–2025. 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 Frequent prayer and Labour score

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

More links with Frequent prayer or Labour score