placestats

Labour score vs. Undernourishment

Fades after adjusting for income

Across 162 countries, Labour score and Undernourishment rise and fall together at r = -0.61 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.18, which could easily be chance (q = 0.074, 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 undernourishment, 2.5% to 14.1% of population, labour score is worse by 0.443 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.132 typical gaps.

Link before adjusting (r)-0.607p = 1.2e-17 · 162 countries
After adjusting for national income-0.181p = 0.022 · likely range [-0.33, -0.03]
p, allowing for regions0.019standard error 0.057 · 7 regions
Check for luck (q)0.074across 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.

Labour score · typical gaps (standard deviations)link with income +0.79 · high
Undernourishment · % of populationlink with income -0.83 · 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

Labour score · typical gaps (standard deviations) →Undernourishment · % of population →-1.971.172.5%54.2%Afghanistan: -1.26 typical gaps (standard deviations), 28.1% of populationAngola: -0.939 typical gaps (standard deviations), 22.5% of populationAlbania: -0.39 typical gaps (standard deviations), 5.4% of populationUnited Arab Emirates: 1.17 typical gaps (standard deviations), 2.5% of populationArgentina: 0.34 typical gaps (standard deviations), 3.4% of populationArmenia: -0.302 typical gaps (standard deviations), 2.5% of populationAustralia: 0.867 typical gaps (standard deviations), 2.5% of populationAustria: 0.671 typical gaps (standard deviations), 2.5% of populationAzerbaijan: -0.0868 typical gaps (standard deviations), 2.5% of populationBelgium: 0.682 typical gaps (standard deviations), 2.5% of populationBenin: -0.233 typical gaps (standard deviations), 14.3% of populationBurkina Faso: -0.52 typical gaps (standard deviations), 13.1% of populationBangladesh: -0.0147 typical gaps (standard deviations), 10.4% of populationBulgaria: 0.954 typical gaps (standard deviations), 2.5% of populationBosnia and Herzegovina: 0.126 typical gaps (standard deviations), 2.5% of populationBelarus: 0.978 typical gaps (standard deviations), 2.5% of populationBelize: 0.0851 typical gaps (standard deviations), 7% of populationBolivia: -0.0485 typical gaps (standard deviations), 21.8% of populationBrazil: 0.16 typical gaps (standard deviations), 2.5% of populationBarbados: 0.485 typical gaps (standard deviations), 4.4% of populationBotswana: -1.12 typical gaps (standard deviations), 24% of populationCentral African Republic: -0.633 typical gaps (standard deviations), 29.8% of populationCanada: 0.581 typical gaps (standard deviations), 2.5% of populationSwitzerland: 0.916 typical gaps (standard deviations), 2.5% of populationChile: 0.344 typical gaps (standard deviations), 2.5% of populationChina: 0.367 typical gaps (standard deviations), 2.5% of populationCote d'Ivoire: -0.122 typical gaps (standard deviations), 11.1% of populationCameroon: -0.192 typical gaps (standard deviations), 4.8% of populationCongo, Dem. Rep.: -0.523 typical gaps (standard deviations), 38.5% of populationCongo, Rep.: -1.69 typical gaps (standard deviations), 26.4% of populationColombia: -0.377 typical gaps (standard deviations), 3.9% of populationComoros: -0.00862 typical gaps (standard deviations), 15.4% of populationCabo Verde: -0.252 typical gaps (standard deviations), 13.5% of populationCosta Rica: 0.133 typical gaps (standard deviations), 2.5% of populationCyprus: 0.812 typical gaps (standard deviations), 2.5% of populationCzechia: 0.889 typical gaps (standard deviations), 2.5% of populationGermany: 0.988 typical gaps (standard deviations), 2.5% of populationDjibouti: -1.82 typical gaps (standard deviations), 12.9% of populationDenmark: 0.819 typical gaps (standard deviations), 2.5% of populationDominican Republic: -0.0819 typical gaps (standard deviations), 3.6% of populationAlgeria: -0.0627 typical gaps (standard deviations), 2.5% of populationEcuador: -0.398 typical gaps (standard deviations), 12.1% of populationEgypt, Arab Rep.: 0.367 typical gaps (standard deviations), 9.4% of populationSpain: 0.483 typical gaps (standard deviations), 2.5% of