placestats

Equality score vs. Physicians

Fades after adjusting for income

Across 162 countries, Equality score and Physicians rise and fall together at r = +0.58 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.13, which could easily be chance (q = 0.42, 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 physicians, 0.379 to 3.44 per 1,000 people, equality score is better by 0.811 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.227 typical gaps.

Link before adjusting (r)+0.585p = 3.2e-16 · 162 countries
After adjusting for national income+0.127p = 0.108 · likely range [-0.03, +0.28]
p, allowing for regions0.217standard error 0.092 · 7 regions
Check for luck (q)0.416across 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.

Equality score · typical gaps (standard deviations)link with income +0.64 · high
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

Equality score · typical gaps (standard deviations) →Physicians · per 1,000 people →-2.511.060.0386.6Angola: -1.52 typical gaps (standard deviations), 0.244 per 1,000 peopleAlbania: 0.741 typical gaps (standard deviations), 1.88 per 1,000 peopleUnited Arab Emirates: 0.921 typical gaps (standard deviations), 2.99 per 1,000 peopleArgentina: -0.0236 typical gaps (standard deviations), 5.11 per 1,000 peopleArmenia: 0.843 typical gaps (standard deviations), 3.36 per 1,000 peopleAustralia: 0.473 typical gaps (standard deviations), 4.09 per 1,000 peopleAustria: 0.632 typical gaps (standard deviations), 5.51 per 1,000 peopleBurundi: -1.63 typical gaps (standard deviations), 0.078 per 1,000 peopleBelgium: 0.895 typical gaps (standard deviations), 3.57 per 1,000 peopleBenin: -0.24 typical gaps (standard deviations), 0.216 per 1,000 peopleBurkina Faso: -0.796 typical gaps (standard deviations), 0.148 per 1,000 peopleBangladesh: 0.51 typical gaps (standard deviations), 0.722 per 1,000 peopleBulgaria: 0.143 typical gaps (standard deviations), 4.33 per 1,000 peopleBosnia and Herzegovina: 0.689 typical gaps (standard deviations), 2.58 per 1,000 peopleBelarus: 1.04 typical gaps (standard deviations), 4.72 per 1,000 peopleBelize: 0.12 typical gaps (standard deviations), 1.09 per 1,000 peopleBolivia: 0.0341 typical gaps (standard deviations), 1.28 per 1,000 peopleBrazil: -0.529 typical gaps (standard deviations), 2.36 per 1,000 peopleBarbados: 0.435 typical gaps (standard deviations), 2.96 per 1,000 peopleBhutan: 0.8 typical gaps (standard deviations), 0.553 per 1,000 peopleBotswana: -1.27 typical gaps (standard deviations), 0.379 per 1,000 peopleCentral African Republic: -1.88 typical gaps (standard deviations), 0.074 per 1,000 peopleCanada: 0.62 typical gaps (standard deviations), 2.82 per 1,000 peopleSwitzerland: 0.491 typical gaps (standard deviations), 4.48 per 1,000 peopleChile: -0.0426 typical gaps (standard deviations), 3.33 per 1,000 peopleChina: 0.37 typical gaps (standard deviations), 3.11 per 1,000 peopleCote d'Ivoire: -0.126 typical gaps (standard deviations), 0.166 per 1,000 peopleCameroon: -0.674 typical gaps (standard deviations), 0.136 per 1,000 peopleCongo, Dem. Rep.: -2.33 typical gaps (standard deviations), 0.208 per 1,000 peopleCongo, Rep.: -1.38 typical gaps (standard deviations), 0.173 per 1,000 peopleColombia: -0.906 typical gaps (standard deviations), 2.54 per 1,000 peopleComoros: 0.565 typical gaps (standard deviations), 0.424 per 1,000 peopleCabo Verde: -0.374 typical gaps (standard deviations), 0.73 per 1,000 peopleCosta Rica: -0.196 typical gaps (standard deviations), 2.69 per 1,000 peopleCyprus: 0.611 typical gaps (standard deviations), 3.56 per 1,000 peopleCzechia: 0.961 typical gaps (standard deviations), 4.35 per 1,000 peopleGermany: 0.484 typical gaps (standard deviations), 4.53 per 1,000 peopleDjibouti: -0.607 typical gaps (standard deviations), 0.211 per 1,000 peopleDenmark: 0.709 typical gaps (standard deviations), 4.5 per 1,000 peopleDominican Republic: 0.177 typical gaps (standard deviations), 2.43 per 1,000 peopleAlgeria: 0.852 typical gaps (standard deviations), 1.66 per 1,000 peopleEcuador: -0.286 typical gaps (standard deviations), 2.31 per 1,000 peopleEgypt, Arab Rep.: 0.764 typical gaps (standard deviations), 0.671 per 1,000 peopleSpain: 0.498 typical gaps (standard deviations), 4.29 per 1,000 peopleEstonia: 0.666 typical gaps (standard deviations), 3.47 per 1,000 