Across 177 countries, Labour score and Vulnerable employment 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 -0.11, which could easily be chance (q = 0.69, 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. Vulnerable employment 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 vulnerable employment, 11.7% to 56.8% of employment, labour score is worse by 0.756 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.164 typical gaps.
Link before adjusting (r)-0.660p = 1.7e-23 · 177 countries
After adjusting for national income-0.109p = 0.148 · likely range [-0.25, +0.04]
p, allowing for regions0.534standard error 0.166 · 7 regions
Check for luck (q)0.693across 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
Vulnerable employment · % of employmentlink with income -0.87 · 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.
Built from the same numbers. Vulnerable employment 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
best-fit line line after adjusting
Four ways to measure the link
Method
r
p
Countries
Standard (Pearson), not adjusted
-0.660
1.7e-23
177
By rank (Spearman), not adjusted
-0.777
5.3e-37
177
Standard (Pearson), adjusted for national income
-0.109
0.148
177
By rank (Spearman), adjusted for national income
-0.281
0.000
177
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 177 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: +0.550 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.155 (without ZAF) to -0.079 (without COG). 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: ZAF (-0.046).
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.
n
r
East Asia & Pacific
25
-0.64
Europe & Central Asia
48
-0.57
Latin America & Caribbean
29
-0.59
Middle East, North Africa, Afghanistan & Pakistan
22
-0.12
Sub-Saharan Africa
45
+0.19
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.
n
r
Poorest third
59
-0.09
Middle third
59
-0.01
Richest third
59
-0.68
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.
n
r
Christians
106
-0.19
Muslims
49
+0.05
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.
n
r
France
25
+0.07
Never a European colony
21
-0.73
Spain
19
-0.63
United Kingdom
55
+0.10
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 = -0.153 after dropping 3 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 employment
Years
Qatar QAT
1.36
0.456%
2025
Bahrain BHR
1.27
0.749%
2025
United Arab Emirates ARE
1.17
1.5%
2025
Kuwait KWT
1.15
2.4%
2025
Russian Federation RUS
1.14
4.93%
2025
Macao SAR, China MAC
1.12
3.33%
2025
Hong Kong SAR, China HKG
1.11
5.76%
2025
Singapore SGP
1.05
9.3%
2025
Oman OMN
1.04
3.81%
2025
Japan JPN
1.03
7.49%
2025
Germany DEU
0.988
4.34%
2025
Belarus BLR
0.978
6.33%
2025
Bulgaria BGR
0.954
6.92%
2025
Malta MLT
0.947
10.1%
2025
Israel ISR
0.943
7.7%
2025
un_sdg; wdi World Bank, World Development Indicators
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.
Vulnerable employment, total (% of total employment)
International Labour Organization · CC BY 4.0 · updated quarterly
Self-employed workers and unpaid family workers, who have no employer and usually no protection. The ILO estimates it with a model for every country and year, which is why it is complete where surveys are not.
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 Vulnerable employment
This page shows that two measures go together. It does not show that Labour score changes Vulnerable employment, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Labour score or Vulnerable employment