placestats

Informal employment vs. PISA reading

Fades after adjusting for income

Across 63 countries, Informal employment and PISA reading rise and fall together at r = -0.74 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.27, which could easily be chance (q = 0.29, 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 reading, 396 to 477 PISA points, informal employment is better by 31.2% of employment at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 11.1% of employment.

Link before adjusting (r)-0.737p = 5.6e-12 · 63 countries
After adjusting for national income-0.271p = 0.033 · likely range [-0.49, -0.02]
p, allowing for regions0.043standard error 0.080 · 4 regions
Check for luck (q)0.288across 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 reading · PISA pointslink with income +0.77 · 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 reading · PISA points →0.63%89.4%329516Argentina: 51.6% of employment, 401 PISA pointsAustria: 1.32% of employment, 480 PISA pointsBelgium: 1.37% of employment, 479 PISA pointsBulgaria: 1.2% of employment, 404 PISA pointsBosnia and Herzegovina: 16.6% of employment, 403 PISA pointsBrazil: 35.6% of employment, 410 PISA pointsBrunei Darussalam: 21.4% of employment, 429 PISA pointsSwitzerland: 1.48% of employment, 483 PISA pointsChile: 26.4% of employment, 448 PISA pointsColombia: 56% of employment, 409 PISA pointsCosta Rica: 36.1% of employment, 415 PISA pointsCyprus: 1.68% of employment, 381 PISA pointsCzechia: 2.26% of employment, 489 PISA pointsGermany: 2.49% of employment, 480 PISA pointsDenmark: 2.82% of employment, 489 PISA pointsDominican Republic: 54.7% of employment, 351 PISA pointsSpain: 2.2% of employment, 474 PISA pointsEstonia: 2.4% of employment, 511 PISA pointsFinland: 2.19% of employment, 490 PISA pointsFrance: 3.15% of employment, 474 PISA pointsUnited Kingdom: 6.51% of employment, 494 PISA pointsGeorgia: 51.5% of employment, 374 PISA pointsGreece: 3.58% of employment, 438 PISA pointsGuatemala: 75.9% of employment, 374 PISA pointsCroatia: 1.17% of employment, 475 PISA pointsHungary: 1.31% of employment, 473 PISA pointsIndonesia: 81% of employment, 359 PISA pointsIreland: 1.9% of employment, 516 PISA pointsIceland: 1.97% of employment, 436 PISA pointsItaly: 3.42% of employment, 482 PISA pointsJamaica: 54.6% of employment, 410 PISA pointsJordan: 55.1% of employment, 342 PISA pointsCambodia: 89.4% of employment, 329 PISA pointsKorea, Rep.: 29.1% of employment, 515 PISA pointsLebanon: 55.5% of employment, 353 PISA pointsLithuania: 3.2% of employment, 472 PISA pointsLuxembourg: 1.93% of employment, 470 PISA pointsLatvia: 2.17% of employment, 475 PISA pointsMoldova: 52.2% of employment, 411 PISA pointsMexico: 56.4% of employment, 415 PISA pointsNorth Macedonia: 11.1% of employment, 359 PISA pointsMalta: 0.63% of employment, 445 PISA pointsMongolia: 27.1% of employment, 378 PISA pointsNetherlands: 2.63% of employment, 459 PISA pointsNorway: 1.43% of employment, 477 PISA pointsPanama: 58.7% of employment, 392 PISA pointsPeru: 70.5% of employment, 408 PISA pointsPoland: 8.62% of employment, 489 PISA pointsPortugal: 4.73% of employment, 477 PISA pointsParaguay: 64.5% of employment, 373 PISA pointsWest Bank and Gaza: 48.8% of employment, 349 PISA pointsRomania: 2.21% of employment, 428 PISA pointsRussian Federation: 19.3% of employment, 479 PISA pointsEl Salvador: 64% of employment, 365 PISA pointsSerbia: 17.8% of employment, 440 PISA pointsSlovak Republic: 4.37% of employment, 447 PISA pointsSlovenia: 0.7% of employment, 469 PISA pointsSweden: 2.83% of employment, 487 PISA pointsThailand: 63.2% of employment, 379 PISA pointsTunisia: 36.9% of employment, 361 PISA pointsTurkiye: 27.7% of employment, 456 PISA pointsUruguay: 28.5% of employment, 430 PISA pointsViet Nam: 67% of employment, 462 PISA pointsBGRCYPESTFINGTMIDNIRLKHMKORMKDPERVNM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7375.6e-1263
By rank (Spearman), not adjusted-0.6391.7e-863
Standard (Pearson), adjusted for national income-0.2710.03363
By rank (Spearman), adjusted for national income-0.0440.73663

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: +0.466 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.336 (without VNM) to -0.225 (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 (-0.065).

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 Asia37-0.16
Latin America & Caribbean14-0.62

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+0.06
Middle third21-0.41
Richest third21+0.13

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
Christians47-0.34

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
Spain12-0.56

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 = -0.271 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%3292019, 2022
Indonesia IDN81%3592023, 2022
Guatemala GTM75.9%3742024, 2022
Peru PER70.5%4082025, 2022
Viet Nam VNM67%4622024, 2022
Paraguay PRY64.5%3732025, 2022
El Salvador SLV64%3652024, 2022
Thailand THA63.2%3792024, 2022
Panama PAN58.7%3922024, 2022
Mexico MEX56.4%4152024, 2022
Colombia COL56%4092025, 2022
Lebanon LBN55.5%3532019, 2018
Jordan JOR55.1%3422023, 2022
Dominican Republic DOM54.7%3512024, 2022
Jamaica JAM54.6%4102023, 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 reading

PISA mean reading score

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

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 reading

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

More links with Informal employment or PISA reading