peopleEthiopia: -0.344 typical gaps (standard deviations), 0.143 per 1,000 peopleFinland: 0.858 typical gaps (standard deviations), 3.61 per 1,000 peopleFiji: 0.553 typical gaps (standard deviations), 0.813 per 1,000 peopleFrance: 0.608 typical gaps (standard deviations), 3.28 per 1,000 peopleMicronesia, Fed. Sts.: -0.538 typical gaps (standard deviations), 0.974 per 1,000 peopleGabon: 0.157 typical gaps (standard deviations), 0.521 per 1,000 peopleUnited Kingdom: 0.566 typical gaps (standard deviations), 3.3 per 1,000 peopleGeorgia: 0.382 typical gaps (standard deviations), 5.64 per 1,000 peopleGhana: -1.07 typical gaps (standard deviations), 0.266 per 1,000 peopleGuinea: 0.435 typical gaps (standard deviations), 0.21 per 1,000 peopleGambia, The: -0.358 typical gaps (standard deviations), 0.09 per 1,000 peopleGuinea-Bissau: -0.509 typical gaps (standard deviations), 0.252 per 1,000 peopleEquatorial Guinea: -0.000739 typical gaps (standard deviations), 0.153 per 1,000 peopleGreece: 0.503 typical gaps (standard deviations), 6.58 per 1,000 peopleGrenada: -0.0989 typical gaps (standard deviations), 1.38 per 1,000 peopleGuatemala: -0.409 typical gaps (standard deviations), 1.28 per 1,000 peopleHonduras: -0.592 typical gaps (standard deviations), 0.489 per 1,000 peopleCroatia: 0.701 typical gaps (standard deviations), 3.91 per 1,000 peopleHaiti: -0.964 typical gaps (standard deviations), 0.292 per 1,000 peopleHungary: 0.656 typical gaps (standard deviations), 3.46 per 1,000 peopleIndonesia: 0.358 typical gaps (standard deviations), 0.524 per 1,000 peopleIndia: 0.836 typical gaps (standard deviations), 0.723 per 1,000 peopleIreland: 0.769 typical gaps (standard deviations), 3.88 per 1,000 peopleIran, Islamic Rep.: 0.311 typical gaps (standard deviations), 1.81 per 1,000 peopleIraq: 0.713 typical gaps (standard deviations), 1.02 per 1,000 peopleIceland: 0.895 typical gaps (standard deviations), 4.37 per 1,000 peopleIsrael: 0.227 typical gaps (standard deviations), 3.8 per 1,000 peopleItaly: 0.444 typical gaps (standard deviations), 4.19 per 1,000 peopleJamaica: 0.11 typical gaps (standard deviations), 0.46 per 1,000 peopleJordan: 0.499 typical gaps (standard deviations), 2.85 per 1,000 peopleJapan: 0.551 typical gaps (standard deviations), 2.65 per 1,000 peopleKazakhstan: 0.76 typical gaps (standard deviations), 3.75 per 1,000 peopleKenya: -0.947 typical gaps (standard deviations), 0.289 per 1,000 peopleKyrgyz Republic: 0.801 typical gaps (standard deviations), 1.57 per 1,000 peopleKiribati: 0.995 typical gaps (standard deviations), 0.193 per 1,000 peopleKorea, Rep.: 0.545 typical gaps (standard deviations), 2.61 per 1,000 peopleLao PDR: 0.261 typical gaps (standard deviations), 0.331 per 1,000 peopleLebanon: 0.249 typical gaps (standard deviations), 2.68 per 1,000 peopleLiberia: -0.456 typical gaps (standard deviations), 0.178 per 1,000 peopleSt. Lucia: -0.0751 typical gaps (standard deviations), 4.23 per 1,000 peopleSri Lanka: 0.202 typical gaps (standard deviations), 1.14 per 1,000 peopleLesotho: -1.22 typical gaps (standard deviations), 0.235 per 1,000 peopleLithuania: 0.341 typical gaps (standard deviations), 4.47 per 1,000 peopleLuxembourg: 0.505 typical gaps (standard deviations), 2.98 per 1,000 peopleLatvia: 0.474 typical gaps (standard deviations), 3.4 per 1,000 peopleMorocco: 0.0733 typical gaps (standard deviations), 0.74 per 1,000 peopleMoldova: 0.895 typical gaps (standard deviations), 4.02 per 1,000 peopleMadagascar: -1.46 typical gaps (standard deviations), 0.172 per 1,000 peopleMaldives: 0.754 typical gaps (standard deviations), 2.23 per 1,000 peopleMexico: -0.0506 typical gaps (standard deviations), 2.59 per 1,000 peopleMarshall Islands: 0.344 typical gaps (standard deviations), 0.467 per 1,000 peopleNorth Macedonia: 0.413 typical gaps (standard deviations), 2.94 per 1,000 peopleMali: -0.544 typical gaps (standard deviations), 0.188 per 1,000 peopleMalta: 0.6 typical gaps (standard deviations), 4.51 per 1,000 peopleMyanmar: 0.408 typical gaps (standard deviations), 0.757 per 1,000 peopleMontenegro: 0.403 typical gaps (standard deviations), 2.78 per 1,000 peopleMongolia: 0.623 typical gaps (standard deviations), 4.13 per 1,000 peopleMozambique: -2.51 typical gaps (standard deviations), 0.175 per 1,000 peopleMauritania: 0.336 typical gaps (standard deviations), 