placestats

Labour score vs. Youth not in work or study

Fades after adjusting for income

Across 170 countries, Labour score and Youth not in work or study 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 -0.44, which could easily be chance (q = 0.14, 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. Youth not in work or study is one of the measures Labour score is built from, so the two are bound to match. Their link is arithmetic, not a relationship between two things.

Comparing countries across the middle half of youth not in work or study, 11.7% to 25.7% of 15 to 24 year olds, labour score is worse by 0.521 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.296 typical gaps.

Link before adjusting (r)-0.654p = 4.2e-22 · 170 countries
After adjusting for national income-0.444p = 1.4e-9 · likely range [-0.56, -0.31]
p, allowing for regions0.007standard error 0.110 · 7 regions
Check for luck (q)0.140across 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
Youth not in work or study · % of 15 to 24 year oldslink with income -0.63 · 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.

Built from the same numbers. Youth not in work or study is one of the measures Labour score is built from, so the two are bound to match. Their link is arithmetic, not a relationship between two things.

Every country as a dot

Labour score · typical gaps (standard deviations) →Youth not in work or study · % of 15 to 24 year olds →-1.971.173.14%65.7%Afghanistan: -1.26 typical gaps (standard deviations), 43.8% of 15 to 24 year oldsAngola: -0.939 typical gaps (standard deviations), 24.9% of 15 to 24 year oldsAlbania: -0.39 typical gaps (standard deviations), 21% of 15 to 24 year oldsUnited Arab Emirates: 1.17 typical gaps (standard deviations), 8.92% of 15 to 24 year oldsArgentina: 0.34 typical gaps (standard deviations), 15.2% of 15 to 24 year oldsArmenia: -0.302 typical gaps (standard deviations), 20.2% of 15 to 24 year oldsAntigua and Barbuda: 0.235 typical gaps (standard deviations), 20.8% of 15 to 24 year oldsAustralia: 0.867 typical gaps (standard deviations), 8.53% of 15 to 24 year oldsAustria: 0.671 typical gaps (standard deviations), 13.4% of 15 to 24 year oldsAzerbaijan: -0.0868 typical gaps (standard deviations), 9.64% of 15 to 24 year oldsBurundi: -0.275 typical gaps (standard deviations), 11.2% of 15 to 24 year oldsBelgium: 0.682 typical gaps (standard deviations), 8.15% of 15 to 24 year oldsBenin: -0.233 typical gaps (standard deviations), 14.2% of 15 to 24 year oldsBurkina Faso: -0.52 typical gaps (standard deviations), 24.5% of 15 to 24 year oldsBangladesh: -0.0147 typical gaps (standard deviations), 15.4% of 15 to 24 year oldsBulgaria: 0.954 typical gaps (standard deviations), 10.9% of 15 to 24 year oldsBosnia and Herzegovina: 0.126 typical gaps (standard deviations), 14.3% of 15 to 24 year oldsBelarus: 0.978 typical gaps (standard deviations), 65.7% of 15 to 24 year oldsBelize: 0.0851 typical gaps (standard deviations), 24.9% of 15 to 24 year oldsBolivia: -0.0485 typical gaps (standard deviations), 8.02% of 15 to 24 year oldsBrazil: 0.16 typical gaps (standard deviations), 18.7% of 15 to 24 year oldsBarbados: 0.485 typical gaps (standard deviations), 29% of 15 to 24 year oldsBrunei Darussalam: 0.857 typical gaps (standard deviations), 19% of 15 to 24 year oldsBhutan: -0.0349 typical gaps (standard deviations), 15.3% of 15 to 24 year oldsBotswana: -1.12 typical gaps (standard deviations), 38% of 15 to 24 year oldsCanada: 0.581 typical gaps (standard deviations), 13.3% of 15 to 24 year oldsSwitzerland: 0.916 typical gaps (standard deviations), 6.99% of 15 to 24 year oldsChile: 0.344 typical gaps (standard deviations), 13.4% of 15 to 24 year oldsCote d'Ivoire: -0.122 typical gaps (standard