placestats

Labour score vs. Premature NCD mortality

Fades after adjusting for income

Across 178 countries, Labour score and Premature NCD mortality rise and fall together at r = -0.54 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.11, which could easily be chance (q = 0.68, 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 premature ncd mortality, 13.1% to 22.9% probability, ages 30-70, labour score is worse by 0.487 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0906 typical gaps.

Link before adjusting (r)-0.538p = 9.0e-15 · 178 countries
After adjusting for national income-0.108p = 0.152 · likely range [-0.25, +0.04]
p, allowing for regions0.367standard error 0.111 · 7 regions
Check for luck (q)0.679across 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
Premature NCD mortality · % probability, ages 30-70link with income -0.70 · 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.

Every country as a dot

Labour score · typical gaps (standard deviations) →Premature NCD mortality · % probability, ages 30-70 →-1.971.366.9%44.1%Afghanistan: -1.26 typical gaps (standard deviations), 32.7% probability, ages 30-70Angola: -0.939 typical gaps (standard deviations), 24.7% probability, ages 30-70Albania: -0.39 typical gaps (standard deviations), 10% probability, ages 30-70United Arab Emirates: 1.17 typical gaps (standard deviations), 11.6% probability, ages 30-70Argentina: 0.34 typical gaps (standard deviations), 13.4% probability, ages 30-70Armenia: -0.302 typical gaps (standard deviations), 20.6% probability, ages 30-70Antigua and Barbuda: 0.235 typical gaps (standard deviations), 12.1% probability, ages 30-70Australia: 0.867 typical gaps (standard deviations), 8.3% probability, ages 30-70Austria: 0.671 typical gaps (standard deviations), 9.9% probability, ages 30-70Azerbaijan: -0.0868 typical gaps (standard deviations), 17.4% probability, ages 30-70Burundi: -0.275 typical gaps (standard deviations), 24.8% probability, ages 30-70Belgium: 0.682 typical gaps (standard deviations), 9.2% probability, ages 30-70Benin: -0.233 typical gaps (standard deviations), 20.8% probability, ages 30-70Burkina Faso: -0.52 typical gaps (standard deviations), 23.4% probability, ages 30-70Bangladesh: -0.0147 typical gaps (standard deviations), 18.1% probability, ages 30-70Bulgaria: 0.954 typical gaps (standard deviations), 25.6% probability, ages 30-70Bahrain: 1.27 typical gaps (standard deviations), 14.8% probability, ages 30-70Bahamas, The: 0.318 typical gaps (standard deviations), 20.4% probability, ages 30-70Bosnia and Herzegovina: 0.126 typical gaps (standard deviations), 17.2% probability, ages 30-70Belarus: 0.978 typical gaps (standard deviations), 23.8% probability, ages 30-70Belize: 0.0851 typical gaps (standard deviations), 15.4% probability, ages 30-70Bolivia: -0.0485 typical gaps (standard deviations), 19.9% probability, ages 30-70Brazil: 0.16 typical gaps (standard deviations), 14.5% probability, ages 30-70Barbados: 0.485 typical gaps (standard deviations), 14% probability, ages 30-70Brunei Darussalam: 0.857 typical gaps (standard deviations), 14.9% probability, ages 30-70Bhutan: -0.0349 typical gaps (standard deviations), 15.9% probability, ages 30-70Botswana: -1.12 typical gaps (standard deviations), 19% probability, ages 30-70Central African Republic: -0.633 typical gaps (standard deviations), 30.8% probability, ages 30-70Canada: 0.581 typical gaps (standard deviations), 9.7% probability, ages 30-70Switzerland: 0.916 typical gaps (standard deviations), 7.5% probability, ages 30-70Chile: 0.344 typical gaps (standard deviations), 9.4% probability, ages 30-70China: 0.367 typical gaps (standard deviations), 15.9% probability, ages 30-70Cote d'Ivoire: -0.122 typical gaps (standard deviations), 22.7% probability, ages 30-70Cameroon: -0.192 typical gaps (standard deviations), 24.3% probability, ages 30-70Congo, Dem. Rep.: -0.523 typical gaps (standard