placestats

Equality score vs. Infant mortality

Fades after adjusting for income

Across 163 countries, Equality score and Infant mortality rise and fall together at r = -0.66 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.31, which could easily be chance (q = 0.053, 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 infant mortality, 4.7 to 29.1 per 1,000 live births, equality score is worse by 0.744 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.437 typical gaps.

Link before adjusting (r)-0.663p = 5.8e-22 · 163 countries
After adjusting for national income-0.305p = 7.8e-5 · likely range [-0.44, -0.16]
p, allowing for regions0.002standard error 0.058 · 7 regions
Check for luck (q)0.053across 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
Infant mortality · per 1,000 live birthslink with income -0.86 · 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) →Infant mortality · per 1,000 live births →-2.511.061.569.8Angola: -1.52 typical gaps (standard deviations), 32.1 per 1,000 live birthsAlbania: 0.741 typical gaps (standard deviations), 8.3 per 1,000 live birthsUnited Arab Emirates: 0.921 typical gaps (standard deviations), 3.8 per 1,000 live birthsArgentina: -0.0236 typical gaps (standard deviations), 8 per 1,000 live birthsArmenia: 0.843 typical gaps (standard deviations), 8.5 per 1,000 live birthsAustralia: 0.473 typical gaps (standard deviations), 3.1 per 1,000 live birthsAustria: 0.632 typical gaps (standard deviations), 2.8 per 1,000 live birthsBurundi: -1.63 typical gaps (standard deviations), 30.5 per 1,000 live birthsBelgium: 0.895 typical gaps (standard deviations), 3.1 per 1,000 live birthsBenin: -0.24 typical gaps (standard deviations), 45.2 per 1,000 live birthsBurkina Faso: -0.796 typical gaps (standard deviations), 43.6 per 1,000 live birthsBangladesh: 0.51 typical gaps (standard deviations), 24.4 per 1,000 live birthsBulgaria: 0.143 typical gaps (standard deviations), 4.6 per 1,000 live birthsBosnia and Herzegovina: 0.689 typical gaps (standard deviations), 5.5 per 1,000 live birthsBelarus: 1.04 typical gaps (standard deviations), 1.8 per 1,000 live birthsBelize: 0.12 typical gaps (standard deviations), 10.8 per 1,000 live birthsBolivia: 0.0341 typical gaps (standard deviations), 14.2 per 1,000 live birthsBrazil: -0.529 typical gaps (standard deviations), 12.3 per 1,000 live birthsBarbados: 0.435 typical gaps (standard deviations), 9.1 per 1,000 live birthsBhutan: 0.8 typical gaps (standard deviations), 13.7 per 1,000 live birthsBotswana: -1.27 typical gaps (standard deviations), 32.1 per 1,000 live birthsCentral African Republic: -1.88 typical gaps (standard deviations), 58.8 per 1,000 live birthsCanada: 0.62 typical gaps (standard deviations), 4.7 per 1,000 live birthsSwitzerland: 0.491 typical gaps (standard deviations), 3.4 per 1,000 live birthsChile: -0.0426 typical gaps (standard deviations), 5.8 per 1,000 live birthsChina: 0.37 typical gaps (standard deviations), 4.1 per 1,000 live birthsCote d'Ivoire: -0.126 typical gaps (standard deviations), 44.8 per 1,000 live birthsCameroon: -0.674 typical gaps (standard deviations), 40.2 per 1,000 live birthsCongo, Dem. Rep.: -2.33 typical gaps (standard deviations), 56.4 per 1,000 live birthsCongo, Rep.: -1.38 typical gaps (standard deviations), 27 per 1,000 live birthsColombia: -0.906 typical gaps (standard deviations), 10.5 per 1,000 live birthsComoros: 0.565 typical gaps (standard deviations), 35 per 1,000 live birthsCabo Verde: -0.374 typical gaps (standard deviations), 10.6 per 1,000 live birthsCosta Rica: -0.196 typical gaps (standard deviations), 9.1 per 1,000 live birthsCyprus: 0.611 typical gaps (standard deviations), 4.1 per 1,000 live birthsCzechia: 0.961 typical gaps (standard deviations), 2.1 per 1,000 live birthsGermany: 0.484 typical gaps (standard deviations), 3.1 per 1,000 live birthsDjibouti: -0.607 typical gaps (standard deviations), 43 per 1,000 live birthsDenmark: 0.709 typical gaps (standard deviations), 3.5 per 1,000 live birthsDominican Republic: 0.177 typical gaps (standard deviations), 27.7 per 1,000 live birthsAlgeria: 0.852 typical gaps (standard deviations), 19.5 per 1,000 live birthsEcuador: -0.286 typical gaps (standard deviations), 10.9 per 1,000 live birthsEgypt, Arab Rep.: 0.764 typical gaps (standard deviations), 20.8 per 1,000 live birthsSpain: 0.498 typical gaps (standard deviations), 2.6 per 1,000 live birthsEstonia: 0.666 typical gaps (standard deviations), 1.5 per 1,000 live birthsEthiopia: -0.344 typical gaps (standard deviations), 34.5 per 1,000 live birthsFinland: 0.858 typical gaps (standard deviations), 1.9 per 1,000 live birthsFiji: 0.553 