placestats

Extreme poverty rate vs. Physicians

Fades after adjusting for income

Across 162 countries, Extreme poverty rate and Physicians rise and fall together at r = -0.60 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.03, which could easily be chance (q = 0.81, 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: sign flips once outliers are trimmed.

Comparing countries across the middle half of extreme poverty rate, 0.4% to 18% of population, physicians is worse by 0.9 per 1,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 0.0372 per 1,000 people the other way.

Link before adjusting (r)-0.600p = 3.2e-17 · 162 countries
After adjusting for national income+0.028p = 0.728 · likely range [-0.13, +0.18]
p, allowing for regions0.677standard error 0.063 · 7 regions
Check for luck (q)0.808across 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
Physicians · per 1,000 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

Extreme poverty rate · % of population →Physicians · per 1,000 people →0%85.3%0.0386.6Angola: 39.3% of population, 0.244 per 1,000 peopleAlbania: 0.3% of population, 1.88 per 1,000 peopleUnited Arab Emirates: 0% of population, 2.99 per 1,000 peopleArgentina: 1% of population, 5.11 per 1,000 peopleArmenia: 0.8% of population, 3.36 per 1,000 peopleAustralia: 0.9% of population, 4.09 per 1,000 peopleAustria: 0.5% of population, 5.51 per 1,000 peopleBurundi: 74.2% of population, 0.078 per 1,000 peopleBelgium: 0.1% of population, 3.57 per 1,000 peopleBenin: 27.2% of population, 0.216 per 1,000 peopleBurkina Faso: 42.1% of population, 0.148 per 1,000 peopleBangladesh: 5.9% of population, 0.722 per 1,000 peopleBulgaria: 1% of population, 4.33 per 1,000 peopleBosnia and Herzegovina: 0.3% of population, 2.58 per 1,000 peopleBelarus: 0% of population, 4.72 per 1,000 peopleBelize: 1% of population, 1.09 per 1,000 peopleBolivia: 2.1% of population, 1.28 per 1,000 peopleBrazil: 3% of population, 2.36 per 1,000 peopleBarbados: 1.7% of population, 2.96 per 1,000 peopleBhutan: 0% of population, 0.553 per 1,000 peopleBotswana: 21.4% of population, 0.379 per 1,000 peopleCentral African Republic: 71.6% of population, 0.074 per 1,000 peopleCanada: 0.3% of population, 2.82 per 1,000 peopleSwitzerland: 0.2% of population, 4.48 per 1,000 peopleChile: 0.4% of population, 3.33 per 1,000 peopleChina: 0% of population, 3.11 per 1,000 peopleCote d'Ivoire: 20.8% of population, 0.166 per 1,000 peopleCameroon: 26.7% of population, 0.136 per 1,000 peopleCongo, Dem. Rep.: 85.3% of population, 0.208 per 1,000 peopleCongo, Rep.: 39.2% of population, 0.173 per 1,000 peopleColombia: 8.5% of population, 2.54 per 1,000 peopleComoros: 5.1% of population, 0.424 per 1,000 peopleCabo Verde: 14.6% of population, 0.73 per 1,000 peopleCosta Rica: 0.8% of population, 2.69 per 1,000 peopleCyprus: 0% of population, 3.56 per 1,000 peopleCzechia: 0% of population, 4.35 per 1,000 peopleGermany: 0.7% of population, 4.53 per 1,000 peopleDjibouti: 25.4% of population, 0.211 per 1,000 peopleDenmark: 0.4% of population, 4.5 per 1,000 peopleDominican Republic: 0.8% of population, 2.43 per 1,000 peopleAlgeria: 0% of population, 