deviations), 25.1% probability, ages 30-70Congo, Rep.: -1.69 typical gaps (standard deviations), 21.2% probability, ages 30-70Colombia: -0.377 typical gaps (standard deviations), 10.3% probability, ages 30-70Comoros: -0.00862 typical gaps (standard deviations), 20.2% probability, ages 30-70Cabo Verde: -0.252 typical gaps (standard deviations), 17.2% probability, ages 30-70Costa Rica: 0.133 typical gaps (standard deviations), 9.9% probability, ages 30-70Cyprus: 0.812 typical gaps (standard deviations), 8.5% probability, ages 30-70Czechia: 0.889 typical gaps (standard deviations), 14.2% probability, ages 30-70Germany: 0.988 typical gaps (standard deviations), 11.6% probability, ages 30-70Djibouti: -1.82 typical gaps (standard deviations), 20.6% probability, ages 30-70Denmark: 0.819 typical gaps (standard deviations), 10.7% probability, ages 30-70Dominican Republic: -0.0819 typical gaps (standard deviations), 17.4% probability, ages 30-70Algeria: -0.0627 typical gaps (standard deviations), 13.3% probability, ages 30-70Ecuador: -0.398 typical gaps (standard deviations), 13.2% probability, ages 30-70Egypt, Arab Rep.: 0.367 typical gaps (standard deviations), 26% probability, ages 30-70Spain: 0.483 typical gaps (standard deviations), 9.3% probability, ages 30-70Estonia: 0.558 typical gaps (standard deviations), 15.5% probability, ages 30-70Ethiopia: -0.481 typical gaps (standard deviations), 16.8% probability, ages 30-70Finland: 0.419 typical gaps (standard deviations), 9.6% probability, ages 30-70Fiji: 0.318 typical gaps (standard deviations), 37.9% probability, ages 30-70France: 0.595 typical gaps (standard deviations), 10.2% probability, ages 30-70Gabon: -0.844 typical gaps (standard deviations), 18.1% probability, ages 30-70United Kingdom: 0.635 typical gaps (standard deviations), 11% probability, ages 30-70Georgia: -0.418 typical gaps (standard deviations), 22.1% probability, ages 30-70Ghana: 0.00187 typical gaps (standard deviations), 22.2% probability, ages 30-70Guinea: -0.539 typical gaps (standard deviations), 23.6% probability, ages 30-70Gambia, The: -0.899 typical gaps (standard deviations), 21.8% probability, ages 30-70Guinea-Bissau: -0.255 typical gaps (standard deviations), 25.3% probability, ages 30-70Equatorial Guinea: 0.255 typical gaps (standard deviations), 20.3% probability, ages 30-70Greece: 0.426 typical gaps (standard deviations), 12% probability, ages 30-70Grenada: -0.263 typical gaps (standard deviations), 17.1% probability, ages 30-70Guatemala: 0.48 typical gaps (standard deviations), 14.6% probability, ages 30-70Guyana: -0.0857 typical gaps (standard deviations), 25.4% probability, ages 30-70Honduras: -0.175 typical gaps (standard deviations), 17.8% probability, ages 30-70Croatia: 0.844 typical gaps (standard deviations), 15.9% probability, ages 30-70Haiti: -1.28 typical gaps (standard deviations), 31.6% probability, ages 30-70Hungary: 0.856 typical gaps (standard deviations), 21.7% probability, ages 30-70Indonesia: 0.169 typical gaps (standard deviations), 21.9% probability, ages 30-70India: -0.486 typical gaps (standard deviations), 23.6% probability, ages 30-70Ireland: 0.813 typical gaps (standard deviations), 9.6% probability, ages 30-70Iran, Islamic Rep.: -0.0514 typical gaps (standard deviations), 13.9% probability, ages 30-70Iraq: -0.409 typical gaps (standard deviations), 22.7% probability, ages 30-70Iceland: 0.919 typical gaps (standard deviations), 8.5% probability, ages 30-70Israel: 0.943 typical gaps (standard deviations), 7.6% probability, ages 30-70Italy: 0.579 typical gaps (standard deviations), 9.1% probability, ages 30-70Jamaica: 0.511 typical gaps (standard deviations), 20.2% probability, ages 30-70Jordan: -0.133 typical gaps (standard deviations), 11.6% probability, ages 30-70Japan: 1.03 typical gaps (standard deviations), 8% probability, ages 30-70Kazakhstan: 0.516 typical gaps (standard deviations), 21.2% probability, ages 