placestats

Informal employment vs. Religious attendance

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 79 countries, Informal employment and Religious attendance rise and fall together at r = +0.70 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.28 (q = 0.047, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak positive link remains.

Comparing countries across the middle half of religious attendance, 19.7% to 60.1% of respondents, informal employment is worse by 40.8% of employment at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 10.3% of employment.

Link before adjusting (r)+0.701p = 6.3e-13 · 79 countries
After adjusting for national income+0.284p = 0.012 · likely range [+0.07, +0.48]
p, allowing for regions0.001standard error 0.043 · 6 regions
Check for luck (q)0.047across 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.

Informal employment · % of employmentlink with income -0.88 · 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

Informal employment · % of employment →Religious attendance · % of respondents →0.63%94.4%6.48%93%Argentina: 51.6% of employment, 36% of respondentsArmenia: 76.1% of employment, 35.8% of respondentsAustria: 1.32% of employment, 26.8% of respondentsBelgium: 1.37% of employment, 18.9% of respondentsBurkina Faso: 93.8% of employment, 76.9% of respondentsBangladesh: 84% of employment, 54.2% of respondentsBulgaria: 1.2% of employment, 17.3% of respondentsBosnia and Herzegovina: 16.6% of employment, 43.3% of respondentsBolivia: 83.9% of employment, 65% of respondentsBrazil: 35.6% of employment, 59.6% of respondentsSwitzerland: 1.48% of employment, 16.2% of respondentsChile: 26.4% of employment, 31% of respondentsColombia: 56% of employment, 58.3% of respondentsCyprus: 1.68% of employment, 50.2% of respondentsCzechia: 2.26% of employment, 10% of respondentsGermany: 2.49% of employment, 19.2% of respondentsDenmark: 2.82% of employment, 6.48% of respondentsEcuador: 68.5% of employment, 61.3% of respondentsEgypt, Arab Rep.: 68.6% of employment, 58.8% of respondentsSpain: 2.2% of employment, 22.8% of respondentsEstonia: 2.4% of employment, 8% of respondentsEthiopia: 77.7% of employment, 93% of respondentsFinland: 2.19% of employment, 11.1% of respondentsFrance: 3.15% of employment, 12.4% of respondentsUnited Kingdom: 6.51% of employment, 16.1% of respondentsGeorgia: 51.5% of employment, 33.7% of respondentsGhana: 78% of employment, 83.8% of respondentsGreece: 3.58% of employment, 44.5% of respondentsGuatemala: 75.9% of employment, 63.4% of respondentsCroatia: 1.17% of employment, 33.7% of respondentsHaiti: 91.6% of employment, 38.6% of respondentsHungary: 1.31% of employment, 17.3% of respondentsIndonesia: 81% of employment, 77.8% of respondentsIndia: 88.1% of employment, 62.7% of respondentsIreland: 1.9% of employment, 53.3% of respondentsIraq: 67.6% of employment, 43.7% of respondentsIceland: 1.97% of employment, 8.48% of respondentsItaly: 3.42% of employment, 40.7% of respondentsJordan: 55.1% of employment, 56.4% of respondentsKenya: 86.5% of employment, 84.6% of respondentsKyrgyz Republic: 57.5% of employment, 39.2% of respondentsKorea, Rep.: 29.1% of employment, 22.8% of respondentsLebanon: 55.5% of employment, 53.3% of respondentsLithuania: 3.2% of employment, 30% of respondentsLuxembourg: 1.93% of employment, 22.7% of respondentsLatvia: 2.17% of employment, 12.8% of respondentsMoldova: 52.2% of employment, 27.8% of respondentsMaldives: 47.3% of employment, 64.8% of respondentsMexico: 56.4% of employment, 60.6% of respondentsNorth Macedonia: 11.1% of employment, 40.1% of respondentsMali: 94.4% of employment, 73.9% of respondentsMalta: 0.63% of employment, 81.5% of respondentsMyanmar: 80.7% of employment, 43.5% of respondentsMongolia: 27.1% of employment, 14.1% of respondentsNigeria: 93.1% of employment, 90.3% of respondentsNicaragua: 81.8% of employment, 54.2% of respondentsNetherlands: 2.63% of employment, 14.3% of respondentsNorway: 1.43% of employment, 11.8% of respondentsPakistan: 88.7% of employment, 60.9% of respondentsPeru: 70.5% of employment, 57.1% of respondentsPoland: 8.62% of employment, 64.3% of respondentsPortugal: 4.73% of employment, 29.7% of respondentsWest Bank and Gaza: 48.8% of employment, 58.5% of respondentsRomania: 2.21% of employment, 46% of respondentsRussian Federation: 19.3% of employment, 16.3% of respondentsRwanda: 84.3% of employment, 78.2% of respondentsSerbia: 17.8% of employment, 20.1% of respondentsSlovak Republic: 4.37% of employment, 36.1% of respondentsSlovenia: 0.7% of employment, 23.8% of respondentsSweden: 2.83% of employment, 9.65% of respondentsThailand: 63.2% of employment, 38.1% of respondentsTajikistan: 63.6% of employment, 40.7% of respondentsTunisia: 36.9% of employment, 43.5% of respondentsTurkiye: 27.7% of employment, 39.5% of respondentsUruguay: 28.5% of employment, 13.3% of respondentsViet Nam: 67% of employment, 17.2% of respondentsSouth Africa: 34.8% of employment, 69% of respondentsZambia: 82.7% of employment, 80.9% of respondentsZimbabwe: 85% of employment, 84.8% of respondentsARMDNKESTETHHTIIRLISLKENMLTNGAPOLVNM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7016.3e-1379
By rank (Spearman), not adjusted+0.6632.8e-1179
Standard (Pearson), adjusted for national income+0.2840.01279
By rank (Spearman), adjusted for national income+0.1690.13979

