placestats

Contraceptive use (any method) vs. Maternal mortality

Fades after adjusting for income

Across 145 countries, Contraceptive use (any method) and Maternal mortality rise and fall together at r = -0.59 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.33, which could easily be chance (q = 0.17, 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 contraceptive use (any method), 29.9% to 65.7%, maternal mortality is better by 177 per 100,000 live births at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 85.7 per 100,000 live births.

Link before adjusting (r)-0.591p = 4.8e-15 · 145 countries
After adjusting for national income-0.327p = 6.2e-5 · likely range [-0.47, -0.17]
p, allowing for regions0.045standard error 0.130 · 7 regions
Check for luck (q)0.168across 14,952 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.

Contraceptive use (any method) · %link with income +0.56 · moderate
Maternal mortality · per 100,000 live birthslink with income -0.85 · proxy

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

Every country as a dot

Contraceptive use (any method) · % →Maternal mortality · per 100,000 live births →6.9%85.5%1993Afghanistan: 22.5%, 521 per 100,000 live birthsAngola: 13.7%, 183 per 100,000 live birthsAlbania: 46%, 7 per 100,000 live birthsArgentina: 70.1%, 33 per 100,000 live birthsArmenia: 57.1%, 19 per 100,000 live birthsAustralia: 66.9%, 2 per 100,000 live birthsAustria: 65.7%, 6 per 100,000 live birthsAzerbaijan: 54.9%, 18 per 100,000 live birthsBurundi: 28.5%, 392 per 100,000 live birthsBelgium: 66.7%, 4 per 100,000 live birthsBenin: 22.4%, 518 per 100,000 live birthsBurkina Faso: 34%, 242 per 100,000 live birthsBangladesh: 64%, 115 per 100,000 live birthsBosnia and Herzegovina: 46%, 6 per 100,000 live birthsBelarus: 52.6%, 1 per 100,000 live birthsBelize: 51.4%, 67 per 100,000 live birthsBolivia: 66.5%, 146 per 100,000 live birthsBrazil: 80.2%, 67 per 100,000 live birthsBarbados: 59.2%, 35 per 100,000 live birthsBhutan: 65.6%, 47 per 100,000 live birthsBotswana: 67.4%, 155 per 100,000 live birthsCentral African Republic: 17.8%, 692 per 100,000 live birthsSwitzerland: 71.6%, 5 per 100,000 live birthsChile: 76.3%, 10 per 100,000 live birthsChina: 84.5%, 16 per 100,000 live birthsCote d'Ivoire: 32%, 359 per 100,000 live birthsCameroon: 19.3%, 258 per 100,000 live birthsCongo, Dem. Rep.: 28.1%, 427 per 100,000 live birthsCongo, Rep.: 30.1%, 241 per 100,000 live birthsColombia: 81%, 59 per 100,000 live birthsComoros: 11.6%, 179 per 100,000 live birthsCabo Verde: 55.8%, 40 per 100,000 live birthsCosta Rica: 70.9%, 24 per 100,000 live birthsGermany: 80.3%, 4 per 100,000 live birthsDjibouti: 19%, 162 per 100,000 live birthsDominican Republic: 62.8%, 124 per 100,000 live birthsAlgeria: 53.6%, 62 per 100,000 live birthsEcuador: 77.9%, 55 per 100,000 live birthsEgypt, Arab Rep.: 58.5%, 17 per 100,000 live birthsSpain: 62.1%, 3 per 100,000 live birthsEstonia: 75.1%, 5 per 100,000 live birthsEthiopia: 36.1%, 195 per 100,000 live birthsFinland: 85.5%, 8 per 100,000 live birthsFiji: 35.4%, 30 per 100,000 live birthsFrance: 78.4%, 7 per 100,000 live birthsGabon: 24.8%, 233 per 100,000 live birthsUnited Kingdom: 76.1%, 8 per 100,000 live birthsGeorgia: 39.3%, 20 per 100,000 live birthsGhana: 36.3%, 234 per 100,000 live birthsGuinea: 10.9%, 494 per 100,000 live birthsGambia, The: 18.9%, 354 per 100,000 live birthsGuinea-Bissau: 21.2%, 505 per 100,000 live birthsEquatorial Guinea: 12.6%, 174 per 100,000 live birthsGuatemala: 60.6%, 94 per 100,000 live birthsGuyana: 29.9%, 75 per 100,000 live birthsHonduras: 69.4%, 47 per 100,000 live birthsHaiti: 34.3%, 328 per 100,000 live birthsIndonesia: 52.6%, 140 per 100,000 live birthsIndia: 66.7%, 80 per 100,000 live birthsIreland: 73.3%, 4 per 100,000 live birthsIran, Islamic Rep.: 77.4%, 16 per 100,000 live birthsIraq: 52.8%, 66 per 100,000 live birthsItaly: 