placestats

Informal employment vs. Slum share of urban population

Holds up after adjusting for income

Across 137 countries, Informal employment and Slum share of urban population 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 +0.36 (q = 0.010, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak positive link remains.

Comparing countries across the middle half of informal employment, 14.2% to 84% of employment, slum share of urban population is worse by 38.6% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 24.2% of urban population.

Link before adjusting (r)+0.766p = 1.2e-27 · 137 countries
After adjusting for national income+0.358p = 1.9e-5 · likely range [+0.20, +0.50]
p, allowing for regions0.000standard error 0.039 · 6 regions
Check for luck (q)0.010across 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
Slum share of urban population · % of urban populationlink 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 →Slum share of urban population · % of urban population →0.63%98.5%0%92.5%Afghanistan: 86.1% of employment, 71.6% of urban populationAngola: 95.3% of employment, 62.7% of urban populationArgentina: 51.6% of employment, 14.5% of urban populationArmenia: 76.1% of employment, 8.39% of urban populationAntigua and Barbuda: 12.8% of employment, 2.65% of urban populationAustria: 1.32% of employment, 0.0466% of urban populationBurundi: 98% of employment, 36.8% of urban populationBelgium: 1.37% of employment, 0% of urban populationBenin: 96.3% of employment, 64% of urban populationBurkina Faso: 93.8% of employment, 87.9% of urban populationBangladesh: 84% of employment, 51.5% of urban populationBulgaria: 1.2% of employment, 0.197% of urban populationBosnia and Herzegovina: 16.6% of employment, 0.313% of urban populationBolivia: 83.9% of employment, 46.6% of urban populationBrazil: 35.6% of employment, 14.9% of urban populationBrunei Darussalam: 21.4% of employment, 21.6% of urban populationBotswana: 75.6% of employment, 39.6% of urban populationSwitzerland: 1.48% of employment, 0% of urban populationChile: 26.4% of employment, 7.32% of urban populationCote d'Ivoire: 92.1% of employment, 48.3% of urban populationCameroon: 87.1% of employment, 32.7% of urban populationCongo, Dem. Rep.: 96.3% of employment, 78.4% of urban populationColombia: 56% of employment, 9.7% of urban populationComoros: 88.8% of employment, 48.5% of urban populationCabo Verde: 60.6% of employment, 46.4% of urban populationCosta Rica: 36.1% of employment, 3.55% of urban populationCyprus: 1.68% of employment, 0.272% of urban populationCzechia: 2.26% of employment, 0.0273% of urban populationGermany: 2.49% of employment, 0% of urban populationDjibouti: 47.6% of employment, 48.7% of urban populationDenmark: 2.82% of employment, 0% of urban populationDominican Republic: 54.7% of employment, 11.2% of urban populationEcuador: 68.5% of employment, 57.8% of urban populationEgypt, Arab Rep.: 68.6% of employment, 3.84% of urban populationSpain: 2.2% of employment, 0.046% of urban populationEstonia: 2.4% of employment, 0.345% of urban populationEthiopia: 77.7% of employment, 64.3% of urban populationFinland: 2.19% of employment, 0% of urban populationFiji: 45.7% of employment, 9.4% of urban populationFrance: 3.15% of employment, 0% of urban populationUnited Kingdom: 6.51% of employment, 0.163% of urban populationGeorgia: 51.5% of employment, 7.08% of urban populationGhana: 78% of employment, 33.5% of urban populationGambia, The: 83.9% of employment, 37.1% of urban populationGuinea-Bissau: 94.8% of employment, 59% of urban populationGreece: 3.58% of employment, 0.05% of urban populationGuatemala: 75.9% of employment, 37.6% of urban populationGuyana: 51% of employment, 11.3% of urban populationHonduras: 82.6% of employment, 31.5% of urban populationCroatia: 1.17% of employment, 0.527% of urban populationHaiti: 91.6% of employment, 51.1% of urban populationHungary: 1.31% of employment, 7.2% of urban populationIndonesia: 81% of employment, 19.4% of urban populationIndia: 88.1% of employment, 5.41% of urban populationIreland: 1.9% of employment, 8.5% of urban populationIraq: 67.6% of employment, 49.3% of urban populationIceland: 1.97% of employment, 0% of urban populationItaly: 3.42% of employment, 0.02% of urban populationJamaica: 54.6% of employment, 0.883% of urban populationJordan: 55.1% of employment, 23.4% of urban populationKenya: 86.5% of employment, 40.5% of urban populationKyrgyz Republic: 57.5% of employment, 2.44% of