populationEstonia: 0.558 typical gaps (standard deviations), 2.5% of populationEthiopia: -0.481 typical gaps (standard deviations), 19.7% of populationFinland: 0.419 typical gaps (standard deviations), 2.5% of populationFiji: 0.318 typical gaps (standard deviations), 6.8% of populationFrance: 0.595 typical gaps (standard deviations), 2.5% of populationGabon: -0.844 typical gaps (standard deviations), 25.3% of populationUnited Kingdom: 0.635 typical gaps (standard deviations), 2.5% of populationGeorgia: -0.418 typical gaps (standard deviations), 2.5% of populationGhana: 0.00187 typical gaps (standard deviations), 6.3% of populationGuinea: -0.539 typical gaps (standard deviations), 11.4% of populationGambia, The: -0.899 typical gaps (standard deviations), 16.8% of populationGuinea-Bissau: -0.255 typical gaps (standard deviations), 22.1% of populationGreece: 0.426 typical gaps (standard deviations), 2.5% of populationGuatemala: 0.48 typical gaps (standard deviations), 11.8% of populationGuyana: -0.0857 typical gaps (standard deviations), 2.5% of populationHong Kong SAR, China: 1.11 typical gaps (standard deviations), 3.1% of populationHonduras: -0.175 typical gaps (standard deviations), 14.8% of populationCroatia: 0.844 typical gaps (standard deviations), 2.5% of populationHaiti: -1.28 typical gaps (standard deviations), 54.2% of populationHungary: 0.856 typical gaps (standard deviations), 2.5% of populationIndonesia: 0.169 typical gaps (standard deviations), 6.3% of populationIndia: -0.486 typical gaps (standard deviations), 12% of populationIreland: 0.813 typical gaps (standard deviations), 2.5% of populationIran, Islamic Rep.: -0.0514 typical gaps (standard deviations), 6.8% of populationIraq: -0.409 typical gaps (standard deviations), 14.9% of populationIceland: 0.919 typical gaps (standard deviations), 2.5% of populationIsrael: 0.943 typical gaps (standard deviations), 2.5% of populationItaly: 0.579 typical gaps (standard deviations), 2.5% of populationJamaica: 0.511 typical gaps (standard deviations), 7.7% of populationJordan: -0.133 typical gaps (standard deviations), 14.3% of populationKazakhstan: 0.516 typical gaps (standard deviations), 2.5% of populationKenya: -0.274 typical gaps (standard deviations), 36.8% of populationKyrgyz Republic: 0.54 typical gaps (standard deviations), 5.1% of populationCambodia: 0.369 typical gaps (standard deviations), 5.2% of populationKiribati: -1.29 typical gaps (standard deviations), 4.1% of populationKorea, Rep.: 0.822 typical gaps (standard deviations), 2.5% of populationKuwait: 1.15 typical gaps (standard deviations), 2.5% of populationLebanon: 0.107 typical gaps (standard deviations), 8.7% of populationLiberia: -0.308 typical gaps (standard deviations), 35.5% of populationLibya: -0.574 typical gaps (standard deviations), 16.5% of populationSri Lanka: 0.307 typical gaps (standard deviations), 7.4% of populationLithuania: 0.626 typical gaps (standard deviations), 2.5% of populationLuxembourg: 0.723 typical gaps (standard deviations), 2.5% of populationLatvia: 0.673 typical gaps (standard deviations), 2.5% of populationMacao SAR, China: 1.12 typical gaps (standard deviations), 11.2% of populationMorocco: -0.0339 typical gaps (standard deviations), 7% of populationMoldova: 0.412 typical gaps (standard deviations), 2.5% of populationMadagascar: -0.463 typical gaps (standard deviations), 39.5% of populationMexico: 0.513 typical gaps (standard deviations), 2.7% of populationNorth Macedonia: 0.0322 typical gaps (standard deviations), 2.5% of populationMali: -0.283 typical gaps (standard deviations), 12.3% of populationMalta: 0.947 typical gaps (standard deviations), 2.5% of populationMyanmar: -0.0589 typical gaps (standard deviations), 5.4% of populationMontenegro: -0.0101 typical gaps (standard deviations), 2.5% of populationMongolia: 0.223 typical gaps (standard deviations), 2.5% of populationMozambique: -0.745 typical gaps (standard deviations), 21.8% of populationMauritania: -0.477 typical gaps (standard deviations), 8.7% of populationMauritius: 0.632 typical gaps (standard deviations), 8.7% of populationMalawi: -0.169 typical gaps (standard deviations), 21.4% of populationMalaysia: 0.654 typical gaps (standard deviations), 