1.66 per 1,000 peopleEcuador: 3.4% of population, 2.31 per 1,000 peopleEgypt, Arab Rep.: 1.4% of population, 0.671 per 1,000 peopleSpain: 0.8% of population, 4.29 per 1,000 peopleEstonia: 0.3% of population, 3.47 per 1,000 peopleEthiopia: 38.6% of population, 0.143 per 1,000 peopleFinland: 0.2% of population, 3.61 per 1,000 peopleFiji: 4.7% of population, 0.813 per 1,000 peopleFrance: 0.1% of population, 3.28 per 1,000 peopleMicronesia, Fed. Sts.: 26.1% of population, 0.974 per 1,000 peopleGabon: 3.8% of population, 0.521 per 1,000 peopleUnited Kingdom: 0.4% of population, 3.3 per 1,000 peopleGeorgia: 4.2% of population, 5.64 per 1,000 peopleGhana: 39% of population, 0.266 per 1,000 peopleGuinea: 11.7% of population, 0.21 per 1,000 peopleGambia, The: 22% of population, 0.09 per 1,000 peopleGuinea-Bissau: 39.9% of population, 0.252 per 1,000 peopleEquatorial Guinea: 8.8% of population, 0.153 per 1,000 peopleGreece: 0.6% of population, 6.58 per 1,000 peopleGrenada: 0.8% of population, 1.38 per 1,000 peopleGuatemala: 9.7% of population, 1.28 per 1,000 peopleHonduras: 15.7% of population, 0.489 per 1,000 peopleCroatia: 0.3% of population, 3.91 per 1,000 peopleHaiti: 40.4% of population, 0.292 per 1,000 peopleHungary: 0.9% of population, 3.46 per 1,000 peopleIndonesia: 4% of population, 0.524 per 1,000 peopleIndia: 5.3% of population, 0.723 per 1,000 peopleIreland: 0.1% of population, 3.88 per 1,000 peopleIran, Islamic Rep.: 2.5% of population, 1.81 per 1,000 peopleIraq: 0.5% of population, 1.02 per 1,000 peopleIceland: 0.1% of population, 4.37 per 1,000 peopleIsrael: 0.4% of population, 3.8 per 1,000 peopleItaly: 0.9% of population, 4.19 per 1,000 peopleJamaica: 1.4% of population, 0.46 per 1,000 peopleJordan: 0.1% of population, 2.85 per 1,000 peopleJapan: 1.2% of population, 2.65 per 1,000 peopleKazakhstan: 0% of population, 3.75 per 1,000 peopleKenya: 45.5% of population, 0.289 per 1,000 peopleKyrgyz Republic: 2.2% of population, 1.57 per 1,000 peopleKiribati: 0.9% of population, 0.193 per 1,000 peopleKorea, Rep.: 0.1% of population, 2.61 per 1,000 peopleLao PDR: 7.1% of population, 0.331 per 1,000 peopleLebanon: 5.8% of population, 2.68 per 1,000 peopleLiberia: 33.6% of population, 0.178 per 1,000 peopleSt. Lucia: 0.1% of population, 4.23 per 1,000 peopleSri Lanka: 2.7% of population, 1.14 per 1,000 peopleLesotho: 41.9% of population, 0.235 per 1,000 peopleLithuania: 1.1% of population, 4.47 per 1,000 peopleLuxembourg: 0.1% of population, 2.98 per 1,000 peopleLatvia: 0.4% of population, 3.4 per 1,000 peopleMorocco: 3.7% of population, 0.74 per 1,000 peopleMoldova: 0.1% of population, 4.02 per 1,000 peopleMadagascar: 69.2% of population, 0.172 per 1,000 peopleMaldives: 0% of population, 2.23 per 1,000 peopleMexico: 1.6% of population, 2.59 per 1,000 peopleMarshall Islands: 2.1% of population, 0.467 per 1,000 peopleNorth Macedonia: 3.9% of population, 2.94 per 1,000 peopleMali: 36.1% of population, 0.188 per 1,000 peopleMalta: 0.4% of population, 4.51 per 1,000 peopleMyanmar: 10.3% of population, 0.757 per 1,000 peopleMontenegro: 2.5% of population, 2.78 per 1,000 peopleMongolia: 0.4% of population, 4.13 