deviations), 18.8% of 15 to 24 year oldsCameroon: -0.192 typical gaps (standard deviations), 23.2% of 15 to 24 year oldsCongo, Dem. Rep.: -0.523 typical gaps (standard deviations), 31.4% of 15 to 24 year oldsColombia: -0.377 typical gaps (standard deviations), 22% of 15 to 24 year oldsComoros: -0.00862 typical gaps (standard deviations), 21.2% of 15 to 24 year oldsCabo Verde: -0.252 typical gaps (standard deviations), 28% of 15 to 24 year oldsCosta Rica: 0.133 typical gaps (standard deviations), 19.4% of 15 to 24 year oldsCyprus: 0.812 typical gaps (standard deviations), 13.2% of 15 to 24 year oldsCzechia: 0.889 typical gaps (standard deviations), 4.62% of 15 to 24 year oldsGermany: 0.988 typical gaps (standard deviations), 7.58% of 15 to 24 year oldsDjibouti: -1.82 typical gaps (standard deviations), 44% of 15 to 24 year oldsDenmark: 0.819 typical gaps (standard deviations), 6.9% of 15 to 24 year oldsDominican Republic: -0.0819 typical gaps (standard deviations), 24.7% of 15 to 24 year oldsAlgeria: -0.0627 typical gaps (standard deviations), 20.9% of 15 to 24 year oldsEcuador: -0.398 typical gaps (standard deviations), 17.7% of 15 to 24 year oldsEgypt, Arab Rep.: 0.367 typical gaps (standard deviations), 23.8% of 15 to 24 year oldsSpain: 0.483 typical gaps (standard deviations), 10.5% of 15 to 24 year oldsEstonia: 0.558 typical gaps (standard deviations), 10.2% of 15 to 24 year oldsEthiopia: -0.481 typical gaps (standard deviations), 17.5% of 15 to 24 year oldsFinland: 0.419 typical gaps (standard deviations), 8.28% of 15 to 24 year oldsFiji: 0.318 typical gaps (standard deviations), 20.8% of 15 to 24 year oldsFrance: 0.595 typical gaps (standard deviations), 12.6% of 15 to 24 year oldsGabon: -0.844 typical gaps (standard deviations), 20.2% of 15 to 24 year oldsUnited Kingdom: 0.635 typical gaps (standard deviations), 14.4% of 15 to 24 year oldsGeorgia: -0.418 typical gaps (standard deviations), 14.7% of 15 to 24 year oldsGhana: 0.00187 typical gaps (standard deviations), 23.9% of 15 to 24 year oldsGuinea: -0.539 typical gaps (standard deviations), 34.3% of 15 to 24 year oldsGambia, The: -0.899 typical gaps (standard deviations), 36.5% of 15 to 24 year oldsGuinea-Bissau: -0.255 typical gaps (standard deviations), 13.2% of 15 to 24 year oldsGreece: 0.426 typical gaps (standard deviations), 10.4% of 15 to 24 year oldsGrenada: -0.263 typical gaps (standard deviations), 20.1% of 15 to 24 year oldsGuatemala: 0.48 typical gaps (standard deviations), 24% of 15 to 24 year oldsGuyana: -0.0857 typical gaps (standard deviations), 44.8% of 15 to 24 year oldsHong Kong SAR, China: 1.11 typical gaps (standard deviations), 5.95% of 15 to 24 year oldsHonduras: -0.175 typical gaps (standard deviations), 29% of 15 to 24 year oldsCroatia: 0.844 typical gaps (standard deviations), 8.08% of 15 to 24 year oldsHaiti: -1.28 typical gaps (standard deviations), 21.1% of 15 to 24 year oldsHungary: 0.856 typical gaps (standard deviations), 9.98% of 15 to 24 year oldsIndonesia: 0.169 typical gaps (standard deviations), 21.4% of 15 to 24 year oldsIndia: -0.486 typical gaps (standard deviations), 25.6% of 15 to 24 year oldsIreland: 0.813 typical gaps (standard deviations), 5.67% of 15 to 24 year oldsIran, Islamic Rep.: -0.0514 typical gaps (standard deviations), 24.3% of 15 to 24 year oldsIraq: -0.409 typical gaps (standard deviations), 36.9% of 15 to 24 year oldsIceland: 0.919 typical gaps (standard deviations), 3.98% of 15 to 24 year oldsIsrael: 0.943 typical gaps (standard deviations), 15.3% of 15 to 24 year oldsItaly: 0.579 typical gaps (standard deviations), 12% of 15 to 24 year oldsJamaica: 0.511 typical gaps (standard deviations), 19.7% of 15 to 24 year oldsJordan: -0.133 typical gaps (standard deviations), 29.4% of 15 to 24 year oldsJapan: 