placestats

Extreme poverty rate vs. Skilled birth attendance

Fades after adjusting for income

Across 152 countries, Extreme poverty rate and Skilled birth attendance rise and fall together at r = -0.65 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.30, which could easily be chance (q = 0.23, 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 extreme poverty rate, 0.4% to 19.8% of population, skilled birth attendance is worse by 9.46% of births at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 5.03% of births.

Link before adjusting (r)-0.654p = 6.8e-20 · 152 countries
After adjusting for national income-0.303p = 0.000 · likely range [-0.44, -0.15]
p, allowing for regions0.076standard error 0.141 · 7 regions
Check for luck (q)0.230across 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.

Extreme poverty rate · % of populationlink with income -0.83 · proxy
Skilled birth attendance · % of birthslink with income +0.69 · 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

Extreme poverty rate · % of population →Skilled birth attendance · % of births →0%85.3%40.3%100%Angola: 39.3% of population, 49.6% of birthsAlbania: 0.3% of population, 99.8% of birthsUnited Arab Emirates: 0% of population, 99.2% of birthsArgentina: 1% of population, 98.8% of birthsArmenia: 0.8% of population, 99.8% of birthsAustralia: 0.9% of population, 96% of birthsAustria: 0.5% of population, 98.1% of birthsBurundi: 74.2% of population, 76.5% of birthsBenin: 27.2% of population, 78.1% of birthsBurkina Faso: 42.1% of population, 79.8% of birthsBangladesh: 5.9% of population, 59% of birthsBulgaria: 1% of population, 94.5% of birthsBosnia and Herzegovina: 0.3% of population, 99.9% of birthsBelarus: 0% of population, 99.9% of birthsBelize: 1% of population, 94.6% of birthsBolivia: 2.1% of population, 81.1% of birthsBrazil: 3% of population, 98.4% of birthsBarbados: 1.7% of population, 98.4% of birthsBhutan: 0% of population, 98.9% of birthsBotswana: 21.4% of population, 99.8% of birthsCentral African Republic: 71.6% of population, 40.3% of birthsCanada: 0.3% of population, 98% of birthsChile: 0.4% of population, 99.8% of birthsChina: 0% of population, 99.9% of birthsCote d'Ivoire: 20.8% of population, 84% of birthsCameroon: 26.7% of population, 69% of birthsCongo, Dem. Rep.: 85.3% of population, 85.2% of birthsCongo, Rep.: 39.2% of population, 91.2% of birthsColombia: 8.5% of population, 98.8% of birthsComoros: 5.1% of population, 82.2% of birthsCabo Verde: 14.6% of population, 97.3% of birthsCosta Rica: 0.8% of population, 99% of birthsCyprus: 0% of population, 99.3% of birthsCzechia: 0% of population, 99.8% of birthsGermany: 0.7% of population, 96.2% of birthsDjibouti: 25.4% of population, 87.4% of birthsDenmark: 0.4% of population, 95.3% of birthsDominican Republic: 0.8% of population, 99.2% of birthsAlgeria: 0% of population, 98.8% of birthsEcuador: 3.4% of population, 99.4% of birthsEgypt, Arab Rep.: 1.4% of population, 97.1% of birthsSpain: 0.8% of population, 99.9% of birthsEstonia: 0.3% of population, 99.6% of birthsEthiopia: 38.6% of population, 49.8% of birthsFinland: 0.2% of population, 99.5% of birthsFiji: 4.7% of population, 99.8% of birthsFrance: 0.1% of population, 98.2% of birthsGabon: 3.8% of population, 89.3% of birthsGeorgia: 4.2% of population, 99.8% of birthsGhana: 39% of population, 78.9% of birthsGuinea: 11.7% of population, 55.3% of birthsGambia, The: 22% of population, 83.8% of birthsGuinea-Bissau: 39.9% of population, 53.8% of birthsEquatorial Guinea: 8.8% of population, 68.3% of birthsGreece: 0.6% of population, 99.9% of birthsGrenada: 0.8% of population, 100% of birthsGuatemala: 9.7% of population, 69.6% of birthsHonduras: 15.7% of population, 94.1% of birthsCroatia: 0.3% of population, 99.9% of birthsHaiti: 40.4% of population, 41.6% of birthsHungary: 0.9% of population, 99.7% of birthsIndonesia: 4% of population, 94.7% of birthsIndia: 5.3% of population, 89.4% of birthsIreland: 0.1% of population, 99.7% of