placestats

Informal employment vs. PISA maths

Fades after adjusting for income

Across 63 countries, Informal employment and PISA maths rise and fall together at r = -0.81 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.46, which could easily be chance (q = 0.22, 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 pisa maths, 389 to 482 PISA points, informal employment is better by 37.8% of employment at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 21.1% of employment.

Link before adjusting (r)-0.806p = 1.6e-15 · 63 countries
After adjusting for national income-0.462p = 0.000 · likely range [-0.64, -0.24]
p, allowing for regions0.109standard error 0.205 · 4 regions
Check for luck (q)0.222across 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.

Informal employment · % of employmentlink with income -0.88 · proxy
PISA maths · PISA pointslink with income +0.79 · 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

Informal employment · % of employment →PISA maths · PISA points →0.63%89.4%336527Argentina: 51.6% of employment, 378 PISA pointsAustria: 1.32% of employment, 487 PISA pointsBelgium: 1.37% of employment, 489 PISA pointsBulgaria: 1.2% of employment, 417 PISA pointsBosnia and Herzegovina: 16.6% of employment, 406 PISA pointsBrazil: 35.6% of employment, 379 PISA pointsBrunei Darussalam: 21.4% of employment, 442 PISA pointsSwitzerland: 1.48% of employment, 508 PISA pointsChile: 26.4% of employment, 412 PISA pointsColombia: 56% of employment, 383 PISA pointsCosta Rica: 36.1% of employment, 385 PISA pointsCyprus: 1.68% of employment, 418 PISA pointsCzechia: 2.26% of employment, 487 PISA pointsGermany: 2.49% of employment, 475 PISA pointsDenmark: 2.82% of employment, 489 PISA pointsDominican Republic: 54.7% of employment, 339 PISA pointsSpain: 2.2% of employment, 473 PISA pointsEstonia: 2.4% of employment, 510 PISA pointsFinland: 2.19% of employment, 484 PISA pointsFrance: 3.15% of employment, 474 PISA pointsUnited Kingdom: 6.51% of employment, 489 PISA pointsGeorgia: 51.5% of employment, 390 PISA pointsGreece: 3.58% of employment, 430 PISA pointsGuatemala: 75.9% of employment, 344 PISA pointsCroatia: 1.17% of employment, 463 PISA pointsHungary: 1.31% of employment, 473 PISA pointsIndonesia: 81% of employment, 366 PISA pointsIreland: 1.9% of employment, 492 PISA pointsIceland: 1.97% of employment, 459 PISA pointsItaly: 3.42% of employment, 471 PISA pointsJamaica: 54.6% of employment, 377 PISA pointsJordan: 55.1% of employment, 361 PISA pointsCambodia: 89.4% of employment, 336 PISA pointsKorea, Rep.: 29.1% of employment, 527 PISA pointsLebanon: 55.5% of employment, 393 PISA pointsLithuania: 3.2% of employment, 475 PISA pointsLuxembourg: 1.93% of employment, 483 PISA pointsLatvia: 2.17% of employment, 483 PISA pointsMoldova: 52.2% of employment, 414 PISA pointsMexico: 56.4% of employment, 395 PISA pointsNorth Macedonia: 11.1% of employment, 389 PISA pointsMalta: 0.63% of employment, 466 PISA pointsMongolia: 27.1% of employment, 425 PISA pointsNetherlands: 2.63% of employment, 493 PISA pointsNorway: 1.43% of employment, 468 PISA pointsPanama: 58.7% of employment, 357 PISA pointsPeru: 70.5% of employment, 391 PISA pointsPoland: 8.62% of employment, 489 PISA pointsPortugal: 4.73% of employment, 472 PISA pointsParaguay: 64.5% of employment, 338 PISA pointsWest Bank and Gaza: 48.8% of employment, 366 PISA pointsRomania: 2.21% of employment, 428 PISA pointsRussian Federation: 19.3% of employment, 488 PISA pointsEl Salvador: 64% of employment, 343 PISA pointsSerbia: 17.8% of employment, 440 PISA pointsSlovak Republic: 4.37% of employment, 464 PISA pointsSlovenia: 0.7% of employment, 485 PISA pointsSweden: 2.83% of employment, 482 PISA pointsThailand: 63.2% of employment, 394 PISA pointsTunisia: 36.9% of employment, 367 PISA pointsTurkiye: 27.7% of employment, 453 PISA pointsUruguay: 28.5% of employment, 409 PISA pointsViet Nam: 67% of employment, 469 PISA pointsBGRCHECYPESTGTMIDNIRLKHMKORMKDPERVNM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.8061.6e-1563
By rank (Spearman), not adjusted-0.7193.3e-1163
Standard (Pearson), adjusted for national income-0.4620.00063
By rank (Spearman), adjusted for national income-0.2040.11163

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 63 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: +0.344 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.547 (without VNM) to -0.402 (without PAN). 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: VNM (-0.085).

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
Europe & Central Asia37-0.20
Latin America & Caribbean14-0.58

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 third21-0.26
Middle third21-0.58
Richest third21+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
Christians47-0.59

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
Spain12-0.55

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 0 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


% of employment
PISA points		Years
Cambodia KHM89.4%3362019, 2022
Indonesia IDN81%3662023, 2022
Guatemala GTM75.9%3442024, 2022
Peru PER70.5%3912025, 2022
Viet Nam VNM67%4692024, 2022
Paraguay PRY64.5%3382025, 2022
El Salvador SLV64%3432024, 2022
Thailand THA63.2%3942024, 2022
Panama PAN58.7%3572024, 2022
Mexico MEX56.4%3952024, 2022
Colombia COL56%3832025, 2022
Lebanon LBN55.5%3932019, 2018
Jordan JOR55.1%3612023, 2022
Dominican Republic DOM54.7%3392024, 2022
Jamaica JAM54.6%3772023, 2022

United Nations Global SDG Indicators Database; series compiled by the International Labour Organization (8.3.1) and the UN Office on Drugs and Crime (16.3.2)
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

Informal employment

Share of employment that is informal

International Labour Organization · via Our World in Data · CC BY 3.0 IGO · updated annual

Workers with no contract, no social protection or no registered employer, as the ILO (the UN's labour agency) makes them comparable across national labour force surveys. The World Bank's copy of this series was retired, so it comes from the UN's database for the Sustainable Development Goals.

PISA maths

PISA mean mathematics score

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

The average score of 15-year-olds on the same test in every country taking part. It is scaled so the OECD average in the starting year was 500, with a standard deviation (a typical gap between students) of 100. Held every three years. The 2021 round was held in 2022, and the OECD says it can still be compared with earlier rounds despite the pandemic. The three subjects are treated as measuring the same thing. Their links with each other are above 0.95, and that close match is a fact about the test, not about schooling.

How this was computed

Data used: most recent value in 2010-2025; values come from 2015–2025. 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 4 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 Informal employment and PISA maths

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

More links with Informal employment or PISA maths