1.03 typical gaps (standard deviations), 3.14% of 15 to 24 year oldsKazakhstan: 0.516 typical gaps (standard deviations), 9.49% of 15 to 24 year oldsKenya: -0.274 typical gaps (standard deviations), 18.5% of 15 to 24 year oldsKyrgyz Republic: 0.54 typical gaps (standard deviations), 19.3% of 15 to 24 year oldsCambodia: 0.369 typical gaps (standard deviations), 6.32% of 15 to 24 year oldsKiribati: -1.29 typical gaps (standard deviations), 35.4% of 15 to 24 year oldsLao PDR: -0.16 typical gaps (standard deviations), 22.5% of 15 to 24 year oldsLebanon: 0.107 typical gaps (standard deviations), 23.5% of 15 to 24 year oldsLiberia: -0.308 typical gaps (standard deviations), 39.9% of 15 to 24 year oldsSt. Lucia: -0.149 typical gaps (standard deviations), 25.5% of 15 to 24 year oldsSri Lanka: 0.307 typical gaps (standard deviations), 17.2% of 15 to 24 year oldsLesotho: -0.581 typical gaps (standard deviations), 38% of 15 to 24 year oldsLithuania: 0.626 typical gaps (standard deviations), 14.9% of 15 to 24 year oldsLuxembourg: 0.723 typical gaps (standard deviations), 10.2% of 15 to 24 year oldsLatvia: 0.673 typical gaps (standard deviations), 9.89% of 15 to 24 year oldsMacao SAR, China: 1.12 typical gaps (standard deviations), 4.96% of 15 to 24 year oldsMorocco: -0.0339 typical gaps (standard deviations), 35.3% of 15 to 24 year oldsMoldova: 0.412 typical gaps (standard deviations), 9.05% of 15 to 24 year oldsMadagascar: -0.463 typical gaps (standard deviations), 24.3% of 15 to 24 year oldsMaldives: 0.686 typical gaps (standard deviations), 26.3% of 15 to 24 year oldsMexico: 0.513 typical gaps (standard deviations), 16.2% of 15 to 24 year oldsMarshall Islands: -1.09 typical gaps (standard deviations), 40% of 15 to 24 year oldsNorth Macedonia: 0.0322 typical gaps (standard deviations), 19.5% of 15 to 24 year oldsMali: -0.283 typical gaps (standard deviations), 30.1% of 15 to 24 year oldsMalta: 0.947 typical gaps (standard deviations), 7.84% of 15 to 24 year oldsMyanmar: -0.0589 typical gaps (standard deviations), 15% of 15 to 24 year oldsMontenegro: -0.0101 typical gaps (standard deviations), 14.2% of 15 to 24 year oldsMongolia: 0.223 typical gaps (standard deviations), 9.9% of 15 to 24 year oldsMozambique: -0.745 typical gaps (standard deviations), 19.2% of 15 to 24 year oldsMauritania: -0.477 typical gaps (standard deviations), 44.1% of 15 to 24 year oldsMauritius: 0.632 typical gaps (standard deviations), 13.9% of 15 to 24 year oldsMalawi: -0.169 typical gaps (standard deviations), 39.4% of 15 to 24 year oldsMalaysia: 0.654 typical gaps (standard deviations), 10.2% of 15 to 24 year oldsNamibia: -0.775 typical gaps (standard deviations), 31.9% of 15 to 24 year oldsNiger: -0.233 typical gaps (standard deviations), 15.7% of 15 to 24 year oldsNigeria: -0.128 typical gaps (standard deviations), 11.9% of 15 to 24 year oldsNicaragua: 0.229 typical gaps (standard deviations), 24.5% of 15 to 24 year oldsNetherlands: 0.804 typical gaps (standard deviations), 4.14% of 15 to 24 year oldsNorway: 0.915 typical gaps (standard deviations), 5.82% of 15 to 24 year oldsNepal: -0.934 typical gaps (standard deviations), 34.8% of 15 to 24 year oldsNaoero: -1.38 typical gaps (standard deviations), 50.4% of 15 to 24 year oldsNew Zealand: 0.721 typical gaps (standard deviations), 12.8% of 15 to 24 year oldsOman: 1.04 typical gaps (standard deviations), 25.5% of 15 to 24 year oldsPakistan: -0.988 typical gaps (standard deviations), 28.5% of 15 to 24 year oldsPanama: 0.0819 typical gaps (standard deviations), 15.1% of 15 to 24 year oldsPeru: -0.454 typical gaps (standard deviations), 22.8% of 15 to 24 year oldsPhilippines: 0.559 typical gaps (standard deviations), 12.4% of 15 to 24 year oldsPalau: 0.825 typical gaps (standard deviations), 