placestats

Fertility rate vs. Researchers

Fades after adjusting for income

Across 131 countries, Fertility rate and Researchers rise and fall together at r = -0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.11, which could easily be chance (q = 0.66, 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 fertility rate, 1.41 to 2.7 births per woman, researchers is worse by 1,419 per million people at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 383 per million people the other way.

Link before adjusting (r)-0.531p = 7.1e-11 · 131 countries
After adjusting for national income+0.108p = 0.223 · likely range [-0.07, +0.27]
p, allowing for regions0.473standard error 0.141 · 7 regions
Check for luck (q)0.661across 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.

Fertility rate · births per womanlink with income -0.81 · proxy
Researchers · per million 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

Fertility rate · births per woman →Researchers · per million people →0.5826.031.459,472Angola: 5.05 births per woman, 18.9 per million peopleAlbania: 1.34 births per woman, 429 per million peopleUnited Arab Emirates: 1.21 births per woman, 2,607 per million peopleArgentina: 1.5 births per woman, 1,313 per million peopleArmenia: 1.7 births per woman, 942 per million peopleAustralia: 1.48 births per woman, 4,569 per million peopleAustria: 1.31 births per woman, 6,634 per million peopleAzerbaijan: 1.43 births per woman, 1,663 per million peopleBurundi: 4.79 births per woman, 22.5 per million peopleBelgium: 1.44 births per woman, 5,990 per million peopleBurkina Faso: 4.11 births per woman, 46.6 per million peopleBulgaria: 1.72 births per woman, 2,401 per million peopleBahrain: 1.8 births per woman, 849 per million peopleBosnia and Herzegovina: 1.49 births per woman, 705 per million peopleBelarus: 1.22 births per woman, 1,568 per million peopleBolivia: 2.52 births per woman, 63.1 per million peopleBrazil: 1.61 births per woman, 903 per million peopleBrunei Darussalam: 1.73 births per woman, 506 per million peopleBhutan: 1.45 births per woman, 236 per million peopleBotswana: 2.7 births per woman, 180 per million peopleCanada: 1.25 births per woman, 5,624 per million peopleSwitzerland: 1.29 births per woman, 6,108 per million peopleChile: 1.14 births per woman, 707 per million peopleChina: 1.01 births per woman, 2,107 per million peopleCongo, Dem. Rep.: 5.98 births per woman, 10.1 per million peopleCongo, Rep.: 4.11 births per woman, 127 per million peopleColombia: 1.63 births per woman, 90.2 per million peopleCabo Verde: 1.51 births per woman, 125 per million peopleCosta Rica: 1.32 births per woman, 508 per million peopleCyprus: 1.38 births per woman, 1,686 per million peopleCzechia: 1.36 births per woman, 4,505 per million peopleGermany: 1.36 births per woman, 5,926 per million peopleDenmark: 1.47 births per woman, 8,838 per million peopleDominican Republic: 2.22 births per woman, 20 per million peopleAlgeria: 2.72 births per woman, 1,764 per million peopleEcuador: 1.81 births per woman, 400 per million peopleEgypt, Arab Rep.: 2.73 births per woman, 1,120 per million peopleSpain: 1.1 births per woman, 3,654 per million peopleEstonia: 1.18 births per woman, 4,800 per million peopleEthiopia: 3.91 births per woman, 89 per million peopleFinland: 1.25 births per woman, 8,315 per million peopleFrance: 1.61 births per woman, 5,369 per million peopleUnited Kingdom: 1.55 births per woman, 4,473 per million peopleGeorgia: 1.8 births per woman, 1,896 per million peopleGhana: 3.34 births per woman, 87.5 per million peopleGambia, The: 3.91 births per woman, 50.9 per million peopleGreece: 1.24 births per woman, 5,283 per million peopleGuatemala: 2.29 births per woman, 1.45 per million peopleHong Kong SAR, China: 0.841 births per woman, 5,874 per million peopleHonduras: 2.48 births per woman, 188 per million peopleCroatia: 1.46 births per woman, 2,440 per million peopleHungary: 1.41 births per woman, 4,567 per million peopleIndonesia: 2.12 births per woman, 395 per million peopleIndia: 1.96 births per woman, 259 per million peopleIreland: 1.47 births per woman, 5,753 per million peopleIran, Islamic Rep.: 1.68 births per woman, 2,240 per million peopleIraq: 3.22 births