placestats

Informal employment vs. Researchers

Fades after adjusting for income

Across 98 countries, Informal employment and Researchers rise and fall together at r = -0.76 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.36, which could easily be chance (q = 0.098, 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, 3.25% to 82.2% of employment, researchers is worse by 4,155 per million people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 3,122 per million people.

Link before adjusting (r)-0.756p = 2.3e-19 · 98 countries
After adjusting for national income-0.363p = 0.000 · likely range [-0.52, -0.18]
p, allowing for regions0.017standard error 0.104 · 6 regions
Check for luck (q)0.098across 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
Researchers · per million peoplelink with income +0.85 · proxy

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 →Researchers · per million people →0.63%98.5%1.459,472Angola: 95.3% of employment, 18.9 per million peopleArgentina: 51.6% of employment, 1,313 per million peopleArmenia: 76.1% of employment, 942 per million peopleAustria: 1.32% of employment, 6,634 per million peopleBurundi: 98% of employment, 22.5 per million peopleBelgium: 1.37% of employment, 5,990 per million peopleBurkina Faso: 93.8% of employment, 46.6 per million peopleBulgaria: 1.2% of employment, 2,401 per million peopleBosnia and Herzegovina: 16.6% of employment, 705 per million peopleBolivia: 83.9% of employment, 63.1 per million peopleBrazil: 35.6% of employment, 903 per million peopleBrunei Darussalam: 21.4% of employment, 506 per million peopleBotswana: 75.6% of employment, 180 per million peopleSwitzerland: 1.48% of employment, 6,108 per million peopleChile: 26.4% of employment, 707 per million peopleCongo, Dem. Rep.: 96.3% of employment, 10.1 per million peopleColombia: 56% of employment, 90.2 per million peopleCabo Verde: 60.6% of employment, 125 per million peopleCosta Rica: 36.1% of employment, 508 per million peopleCyprus: 1.68% of employment, 1,686 per million peopleCzechia: 2.26% of employment, 4,505 per million peopleGermany: 2.49% of employment, 5,926 per million peopleDenmark: 2.82% of employment, 8,838 per million peopleDominican Republic: 54.7% of employment, 20 per million peopleEcuador: 68.5% of employment, 400 per million peopleEgypt, Arab Rep.: 68.6% of employment, 1,120 per million peopleSpain: 2.2% of employment, 3,654 per million peopleEstonia: 2.4% of employment, 4,800 per million peopleEthiopia: 77.7% of employment, 89 per million peopleFinland: 2.19% of employment, 8,315 per million peopleFrance: 3.15% of employment, 5,369 per million peopleUnited Kingdom: 6.51% of employment, 4,473 per million peopleGeorgia: 51.5% of employment, 1,896 per million peopleGhana: 78% of employment, 87.5 per million peopleGambia, The: 83.9% of employment, 50.9 per million peopleGreece: 3.58% of employment, 5,283 per million peopleGuatemala: 75.9% of employment, 1.45 per million peopleHonduras: 82.6% of employment, 188 per million peopleCroatia: 1.17% of employment, 2,440 per million peopleHungary: 1.31% of employment, 4,567 per million peopleIndonesia: 81% of employment, 395 per million peopleIndia: 88.1% of employment, 259 per million peopleIreland: 1.9% of employment, 5,753 per million peopleIraq: 67.6% of employment, 164 per million peopleIceland: 1.97% of employment, 5,953 per million peopleItaly: 3.42% of employment, 2,855 per million peopleJordan: 55.1% of employment, 576 per million peopleKenya: 86.5% of employment, 384 per million peopleCambodia: 89.4% of employment, 30.4 per million peopleKorea, Rep.: 29.1% of employment, 9,472 per million peopleSri Lanka: 67.6% of employment, 94.5 per million peopleLesotho: 89.1% of employment, 32.2 per million peopleLithuania: 3.2% of employment, 3,704 per million peopleLuxembourg: 1.93% of employment, 5,359 per million peopleLatvia: 2.17% of employment, 2,191 per million peopleMoldova: 52.2% of employment, 650 per million peopleMadagascar: 96.1% of employment, 32.9 per million peopleMexico: 56.4% of employment, 438 per million peopleNorth Macedonia: 11.1% of employment, 888 per million peopleMali: 94.4% of employment, 26 per million peopleMalta: 0.63% of employment, 2,355 per million peopleMyanmar: 80.7% of employment, 28 per million peopleMongolia: 27.1% of employment, 535 per million peopleMozambique: 95.5% of employment, 44.4 per million peopleMauritius: 23.4% of employment, 689 per million peopleMalawi: 92.7% of employment, 25.2 per million peopleNamibia: 55.6% of employment, 225 per million peopleNiger: 98.5% of employment, 26.9 per million peopleNigeria: 93.1% of employment, 22.1 per million peopleNetherlands: 2.63% of employment, 6,563 per million peopleNorway: 1.43% of employment, 7,451 per million peoplePakistan: 88.7% of employment, 474 per million peoplePanama: 58.7% of employment, 250 per million peoplePoland: 8.62% of employment, 3,675 per million peoplePortugal: 4.73% of employment, 5,983 per million peopleParaguay: 64.5% of employment, 139 per million peopleWest Bank and Gaza: 48.8% of employment, 584 per million peopleRomania: 2.21% of employment, 1,082 per million peopleRussian Federation: 19.3% of employment, 2,639 per million peopleRwanda: 84.3% of employment, 133 per million peopleSenegal: 89.2% of employment, 572 per million peopleEl Salvador: 64% of employment, 55.5 per million peopleSerbia: 17.8% of employment, 2,688 per million peopleSlovak Republic: 4.37% of employment, 3,507 per million peopleSlovenia: 0.7% of employment, 5,616 per million peopleSweden: 2.83% of employment, 8,911 per million peopleEswatini: 53.3% of employment, 138 per million peopleChad: 96.9% of employment, 56.7 per million peopleTogo: 92.3% of employment, 57.8 per million peopleThailand: 63.2% of employment, 1,592 per million peopleTunisia: 36.9% of employment, 1,668 per million peopleTurkiye: 27.7% of employment, 2,645 per million peopleTanzania: 90.8% of employment, 19.3 per million peopleUganda: 94.5% of employment, 81.3 per million peopleUruguay: 28.5% of employment, 938 per million peopleViet Nam: 67% of employment, 836 per million peopleSouth Africa: 34.8% of employment, 444 per million peopleZimbabwe: 85% of employment, 95.1 per million peopleBDICODCYPDNKFINKORMKDNERROUSWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7562.3e-1998
By rank (Spearman), not adjusted-0.8703.6e-3198
Standard (Pearson), adjusted for national income-0.3630.00098
By rank (Spearman), adjusted for national income-0.3360.00198

