Education score vs. Informal employment

Fades after adjusting for income

Across 139 countries, Education score and Informal employment rise and fall together at r = -0.78 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.23, which could easily be chance (q = 0.21, 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.

Comparing countries across the middle half of informal employment, 15.4% to 83.9% of employment, education score is worse by 1.02 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.35 typical gaps.

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.

Every country as a dot

Four ways to measure the link

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 139 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: +0.543 (a minus sign means it grew).

How solid is it?

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

Sources

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 6 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 Education score and Informal employment

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

More links with Education score or Informal employment

• Informal employment vs Retirement age, men before +0.63 → after adjusting +0.57
• Informal employment vs Population 65+ before -0.87 → after adjusting -0.54
• Informal employment vs Median age before -0.88 → after adjusting -0.51
• Education score vs Married by 18 before -0.72 → after adjusting -0.48
• Government expense vs Informal employment before -0.52 → after adjusting -0.46
• Informal employment vs PISA maths before -0.81 → after adjusting -0.46
• Informal employment vs Rule of law before -0.79 → after adjusting -0.45
• Education score vs Population growth before -0.59 → after adjusting -0.42