0.261 per 1,000 peopleMauritius: 0.311 typical gaps (standard deviations), 1.44 per 1,000 peopleMalawi: -1.72 typical gaps (standard deviations), 0.054 per 1,000 peopleMalaysia: 0.0997 typical gaps (standard deviations), 2.34 per 1,000 peopleNamibia: -1.55 typical gaps (standard deviations), 0.553 per 1,000 peopleNiger: -1.01 typical gaps (standard deviations), 0.038 per 1,000 peopleNigeria: -0.587 typical gaps (standard deviations), 0.38 per 1,000 peopleNicaragua: -0.386 typical gaps (standard deviations), 0.682 per 1,000 peopleNetherlands: 0.958 typical gaps (standard deviations), 3.88 per 1,000 peopleNorway: 0.91 typical gaps (standard deviations), 4.97 per 1,000 peopleNepal: 0.652 typical gaps (standard deviations), 1.01 per 1,000 peopleNaoero: 0.326 typical gaps (standard deviations), 1.27 per 1,000 peoplePakistan: -0.0796 typical gaps (standard deviations), 1.16 per 1,000 peoplePanama: -0.497 typical gaps (standard deviations), 1.63 per 1,000 peoplePeru: 0.00275 typical gaps (standard deviations), 1.69 per 1,000 peoplePhilippines: 0.0435 typical gaps (standard deviations), 0.792 per 1,000 peoplePoland: 0.795 typical gaps (standard deviations), 4.03 per 1,000 peoplePortugal: 0.48 typical gaps (standard deviations), 5.85 per 1,000 peopleParaguay: -0.155 typical gaps (standard deviations), 3.89 per 1,000 peopleWest Bank and Gaza: 0.292 typical gaps (standard deviations), 2.17 per 1,000 peopleQatar: 0.421 typical gaps (standard deviations), 3.02 per 1,000 peopleRomania: 0.708 typical gaps (standard deviations), 3.63 per 1,000 peopleRussian Federation: 0.539 typical gaps (standard deviations), 5.11 per 1,000 peopleRwanda: -0.82 typical gaps (standard deviations), 0.09 per 1,000 peopleSudan: 0.213 typical gaps (standard deviations), 0.25 per 1,000 peopleSenegal: -0.103 typical gaps (standard deviations), 0.109 per 1,000 peopleSolomon Islands: -0.732 typical gaps (standard deviations), 0.238 per 1,000 peopleSierra Leone: -0.683 typical gaps (standard deviations), 0.134 per 1,000 peopleEl Salvador: 0.0329 typical gaps (standard deviations), 1.62 per 1,000 peopleSerbia: 0.525 typical gaps (standard deviations), 3.1 per 1,000 peopleSao Tome and Principe: -0.235 typical gaps (standard deviations), 0.464 per 1,000 peopleSuriname: 0.129 typical gaps (standard deviations), 1.35 per 1,000 peopleSlovak Republic: 1.06 typical gaps (standard deviations), 3.7 per 1,000 peopleSlovenia: 1.02 typical gaps (standard deviations), 3.37 per 1,000 peopleSweden: 0.734 typical gaps (standard deviations), 4.41 per 1,000 peopleEswatini: -1.85 typical gaps (standard deviations), 0.557 per 1,000 peopleSeychelles: 0.575 typical gaps (standard deviations), 6.6 per 1,000 peopleSyrian Arab Republic: 0.457 typical gaps (standard deviations), 1.52 per 1,000 peopleChad: -0.729 typical gaps (standard deviations), 0.085 per 1,000 peopleTogo: -0.634 typical gaps (standard deviations), 0.08 per 1,000 peopleThailand: 0.525 typical gaps (standard deviations), 0.541 per 1,000 peopleTajikistan: 0.207 typical gaps (standard deviations), 1.87 per 1,000 peopleTimor-Leste: -0.345 typical gaps (standard deviations), 0.752 per 1,000 peopleTonga: 0.87 typical gaps (standard deviations), 1.01 per 1,000 peopleTunisia: 0.484 typical gaps (standard deviations), 1.31 per 1,000 peopleTurkiye: -0.0777 typical gaps (standard deviations), 2.24 per 1,000 peopleTuvalu: -0.0403 typical gaps (standard deviations), 1.35 per 1,000 peopleTanzania: -1.21 typical gaps (standard deviations), 0.134 per 1,000 peopleUganda: -1.56 typical gaps (standard deviations), 0.191 per 1,000 peopleUkraine: 0.967 typical gaps (standard deviations), 3.53 per 1,000 peopleUruguay: 0.135 typical gaps (standard deviations), 4.67 per 1,000 peopleUnited States: 0.00824 typical gaps (standard deviations), 3.68 per 1,000 peopleUzbekistan: 0.5 typical gaps (standard deviations), 2.81 per 1,000 peopleViet Nam: 0.323 typical gaps (standard deviations), 1.11 per 1,000 peopleVanuatu: 0.0795 typical gaps (standard deviations), 0.164 per 1,000 peopleSamoa: 0.0986 typical gaps (standard deviations), 0.561 per 1,000 peopleSouth Africa: -1.12 typical gaps (standard deviations), 0.794 per 1,000 peopleZambia: -2.37 typical gaps (standard deviations), 0.324 per 1,000 peopleZimbabwe: -1.72 typical gaps (standard deviations), 0.136 per 1,000 peopleBLRBTNCODGRCINDKIRMOZSYCZMB