birthsIran, Islamic Rep.: 2.5% of population, 99% of birthsIraq: 0.5% of population, 95.6% of birthsIceland: 0.1% of population, 97.4% of birthsItaly: 0.9% of population, 99.8% of birthsJamaica: 1.4% of population, 99.7% of birthsJordan: 0.1% of population, 99.7% of birthsJapan: 1.2% of population, 99.8% of birthsKazakhstan: 0% of population, 99.9% of birthsKenya: 45.5% of population, 70.2% of birthsKyrgyz Republic: 2.2% of population, 100% of birthsKiribati: 0.9% of population, 91.9% of birthsKorea, Rep.: 0.1% of population, 100% of birthsLao PDR: 7.1% of population, 64.4% of birthsLiberia: 33.6% of population, 84.4% of birthsSt. Lucia: 0.1% of population, 100% of birthsSri Lanka: 2.7% of population, 99.5% of birthsLesotho: 41.9% of population, 86.6% of birthsLithuania: 1.1% of population, 100% of birthsLatvia: 0.4% of population, 99.9% of birthsMorocco: 3.7% of population, 86.6% of birthsMoldova: 0.1% of population, 99.6% of birthsMadagascar: 69.2% of population, 45.8% of birthsMaldives: 0% of population, 99.5% of birthsMexico: 1.6% of population, 96.6% of birthsMarshall Islands: 2.1% of population, 92.4% of birthsNorth Macedonia: 3.9% of population, 100% of birthsMali: 36.1% of population, 67.3% of birthsMalta: 0.4% of population, 99.8% of birthsMyanmar: 10.3% of population, 60.2% of birthsMontenegro: 2.5% of population, 98.8% of birthsMongolia: 0.4% of population, 99.3% of birthsMozambique: 81.4% of population, 73% of birthsMauritania: 10.2% of population, 70.4% of birthsMauritius: 0.5% of population, 99.7% of birthsMalawi: 75.4% of population, 96.4% of birthsMalaysia: 0% of population, 99.6% of birthsNamibia: 22.9% of population, 88.2% of birthsNiger: 60.5% of population, 43.7% of birthsNigeria: 41.8% of population, 50.7% of birthsNicaragua: 6.6% of population, 94.1% of birthsNorway: 0.2% of population, 99.2% of birthsNepal: 2.4% of population, 77.2% of birthsPakistan: 23% of population, 68% of birthsPanama: 3.1% of population, 95% of birthsPeru: 5.1% of population, 94.9% of birthsPhilippines: 5.3% of population, 84.4% of birthsPoland: 0.2% of population, 99.6% of birthsPortugal: 0.4% of population, 98.6% of birthsParaguay: 2.1% of population, 92.2% of birthsWest Bank and Gaza: 2.1% of population, 99.7% of birthsQatar: 0% of population, 100% of birthsRomania: 0.7% of population, 93% of birthsRussian Federation: 0.1% of population, 99.6% of birthsRwanda: 38.6% of population, 94.2% of birthsSudan: 10.1% of population, 77.7% of birthsSenegal: 17.9% of population, 74.5% of birthsSolomon Islands: 40.3% of population, 86.2% of birthsSierra Leone: 41.5% of population, 86.9% of birthsEl Salvador: 4.6% of population, 99.9% of birthsSerbia: 1.1% of population, 99.9% of birthsSao Tome and Principe: 13% of population, 96.8% of birthsSuriname: 2.2% of population, 98.4% of birthsSlovak Republic: 0.5% of population, 98% of birthsSlovenia: 0% of population, 99.8% of birthsEswatini: 44.5% of population, 88.3% of birthsSeychelles: 0.7% of population, 99.5% of birthsChad: 39.5% of population, 47.2% of birthsTogo: 34.7% of population, 69.4% of birthsThailand: 0% of population, 99.1% of birthsTajikistan: 6.1% of population, 94.8% of birthsTimor-Leste: 44% of population, 56.7% of birthsTonga: 0.4% of population, 98.3% of birthsTunisia: 0.7% of population, 99.5% of birthsTurkiye: 0.2% of population, 97% of birthsTuvalu: 9% of population, 99.5% of birthsTanzania: 51.3% of population, 63.5% of birthsUganda: 59.8% of population, 74.2% of birthsUkraine: 0% of population, 99.9% of birthsUruguay: 0.2% of population, 100% of birthsUnited States: 1.1% of population, 99.1% of birthsUzbekistan: 2.3% of population, 99% of birthsViet Nam: 1.6% of population, 96.1% of birthsVanuatu: 19.5% of population, 89.4% of birthsSamoa: 4.5% of population, 88.9% of birthsKosovo: 10% of population, 99% of birthsSouth Africa: 17.4% of population, 96.7% of birthsZambia: 71.7% of population, 80.4% of birthsZimbabwe: 49.2% of population, 86% of birthsBGDCAFCODGINKORLCAMDGMMRMWINERQATZMB