per 1,000 peopleMozambique: 81.4% of population, 0.175 per 1,000 peopleMauritania: 10.2% of population, 0.261 per 1,000 peopleMauritius: 0.5% of population, 1.44 per 1,000 peopleMalawi: 75.4% of population, 0.054 per 1,000 peopleMalaysia: 0% of population, 2.34 per 1,000 peopleNamibia: 22.9% of population, 0.553 per 1,000 peopleNiger: 60.5% of population, 0.038 per 1,000 peopleNigeria: 41.8% of population, 0.38 per 1,000 peopleNicaragua: 6.6% of population, 0.682 per 1,000 peopleNetherlands: 0.1% of population, 3.88 per 1,000 peopleNorway: 0.2% of population, 4.97 per 1,000 peopleNepal: 2.4% of population, 1.01 per 1,000 peopleNaoero: 9.7% of population, 1.27 per 1,000 peoplePakistan: 23% of population, 1.16 per 1,000 peoplePanama: 3.1% of population, 1.63 per 1,000 peoplePeru: 5.1% of population, 1.69 per 1,000 peoplePhilippines: 5.3% of population, 0.792 per 1,000 peoplePoland: 0.2% of population, 4.03 per 1,000 peoplePortugal: 0.4% of population, 5.85 per 1,000 peopleParaguay: 2.1% of population, 3.89 per 1,000 peopleWest Bank and Gaza: 2.1% of population, 2.17 per 1,000 peopleQatar: 0% of population, 3.02 per 1,000 peopleRomania: 0.7% of population, 3.63 per 1,000 peopleRussian Federation: 0.1% of population, 5.11 per 1,000 peopleRwanda: 38.6% of population, 0.09 per 1,000 peopleSudan: 10.1% of population, 0.25 per 1,000 peopleSenegal: 17.9% of population, 0.109 per 1,000 peopleSolomon Islands: 40.3% of population, 0.238 per 1,000 peopleSierra Leone: 41.5% of population, 0.134 per 1,000 peopleEl Salvador: 4.6% of population, 1.62 per 1,000 peopleSerbia: 1.1% of population, 3.1 per 1,000 peopleSao Tome and Principe: 13% of population, 0.464 per 1,000 peopleSuriname: 2.2% of population, 1.35 per 1,000 peopleSlovak Republic: 0.5% of population, 3.7 per 1,000 peopleSlovenia: 0% of population, 3.37 per 1,000 peopleSweden: 0.8% of population, 4.41 per 1,000 peopleEswatini: 44.5% of population, 0.557 per 1,000 peopleSeychelles: 0.7% of population, 6.6 per 1,000 peopleSyrian Arab Republic: 18% of population, 1.52 per 1,000 peopleChad: 39.5% of population, 0.085 per 1,000 peopleTogo: 34.7% of population, 0.08 per 1,000 peopleThailand: 0% of population, 0.541 per 1,000 peopleTajikistan: 6.1% of population, 1.87 per 1,000 peopleTimor-Leste: 44% of population, 0.752 per 1,000 peopleTonga: 0.4% of population, 1.01 per 1,000 peopleTunisia: 0.7% of population, 1.31 per 1,000 peopleTurkiye: 0.2% of population, 2.24 per 1,000 peopleTuvalu: 9% of population, 1.35 per 1,000 peopleTanzania: 51.3% of population, 0.134 per 1,000 peopleUganda: 59.8% of population, 0.191 per 1,000 peopleUkraine: 0% of population, 3.53 per 1,000 peopleUruguay: 0.2% of population, 4.67 per 1,000 peopleUnited States: 1.1% of population, 3.68 per 1,000 peopleUzbekistan: 2.3% of population, 2.81 per 1,000 peopleViet Nam: 1.6% of population, 1.11 per 1,000 peopleVanuatu: 19.5% of population, 0.164 per 1,000 peopleSamoa: 4.5% of population, 0.561 per 1,000 peopleSouth Africa: 17.4% of population, 0.794 per 1,000 peopleZambia: 71.7% of population, 0.324 per 1,000 peopleZimbabwe: 49.2% of population, 0.136 per 1,000 peopleBTNCODGRCKIRMOZMWIPRTSYCTHA