65.1%, 6 per 100,000 live birthsJamaica: 60.6%, 130 per 100,000 live birthsJordan: 60.1%, 31 per 100,000 live birthsJapan: 58.3%, 3 per 100,000 live birthsKazakhstan: 53%, 10 per 100,000 live birthsKenya: 62.5%, 149 per 100,000 live birthsKyrgyz Republic: 39.4%, 42 per 100,000 live birthsCambodia: 61.9%, 137 per 100,000 live birthsKiribati: 33.6%, 80 per 100,000 live birthsKorea, Rep.: 84.2%, 4 per 100,000 live birthsLao PDR: 56.3%, 112 per 100,000 live birthsLiberia: 24.9%, 628 per 100,000 live birthsLibya: 27.7%, 59 per 100,000 live birthsSt. Lucia: 55.5%, 44 per 100,000 live birthsSri Lanka: 64.6%, 18 per 100,000 live birthsLesotho: 67.4%, 478 per 100,000 live birthsMorocco: 70.8%, 70 per 100,000 live birthsMoldova: 56%, 19 per 100,000 live birthsMadagascar: 49.7%, 445 per 100,000 live birthsMaldives: 18.8%, 32 per 100,000 live birthsMexico: 71.8%, 42 per 100,000 live birthsNorth Macedonia: 59.9%, 3 per 100,000 live birthsMali: 17.2%, 367 per 100,000 live birthsMyanmar: 52.2%, 185 per 100,000 live birthsMontenegro: 20.7%, 6 per 100,000 live birthsMongolia: 48.1%, 41 per 100,000 live birthsMozambique: 26.4%, 99 per 100,000 live birthsMauritania: 14.3%, 381 per 100,000 live birthsMauritius: 63.8%, 66 per 100,000 live birthsMalawi: 65.6%, 225 per 100,000 live birthsMalaysia: 42.8%, 26 per 100,000 live birthsNamibia: 56.1%, 139 per 100,000 live birthsNiger: 15.3%, 350 per 100,000 live birthsNigeria: 21.6%, 993 per 100,000 live birthsNicaragua: 80.4%, 60 per 100,000 live birthsNetherlands: 73%, 4 per 100,000 live birthsNepal: 57.2%, 142 per 100,000 live birthsOman: 29.7%, 13 per 100,000 live birthsPakistan: 34%, 155 per 100,000 live birthsPanama: 50.8%, 37 per 100,000 live birthsPeru: 77.3%, 51 per 100,000 live birthsPhilippines: 58.3%, 84 per 100,000 live birthsPapua New Guinea: 36.7%, 189 per 100,000 live birthsPoland: 63.2%, 2 per 100,000 live birthsPortugal: 73.9%, 15 per 100,000 live birthsParaguay: 68.4%, 58 per 100,000 live birthsWest Bank and Gaza: 57.3%, 16 per 100,000 live birthsQatar: 37.5%, 4 per 100,000 live birthsRussian Federation: 68%, 9 per 100,000 live birthsRwanda: 64.1%, 229 per 100,000 live birthsSaudi Arabia: 40%, 7 per 100,000 live birthsSudan: 12.8%, 256 per 100,000 live birthsSenegal: 26.5%, 237 per 100,000 live birthsSolomon Islands: 29.3%, 123 per 100,000 live birthsSierra Leone: 21.2%, 354 per 100,000 live birthsEl Salvador: 53.6%, 39 per 100,000 live birthsSomalia, Fed. Rep.: 6.9%, 563 per 100,000 live birthsSerbia: 62.3%, 11 per 100,000 live birthsSao Tome and Principe: 49.7%, 75 per 100,000 live birthsSuriname: 39.1%, 84 per 100,000 live birthsEswatini: 57.7%, 118 per 100,000 live birthsChad: 8.1%, 748 per 100,000 live birthsTogo: 23.8%, 349 per 100,000 live birthsThailand: 72.8%, 34 per 100,000 live birthsTajikistan: 29.3%, 14 per 100,000 live birthsTurkmenistan: 49.7%, 5 per 100,000 live birthsTimor-Leste: 26%, 192 per 100,000 live birthsTonga: 29.3%, 67 per 100,000 live birthsTrinidad and Tobago: 36.5%, 54 per 100,000 live birthsTunisia: 54%, 36 per 100,000 live birthsTurkiye: 69.8%, 15 per 100,000 live birthsTuvalu: 23.7%, 170 per 100,000 live birthsTanzania: 37.6%, 276 per 100,000 live birthsUganda: 43%, 170 per 100,000 live birthsUkraine: 65.5%, 15 per 100,000 live birthsUnited States: 73.9%, 17 per 100,000 live birthsUzbekistan: 58.8%, 26 per 100,000 live birthsViet Nam: 72.8%, 48 per 100,000 live birthsVanuatu: 49%, 100 per 100,000 live birthsSamoa: 16.6%, 101 per 100,000 live birthsSouth Africa: 54.6%, 118 per 100,000 live birthsZambia: 49.6%, 85 per 100,000 live birthsZimbabwe: 66.8%, 358 per 100,000 live birthsCAFCHNFINKORLSOMDVMNENGATCDWSMZWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5914.8e-15145
By rank (Spearman), not adjusted-0.5782.6e-14145
Standard (Pearson), adjusted for national income-0.3276.2e-5145
By rank (Spearman), adjusted for national income-0.2210.008145