2.5% of populationNamibia: -0.775 typical gaps (standard deviations), 18.1% of populationNiger: -0.233 typical gaps (standard deviations), 12.9% of populationNigeria: -0.128 typical gaps (standard deviations), 19.9% of populationNicaragua: 0.229 typical gaps (standard deviations), 17.3% of populationNetherlands: 0.804 typical gaps (standard deviations), 2.5% of populationNorway: 0.915 typical gaps (standard deviations), 2.5% of populationNepal: -0.934 typical gaps (standard deviations), 5.3% of populationNew Zealand: 0.721 typical gaps (standard deviations), 2.5% of populationOman: 1.04 typical gaps (standard deviations), 5.9% of populationPakistan: -0.988 typical gaps (standard deviations), 16.5% of populationPanama: 0.0819 typical gaps (standard deviations), 5.7% of populationPeru: -0.454 typical gaps (standard deviations), 6.9% of populationPhilippines: 0.559 typical gaps (standard deviations), 3% of populationPapua New Guinea: -0.175 typical gaps (standard deviations), 28.7% of populationPoland: 0.842 typical gaps (standard deviations), 2.5% of populationPortugal: 0.843 typical gaps (standard deviations), 2.5% of populationParaguay: -0.242 typical gaps (standard deviations), 5.2% of populationRomania: 0.637 typical gaps (standard deviations), 2.5% of populationRussian Federation: 1.14 typical gaps (standard deviations), 2.5% of populationRwanda: -0.108 typical gaps (standard deviations), 24.4% of populationSaudi Arabia: 0.782 typical gaps (standard deviations), 2.5% of populationSudan: -1.35 typical gaps (standard deviations), 11% of populationSenegal: -0.0349 typical gaps (standard deviations), 5.1% of populationSolomon Islands: -0.0344 typical gaps (standard deviations), 20% of populationSierra Leone: -0.503 typical gaps (standard deviations), 24.1% of populationEl Salvador: 0.523 typical gaps (standard deviations), 6.7% of populationSomalia, Fed. Rep.: -1.05 typical gaps (standard deviations), 53.2% of populationSerbia: 0.409 typical gaps (standard deviations), 2.5% of populationSao Tome and Principe: -0.0345 typical gaps (standard deviations), 17.4% of populationSuriname: 0.385 typical gaps (standard deviations), 9.7% of populationSlovak Republic: 0.713 typical gaps (standard deviations), 3.1% of populationSlovenia: 0.914 typical gaps (standard deviations), 2.5% of populationSweden: 0.531 typical gaps (standard deviations), 2.5% of populationEswatini: -1.97 typical gaps (standard deviations), 14.7% of populationSeychelles: 0.418 typical gaps (standard deviations), 2.5% of populationSyrian Arab Republic: -0.472 typical gaps (standard deviations), 39% of populationChad: -0.409 typical gaps (standard deviations), 32% of populationTogo: -0.201 typical gaps (standard deviations), 9.1% of populationThailand: 0.466 typical gaps (standard deviations), 4.6% of populationTajikistan: 0.278 typical gaps (standard deviations), 8.4% of populationTurkmenistan: 0.492 typical gaps (standard deviations), 4.3% of populationTimor-Leste: 0.141 typical gaps (standard deviations), 18.7% of populationTrinidad and Tobago: 0.701 typical gaps (standard deviations), 11.2% of populationTunisia: -0.205 typical gaps (standard deviations), 3% of populationTurkiye: 0.224 typical gaps (standard deviations), 2.5% of populationTanzania: -0.328 typical gaps (standard deviations), 20.2% of populationUganda: -0.25 typical gaps (standard deviations), 22% of populationUkraine: 0.208 typical gaps (standard deviations), 6.9% of populationUruguay: 0.298 typical gaps (standard deviations), 2.5% of populationUnited States: 0.862 typical gaps (standard deviations), 2.5% of populationUzbekistan: 0.368 typical gaps (standard deviations), 2.5% of populationSt. Vincent and the Grenadines: -0.519 typical gaps (standard deviations), 6.1% of populationViet Nam: 0.283 typical gaps (standard deviations), 5.3% of populationVanuatu: -0.495 typical gaps (standard deviations), 7.2% of populationSamoa: 0.181 typical gaps (standard deviations), 3.6% of populationSouth Africa: -1.51 typical gaps (standard deviations), 10% of populationZambia: -0.432 typical gaps (standard deviations), 37.2% of populationZimbabwe: -0.673 typical gaps (standard deviations), 19.7% of populationARECOGDJIHTIKENKIRKWTRUSSOMSWZ