per woman, 164 per million peopleIceland: 1.56 births per woman, 5,953 per million peopleItaly: 1.18 births per woman, 2,855 per million peopleJordan: 2.6 births per woman, 576 per million peopleJapan: 1.15 births per woman, 5,609 per million peopleKazakhstan: 2.98 births per woman, 758 per million peopleKenya: 3.17 births per woman, 384 per million peopleCambodia: 2.55 births per woman, 30.4 per million peopleKorea, Rep.: 0.748 births per woman, 9,472 per million peopleKuwait: 1.56 births per woman, 145 per million peopleSri Lanka: 1.95 births per woman, 94.5 per million peopleLesotho: 2.66 births per woman, 32.2 per million peopleLithuania: 1.11 births per woman, 3,704 per million peopleLuxembourg: 1.25 births per woman, 5,359 per million peopleLatvia: 1.24 births per woman, 2,191 per million peopleMacao SAR, China: 0.582 births per woman, 4,634 per million peopleMorocco: 2.21 births per woman, 1,083 per million peopleMoldova: 1.73 births per woman, 650 per million peopleMadagascar: 3.91 births per woman, 32.9 per million peopleMexico: 1.89 births per woman, 438 per million peopleNorth Macedonia: 1.4 births per woman, 888 per million peopleMali: 5.51 births per woman, 26 per million peopleMalta: 1.01 births per woman, 2,355 per million peopleMyanmar: 2.1 births per woman, 28 per million peopleMontenegro: 1.8 births per woman, 764 per million peopleMongolia: 2.5 births per woman, 535 per million peopleMozambique: 4.69 births per woman, 44.4 per million peopleMauritius: 1.44 births per woman, 689 per million peopleMalawi: 3.59 births per woman, 25.2 per million peopleMalaysia: 1.54 births per woman, 1,218 per million peopleNamibia: 3.21 births per woman, 225 per million peopleNiger: 5.93 births per woman, 26.9 per million peopleNigeria: 4.38 births per woman, 22.1 per million peopleNetherlands: 1.43 births per woman, 6,563 per million peopleNorway: 1.45 births per woman, 7,451 per million peopleNew Zealand: 1.57 births per woman, 5,435 per million peopleOman: 2.51 births per woman, 739 per million peoplePakistan: 3.55 births per woman, 474 per million peoplePanama: 2.11 births per woman, 250 per million peoplePhilippines: 1.89 births per woman, 320 per million peoplePapua New Guinea: 3.07 births per woman, 33.2 per million peoplePoland: 1.14 births per woman, 3,675 per million peoplePuerto Rico (US): 0.92 births per woman, 332 per million peoplePortugal: 1.41 births per woman, 5,983 per million peopleParaguay: 2.42 births per woman, 139 per million peopleWest Bank and Gaza: 3.25 births per woman, 584 per million peopleQatar: 1.72 births per woman, 948 per million peopleRomania: 1.39 births per woman, 1,082 per million peopleRussian Federation: 1.42 births per woman, 2,639 per million peopleRwanda: 3.65 births per woman, 133 per million peopleSaudi Arabia: 2.31 births per woman, 1,235 per million peopleSenegal: 3.77 births per woman, 572 per million peopleSingapore: 0.97 births per woman, 8,782 per million peopleEl Salvador: 1.77 births per woman, 55.5 per million peopleSerbia: 1.64 births per woman, 2,688 per million peopleSlovak Republic: 1.46 births per woman, 3,507 per million peopleSlovenia: 1.52 births per woman, 5,616 per million peopleSweden: 1.43 births per woman, 8,911 per million peopleEswatini: 2.72 births per woman, 138 per million peopleSyrian Arab Republic: 2.7 births per woman, 140 per million peopleChad: 6.03 births per woman, 56.7 per million peopleTogo: 4.12 births per woman, 57.8 per million peopleThailand: 1.2 births per woman, 1,592 per million peopleTrinidad and Tobago: 1.54 births per woman, 493 per million peopleTunisia: 1.82 births per woman, 1,668 per million peopleTurkiye: 1.48 births per woman, 2,645 per million peopleTanzania: 4.54 births per woman, 19.3 per million peopleUganda: 4.17 births per woman, 81.3 per million peopleUkraine: 0.988 births per woman, 586 per million peopleUruguay: 1.4 births per woman, 938 per million peopleUnited States: 1.63 births per woman, 4,937 per million peopleUzbekistan: 3.49 births per woman, 640 per million peopleViet Nam: 1.9 births per woman, 836 per million peopleSouth Africa: 2.21 births per woman, 444 per million peopleZimbabwe: 3.67 births per woman, 95.1 per million peopleCHLCODKORNERPRISGPSWETCDUKR