30-70Kenya: -0.274 typical gaps (standard deviations), 18% probability, ages 30-70Kyrgyz Republic: 0.54 typical gaps (standard deviations), 18.8% probability, ages 30-70Cambodia: 0.369 typical gaps (standard deviations), 22.6% probability, ages 30-70Kiribati: -1.29 typical gaps (standard deviations), 44.1% probability, ages 30-70Korea, Rep.: 0.822 typical gaps (standard deviations), 6.9% probability, ages 30-70Kuwait: 1.15 typical gaps (standard deviations), 9.3% probability, ages 30-70Lao PDR: -0.16 typical gaps (standard deviations), 26.5% probability, ages 30-70Lebanon: 0.107 typical gaps (standard deviations), 11.9% probability, ages 30-70Liberia: -0.308 typical gaps (standard deviations), 22% probability, ages 30-70Libya: -0.574 typical gaps (standard deviations), 19.8% probability, ages 30-70St. Lucia: -0.149 typical gaps (standard deviations), 15.5% probability, ages 30-70Sri Lanka: 0.307 typical gaps (standard deviations), 13.9% probability, ages 30-70Lesotho: -0.581 typical gaps (standard deviations), 36.3% probability, ages 30-70Lithuania: 0.626 typical gaps (standard deviations), 19.8% probability, ages 30-70Luxembourg: 0.723 typical gaps (standard deviations), 7.8% probability, ages 30-70Latvia: 0.673 typical gaps (standard deviations), 22.7% probability, ages 30-70Morocco: -0.0339 typical gaps (standard deviations), 22% probability, ages 30-70Moldova: 0.412 typical gaps (standard deviations), 24.7% probability, ages 30-70Madagascar: -0.463 typical gaps (standard deviations), 25.9% probability, ages 30-70Maldives: 0.686 typical gaps (standard deviations), 9% probability, ages 30-70Mexico: 0.513 typical gaps (standard deviations), 16.3% probability, ages 30-70North Macedonia: 0.0322 typical gaps (standard deviations), 19.6% probability, ages 30-70Mali: -0.283 typical gaps (standard deviations), 23% probability, ages 30-70Malta: 0.947 typical gaps (standard deviations), 9.8% probability, ages 30-70Myanmar: -0.0589 typical gaps (standard deviations), 24% probability, ages 30-70Montenegro: -0.0101 typical gaps (standard deviations), 18% probability, ages 30-70Mongolia: 0.223 typical gaps (standard deviations), 26.3% probability, ages 30-70Mozambique: -0.745 typical gaps (standard deviations), 26.6% probability, ages 30-70Mauritania: -0.477 typical gaps (standard deviations), 18.7% probability, ages 30-70Mauritius: 0.632 typical gaps (standard deviations), 21.7% probability, ages 30-70Malawi: -0.169 typical gaps (standard deviations), 24.9% probability, ages 30-70Malaysia: 0.654 typical gaps (standard deviations), 19.9% probability, ages 30-70Namibia: -0.775 typical gaps (standard deviations), 23.1% probability, ages 30-70Niger: -0.233 typical gaps (standard deviations), 20.2% probability, ages 30-70Nigeria: -0.128 typical gaps (standard deviations), 17.7% probability, ages 30-70Nicaragua: 0.229 typical gaps (standard deviations), 12.6% probability, ages 30-70Netherlands: 0.804 typical gaps (standard deviations), 9.9% probability, ages 30-70Norway: 0.915 typical gaps (standard deviations), 7.8% probability, ages 30-70Nepal: -0.934 typical gaps (standard deviations), 19.4% probability, ages 30-70New Zealand: 0.721 typical gaps (standard deviations), 10.3% probability, ages 30-70Oman: 1.04 typical gaps (standard deviations), 14.6% probability, ages 30-70Pakistan: -0.988 typical gaps (standard deviations), 25.5% probability, ages 30-70Panama: 0.0819 typical gaps (standard deviations), 10.9% probability, ages 30-70Peru: -0.454 typical gaps (standard deviations), 12.3% probability, ages 30-70Philippines: 0.559 typical gaps (standard deviations), 31.9% probability, ages 30-70Papua New Guinea: -0.175 typical gaps (standard deviations), 28.5% probability, ages 30-70Poland: 0.842 typical gaps (standard deviations), 17% probability, ages 30-70Puerto Rico (US): 0.688 typical gaps (standard deviations), 10.1% probability, ages 30-70Portugal: 0.843 typical