Do Labour score and Market-income Gini go together across countries?

Across 45 countries the link before adjusting for anything is r = -0.72, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.49.

Does the link hold within countries over time, not just across countries?

There are not enough years of data within countries to check this over time.

Does Labour score cause Market-income Gini?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Labour score vs. Market-income Gini

Fades after adjusting for income

Across 45 countries, Labour score and Market-income Gini rise and fall together at r = -0.72 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) leaves r = -0.62, which could easily be chance (q = 0.13, allowing for the many pairs tested, and allowing for countries in the same region being alike). Caution: leave-one-out range spans 0.30 in r.

Comparing countries across the middle half of market-income gini, 0.434 to 0.508 points, labour score is worse by 0.428 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.301 typical gaps.

Link before adjusting (r)-0.723p = 2.1e-8 · 45 countries

After adjusting for national income-0.621p = 6.9e-6 · likely range [-0.77, -0.40]

p, allowing for regions0.002standard error 0.118 · 7 regions

Check for luck (q)0.131across 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

Market-income Gini · index, 0 to 1link with income -0.36 · moderate

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

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	-0.723	2.1e-8	45
By rank (Spearman), not adjusted	-0.491	0.001	45
Standard (Pearson), adjusted for national income	-0.621	6.9e-6	45
By rank (Spearman), adjusted for national income	-0.394	0.008	45

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 45 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: +0.102 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.668 (without BGR) to -0.364 (without ZAF). 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 (+0.257).

Fragile: this link may not hold up.

leave-one-out range spans 0.30 in r
correlation moves sharply once outliers are trimmed

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
Europe & Central Asia	31	-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.

	n	r
Poorest third	15	-0.77
Middle third	15	-0.48
Richest third	15	-0.26

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	36	-0.70

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 = -0.413 after dropping 2 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)	index, 0 to 1	Years
Russian Federation RUS	1.14	0.434	2025, 2017
Japan JPN	1.03	0.513	2025, 2021
Germany DEU	0.988	0.505	2025, 2022
Bulgaria BGR	0.954	0.52	2025, 2023
Israel ISR	0.943	0.449	2025, 2022
Iceland ISL	0.919	0.384	2025, 2019
Switzerland CHE	0.916	0.402	2025, 2022
Norway NOR	0.915	0.434	2025, 2022
Slovenia SVN	0.914	0.429	2025, 2023
Czechia CZE	0.889	0.429	2025, 2023
Australia AUS	0.867	0.441	2025, 2020
United States USA	0.862	0.506	2025, 2023
Hungary HUN	0.856	0.417	2025, 2022
Croatia HRV	0.844	0.451	2025, 2023
Portugal PRT	0.843	0.504	2025, 2023

un_sdg; wdi
OECD databases as named on each indicator: PISA, Social Expenditure Database (SOCX), Income Distribution Database, Development Assistance Committee statistics, Family Database, Government at a Glance, Corporate Tax Statistics, employment and environment statistics

Sources

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.

Market-income Gini

Gini coefficient of market income

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator · comparable from 2011 only

Inequality of household income before taxes and government payments, for the whole population. It is not the Gini already on the site, which is of income after taxes and payments. The gap between the two shows what the government does. The number of countries jumps from 24 to 34 in 2011, when the OECD changed its definition of income, so the series is used from 2011 on.

How this was computed

Data used: most recent value in 2010-2025; values come from 2011–2025. 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 Market-income Gini

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