placestats

Informal employment vs. Migrant share of population

Fades after adjusting for income

Across 140 countries, Informal employment and Migrant share of population rise and fall together at r = -0.60 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.24, which could easily be chance (q = 0.32, 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. Caution: direction is not consistent across regions.

Comparing countries across the middle half of informal employment, 16% to 83.9% of employment, migrant share of population is 10.9% of population lower at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 7.08% of population.

Link before adjusting (r)-0.599p = 5.6e-15 · 140 countries
After adjusting for national income-0.238p = 0.005 · likely range [-0.39, -0.07]
p, allowing for regions0.131standard error 0.132 · 6 regions
Check for luck (q)0.316across 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
Migrant share of population · % of populationlink with income +0.73 · 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 →Migrant share of population · % of population →0.63%98.5%0.1%51.2%Afghanistan: 86.1% of employment, 0.2% of populationAngola: 95.3% of employment, 1.8% of populationArgentina: 51.6% of employment, 4.3% of populationArmenia: 76.1% of employment, 9.2% of populationAntigua and Barbuda: 12.8% of employment, 32.5% of populationAustria: 1.32% of employment, 25.5% of populationBurundi: 98% of employment, 2.8% of populationBelgium: 1.37% of employment, 20% of populationBenin: 96.3% of employment, 2.9% of populationBurkina Faso: 93.8% of employment, 3.1% of populationBangladesh: 84% of employment, 1.7% of populationBulgaria: 1.2% of employment, 4.4% of populationBahamas, The: 18.3% of employment, 16.8% of populationBosnia and Herzegovina: 16.6% of employment, 1.1% of populationBolivia: 83.9% of employment, 1.5% of populationBrazil: 35.6% of employment, 0.7% of populationBarbados: 61.8% of employment, 12.5% of populationBrunei Darussalam: 21.4% of employment, 25.9% of populationBotswana: 75.6% of employment, 4.6% of populationSwitzerland: 1.48% of employment, 31.1% of populationChile: 26.4% of employment, 7.8% of populationCote d'Ivoire: 92.1% of employment, 9% of populationCameroon: 87.1% of employment, 2.2% of populationCongo, Dem. Rep.: 96.3% of employment, 1% of populationColombia: 56% of employment, 5.8% of populationComoros: 88.8% of employment, 1.4% of populationCabo Verde: 60.6% of employment, 3.1% of populationCosta Rica: 36.1% of employment, 12.2% of populationCyprus: 1.68% of employment, 14.9% of populationCzechia: 2.26% of employment, 9.5% of populationGermany: 2.49% of employment, 19.8% of populationDjibouti: 47.6% of employment, 10.8% of populationDenmark: 2.82% of employment, 14.2% of populationDominican Republic: 54.7% of employment, 6.5% of populationEcuador: 68.5% of employment, 4.1% of populationEgypt, Arab Rep.: 68.6% of employment, 1% of populationSpain: 2.2% of employment, 18.5% of populationEstonia: 2.4% of employment, 14.9% of populationEthiopia: 77.7% of employment, 0.9% of populationFinland: 2.19% of employment, 9.2% of populationFiji: 45.7% of employment, 1.5% of populationFrance: 3.15% of employment, 13.8% of populationUnited Kingdom: 6.51% of employment, 17.1% of populationGeorgia: 51.5% of employment, 2.1% of populationGhana: 78% of employment, 1.5% of populationGambia, The: 83.9% of employment, 8.6% of populationGuinea-Bissau: 94.8% of employment, 0.7% of populationGreece: 3.58% of employment, 14.2% of populationGrenada: 48% of employment, 6.3% of populationGuatemala: 75.9% of employment, 0.5% of populationGuyana: 51% of employment, 6.5% of populationHonduras: 82.6% of employment, 0.4% of populationCroatia: 1.17% of employment, 13.6% of populationHaiti: 91.6% of employment, 0.2% of populationHungary: 1.31% of employment, 7.1% of populationIndonesia: 81% of employment, 0.2% of populationIndia: 88.1% of employment, 0.3% of populationIreland: 1.9% of employment, 23.1% of populationIraq: 67.6% of employment, 0.8% of populationIceland: 1.97% of employment, 25.1% of populationItaly: 3.42% of employment, 11% of populationJamaica: 54.6% of employment, 0.8% of populationJordan: 55.1% of employment, 45.7% of populationKenya: 86.5% of employment, 1.8% of populationKyrgyz Republic: 57.5% of employment, 2.7% of populationCambodia: 89.4% of employment, 0.5% of populationKiribati: 61.5% of employment, 2.5% of populationKorea, Rep.: 29.1% of employment, 3.5% of populationLao PDR: 90.5% of employment, 0.7% of populationLebanon: 55.5% of employment, 24.5% of populationLiberia: 89.3% of employment, 1.3% of populationSt. Lucia: 28.3% of employment, 4.5% of populationSri Lanka: 67.6% of employment, 0.2% of populationLesotho: 89.1% of employment, 0.6% of populationLithuania: 3.2% of employment, 6.1% of populationLuxembourg: 1.93% of employment, 51.2% of populationLatvia: 2.17% of employment, 11.8% of populationMoldova: 52.2% of employment, 6.2% of populationMadagascar: 96.1% of employment, 0.1% of populationMaldives: 47.3% of employment, 14.2% of populationMexico: 56.4% of employment, 1.3% of populationMarshall Islands: 32.6% of employment, 8.8% of populationNorth Macedonia: 11.1% of employment, 8.3% of populationMali: 94.4% of employment, 2.2% of populationMalta: 0.63% of employment, 37% of populationMyanmar: 80.7% of employment, 0.1% of populationMongolia: 27.1% of employment, 0.6% of populationMozambique: 95.5% of employment, 1% of populationMauritania: 89.3% of employment, 3.8% of populationMauritius: 23.4% of employment, 2.3% of populationMalawi: 92.7% of employment, 0.9% of populationNamibia: 55.6% of employment, 3.8% of populationNiger: 98.5% of employment, 1.7% of populationNigeria: 93.1% of employment, 0.6% of populationNicaragua: 81.8% of employment, 0.6% of populationNetherlands: 2.63% of employment, 16.2% of populationNorway: 1.43% of employment, 18.2% of populationNepal: 81.5% of employment, 1.6% of populationNaoero: 47.9% of employment, 21.3% of populationPakistan: 88.7% of employment, 1.7% of populationPanama: 58.7% of employment, 10.6% of populationPeru: 70.5% of employment, 5.4% of populationPalau: 7.27% of employment, 29.5% of populationPoland: 8.62% of employment, 4.5% of populationPortugal: 4.73% of employment, 10.8% of populationParaguay: 64.5% of employment, 2.6% of populationWest Bank and Gaza: 48.8% of employment, 5% of populationRomania: 2.21% of employment, 3.4% of populationRussian Federation: 19.3% of employment, 5.3% of populationRwanda: 84.3% of employment, 3.6% of populationSudan: 94.2% of employment, 4.8% of populationSenegal: 89.2% of employment, 1.5% of populationSierra Leone: 93.1% of employment, 0.6% of populationEl Salvador: 64% of employment, 0.7% of populationSomalia, Fed. Rep.: 81.9% of employment, 0.4% of populationSerbia: 17.8% of employment, 10.6% of populationSuriname: 50.9% of employment, 8.2% of populationSlovak Republic: 4.37% of employment, 5.9% of populationSlovenia: 0.7% of employment, 14.9% of populationSweden: 2.83% of employment, 21.4% of populationEswatini: 53.3% of employment, 2.7% of populationSeychelles: 14.2% of employment, 10.2% of populationChad: 96.9% of employment, 6.3% of populationTogo: 92.3% of employment, 3% of populationThailand: 63.2% of employment, 4.4% of populationTajikistan: 63.6% of employment, 2.6% of populationTimor-Leste: 80.6% of employment, 0.6% of populationTonga: 59.2% of employment, 3.4% of populationTunisia: 36.9% of employment, 0.5% of populationTurkiye: 27.7% of employment, 8.1% of populationTuvalu: 20.1% of employment, 2.6% of populationTanzania: 90.8% of employment, 0.7% of populationUganda: 94.5% of employment, 4.1% of populationUruguay: 28.5% of employment, 4.7% of populationViet Nam: 67% of employment, 0.3% of populationVanuatu: 72.4% of employment, 1% of populationSamoa: 51.5% of employment, 1.8% of populationSouth Africa: 34.8% of employment, 4.1% of populationZambia: 82.7% of employment, 1.2% of populationZimbabwe: 85% of employment, 2.6% of populationBGRBIHCODGNBJORLBNLUXMDGMLTROU

