placestats

Informal employment vs. PISA science

Fades after adjusting for income

Across 63 countries, Informal employment and PISA science rise and fall together at r = -0.77 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.37, which could easily be chance (q = 0.33, 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 science, 405 to 488 PISA points, informal employment is better by 34.6% of employment at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 15.9% of employment.

Link before adjusting (r)-0.770p = 1.6e-13 · 63 countries
After adjusting for national income-0.367p = 0.003 · likely range [-0.56, -0.13]
p, allowing for regions0.095standard error 0.152 · 4 regions
Check for luck (q)0.333across 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 science · PISA pointslink with income +0.78 · 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 science · PISA points →0.63%89.4%347528Argentina: 51.6% of employment, 406 PISA pointsAustria: 1.32% of employment, 491 PISA pointsBelgium: 1.37% of employment, 491 PISA pointsBulgaria: 1.2% of employment, 421 PISA pointsBosnia and Herzegovina: 16.6% of employment, 398 PISA pointsBrazil: 35.6% of employment, 403 PISA pointsBrunei Darussalam: 21.4% of employment, 446 PISA pointsSwitzerland: 1.48% of employment, 503 PISA pointsChile: 26.4% of employment, 444 PISA pointsColombia: 56% of employment, 411 PISA pointsCosta Rica: 36.1% of employment, 411 PISA pointsCyprus: 1.68% of employment, 411 PISA pointsCzechia: 2.26% of employment, 498 PISA pointsGermany: 2.49% of employment, 492 PISA pointsDenmark: 2.82% of employment, 494 PISA pointsDominican Republic: 54.7% of employment, 360 PISA pointsSpain: 2.2% of employment, 485 PISA pointsEstonia: 2.4% of employment, 526 PISA pointsFinland: 2.19% of employment, 511 PISA pointsFrance: 3.15% of employment, 487 PISA pointsUnited Kingdom: 6.51% of employment, 500 PISA pointsGeorgia: 51.5% of employment, 384 PISA pointsGreece: 3.58% of employment, 441 PISA pointsGuatemala: 75.9% of employment, 373 PISA pointsCroatia: 1.17% of employment, 483 PISA pointsHungary: 1.31% of employment, 486 PISA pointsIndonesia: 81% of employment, 383 PISA pointsIreland: 1.9% of employment, 504 PISA pointsIceland: 1.97% of employment, 447 PISA pointsItaly: 3.42% of employment, 477 PISA pointsJamaica: 54.6% of employment, 403 PISA pointsJordan: 55.1% of employment, 375 PISA pointsCambodia: 89.4% of employment, 347 PISA pointsKorea, Rep.: 29.1% of employment, 528 PISA pointsLebanon: 55.5% of employment, 384 PISA pointsLithuania: 3.2% of employment, 484 PISA pointsLuxembourg: 1.93% of employment, 477 PISA pointsLatvia: 2.17% of employment, 494 PISA pointsMoldova: 52.2% of employment, 417 PISA pointsMexico: 56.4% of employment, 410 PISA pointsNorth Macedonia: 11.1% of employment, 380 PISA pointsMalta: 0.63% of employment, 466 PISA pointsMongolia: 27.1% of employment, 412 PISA pointsNetherlands: 2.63% of employment, 488 PISA pointsNorway: 1.43% of employment, 478 PISA pointsPanama: 58.7% of employment, 388 PISA pointsPeru: 70.5% of employment, 408 PISA pointsPoland: 8.62% of employment, 499 PISA pointsPortugal: 4.73% of employment, 484 PISA pointsParaguay: 64.5% of employment, 368 PISA pointsWest Bank and Gaza: 48.8% of employment, 369 PISA pointsRomania: 2.21% of employment, 428 PISA pointsRussian Federation: 19.3% of employment, 478 PISA pointsEl Salvador: 64% of employment, 373 PISA pointsSerbia: 17.8% of employment, 447 PISA pointsSlovak Republic: 4.37% of employment, 462 PISA pointsSlovenia: 0.7% of employment, 500 PISA pointsSweden: 2.83% of employment, 494 PISA pointsThailand: 63.2% of employment, 409 PISA pointsTunisia: 36.9% of employment, 386 PISA pointsTurkiye: 27.7% of employment, 476 PISA pointsUruguay: 28.5% of employment, 435 PISA pointsViet Nam: 67% of employment, 472 PISA pointsBGRCYPESTFINGTMIDNIRLKHMKORMKDPERVNM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7701.6e-1363
By rank (Spearman), not adjusted-0.7107.3e-1163
Standard (Pearson), adjusted for national income-0.3670.00363
By rank (Spearman), adjusted for national income-0.2090.10363

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: +0.404 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.438 (without VNM) to -0.312 (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 (-0.071).

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 Asia37-0.23
Latin America & Caribbean14-0.63

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 third21-0.01
Middle third21-0.47
Richest third21+0.16

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
Christians47-0.49

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
Spain12-0.63

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 = -0.367 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.

Loading…

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%3472019, 2022
Indonesia IDN81%3832023, 2022
Guatemala GTM75.9%3732024, 2022
Peru PER70.5%4082025, 2022
Viet Nam VNM67%4722024, 2022
Paraguay PRY64.5%3682025, 2022
El Salvador SLV64%3732024, 2022
Thailand THA63.2%4092024, 2022
Panama PAN58.7%3882024, 2022
Mexico MEX56.4%4102024, 2022
Colombia COL56%4112025, 2022
Lebanon LBN55.5%3842019, 2018
Jordan JOR55.1%3752023, 2022
Dominican Republic DOM54.7%3602024, 2022
Jamaica JAM54.6%4032023, 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 science

PISA mean science 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 science

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

More links with Informal employment or PISA science