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5853.2e-16162
By rank (Spearman), not adjusted+0.6562.6e-21162
Standard (Pearson), adjusted for national income+0.1270.108162
By rank (Spearman), adjusted for national income+0.2580.001162

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: +0.457 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.102 (without BWA) to +0.147 (without MOZ). 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: BWA (-0.025).

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 & Pacific23-0.11
Europe & Central Asia46+0.00
Latin America & Caribbean24-0.02
Middle East, North Africa, Afghanistan & Pakistan16+0.08
Sub-Saharan Africa45+0.15

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+0.21
Richest third54+0.28

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+0.22
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+0.27
Never a European colony16+0.23
Spain20+0.04
United Kingdom48+0.15

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 5 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


typical gaps (standard deviations)

per 1,000 people
Years
Slovak Republic SVK1.063.72023, 2022
Belarus BLR1.044.722020, 2023
Slovenia SVN1.023.372023, 2022
Kiribati KIR0.9950.1932023, 2013
Ukraine UKR0.9673.532020, 2023
Czechia CZE0.9614.352023, 2022
Netherlands NLD0.9583.882021, 2022
United Arab Emirates ARE0.9212.992018, 2023
Norway NOR0.914.972023
Moldova MDA0.8954.022023
Iceland ISL0.8954.372019, 2023
Belgium BEL0.8953.572023, 2022
Tonga TON0.871.012021
Finland FIN0.8583.612023, 2021
Algeria DZA0.8521.662011, 2022

wdi
World Bank, World Development Indicators

Sources

Equality score

Equality score

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

The average of 2 measures from 1 publisher. 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: Extreme poverty rate, Gini index.

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 Equality score and Physicians

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

More links with Equality score or Physicians