placestats

Birth rate vs. Governance score

Fades after adjusting for income

Across 171 countries, Birth rate and Governance score rise and fall together at r = -0.50 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.17, which could easily be chance (q = 0.30, 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 birth rate, 9.75 to 25.7 per 1,000 people, governance score is worse by 0.706 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.411 typical gaps.

Link before adjusting (r)-0.505p = 1.9e-12 · 171 countries
After adjusting for national income-0.170p = 0.026 · likely range [-0.31, -0.02]
p, allowing for regions0.120standard error 0.094 · 7 regions
Check for luck (q)0.301across 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.

Birth rate · per 1,000 peoplelink with income -0.85 · proxy
Governance score · typical gaps (standard deviations)link with income +0.53 · moderate

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

Birth rate · per 1,000 people →Governance score · typical gaps (standard deviations) →4.746.2-1.41.47Afghanistan: 35 per 1,000 people, -1.4 typical gaps (standard deviations)Angola: 37.2 per 1,000 people, -0.522 typical gaps (standard deviations)Albania: 10.1 per 1,000 people, 0.323 typical gaps (standard deviations)Andorra: 6.85 per 1,000 people, 0.558 typical gaps (standard deviations)United Arab Emirates: 9.82 per 1,000 people, -0.981 typical gaps (standard deviations)Argentina: 11.1 per 1,000 people, 0.815 typical gaps (standard deviations)Armenia: 11.1 per 1,000 people, 0.22 typical gaps (standard deviations)Australia: 10.7 per 1,000 people, 1.38 typical gaps (standard deviations)Austria: 8.4 per 1,000 people, 1.33 typical gaps (standard deviations)Azerbaijan: 10 per 1,000 people, -1.33 typical gaps (standard deviations)Burundi: 33.2 per 1,000 people, -1.09 typical gaps (standard deviations)Belgium: 9.2 per 1,000 people, 1.41 typical gaps (standard deviations)Benin: 33.4 per 1,000 people, 0.285 typical gaps (standard deviations)Burkina Faso: 31.3 per 1,000 people, -0.784 typical gaps (standard deviations)Bangladesh: 20 per 1,000 people, -0.359 typical gaps (standard deviations)Bulgaria: 8.3 per 1,000 people, 0.74 typical gaps (standard deviations)Bahrain: 12 per 1,000 people, -1.05 typical gaps (standard deviations)Bosnia and Herzegovina: 7.72 per 1,000 people, 0.0899 typical gaps (standard deviations)Belarus: 7.03 per 1,000 people, -1.26 typical gaps (standard deviations)Belize: 18.1 per 1,000 people, 0.0883 typical gaps (standard deviations)Bolivia: 21 per 1,000 people, 0.552 typical gaps (standard deviations)Brazil: 12.1 per 1,000 people, 0.85 typical gaps (standard deviations)Barbados: 11.1 per 1,000 people, 1.24 typical gaps (standard deviations)Bhutan: 12.5 per 1,000 people, 0.61 typical gaps (standard deviations)Botswana: 24.4 per 1,000 people, 0.872 typical gaps (standard deviations)Central African Republic: 46.2 per 1,000 people, -1.02 typical gaps (standard deviations)Canada: 8.9 per 1,000 people, 1.36 typical gaps (standard deviations)Switzerland: 8.7 per 1,000 people, 1.47 typical gaps (standard deviations)Chile: 8.72 per 1,000 people, 1.32 typical gaps (standard deviations)China: 6.77 per 1,000 people, -1.2 typical gaps (standard deviations)Cote d'Ivoire: 31.6 per 1,000 people, -0.116 typical gaps (standard deviations)Cameroon: 33.3 per 1,000 people, -0.834 typical gaps (standard deviations)Congo, Dem. Rep.: 40.9 per 1,000 people, -0.824 typical gaps (standard deviations)Congo, Rep.: 30.4 per 1,000 people, -0.925 typical gaps (standard deviations)Colombia: 13.3 per 1,000 people, 0.733 typical gaps (standard deviations)Comoros: 28.2 per 1,000 people, -0.464 typical gaps (standard deviations)Cabo Verde: 12.2 per 1,000 people, 1.15 typical gaps (standard deviations)Costa Rica: 9.99 per 1,000 people, 1.34 typical gaps (standard deviations)Cyprus: 10.5 per 1,000 people, 1.1 typical gaps (standard deviations)Czechia: 7.7 per 1,000 people, 1.31 typical gaps (standard deviations)Germany: 8.1 per 1,000 people, 1.36 typical gaps (standard deviations)Djibouti: 20.9 per 1,000 people, -0.784 typical gaps (standard deviations)Denmark: 9.5 per 1,000 people, 1.47 typical gaps (standard deviations)Dominican Republic: 17.6 per 1,000 people, 0.621 typical gaps (standard deviations)Algeria: 18.8 per 1,000 people, -0.695 typical gaps (standard deviations)Ecuador: 14.8 per 1,000 people, 0.447 typical gaps (standard deviations)Egypt, Arab Rep.: 20.9 per 1,000 people, -0.987 typical gaps (standard deviations)Spain: 6.5 per 1,000 people, 1.32 typical gaps (standard deviations)Estonia: 7.1 per 1,000 people, 