typical gaps (standard deviations), 23.5 per 1,000 live birthsFrance: 0.608 typical gaps (standard deviations), 3.4 per 1,000 live birthsMicronesia, Fed. Sts.: -0.538 typical gaps (standard deviations), 20.3 per 1,000 live birthsGabon: 0.157 typical gaps (standard deviations), 25.9 per 1,000 live birthsUnited Kingdom: 0.566 typical gaps (standard deviations), 4.1 per 1,000 live birthsGeorgia: 0.382 typical gaps (standard deviations), 7.5 per 1,000 live birthsGhana: -1.07 typical gaps (standard deviations), 27.5 per 1,000 live birthsGuinea: 0.435 typical gaps (standard deviations), 59.8 per 1,000 live birthsGambia, The: -0.358 typical gaps (standard deviations), 32.7 per 1,000 live birthsGuinea-Bissau: -0.509 typical gaps (standard deviations), 42.1 per 1,000 live birthsEquatorial Guinea: -0.000739 typical gaps (standard deviations), 47.6 per 1,000 live birthsGreece: 0.503 typical gaps (standard deviations), 3.2 per 1,000 live birthsGrenada: -0.0989 typical gaps (standard deviations), 16.4 per 1,000 live birthsGuatemala: -0.409 typical gaps (standard deviations), 17.2 per 1,000 live birthsHonduras: -0.592 typical gaps (standard deviations), 12.8 per 1,000 live birthsCroatia: 0.701 typical gaps (standard deviations), 4.1 per 1,000 live birthsHaiti: -0.964 typical gaps (standard deviations), 39 per 1,000 live birthsHungary: 0.656 typical gaps (standard deviations), 3.1 per 1,000 live birthsIndonesia: 0.358 typical gaps (standard deviations), 15.2 per 1,000 live birthsIndia: 0.836 typical gaps (standard deviations), 23.3 per 1,000 live birthsIreland: 0.769 typical gaps (standard deviations), 3.4 per 1,000 live birthsIran, Islamic Rep.: 0.311 typical gaps (standard deviations), 10.3 per 1,000 live birthsIraq: 0.713 typical gaps (standard deviations), 20.1 per 1,000 live birthsIceland: 0.895 typical gaps (standard deviations), 2.1 per 1,000 live birthsIsrael: 0.227 typical gaps (standard deviations), 2.7 per 1,000 live birthsItaly: 0.444 typical gaps (standard deviations), 2.3 per 1,000 live birthsJamaica: 0.11 typical gaps (standard deviations), 16.4 per 1,000 live birthsJordan: 0.499 typical gaps (standard deviations), 12 per 1,000 live birthsJapan: 0.551 typical gaps (standard deviations), 1.8 per 1,000 live birthsKazakhstan: 0.76 typical gaps (standard deviations), 7.4 per 1,000 live birthsKenya: -0.947 typical gaps (standard deviations), 34.2 per 1,000 live birthsKyrgyz Republic: 0.801 typical gaps (standard deviations), 14.8 per 1,000 live birthsKiribati: 0.995 typical gaps (standard deviations), 38.8 per 1,000 live birthsKorea, Rep.: 0.545 typical gaps (standard deviations), 2.3 per 1,000 live birthsLao PDR: 0.261 typical gaps (standard deviations), 27.2 per 1,000 live birthsLebanon: 0.249 typical gaps (standard deviations), 15.3 per 1,000 live birthsLiberia: -0.456 typical gaps (standard deviations), 63.2 per 1,000 live birthsSt. Lucia: -0.0751 typical gaps (standard deviations), 15.8 per 1,000 live birthsSri Lanka: 0.202 typical gaps (standard deviations), 5.2 per 1,000 live birthsLesotho: -1.22 typical gaps (standard deviations), 56.3 per 1,000 live birthsLithuania: 0.341 typical gaps (standard deviations), 2.8 per 1,000 live birthsLuxembourg: 0.505 typical gaps (standard deviations), 1.9 per 1,000 live birthsLatvia: 0.474 typical gaps (standard deviations), 2.2 per 1,000 live birthsMorocco: 0.0733 typical gaps (standard deviations), 14.7 per 1,000 live birthsMoldova: 0.895 typical gaps (standard deviations), 15.1 per 1,000 live birthsMadagascar: -1.46 typical gaps (standard deviations), 43 per 1,000 live birthsMaldives: 0.754 typical gaps (standard deviations), 4.7 per 1,000 live birthsMexico: -0.0506 typical gaps (standard deviations), 12.1 per 1,000 live birthsMarshall Islands: 0.344 typical gaps (standard deviations), 22.7 per 1,000 live birthsNorth Macedonia: 0.413 typical gaps (standard deviations), 2.5 per 1,000 live birthsMali: -0.544 typical gaps (standard deviations), 47.5 per 1,000 live birthsMalta: 0.6 typical gaps (standard deviations), 4.7 per 1,000 live birthsMyanmar: 0.408 typical gaps (standard deviations), 32.8 per 1,000 live birthsMontenegro: 0.403 typical gaps (standard deviations), 1.9 per 1,000 live birthsMongolia: 0.623 typical gaps (standard deviations), 11.2 per 1,000 live birthsMozambique: -2.51 typical gaps (standard deviations), 43.7 per 1,000 live birthsMauritania: 0.336 typical gaps (standard deviations), 30.2 per 1,000 live birthsMauritius: 0.311 typical gaps (standard deviations), 13.8 per 1,000 live birthsMalawi: -1.72 typical gaps (standard deviations), 38 per 1,000 live birthsMalaysia: 0.0997 typical gaps (standard deviations), 7 per 1,000 live