gaps (standard deviations), 10.5% probability, ages 30-70Paraguay: -0.242 typical gaps (standard deviations), 16.1% probability, ages 30-70West Bank and Gaza: -0.578 typical gaps (standard deviations), 18.3% probability, ages 30-70Qatar: 1.36 typical gaps (standard deviations), 12.1% probability, ages 30-70Romania: 0.637 typical gaps (standard deviations), 21.5% probability, ages 30-70Russian Federation: 1.14 typical gaps (standard deviations), 22.4% probability, ages 30-70Rwanda: -0.108 typical gaps (standard deviations), 20% probability, ages 30-70Saudi Arabia: 0.782 typical gaps (standard deviations), 13.6% probability, ages 30-70Sudan: -1.35 typical gaps (standard deviations), 21.2% probability, ages 30-70Senegal: -0.0349 typical gaps (standard deviations), 21.4% probability, ages 30-70Singapore: 1.05 typical gaps (standard deviations), 10.3% probability, ages 30-70Solomon Islands: -0.0344 typical gaps (standard deviations), 40.6% probability, ages 30-70Sierra Leone: -0.503 typical gaps (standard deviations), 23.9% probability, ages 30-70El Salvador: 0.523 typical gaps (standard deviations), 12.6% probability, ages 30-70Somalia, Fed. Rep.: -1.05 typical gaps (standard deviations), 27.6% probability, ages 30-70Serbia: 0.409 typical gaps (standard deviations), 20.7% probability, ages 30-70Sao Tome and Principe: -0.0345 typical gaps (standard deviations), 19.5% probability, ages 30-70Suriname: 0.385 typical gaps (standard deviations), 18.7% probability, ages 30-70Slovak Republic: 0.713 typical gaps (standard deviations), 16.6% probability, ages 30-70Slovenia: 0.914 typical gaps (standard deviations), 11.6% probability, ages 30-70Sweden: 0.531 typical gaps (standard deviations), 7.9% probability, ages 30-70Eswatini: -1.97 typical gaps (standard deviations), 32.3% probability, ages 30-70Seychelles: 0.418 typical gaps (standard deviations), 15.6% probability, ages 30-70Syrian Arab Republic: -0.472 typical gaps (standard deviations), 21.3% probability, ages 30-70Chad: -0.409 typical gaps (standard deviations), 23.4% probability, ages 30-70Togo: -0.201 typical gaps (standard deviations), 25.4% probability, ages 30-70Thailand: 0.466 typical gaps (standard deviations), 14.5% probability, ages 30-70Tajikistan: 0.278 typical gaps (standard deviations), 18.1% probability, ages 30-70Turkmenistan: 0.492 typical gaps (standard deviations), 25.7% probability, ages 30-70Timor-Leste: 0.141 typical gaps (standard deviations), 20.1% probability, ages 30-70Tonga: 0.576 typical gaps (standard deviations), 26.9% probability, ages 30-70Trinidad and Tobago: 0.701 typical gaps (standard deviations), 20.3% probability, ages 30-70Tunisia: -0.205 typical gaps (standard deviations), 13% probability, ages 30-70Turkiye: 0.224 typical gaps (standard deviations), 15.4% probability, ages 30-70Tanzania: -0.328 typical gaps (standard deviations), 18.8% probability, ages 30-70Uganda: -0.25 typical gaps (standard deviations), 21.5% probability, ages 30-70Ukraine: 0.208 typical gaps (standard deviations), 24.8% probability, ages 30-70Uruguay: 0.298 typical gaps (standard deviations), 17.4% probability, ages 30-70United States: 0.862 typical gaps (standard deviations), 13.7% probability, ages 30-70Uzbekistan: 0.368 typical gaps (standard deviations), 24.6% probability, ages 30-70St. Vincent and the Grenadines: -0.519 typical gaps (standard deviations), 23.6% probability, ages 30-70Viet Nam: 0.283 typical gaps (standard deviations), 20.1% probability, ages 30-70Vanuatu: -0.495 typical gaps (standard deviations), 36.8% probability, ages 30-70Samoa: 0.181 typical gaps (standard deviations), 32.3% probability, ages 30-70South Africa: -1.51 typical gaps (standard deviations), 22.7% probability, ages 30-70Zambia: -0.432 typical gaps (standard deviations), 23.6% probability, ages 30-70Zimbabwe: -0.673 typical gaps (standard deviations), 31.2% probability, ages 30-70AFGALBCOGDJIFJIJPNKIRKWTPHLQATSLBSWZ