1.41 typical gaps (standard deviations)Ethiopia: 31.5 per 1,000 people, -0.741 typical gaps (standard deviations)Finland: 7.8 per 1,000 people, 1.38 typical gaps (standard deviations)Fiji: 17.9 per 1,000 people, 0.57 typical gaps (standard deviations)France: 9.7 per 1,000 people, 1.24 typical gaps (standard deviations)Gabon: 27.1 per 1,000 people, -0.529 typical gaps (standard deviations)United Kingdom: 9.89 per 1,000 people, 1.27 typical gaps (standard deviations)Georgia: 11.3 per 1,000 people, 0.0179 typical gaps (standard deviations)Ghana: 25.9 per 1,000 people, 0.845 typical gaps (standard deviations)Guinea: 33.3 per 1,000 people, -0.676 typical gaps (standard deviations)Gambia, The: 29.9 per 1,000 people, 0.362 typical gaps (standard deviations)Guinea-Bissau: 29.6 per 1,000 people, -0.465 typical gaps (standard deviations)Equatorial Guinea: 30 per 1,000 people, -1.32 typical gaps (standard deviations)Greece: 6.6 per 1,000 people, 0.914 typical gaps (standard deviations)Guatemala: 20.6 per 1,000 people, 0.291 typical gaps (standard deviations)Guyana: 19.9 per 1,000 people, 0.538 typical gaps (standard deviations)Hong Kong SAR, China: 4.9 per 1,000 people, -0.536 typical gaps (standard deviations)Honduras: 21.6 per 1,000 people, 0.146 typical gaps (standard deviations)Croatia: 8.3 per 1,000 people, 0.865 typical gaps (standard deviations)Haiti: 21.9 per 1,000 people, -0.66 typical gaps (standard deviations)Hungary: 8.2 per 1,000 people, 0.225 typical gaps (standard deviations)Indonesia: 15.8 per 1,000 people, 0.125 typical gaps (standard deviations)India: 16 per 1,000 people, 0.146 typical gaps (standard deviations)Ireland: 10 per 1,000 people, 1.41 typical gaps (standard deviations)Iran, Islamic Rep.: 12.6 per 1,000 people, -1.26 typical gaps (standard deviations)Iraq: 25.6 per 1,000 people, -0.488 typical gaps (standard deviations)Iceland: 11.2 per 1,000 people, 1.32 typical gaps (standard deviations)Israel: 18.6 per 1,000 people, 0.783 typical gaps (standard deviations)Italy: 6.3 per 1,000 people, 1 typical gaps (standard deviations)Jamaica: 11.4 per 1,000 people, 1.01 typical gaps (standard deviations)Jordan: 20.5 per 1,000 people, -0.271 typical gaps (standard deviations)Japan: 5.7 per 1,000 people, 1.28 typical gaps (standard deviations)Kazakhstan: 19.5 per 1,000 people, -0.796 typical gaps (standard deviations)Kenya: 27 per 1,000 people, 0.217 typical gaps (standard deviations)Kyrgyz Republic: 19.4 per 1,000 people, -0.725 typical gaps (standard deviations)Cambodia: 20.3 per 1,000 people, -1.01 typical gaps (standard deviations)Korea, Rep.: 4.7 per 1,000 people, 1.05 typical gaps (standard deviations)Kuwait: 10 per 1,000 people, -0.696 typical gaps (standard deviations)Lao PDR: 20.9 per 1,000 people, -1.14 typical gaps (standard deviations)Lebanon: 16 per 1,000 people, -0.112 typical gaps (standard deviations)Liberia: 30.6 per 1,000 people, 0.416 typical gaps (standard deviations)Libya: 16.5 per 1,000 people, -0.885 typical gaps (standard deviations)St. Lucia: 11.2 per 1,000 people, 0.552 typical gaps (standard deviations)Sri Lanka: 10.1 per 1,000 people, 0.602 typical gaps (standard deviations)Lesotho: 23.8 per 1,000 people, 0.671 typical gaps (standard deviations)Lithuania: 6.6 per 1,000 people, 1.14 typical gaps (standard deviations)Luxembourg: 9.5 per 1,000 people, 1.38 typical gaps (standard deviations)Latvia: 6.9 per 1,000 people, 1.22 typical gaps (standard deviations)Morocco: 16.4 per 1,000 people, -0.351 typical gaps (standard deviations)Moldova: 10.6 per 1,000 people, 0.5 typical gaps (standard deviations)Madagascar: 31.7 per 1,000 people, -0.193 typical gaps (standard deviations)Maldives: 10.6 per 1,000 people, 0.0802 typical gaps (standard deviations)Mexico: 15.5 per 1,000 people, 0.134 typical gaps (standard deviations)North Macedonia: 8.8 per 1,000 people, 0.352 typical gaps (standard deviations)Mali: 39.5 per 1,000 people, -0.753 typical gaps (standard deviations)Malta: 7.7 per 1,000 people, 1.12 typical gaps (standard deviations)Myanmar: 16.5 per 1,000 people, -1.39 typical gaps (standard deviations)Montenegro: 11.2 per 1,000 people, 0.55 typical gaps (standard deviations)Mongolia: 16.9 per 1,000 people, 0.716 typical gaps (standard deviations)Mozambique: 37 per 1,000 people, -0.298 typical gaps (standard deviations)Mauritania: 34 per 1,000 people, -0.421 typical gaps (standard deviations)Mauritius: 10.3 per 1,000 people, 1.07 typical gaps (standard deviations)Malawi: 31.1 per 1,000 people, 0.576 typical gaps (standard deviations)Malaysia: 12.4 per 1,000 people, 0.169 typical gaps (standard deviations)Namibia: 25.9 