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5317.1e-11131
By rank (Spearman), not adjusted-0.7552.3e-25131
Standard (Pearson), adjusted for national income+0.1080.223131
By rank (Spearman), adjusted for national income-0.2610.003131

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 131 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: +0.423 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.091 (without COD) to +0.136 (without QAT). 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: QAT (+0.029).

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 & Pacific18-0.36
Europe & Central Asia45-0.02
Latin America & Caribbean17-0.41
Middle East, North Africa, Afghanistan & Pakistan17-0.55
Sub-Saharan Africa29-0.29

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 third44-0.15
Middle third43-0.23
Richest third44-0.29

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
Christians80+0.28
Muslims32-0.38

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
France13+0.07
Never a European colony15-0.50
Spain16-0.44
United Kingdom36-0.05

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 = +0.045 after dropping 12 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 29years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Within countries over time, there is no link that is clearly more than chance (r = +0.198, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose fertility rate moved did not tend to see their researchers move with it.

Across countries (adjusted)+0.108for comparison
Within countries-0.124each country vs itself
…and vs the same year elsewhere+0.198q = 0.055 · 103 countries
Year-on-year changes+0.041q = 0.402
Read with care.
  • Almost all of this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.

1,875 country-years (one country in one year), 103 countries, 1996–2024, about 18 years each on average. Share of the variation that happens within countries over time: 5% for Fertility rate, 14% for Researchers. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.

Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.

With one measure years behind the other

0.40−0.4-100+10years Researchers trails Fertility rate →lag -10: r = 0.020 (n = 1,122)lag -9: r = 0.040 (n = 1,208)lag -8: r = 0.059 (n = 1,280)lag -7: r = 0.085 (n = 1,372)lag -6: r = 0.108 (n = 1,443)lag -5: r = 0.129 (n = 1,534)lag -4: r = 0.141 (n = 1,609)lag -3: r = 0.155 (n = 1,692)lag -2: r = 0.168 (n = 1,767)lag -1: r = 0.182 (n = 1,839)lag +0: r = 0.198 (n = 1,875)lag +1: r = 0.212 (n = 1,875)lag +2: r = 0.223 (n = 1,875)lag +3: r = 0.235 (n = 1,875)lag +4: r = 0.253 (n = 1,875)lag +5: r = 0.274 (n = 1,875)lag +6: r = 0.290 (n = 1,875)lag +7: r = 0.302 (n = 1,832)lag +8: r = 0.297 (n = 1,785)lag +9: r = 0.305 (n = 1,740)lag +10: r = 0.317 (n = 1,693)

Same year (lag 0): +0.198. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.

This curve peaks at the edge of the window (lag +10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.

The data


births per woman
per million people		Years
Chad TCD6.0356.72024, 2016
Congo, Dem. Rep. COD5.9810.12024, 2015
Niger NER5.9326.92024, 2013
Mali MLI5.51262024, 2023
Angola AGO5.0518.92024, 2016
Burundi BDI4.7922.52024, 2018
Mozambique MOZ4.6944.42024, 2015
Tanzania TZA4.5419.32024, 2013
Nigeria NGA4.3822.12024, 2019
Uganda UGA4.1781.32024, 2023
Togo TGO4.1257.82024
Burkina Faso BFA4.1146.62024, 2010
Congo, Rep. COG4.111272024, 2022
Madagascar MDG3.9132.92024, 2018
Ethiopia ETH3.91892024, 2017

World Bank, World Development Indicators
World Bank, World Development Indicators

Sources

Fertility rate

Fertility rate, total (births per woman)

World Bank · CC BY 4.0 · updated quarterly

Researchers

Researchers in R&D (per million people)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

Researchers counted as full-time equivalents (two half-time researchers count as one), from national research surveys that many countries run only now and then. So coverage is thin outside the OECD.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2024. 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 Fertility rate and Researchers

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

More links with Fertility rate or Researchers