11.3% of 15 to 24 year oldsPapua New Guinea: -0.175 typical gaps (standard deviations), 27.7% of 15 to 24 year oldsPoland: 0.842 typical gaps (standard deviations), 10.1% of 15 to 24 year oldsPuerto Rico (US): 0.688 typical gaps (standard deviations), 19.5% of 15 to 24 year oldsPortugal: 0.843 typical gaps (standard deviations), 7.02% of 15 to 24 year oldsParaguay: -0.242 typical gaps (standard deviations), 18.6% of 15 to 24 year oldsWest Bank and Gaza: -0.578 typical gaps (standard deviations), 25.8% of 15 to 24 year oldsRomania: 0.637 typical gaps (standard deviations), 17% of 15 to 24 year oldsRussian Federation: 1.14 typical gaps (standard deviations), 6.68% of 15 to 24 year oldsRwanda: -0.108 typical gaps (standard deviations), 15.3% of 15 to 24 year oldsSaudi Arabia: 0.782 typical gaps (standard deviations), 16.4% of 15 to 24 year oldsSudan: -1.35 typical gaps (standard deviations), 46.5% of 15 to 24 year oldsSenegal: -0.0349 typical gaps (standard deviations), 35.2% of 15 to 24 year oldsSingapore: 1.05 typical gaps (standard deviations), 6.46% of 15 to 24 year oldsSolomon Islands: -0.0344 typical gaps (standard deviations), 7.04% of 15 to 24 year oldsSierra Leone: -0.503 typical gaps (standard deviations), 25.9% of 15 to 24 year oldsEl Salvador: 0.523 typical gaps (standard deviations), 20.5% of 15 to 24 year oldsSomalia, Fed. Rep.: -1.05 typical gaps (standard deviations), 51% of 15 to 24 year oldsSerbia: 0.409 typical gaps (standard deviations), 12.7% of 15 to 24 year oldsSao Tome and Principe: -0.0345 typical gaps (standard deviations), 43.8% of 15 to 24 year oldsSuriname: 0.385 typical gaps (standard deviations), 18.2% of 15 to 24 year oldsSlovak Republic: 0.713 typical gaps (standard deviations), 8.75% of 15 to 24 year oldsSlovenia: 0.914 typical gaps (standard deviations), 7.13% of 15 to 24 year oldsSweden: 0.531 typical gaps (standard deviations), 5.58% of 15 to 24 year oldsEswatini: -1.97 typical gaps (standard deviations), 36.5% of 15 to 24 year oldsSeychelles: 0.418 typical gaps (standard deviations), 16.9% of 15 to 24 year oldsChad: -0.409 typical gaps (standard deviations), 42.3% of 15 to 24 year oldsTogo: -0.201 typical gaps (standard deviations), 13% of 15 to 24 year oldsThailand: 0.466 typical gaps (standard deviations), 12.4% of 15 to 24 year oldsTajikistan: 0.278 typical gaps (standard deviations), 41.3% of 15 to 24 year oldsTimor-Leste: 0.141 typical gaps (standard deviations), 29.1% of 15 to 24 year oldsTonga: 0.576 typical gaps (standard deviations), 25.6% of 15 to 24 year oldsTrinidad and Tobago: 0.701 typical gaps (standard deviations), 15.8% of 15 to 24 year oldsTunisia: -0.205 typical gaps (standard deviations), 22.6% of 15 to 24 year oldsTurkiye: 0.224 typical gaps (standard deviations), 22.9% of 15 to 24 year oldsTuvalu: -0.6 typical gaps (standard deviations), 34.3% of 15 to 24 year oldsTanzania: -0.328 typical gaps (standard deviations), 12.8% of 15 to 24 year oldsUganda: -0.25 typical gaps (standard deviations), 13.8% of 15 to 24 year oldsUkraine: 0.208 typical gaps (standard deviations), 16.5% of 15 to 24 year oldsUruguay: 0.298 typical gaps (standard deviations), 14% of 15 to 24 year oldsUnited States: 0.862 typical gaps (standard deviations), 11.6% of 15 to 24 year oldsViet Nam: 0.283 typical gaps (standard deviations), 10.3% of 15 to 24 year oldsVanuatu: -0.495 typical gaps (standard deviations), 29.3% of 15 to 24 year oldsSamoa: 0.181 typical gaps (standard deviations), 30.1% of 15 to 24 year oldsSouth Africa: -1.51 typical gaps (standard deviations), 34.6% of 15 to 24 year oldsZambia: -0.432 typical gaps (standard deviations), 26.7% of 15 to 24 year oldsZimbabwe: -0.673 typical gaps (standard deviations), 36% of 15 to 24 year oldsBLRDJIGABHKGHTIJPNMACNRUOMNSWZTJK