per 1,000 people, 0.69 typical gaps (standard deviations)Niger: 41.4 per 1,000 people, -0.548 typical gaps (standard deviations)Nigeria: 32.5 per 1,000 people, -0.0132 typical gaps (standard deviations)Nicaragua: 19.1 per 1,000 people, -1.24 typical gaps (standard deviations)Netherlands: 9.2 per 1,000 people, 1.32 typical gaps (standard deviations)Norway: 9.7 per 1,000 people, 1.43 typical gaps (standard deviations)Nepal: 19 per 1,000 people, 0.441 typical gaps (standard deviations)New Zealand: 11 per 1,000 people, 1.32 typical gaps (standard deviations)Oman: 16.8 per 1,000 people, -0.665 typical gaps (standard deviations)Pakistan: 27.4 per 1,000 people, -0.437 typical gaps (standard deviations)Panama: 15.9 per 1,000 people, 0.84 typical gaps (standard deviations)Peru: 15.8 per 1,000 people, 0.522 typical gaps (standard deviations)Philippines: 15.9 per 1,000 people, 0.0585 typical gaps (standard deviations)Papua New Guinea: 24.3 per 1,000 people, 0.227 typical gaps (standard deviations)Poland: 6.9 per 1,000 people, 0.996 typical gaps (standard deviations)Portugal: 7.9 per 1,000 people, 1.26 typical gaps (standard deviations)Paraguay: 19.7 per 1,000 people, 0.349 typical gaps (standard deviations)Qatar: 9.52 per 1,000 people, -0.908 typical gaps (standard deviations)Romania: 7.5 per 1,000 people, 0.749 typical gaps (standard deviations)Russian Federation: 8.4 per 1,000 people, -1.14 typical gaps (standard deviations)Rwanda: 28.1 per 1,000 people, -0.868 typical gaps (standard deviations)Saudi Arabia: 16.5 per 1,000 people, -1.14 typical gaps (standard deviations)Sudan: 33 per 1,000 people, -1.22 typical gaps (standard deviations)Senegal: 29.2 per 1,000 people, 0.683 typical gaps (standard deviations)Singapore: 7.4 per 1,000 people, 0.0449 typical gaps (standard deviations)Solomon Islands: 26.7 per 1,000 people, 0.697 typical gaps (standard deviations)Sierra Leone: 30.1 per 1,000 people, 0.299 typical gaps (standard deviations)El Salvador: 15.6 per 1,000 people, -0.352 typical gaps (standard deviations)Somalia, Fed. Rep.: 42.3 per 1,000 people, -1.01 typical gaps (standard deviations)Serbia: 9.2 per 1,000 people, -0.0738 typical gaps (standard deviations)Sao Tome and Principe: 28.1 per 1,000 people, 0.8 typical gaps (standard deviations)Suriname: 17.2 per 1,000 people, 0.996 typical gaps (standard deviations)Slovak Republic: 8.5 per 1,000 people, 1.02 typical gaps (standard deviations)Slovenia: 7.9 per 1,000 people, 1.19 typical gaps (standard deviations)Sweden: 9.3 per 1,000 people, 1.42 typical gaps (standard deviations)Eswatini: 23.6 per 1,000 people, -1.05 typical gaps (standard deviations)Seychelles: 11 per 1,000 people, 0.975 typical gaps (standard deviations)Syrian Arab Republic: 23.2 per 1,000 people, -1.05 typical gaps (standard deviations)Chad: 43.3 per 1,000 people, -0.999 typical gaps (standard deviations)Togo: 32.1 per 1,000 people, -0.371 typical gaps (standard deviations)Thailand: 8.12 per 1,000 people, -0.383 typical gaps (standard deviations)Tajikistan: 25.3 per 1,000 people, -1.34 typical gaps (standard deviations)Turkmenistan: 20.9 per 1,000 people, -1.39 typical gaps (standard deviations)Timor-Leste: 21.8 per 1,000 people, 0.779 typical gaps (standard deviations)Trinidad and Tobago: 10.5 per 1,000 people, 1.03 typical gaps (standard deviations)Tunisia: 13.4 per 1,000 people, -0.257 typical gaps (standard deviations)Turkiye: 11 per 1,000 people, -0.691 typical gaps (standard deviations)Tanzania: 34.8 per 1,000 people, -0.315 typical gaps (standard deviations)Uganda: 34.4 per 1,000 people, -0.646 typical gaps (standard deviations)Ukraine: 5.47 per 1,000 people, -0.0525 typical gaps (standard deviations)Uruguay: 9.8 per 1,000 people, 1.39 typical gaps (standard deviations)United States: 10.6 per 1,000 people, 0.998 typical gaps (standard deviations)Uzbekistan: 25.6 per 1,000 people, -1.04 typical gaps (standard deviations)Viet Nam: 13.4 per 1,000 people, -0.933 typical gaps (standard deviations)Vanuatu: 27.7 per 1,000 people, 0.885 typical gaps (standard deviations)Kosovo: 11.9 per 1,000 people, 0.371 typical gaps (standard deviations)South Africa: 18.5 per 1,000 people, 0.883 typical gaps (standard deviations)Zambia: 32.7 per 1,000 people, 0.212 typical gaps (standard deviations)Zimbabwe: 29.9 per 1,000 people, -0.609 typical gaps (standard deviations)AZEBLRCAFCHNESPESTJPNMWISOMSTPTCDVUT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5051.9e-12171
By rank (Spearman), not adjusted-0.5222.4e-13171
Standard (Pearson), adjusted for national income-0.1700.026171
By rank (Spearman), adjusted for national income-0.1610.036171

