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 176 countries, Christian share and Gender score rise and fall together at r = +0.45 (on a scale from −1 to +1, where 0 means no link). It holds up almost unchanged when only countries with similar income (GDP per person) are compared (r = +0.48, q = 0.048, allowing for the many pairs tested). Caution: much weaker within regions than across them (largely a between-region difference).
Comparing countries across the middle half of christian share, 8.27% to 87.5% of population, gender score is better by 0.698 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.693 typical gaps.
Link before adjusting (r)+0.451p = 3.2e-10 · 176 countries
After adjusting for national income+0.476p = 2.7e-11 · likely range [+0.35, +0.58]
p, allowing for regions0.005standard error 0.111 · 7 regions
Check for luck (q)0.048across 13,797 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.
Christian share · % of populationlink with income -0.01 · independent
Gender score · typical gaps (standard deviations)link with income +0.37 · moderate
“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
+0.451
3.2e-10
176
By rank (Spearman), not adjusted
+0.366
5.7e-7
176
Standard (Pearson), adjusted for national income
+0.476
2.7e-11
176
By rank (Spearman), adjusted for national income
+0.404
2.9e-8
176
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 176 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: -0.025 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from +0.468 (without IRN) to +0.496 (without PNG). 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: PNG (+0.020).
Fragile: this link may not hold up.
much weaker within regions than across them (largely a between-region difference)
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
25
+0.20
Europe & Central Asia
49
+0.31
Latin America & Caribbean
27
-0.02
Middle East, North Africa, Afghanistan & Pakistan
21
+0.18
Sub-Saharan Africa
46
+0.41
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
59
+0.32
Middle third
58
+0.60
Richest third
59
+0.53
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
107
-0.31
Muslims
49
+0.19
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.
n
r
France
25
+0.13
Never a European colony
21
+0.67
Spain
20
+0.04
United Kingdom
57
+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 = +0.476 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 population
typical gaps (standard deviations)
Years
Timor-Leste TLS
99.5%
0.73
2020, 2024
Moldova MDA
99.5%
1.45
2020, 2024
Papua New Guinea PNG
99.1%
-1.01
2020, 2024
Micronesia, Fed. Sts. FSM
98.8%
-0.508
2020, 2014
Romania ROU
98.5%
0.285
2020, 2024
Zambia ZMB
98.3%
0.211
2020, 2024
Tonga TON
97.8%
-0.456
2020, 2023
Samoa WSM
97.6%
0.528
2020, 2022
Bahamas, The BHS
97.6%
0.783
2020, 2023
Kiribati KIR
97.6%
-0.559
2020, 2023
Lesotho LSO
97.3%
0.261
2020, 2024
Armenia ARM
97.3%
1.26
2020, 2024
Rwanda RWA
97%
1.02
2020, 2025
Congo, Dem. Rep. COD
96.3%
-0.00209
2020, 2024
Solomon Islands SLB
95%
-0.473
2020, 2024
Pew Research Center, Religious Composition by Country, 2010-2020. Pew Research Center bears no responsibility for the analyses or interpretations of the data presented here. vdem; wb_gender; wdi One of these measures is published under a licence that forbids changes. Values are shown exactly as published and must not be republished in a changed or rescaled form.
Pew Research Center · via Our World in Data · Pew Research Center terms of use, excerpts with attribution · updated decennial · comparable from 2010 only
All Christian traditions together. The world's largest religion, and the one whose spread most closely follows income. Adjusting for GDP is there to remove that. No "better" direction is set.
PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel
The average of 5 measures from 3 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: Women's political empowerment, Women ministers, Women in parliament, Women in management, Married by 18.
How this was computed
Data used: most recent value in 2010-2025; values come from 2012–2025. 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 13,797 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Christian share and Gender score
This page shows that two measures go together. It does not show that Christian share changes Gender score, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.