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 145 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: +0.264 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.355 (without LSO) to -0.307 (without TCD). 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: LSO (-0.028).

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 & Pacific22-0.03
Europe & Central Asia32+0.24
Latin America & Caribbean24-0.61
Middle East, North Africa, Afghanistan & Pakistan15-0.62
Sub-Saharan Africa45-0.38

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 third49-0.38
Middle third48-0.34
Richest third48-0.14

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
Christians86-0.19
Muslims43-0.46

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
France23-0.46
Never a European colony21-0.17
Spain18-0.64
United Kingdom46-0.22

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 = -0.317 after dropping 11 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 34years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

The link shows up within countries over time (r = -0.253) but mostly disappears in year-to-year changes (r = —). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.327for comparison
Within countries-0.492each country vs itself
…and vs the same year elsewhere-0.253q = 0.024 · 95 countries
Year-on-year changes—q = —
Read with care.
  • Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.

777 country-years (one country in one year), 95 countries, 1990–2023, about 8 years each on average. Share of the variation that happens within countries over time: 11% for Contraceptive use (any method), 18% for Maternal mortality. 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 Maternal mortality trails Contraceptive use (any method) →lag -10: r = -0.352 (n = 466)lag -9: r = -0.309 (n = 515)lag -8: r = -0.307 (n = 543)lag -7: r = -0.299 (n = 572)lag -6: r = -0.271 (n = 612)lag -5: r = -0.302 (n = 666)lag -4: r = -0.287 (n = 684)lag -3: r = -0.264 (n = 720)lag -2: r = -0.281 (n = 743)lag -1: r = -0.246 (n = 764)lag +0: r = -0.253 (n = 777)lag +1: r = -0.252 (n = 768)lag +2: r = -0.146 (n = 733)lag +3: r = -0.262 (n = 717)lag +4: r = -0.251 (n = 700)lag +5: r = -0.248 (n = 654)lag +6: r = -0.257 (n = 608)lag +7: r = -0.226 (n = 573)lag +8: r = -0.196 (n = 536)lag +9: r = -0.202 (n = 481)lag +10: r = -0.150 (n = 421)

Same year (lag 0): -0.253. 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


%

per 100,000 live births
Years
Finland FIN85.5%82015, 2023
China CHN84.5%162017, 2023
Korea, Rep. KOR84.2%42021, 2023
Colombia COL81%592015, 2023
Nicaragua NIC80.4%602012, 2023
Germany DEU80.3%42011, 2023
Brazil BRA80.2%672013, 2023
France FRA78.4%72010, 2023
Ecuador ECU77.9%552018, 2023
Iran, Islamic Rep. IRN77.4%162010, 2023
Peru PER77.3%512023
Chile CHL76.3%102016, 2023
United Kingdom GBR76.1%82012, 2023
Estonia EST75.1%52014, 2023
United States USA73.9%172019, 2023

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

Sources

Contraceptive use (any method)

Contraceptive prevalence, any method (% of married women ages 15-49)

World Bank · CC BY 4.0 · updated quarterly

The share of married women aged 15 to 49 who use any form of birth control, including traditional ones such as withdrawal, from national surveys. Only married women (and women living with a partner) are counted. Most countries have a survey only every few years.

Maternal mortality

Maternal mortality ratio, modelled estimate (per 100,000 live births)

WHO and the UN Maternal Mortality Estimation Inter-Agency Group · CC BY 4.0 · updated quarterly

Estimated with a model, like stunting, and for the same reason. Deaths in childbirth are rare in small countries with weak death records, so the agencies estimate rather than count.

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 14,952 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Contraceptive use (any method) and Maternal mortality

This page shows that two measures go together. It does not show that Contraceptive use (any method) changes Maternal mortality, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Contraceptive use (any method) or Maternal mortality