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 171 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: +0.334 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.192 (without CHN) to -0.149 (without SAU). 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: CHN (-0.022).

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 & Pacific21+0.42
Europe & Central Asia50-0.30
Latin America & Caribbean25-0.55
Middle East, North Africa, Afghanistan & Pakistan21-0.12
Sub-Saharan Africa46-0.57

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 third57-0.15
Middle third57-0.19
Richest third57-0.35

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
Christians101-0.22
Muslims48-0.25

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
France25+0.01
Never a European colony21-0.39
Spain19-0.57
United Kingdom51-0.23

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 = -0.170 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


per 1,000 people

typical gaps (standard deviations)
Years
Central African Republic CAF46.2-1.022024, 2025
Chad TCD43.3-0.9992024, 2025
Somalia, Fed. Rep. SOM42.3-1.012024, 2025
Niger NER41.4-0.5482024, 2025
Congo, Dem. Rep. COD40.9-0.8242024, 2025
Mali MLI39.5-0.7532024, 2025
Angola AGO37.2-0.5222024, 2025
Mozambique MOZ37-0.2982024, 2025
Afghanistan AFG35-1.42024, 2025
Tanzania TZA34.8-0.3152024, 2025
Uganda UGA34.4-0.6462024, 2025
Mauritania MRT34-0.4212024, 2025
Benin BEN33.40.2852024, 2025
Cameroon CMR33.3-0.8342024, 2025
Guinea GIN33.3-0.6762024, 2025

World Bank, World Development Indicators
fh; un_sdg; vdem; wdi; wgi

Sources

Birth rate

Birth rate, crude (per 1,000 people)

World Bank · CC BY 4.0 · updated quarterly

Governance score

Governance score

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

The average of 19 measures from 5 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: Civil liberties (FH), Political rights (FH), Pre-trial detainee share, Civil society participation, Political corruption, Electoral democracy, Freedom of association, Freedom of expression, Free and fair elections, Liberal democracy, Participatory democracy, Political civil liberties, Birth registration, Control of corruption, Government effectiveness, Political stability, Rule of law, Regulatory quality, Voice and accountability.

How this was computed

Data used: most recent value in 2010-2025; values come from 2011–2025. 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 Birth rate and Governance score

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

More links with Birth rate or Governance score