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5995.6e-15140
By rank (Spearman), not adjusted-0.6841.1e-20140
Standard (Pearson), adjusted for national income-0.2380.005140
By rank (Spearman), adjusted for national income-0.2470.003140

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 140 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: +0.361 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.276 (without LUX) to -0.208 (without PLW). 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: LUX (-0.038).

Fragile: this link may not hold up.
  • direction is not consistent across regions

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
East Asia & Pacific19-0.49
Europe & Central Asia40+0.26
Latin America & Caribbean26-0.47
Sub-Saharan Africa40-0.10

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 third47-0.26
Middle third46-0.28
Richest third47-0.18

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
Christians90-0.18
Muslims31-0.17

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
France19-0.31
Never a European colony12+0.51
Spain16-0.02
United Kingdom43-0.26

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 = -0.294 after dropping 9 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
% of population		Years
Niger NER98.5%1.7%2022, 2024
Burundi BDI98%2.8%2014, 2024
Chad TCD96.9%6.3%2018, 2024
Benin BEN96.3%2.9%2022, 2024
Congo, Dem. Rep. COD96.3%1%2020, 2024
Madagascar MDG96.1%0.1%2022, 2024
Mozambique MOZ95.5%1%2015, 2024
Angola AGO95.3%1.8%2024
Guinea-Bissau GNB94.8%0.7%2022, 2024
Uganda UGA94.5%4.1%2021, 2024
Mali MLI94.4%2.2%2023, 2024
Sudan SDN94.2%4.8%2022, 2024
Burkina Faso BFA93.8%3.1%2024
Nigeria NGA93.1%0.6%2024
Sierra Leone SLE93.1%0.6%2018, 2024

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.

Migrant share of population

International migrant stock (% of population)

UN Population Division, International Migrant Stock · CC BY 4.0 · updated quarterly

People living outside the country they were born in, as a share of the population. It counts people at one moment, while the net migration already on the site counts moves over a year. Published every five years until 2020, and for 2024.

How this was computed

Data used: most recent value in 2010-2025; values come from 2012–2025. 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 Migrant share of population

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

More links with Informal employment or Migrant share of population