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6544.2e-22170
By rank (Spearman), not adjusted-0.6957.9e-26170
Standard (Pearson), adjusted for national income-0.4441.4e-9170
By rank (Spearman), adjusted for national income-0.4171.8e-8170

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 170 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: +0.209 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.532 (without BLR) to -0.412 (without NRU). 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: BLR (-0.088).

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 & Pacific28-0.78
Europe & Central Asia46+0.09
Latin America & Caribbean28-0.26
Middle East, North Africa, Afghanistan & Pakistan17-0.44
Sub-Saharan Africa43-0.49

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 third57-0.46
Middle third56-0.59
Richest third57-0.22

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
Christians103-0.34
Muslims42-0.40

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
France22-0.51
Never a European colony17-0.04
Spain18-0.28
United Kingdom55-0.65

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 = -0.462 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.

Loading…

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 15 to 24 year olds		Years
United Arab Emirates ARE1.178.92%2025, 2023
Russian Federation RUS1.146.68%2025
Macao SAR, China MAC1.124.96%2025, 2016
Hong Kong SAR, China HKG1.115.95%2025
Singapore SGP1.056.46%2025
Oman OMN1.0425.5%2025, 2022
Japan JPN1.033.14%2025, 2019
Germany DEU0.9887.58%2025, 2024
Belarus BLR0.97865.7%2025, 2024
Bulgaria BGR0.95410.9%2025, 2024
Malta MLT0.9477.84%2025, 2024
Israel ISR0.94315.3%2025, 2024
Iceland ISL0.9193.98%2025, 2024
Switzerland CHE0.9166.99%2025
Norway NOR0.9155.82%2025, 2024

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.

Youth not in work or study

Share of youth not in education, employment or training, total (% of youth population)

International Labour Organization · CC BY 4.0 · updated quarterly

From labour force surveys, so yearly for about a hundred countries and occasional elsewhere.

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 Labour score and Youth not in work or study

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

More links with Labour score or Youth not in work or study