urban populationCambodia: 89.4% of employment, 42.3% of urban populationKiribati: 61.5% of employment, 5.93% of urban populationKorea, Rep.: 29.1% of employment, 4.5% of urban populationLao PDR: 90.5% of employment, 54.8% of urban populationLebanon: 55.5% of employment, 4.53% of urban populationLiberia: 89.3% of employment, 60.5% of urban populationSt. Lucia: 28.3% of employment, 0.441% of urban populationSri Lanka: 67.6% of employment, 44.7% of urban populationLesotho: 89.1% of employment, 25.6% of urban populationLithuania: 3.2% of employment, 0.509% of urban populationLuxembourg: 1.93% of employment, 0.0173% of urban populationLatvia: 2.17% of employment, 0.593% of urban populationMoldova: 52.2% of employment, 6.5% of urban populationMadagascar: 96.1% of employment, 65.7% of urban populationMaldives: 47.3% of employment, 34.8% of urban populationMexico: 56.4% of employment, 17.6% of urban populationMarshall Islands: 32.6% of employment, 2.38% of urban populationNorth Macedonia: 11.1% of employment, 0.278% of urban populationMali: 94.4% of employment, 92.5% of urban populationMalta: 0.63% of employment, 0.0375% of urban populationMyanmar: 80.7% of employment, 58.3% of urban populationMongolia: 27.1% of employment, 17.9% of urban populationMozambique: 95.5% of employment, 55% of urban populationMauritania: 89.3% of employment, 58.6% of urban populationMauritius: 23.4% of employment, 48.7% of urban populationMalawi: 92.7% of employment, 38% of urban populationNamibia: 55.6% of employment, 41.4% of urban populationNiger: 98.5% of employment, 70.4% of urban populationNigeria: 93.1% of employment, 48.5% of urban populationNicaragua: 81.8% of employment, 5.52% of urban populationNetherlands: 2.63% of employment, 0% of urban populationNorway: 1.43% of employment, 0% of urban populationNepal: 81.5% of employment, 40.1% of urban populationNaoero: 47.9% of employment, 0.597% of urban populationPakistan: 88.7% of employment, 56% of urban populationPanama: 58.7% of employment, 16.3% of urban populationPeru: 70.5% of employment, 45.1% of urban populationPalau: 7.27% of employment, 0.618% of urban populationPoland: 8.62% of employment, 4.22% of urban populationPortugal: 4.73% of employment, 0.059% of urban populationParaguay: 64.5% of employment, 15.1% of urban populationWest Bank and Gaza: 48.8% of employment, 19.5% of urban populationRomania: 2.21% of employment, 2.52% of urban populationRussian Federation: 19.3% of employment, 2.62% of urban populationRwanda: 84.3% of employment, 38.3% of urban populationSudan: 94.2% of employment, 73.7% of urban populationSenegal: 89.2% of employment, 46.4% of urban populationSierra Leone: 93.1% of employment, 49.3% of urban populationEl Salvador: 64% of employment, 16.5% of urban populationSomalia, Fed. Rep.: 81.9% of employment, 48.7% of urban populationSerbia: 17.8% of employment, 1.42% of urban populationSuriname: 50.9% of employment, 15.8% of urban populationSlovak Republic: 4.37% of employment, 0.066% of urban populationSlovenia: 0.7% of employment, 0.527% of urban populationSweden: 2.83% of employment, 0.336% of urban populationEswatini: 53.3% of employment, 17% of urban populationSeychelles: 14.2% of employment, 48.7% of urban populationChad: 96.9% of employment, 82% of urban populationTogo: 92.3% of employment, 38.5% of urban populationThailand: 63.2% of employment, 2% of urban populationTajikistan: 63.6% of employment, 0.05% of urban populationTimor-Leste: 80.6% of employment, 33.9% of urban populationTonga: 59.2% of employment, 0.308% of urban populationTunisia: 36.9% of employment, 7.64% of urban populationTurkiye: 27.7% of employment, 14.1% of urban populationTuvalu: 20.1% of employment, 50.9% of urban populationTanzania: 90.8% of employment, 70.1% of urban populationUganda: 94.5% of employment, 52.7% of urban populationUruguay: 28.5% of employment, 1.3% of urban populationViet Nam: 67% of employment, 32.5% of urban populationVanuatu: 72.4% of employment, 3.06% of urban populationSamoa: 51.5% of employment, 34.6% of urban populationSouth Africa: 34.8% of employment, 24.2% of urban populationZambia: 82.7% of employment, 48.3% of urban populationZimbabwe: 85% of employment, 54.9% of urban populationAUTBELBFAINDMLIMLTMUSNICSYCTCDTUVVUT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7661.2e-27137
By rank (Spearman), not adjusted+0.8261.8e-35137
Standard (Pearson), adjusted for national income+0.3581.9e-5137
By rank (Spearman), adjusted for national income+0.4764.7e-9137