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5389.0e-15178
By rank (Spearman), not adjusted-0.5558.5e-16178
Standard (Pearson), adjusted for national income-0.1080.152178
By rank (Spearman), adjusted for national income-0.0110.883178

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 178 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: +0.430 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.133 (without SLB) to -0.055 (without SWZ). 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: SWZ (+0.053).

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 & Pacific24-0.36
Europe & Central Asia48+0.19
Latin America & Caribbean30-0.34
Middle East, North Africa, Afghanistan & Pakistan22-0.10
Sub-Saharan Africa46-0.35

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 third60-0.19
Middle third59-0.03
Richest third59-0.03

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
Christians108-0.14
Muslims49-0.06

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
France25-0.22
Never a European colony21-0.33
Spain19+0.32
United Kingdom59-0.23

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 = -0.065 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.

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)

% probability, ages 30-70
Years
Qatar QAT1.3612.1%2025, 2021
Bahrain BHR1.2714.8%2025, 2021
United Arab Emirates ARE1.1711.6%2025, 2021
Kuwait KWT1.159.3%2025, 2021
Russian Federation RUS1.1422.4%2025, 2021
Singapore SGP1.0510.3%2025, 2021
Oman OMN1.0414.6%2025, 2021
Japan JPN1.038%2025, 2021
Germany DEU0.98811.6%2025, 2021
Belarus BLR0.97823.8%2025, 2021
Bulgaria BGR0.95425.6%2025, 2021
Malta MLT0.9479.8%2025, 2021
Israel ISR0.9437.6%2025, 2021
Iceland ISL0.9198.5%2025, 2021
Switzerland CHE0.9167.5%2025, 2021

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.

Premature NCD mortality

Mortality from cardiovascular disease, cancer, diabetes or chronic respiratory disease between ages 30 and 70 (%)

World Health Organization · CC BY 4.0 · updated quarterly

The series ends in 2021.

How this was computed

Data used: most recent value in 2010-2025; values come from 2018–2025. 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 Premature NCD mortality

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

More links with Labour score or Premature NCD mortality