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6071.2e-17162
By rank (Spearman), not adjusted-0.7284.6e-28162
Standard (Pearson), adjusted for national income-0.1810.022162
By rank (Spearman), adjusted for national income-0.2130.007162

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: +0.426 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.208 (without KIR) to -0.149 (without HTI). 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: HTI (+0.032).

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 & Pacific21+0.31
Europe & Central Asia48+0.09
Latin America & Caribbean25-0.42
Middle East, North Africa, Afghanistan & Pakistan19+0.18
Sub-Saharan Africa43-0.25

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 third54-0.22
Middle third54-0.48
Richest third54+0.09

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
Christians100-0.23
Muslims44-0.15

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
France24-0.53
Never a European colony20+0.07
Spain18-0.21
United Kingdom47-0.11

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 = -0.199 after dropping 12 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)
% of population		Years
United Arab Emirates ARE1.172.5%2025, 2023
Kuwait KWT1.152.5%2025, 2023
Russian Federation RUS1.142.5%2025, 2023
Macao SAR, China MAC1.1211.2%2025, 2023
Hong Kong SAR, China HKG1.113.1%2025, 2023
Oman OMN1.045.9%2025, 2023
Germany DEU0.9882.5%2025, 2023
Belarus BLR0.9782.5%2025, 2023
Bulgaria BGR0.9542.5%2025, 2023
Malta MLT0.9472.5%2025, 2023
Israel ISR0.9432.5%2025, 2023
Iceland ISL0.9192.5%2025, 2023
Switzerland CHE0.9162.5%2025, 2023
Norway NOR0.9152.5%2025, 2023
Slovenia SVN0.9142.5%2025, 2023

un_sdg; wdi
World Bank, World Development Indicators

Sources

Labour score

Labour score

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

The average of 4 measures from 2 publishers. 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: Informal employment, Vulnerable employment, Youth not in work or study, Unemployment.

Undernourishment

Prevalence of undernourishment (% of population)

Food and Agriculture Organization · CC BY 4.0 · updated quarterly

Estimated from the food supply and how it is shared out, not by asking anyone. It is the FAO's model of how many people get less food energy than they need. The survey-based food insecurity measure below is treated as measuring the same thing. So their pair reads as two tools agreeing, not as a finding.

How this was computed

Data used: most recent value in 2010-2025; values come from 2021–2025. 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 Labour score and Undernourishment

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

More links with Labour score or Undernourishment