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 98 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: +0.393 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.423 (without KOR) to -0.339 (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: KOR (-0.060).

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 Asia38-0.07
Latin America & Caribbean15-0.48
Sub-Saharan Africa28-0.43

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 third33-0.41
Middle third32-0.33
Richest third33+0.01

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
Christians69-0.43
Muslims17-0.37

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
Spain14-0.37
United Kingdom23-0.36

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 = -0.455 after dropping 4 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 25years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Within countries over time, there is no link that is clearly more than chance (r = -0.024, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose informal employment moved did not tend to see their researchers move with it.

Across countries (adjusted)-0.363for comparison
Within countries-0.212each country vs itself
…and vs the same year elsewhere-0.024q = 0.838 · 56 countries
Year-on-year changes+0.014q = 0.778
Read with care.
  • Almost all of this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.

794 country-years (one country in one year), 56 countries, 2000–2024, about 14 years each on average. Share of the variation that happens within countries over time: 2% for Informal employment, 6% for Researchers. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.

Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.

With one measure years behind the other

0.20−0.2-100+10years Researchers trails Informal employment →lag -10: r = 0.141 (n = 668)lag -9: r = 0.104 (n = 703)lag -8: r = 0.079 (n = 724)lag -7: r = 0.081 (n = 743)lag -6: r = 0.083 (n = 761)lag -5: r = 0.061 (n = 779)lag -4: r = 0.032 (n = 791)lag -3: r = 0.001 (n = 808)lag -2: r = -0.031 (n = 818)lag -1: r = -0.030 (n = 821)lag +0: r = -0.024 (n = 794)lag +1: r = -0.001 (n = 748)lag +2: r = -0.006 (n = 705)lag +3: r = 0.009 (n = 662)lag +4: r = 0.007 (n = 622)lag +5: r = 0.007 (n = 569)lag +6: r = -0.010 (n = 511)lag +7: r = -0.012 (n = 447)lag +8: r = -0.038 (n = 415)lag +9: r = 0.015 (n = 341)lag +10: r = 0.005 (n = 293)

Same year (lag 0): -0.024. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.

This curve peaks at the edge of the window (lag -10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.

The data


% of employment
per million people		Years
Niger NER98.5%26.92022, 2013
Burundi BDI98%22.52014, 2018
Chad TCD96.9%56.72018, 2016
Congo, Dem. Rep. COD96.3%10.12020, 2015
Madagascar MDG96.1%32.92022, 2018
Mozambique MOZ95.5%44.42015
Angola AGO95.3%18.92024, 2016
Uganda UGA94.5%81.32021, 2023
Mali MLI94.4%262023
Burkina Faso BFA93.8%46.62024, 2010
Nigeria NGA93.1%22.12024, 2019
Malawi MWI92.7%25.22024, 2019
Togo TGO92.3%57.82022, 2024
Tanzania TZA90.8%19.32024, 2013
Cambodia KHM89.4%30.42019, 2015

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)
World Bank, World Development Indicators

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.

Researchers

Researchers in R&D (per million people)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

Researchers counted as full-time equivalents (two half-time researchers count as one), from national research surveys that many countries run only now and then. So coverage is thin outside the OECD.

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 Informal employment and Researchers

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

More links with Informal employment or Researchers