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 137 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: +0.408 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.340 (without MHL) to +0.389 (without TUV). 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: TUV (+0.032).

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+0.32
Europe & Central Asia40+0.58
Latin America & Caribbean23+0.55
Sub-Saharan Africa40+0.20

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 third46+0.38
Middle third45+0.50
Richest third46+0.34

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
Christians87+0.39
Muslims31+0.50

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+0.40
Never a European colony12-0.01
Spain16+0.51
United Kingdom40+0.21

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 = +0.358 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 23years. 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 = +0.029, 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 slum share of urban population move with it.

Across countries (adjusted)+0.358for comparison
Within countries+0.281each country vs itself
…and vs the same year elsewhere+0.029q = 0.869 · 53 countries
Year-on-year changes—q = —
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.
  • Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.

405 country-years (one country in one year), 53 countries, 2000–2022, about 8 years each on average. Share of the variation that happens within countries over time: 3% for Informal employment, 5% for Slum share of urban population. 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+6years Slum share of urban population trails Informal employment →lag -10: r = 0.196 (n = 398)lag -9: r = 0.028 (n = 410)lag -8: r = 0.101 (n = 441)lag -7: r = 0.051 (n = 449)lag -6: r = 0.093 (n = 456)lag -5: r = 0.021 (n = 454)lag -4: r = 0.088 (n = 461)lag -3: r = 0.012 (n = 462)lag -2: r = 0.046 (n = 461)lag -1: r = 0.004 (n = 453)lag +0: r = 0.029 (n = 405)lag +1: r = -0.025 (n = 390)lag +2: r = -0.021 (n = 338)lag +3: r = -0.063 (n = 323)lag +4: r = -0.028 (n = 285)lag +5: r = -0.065 (n = 248)lag +6: r = -0.032 (n = 209)

Same year (lag 0): +0.029. 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
% of urban population		Years
Niger NER98.5%70.4%2022
Burundi BDI98%36.8%2014, 2022
Chad TCD96.9%82%2018, 2022
Benin BEN96.3%64%2022
Congo, Dem. Rep. COD96.3%78.4%2020, 2022
Madagascar MDG96.1%65.7%2022
Mozambique MOZ95.5%55%2015, 2022
Angola AGO95.3%62.7%2024, 2022
Guinea-Bissau GNB94.8%59%2022
Uganda UGA94.5%52.7%2021, 2022
Mali MLI94.4%92.5%2023, 2022
Sudan SDN94.2%73.7%2022
Burkina Faso BFA93.8%87.9%2024, 2022
Nigeria NGA93.1%48.5%2024, 2022
Sierra Leone SLE93.1%49.3%2018, 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)
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.

Slum share of urban population

Population living in slums (% of urban population)

UN-Habitat · CC BY 4.0 · updated quarterly

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 Slum share of urban population

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

More links with Informal employment or Slum share of urban population