birthsNamibia: -1.55 typical gaps (standard deviations), 36.4 per 1,000 live birthsNiger: -1.01 typical gaps (standard deviations), 65.6 per 1,000 live birthsNigeria: -0.587 typical gaps (standard deviations), 69.8 per 1,000 live birthsNicaragua: -0.386 typical gaps (standard deviations), 9.4 per 1,000 live birthsNetherlands: 0.958 typical gaps (standard deviations), 3.4 per 1,000 live birthsNorway: 0.91 typical gaps (standard deviations), 2 per 1,000 live birthsNepal: 0.652 typical gaps (standard deviations), 22.2 per 1,000 live birthsNaoero: 0.326 typical gaps (standard deviations), 7.7 per 1,000 live birthsPakistan: -0.0796 typical gaps (standard deviations), 48.2 per 1,000 live birthsPanama: -0.497 typical gaps (standard deviations), 12 per 1,000 live birthsPeru: 0.00275 typical gaps (standard deviations), 11 per 1,000 live birthsPhilippines: 0.0435 typical gaps (standard deviations), 21.7 per 1,000 live birthsPoland: 0.795 typical gaps (standard deviations), 3.6 per 1,000 live birthsPortugal: 0.48 typical gaps (standard deviations), 2.7 per 1,000 live birthsParaguay: -0.155 typical gaps (standard deviations), 14.5 per 1,000 live birthsWest Bank and Gaza: 0.292 typical gaps (standard deviations), 17 per 1,000 live birthsQatar: 0.421 typical gaps (standard deviations), 4.8 per 1,000 live birthsRomania: 0.708 typical gaps (standard deviations), 6 per 1,000 live birthsRussian Federation: 0.539 typical gaps (standard deviations), 4.1 per 1,000 live birthsRwanda: -0.82 typical gaps (standard deviations), 29.4 per 1,000 live birthsSudan: 0.213 typical gaps (standard deviations), 42.9 per 1,000 live birthsSenegal: -0.103 typical gaps (standard deviations), 28.9 per 1,000 live birthsSolomon Islands: -0.732 typical gaps (standard deviations), 16.4 per 1,000 live birthsSierra Leone: -0.683 typical gaps (standard deviations), 54.6 per 1,000 live birthsEl Salvador: 0.0329 typical gaps (standard deviations), 8.8 per 1,000 live birthsSerbia: 0.525 typical gaps (standard deviations), 4.6 per 1,000 live birthsSao Tome and Principe: -0.235 typical gaps (standard deviations), 9.2 per 1,000 live birthsSuriname: 0.129 typical gaps (standard deviations), 14.8 per 1,000 live birthsSlovak Republic: 1.06 typical gaps (standard deviations), 5.3 per 1,000 live birthsSlovenia: 1.02 typical gaps (standard deviations), 1.8 per 1,000 live birthsSweden: 0.734 typical gaps (standard deviations), 2 per 1,000 live birthsEswatini: -1.85 typical gaps (standard deviations), 42.2 per 1,000 live birthsSeychelles: 0.575 typical gaps (standard deviations), 12.9 per 1,000 live birthsSyrian Arab Republic: 0.457 typical gaps (standard deviations), 17.7 per 1,000 live birthsChad: -0.729 typical gaps (standard deviations), 56.3 per 1,000 live birthsTogo: -0.634 typical gaps (standard deviations), 34.8 per 1,000 live birthsThailand: 0.525 typical gaps (standard deviations), 7.7 per 1,000 live birthsTajikistan: 0.207 typical gaps (standard deviations), 23.5 per 1,000 live birthsTimor-Leste: -0.345 typical gaps (standard deviations), 34.6 per 1,000 live birthsTonga: 0.87 typical gaps (standard deviations), 7.8 per 1,000 live birthsTunisia: 0.484 typical gaps (standard deviations), 9.7 per 1,000 live birthsTurkiye: -0.0777 typical gaps (standard deviations), 8 per 1,000 live birthsTuvalu: -0.0403 typical gaps (standard deviations), 16.5 per 1,000 live birthsTanzania: -1.21 typical gaps (standard deviations), 28.7 per 1,000 live birthsUganda: -1.56 typical gaps (standard deviations), 35.9 per 1,000 live birthsUkraine: 0.967 typical gaps (standard deviations), 7.6 per 1,000 live birthsUruguay: 0.135 typical gaps (standard deviations), 6.2 per 1,000 live birthsUnited States: 0.00824 typical gaps (standard deviations), 5.5 per 1,000 live birthsUzbekistan: 0.5 typical gaps (standard deviations), 12.8 per 1,000 live birthsViet Nam: 0.323 typical gaps (standard deviations), 12 per 1,000 live birthsVanuatu: 0.0795 typical gaps (standard deviations), 14.4 per 1,000 live birthsSamoa: 0.0986 typical gaps (standard deviations), 12.3 per 1,000 live birthsKosovo: -0.0202 typical gaps (standard deviations), 8 per 1,000 live birthsSouth Africa: -1.12 typical gaps (standard deviations), 24.2 per 1,000 live birthsZambia: -2.37 typical gaps (standard deviations), 34.6 per 1,000 live birthsZimbabwe: -1.72 typical gaps (standard deviations), 62.4 per 1,000 live birthsBLRCAFCODCOLCZEGINKIRMOZNGASVNZMBZWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6635.8e-22163
By rank (Spearman), not adjusted-0.6644.4e-22163
Standard (Pearson), adjusted for national income-0.3057.8e-5163
By rank (Spearman), adjusted for national income-0.2750.000163