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6003.2e-17162
By rank (Spearman), not adjusted-0.7872.0e-35162
Standard (Pearson), adjusted for national income+0.0280.728162
By rank (Spearman), adjusted for national income-0.2410.002162

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 162 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: +0.572 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.010 (without MOZ) to +0.049 (without PSE). 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: PSE (+0.022).

Fragile: this link may not hold up.
  • sign flips once outliers are trimmed
  • 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 & Pacific23+0.20
Europe & Central Asia46+0.06
Latin America & Caribbean24+0.23
Middle East, North Africa, Afghanistan & Pakistan16-0.19
Sub-Saharan Africa45+0.00

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 third54-0.36
Middle third54-0.23
Richest third54+0.02

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
Christians103+0.05
Muslims39-0.30

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
France24-0.12
Never a European colony16-0.16
Spain20+0.02
United Kingdom48-0.08

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 = -0.097 after dropping 13 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.

Within countries over time, there is no link that is clearly more than chance (r = +0.122, 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 extreme poverty rate moved did not tend to see their physicians move with it.

Across countries (adjusted)+0.028for comparison
Within countries-0.207each country vs itself
…and vs the same year elsewhere+0.122q = 0.142 · 83 countries
Year-on-year changes+0.030q = 0.149
The direction flips. Comparing each country only with itself gives -0.21. Also taking out what happened to the whole world each year gives +0.12. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.

1,509 country-years (one country in one year), 83 countries, 1990–2023, about 18 years each on average. Share of the variation that happens within countries over time: 42% for Extreme poverty rate, 16% for Physicians. 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 Physicians trails Extreme poverty rate →lag -10: r = 0.299 (n = 1,251)lag -9: r = 0.325 (n = 1,300)lag -8: r = 0.314 (n = 1,333)lag -7: r = 0.310 (n = 1,375)lag -6: r = 0.278 (n = 1,403)lag -5: r = 0.260 (n = 1,442)lag -4: r = 0.243 (n = 1,456)lag -3: r = 0.212 (n = 1,496)lag -2: r = 0.213 (n = 1,530)lag -1: r = 0.146 (n = 1,542)lag +0: r = 0.122 (n = 1,509)lag +1: r = 0.084 (n = 1,456)lag +2: r = 0.037 (n = 1,399)lag +3: r = 0.069 (n = 1,350)lag +4: r = 0.030 (n = 1,317)lag +5: r = 0.041 (n = 1,221)lag +6: r = 0.032 (n = 1,162)lag +7: r = 0.034 (n = 1,085)lag +8: r = 0.051 (n = 1,054)lag +9: r = 0.023 (n = 961)lag +10: r = 0.067 (n = 924)

Same year (lag 0): +0.122. 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 largest value on this curve (+0.32 at lag -9) is higher than the same-year one by 0.20. That gap is not a finding. It is the largest of 21 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.

The data


% of population
per 1,000 people		Years
Congo, Dem. Rep. COD85.3%0.2082020, 2022
Mozambique MOZ81.4%0.1752022
Malawi MWI75.4%0.0542019, 2022
Burundi BDI74.2%0.0782020, 2022
Zambia ZMB71.7%0.3242022
Central African Republic CAF71.6%0.0742021, 2023
Madagascar MDG69.2%0.1722021, 2022
Niger NER60.5%0.0382021, 2023
Uganda UGA59.8%0.1912019, 2022
Tanzania TZA51.3%0.1342018, 2022
Zimbabwe ZWE49.2%0.1362019, 2023
Kenya KEN45.5%0.2892022, 2023
Eswatini SWZ44.5%0.5572016, 2023
Timor-Leste TLS44%0.7522014, 2020
Burkina Faso BFA42.1%0.1482021, 2022

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

Physicians

Physicians (per 1,000 people)

World Health Organization · CC BY 4.0 · updated quarterly

Reported irregularly, so the latest value in the time window used is from a different year for different countries. Hospital beds, already on the site, have no "better" direction, since more beds is not plainly better. More doctors is closer to plainly better, so a direction is set on that basis.

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 Physicians

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

More links with Extreme poverty rate or Physicians