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 79 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: +0.418 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.250 (without IDN) to +0.320 (without VNM). 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: VNM (+0.036).

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
Europe & Central Asia40+0.08
Latin America & Caribbean12+0.60

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 third27+0.36
Middle third26+0.25
Richest third26-0.06

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
Christians53+0.19
Muslims17+0.66

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
United Kingdom15+0.07

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 = +0.284 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.

Loading…

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 employment
% of respondents		Years
Mali MLI94.4%73.9%2023, 2010
Burkina Faso BFA93.8%76.9%2024, 2010
Nigeria NGA93.1%90.3%2024, 2022
Haiti HTI91.6%38.6%2012, 2014
Pakistan PAK88.7%60.9%2025, 2022
India IND88.1%62.7%2024, 2022
Kenya KEN86.5%84.6%2019, 2022
Zimbabwe ZWE85%84.8%2024, 2022
Rwanda RWA84.3%78.2%2024, 2014
Bangladesh BGD84%54.2%2024, 2022
Bolivia BOL83.9%65%2024, 2022
Zambia ZMB82.7%80.9%2024, 2010
Nicaragua NIC81.8%54.2%2012, 2022
Indonesia IDN81%77.8%2023, 2022
Myanmar MMR80.7%43.5%2020, 2022

United Nations Global SDG Indicators Database; series compiled by the International Labour Organization (8.3.1) and the UN Office on Drugs and Crime (16.3.2)
Haerpfer, C. et al., World Values Survey; EVS, European Values Study

Sources

Informal employment

Share of employment that is informal

International Labour Organization · via Our World in Data · CC BY 3.0 IGO · updated annual

Workers with no contract, no social protection or no registered employer, as the ILO (the UN's labour agency) makes them comparable across national labour force surveys. The World Bank's copy of this series was retired, so it comes from the UN's database for the Sustainable Development Goals.

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 2010–2025. 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 6 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 Informal employment and Religious attendance

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

More links with Informal employment or Religious attendance