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 163 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: +0.357 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.342 (without GIN) to -0.272 (without ZWE). 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: GIN (-0.037).

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+0.29
Europe & Central Asia47-0.03
Latin America & Caribbean24-0.11
Middle East, North Africa, Afghanistan & Pakistan16-0.48
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 third55-0.21
Middle third54-0.46
Richest third54-0.46

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
Christians103-0.40
Muslims40-0.53

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
France24-0.06
Never a European colony16-0.60
Spain20+0.25
United Kingdom48-0.42

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 = -0.272 after dropping 6 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 live births
Years
Slovak Republic SVK1.065.32023, 2024
Belarus BLR1.041.82020, 2024
Slovenia SVN1.021.82023, 2024
Kiribati KIR0.99538.82023, 2024
Ukraine UKR0.9677.62020, 2024
Czechia CZE0.9612.12023, 2024
Netherlands NLD0.9583.42021, 2024
United Arab Emirates ARE0.9213.82018, 2024
Norway NOR0.9122023, 2024
Moldova MDA0.89515.12023, 2024
Iceland ISL0.8952.12019, 2024
Belgium BEL0.8953.12023, 2024
Tonga TON0.877.82021, 2024
Finland FIN0.8581.92023, 2024
Algeria DZA0.85219.52011, 2024

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.

Infant mortality

Mortality rate, infant (per 1,000 live births)

World Bank · CC BY 4.0 · updated quarterly

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 Infant mortality

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

More links with Equality score or Infant mortality