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6546.8e-20152
By rank (Spearman), not adjusted-0.7491.3e-28152
Standard (Pearson), adjusted for national income-0.3030.000152
By rank (Spearman), adjusted for national income-0.4072.2e-7152

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 152 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: +0.350 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.356 (without MWI) to -0.282 (without NGA). 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: MWI (-0.053).

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 & Pacific21-0.41
Europe & Central Asia41-0.14
Latin America & Caribbean24-0.69
Middle East, North Africa, Afghanistan & Pakistan13-0.76
Sub-Saharan Africa45-0.04

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 third51-0.16
Middle third50-0.42
Richest third51-0.18

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
Christians97-0.31
Muslims38-0.58

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
France22-0.01
Never a European colony16-0.64
Spain19-0.20
United Kingdom46-0.26

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 = -0.335 after dropping 21 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 33years. 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 = -0.403) but mostly disappears in year-to-year changes (r = +0.012). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.303for comparison
Within countries-0.464each country vs itself
…and vs the same year elsewhere-0.403q = 0.025 · 66 countries
Year-on-year changes+0.012q = 0.951
Read with care.
  • The association is much weaker in year-on-year changes than in levels, which points to shared country-specific trends.

1,131 country-years (one country in one year), 66 countries, 1990–2022, about 17 years each on average. Share of the variation that happens within countries over time: 37% for Extreme poverty rate, 25% for Skilled birth attendance. 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

0.50−0.5-100+10years Skilled birth attendance trails Extreme poverty rate →lag -10: r = -0.320 (n = 1,052)lag -9: r = -0.313 (n = 1,083)lag -8: r = -0.339 (n = 1,125)lag -7: r = -0.325 (n = 1,155)lag -6: r = -0.336 (n = 1,187)lag -5: r = -0.267 (n = 1,209)lag -4: r = -0.342 (n = 1,208)lag -3: r = -0.330 (n = 1,210)lag -2: r = -0.384 (n = 1,199)lag -1: r = -0.377 (n = 1,164)lag +0: r = -0.403 (n = 1,131)lag +1: r = -0.422 (n = 1,097)lag +2: r = -0.429 (n = 1,053)lag +3: r = -0.364 (n = 1,007)lag +4: r = -0.370 (n = 959)lag +5: r = -0.400 (n = 911)lag +6: r = -0.305 (n = 877)lag +7: r = -0.376 (n = 825)lag +8: r = -0.227 (n = 780)lag +9: r = -0.350 (n = 733)lag +10: r = -0.254 (n = 684)

Same year (lag 0): -0.403. 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.

The data


% of population
% of births		Years
Congo, Dem. Rep. COD85.3%85.2%2020, 2018
Mozambique MOZ81.4%73%2022, 2015
Malawi MWI75.4%96.4%2019, 2020
Burundi BDI74.2%76.5%2020, 2021
Zambia ZMB71.7%80.4%2022, 2019
Central African Republic CAF71.6%40.3%2021, 2019
Madagascar MDG69.2%45.8%2021
Niger NER60.5%43.7%2021
Uganda UGA59.8%74.2%2019, 2016
Tanzania TZA51.3%63.5%2018, 2016
Zimbabwe ZWE49.2%86%2019
Kenya KEN45.5%70.2%2022, 2016
Eswatini SWZ44.5%88.3%2016, 2014
Timor-Leste TLS44%56.7%2014, 2016
Burkina Faso BFA42.1%79.8%2021, 2015

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

Sources

Extreme poverty rate

Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population)

World Bank · CC BY 4.0 · updated quarterly

Skilled birth attendance

Births attended by skilled health staff (% of total)

UNICEF and WHO · CC BY 4.0 · updated quarterly

Based on surveys, so a country reports it every few years rather than yearly.

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 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 Extreme poverty rate and Skilled birth attendance

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

More links with Extreme poverty rate or Skilled birth attendance