placestats

Slum share of urban population vs. Vulnerable employment

Holds up after adjusting for income

Across 171 countries, Slum share of urban population and Vulnerable employment rise and fall together at r = +0.78 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.39 (q = 0.013, 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 vulnerable employment, 12.5% to 57.7% of employment, slum share of urban population is worse by 34.1% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 20.8% of urban population.

Link before adjusting (r)+0.776p = 1.2e-35 · 171 countries
After adjusting for national income+0.386p = 2.0e-7 · likely range [+0.25, +0.51]
p, allowing for regions0.003standard error 0.078 · 7 regions
Check for luck (q)0.013across 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.

Slum share of urban population · % of urban populationlink with income -0.78 · high
Vulnerable employment · % of employmentlink with income -0.87 · 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

Slum share of urban population · % of urban population →Vulnerable employment · % of employment →0%92.5%0.456%90.2%Afghanistan: 71.6% of urban population, 81.8% of employmentAngola: 62.7% of urban population, 66.3% of employmentAlbania: 2.7% of urban population, 46% of employmentUnited Arab Emirates: 0.113% of urban population, 1.5% of employmentArgentina: 14.5% of urban population, 22.3% of employmentArmenia: 8.39% of urban population, 32.8% of employmentAustralia: 0.035% of urban population, 9.12% of employmentAustria: 0.0466% of urban population, 7.85% of employmentAzerbaijan: 26.9% of urban population, 53.8% of employmentBurundi: 36.8% of urban population, 83.7% of employmentBelgium: 0% of urban population, 11% of employmentBenin: 64% of urban population, 78.5% of employmentBurkina Faso: 87.9% of urban population, 80.6% of employmentBangladesh: 51.5% of urban population, 57.3% of employmentBulgaria: 0.197% of urban population, 6.92% of employmentBahrain: 0% of urban population, 0.749% of employmentBosnia and Herzegovina: 0.313% of urban population, 18.1% of employmentBelarus: 0.004% of urban population, 6.33% of employmentBelize: 15.7% of urban population, 29.8% of employmentBolivia: 46.6% of urban population, 62.7% of employmentBrazil: 14.9% of urban population, 26.1% of employmentBrunei Darussalam: 21.6% of urban population, 4.48% of employmentBhutan: 44.7% of urban population, 69% of employmentBotswana: 39.6% of urban population, 25.6% of employmentCentral African Republic: 68.9% of urban population, 79.4% of employmentCanada: 1.1% of urban population, 9.56% of employmentSwitzerland: 0% of urban population, 9.6% of employmentChile: 7.32% of urban population, 20.9% of employmentChina: 26.3% of urban population, 33.4% of employmentCote d'Ivoire: 48.3% of urban population, 69.4% of employmentCameroon: 32.7% of urban population, 67.6% of employmentCongo, Dem. Rep.: 78.4% of urban population, 81.5% of employmentCongo, Rep.: 75.3% of urban population, 75.9% of employmentColombia: 9.7% of urban population, 43% of employmentComoros: 48.5% of urban population, 56.8% of employmentCabo Verde: 46.4% of urban population, 34.3% of employmentCosta Rica: 3.55% of urban population, 23.4% of employmentCyprus: 0.272% of urban population, 8.38% of employmentCzechia: 0.0273% of urban population, 13.5% of employmentGermany: 0% of urban population, 4.34% of employmentDjibouti: 48.7% of urban population, 30.3% of employmentDenmark: 0% of urban population, 5.4% of employmentDominican Republic: 11.2% of urban population, 38% of employmentAlgeria: 13.2% of urban population, 25.6% of employmentEcuador: 57.8% of urban population, 50.1% of employmentEgypt, Arab Rep.: 3.84% of urban population, 23.9% of employmentSpain: 0.046% of urban population, 10.1% of employmentEstonia: 0.345% of urban population, 7.09% of employmentEthiopia: 64.3% of urban population, 84.1% of employmentFinland: 0% of urban population, 9.46% of employmentFiji: 9.4% of urban population, 32.8% of employmentFrance: 0% of urban population, 8.5% of employmentGabon: 38.8% of urban population, 29.8% of employmentUnited Kingdom: 0.163% of urban population, 11.5% of employmentGeorgia: 7.08% of urban population, 42.4% of employmentGhana: 33.5% of urban population, 60% of employmentGuinea: 44% of urban population, 79.2% of employmentGambia, The: 37.1% of urban population, 63.5% of employmentGuinea-Bissau: 59% of urban population, 74.9% of employmentEquatorial Guinea: 64.7% of urban population, 23.1% of employmentGreece: 0.05% of urban population, 23.1% of employmentGuatemala: 37.6% of urban population, 36.2% of employmentGuyana: 11.3% of urban population, 25.3% of employmentHonduras: 31.5% of urban population, 33.9% of employmentCroatia: 0.527% of urban population, 6.35% of employmentHaiti: 51.1% of urban population, 76% of employmentHungary: 7.2% of urban population, 7.83% of employmentIndonesia: 19.4% of urban population, 50% of employmentIndia: 5.41% of urban population, 71.6% of employmentIreland: 8.5% of urban population, 9.64% of employmentIran, Islamic Rep.: 44.7% of urban population, 39.5% of employmentIraq: 49.3% of urban population, 27.1% of employmentIceland: 0% of urban population, 8.4% of employmentItaly: 0.02% of urban population, 14.3% of employmentJamaica: 0.883% of urban population, 31.6% of employmentJordan: 23.4% of urban population, 7.86% of employmentJapan: 2% of urban population, 7.49% of employmentKazakhstan: 0.794% of urban population, 24.8% of employmentKenya: 40.5% of urban population, 63.8% of employmentKyrgyz Republic: 2.44% of urban population, 29% of employmentCambodia: 42.3% of urban population, 52.3% of employmentKorea, Rep.: 4.5% of urban population, 17.7% of employmentKuwait: 0% of urban population, 2.4% of employmentLao PDR: 54.8% of urban population, 76.4% of employmentLebanon: 4.53% of urban population, 19.4% of employmentLiberia: 60.5% of urban population, 76.9% of employmentLibya: 16.6% of urban population, 21.6% of employmentSt. Lucia: 0.441% of urban population, 22% of employmentSri Lanka: 44.7% of urban population, 39.2% of employmentLesotho: 25.6% of urban population, 32.6% of employmentLithuania: 0.509% of urban population, 10.4% of employmentLuxembourg: 0.0173% of urban population, 6.95% of employmentLatvia: 0.593% of urban population, 8.62% of employmentMorocco: 10.9% of urban population, 35.5% of employmentMoldova: 6.5% of urban population, 58.6% of employmentMadagascar: 65.7% of urban population, 84.4% of employmentMaldives: 34.8% of urban population, 16.9% of employmentMexico: 17.6% of urban population, 25.3% of employmentNorth Macedonia: 0.278% of urban population, 12.3% of employmentMali: 92.5% of urban population, 75.8% of employmentMalta: 0.0375% of urban population, 10.1% of employmentMyanmar: 58.3% of urban population, 62.9% of employmentMontenegro: 8.77% of urban population, 14.3% of employmentMongolia: 17.9% of urban population, 38.4% of employmentMozambique: 55% of urban population, 83.7% of employmentMauritania: 58.6% of urban population, 53.3% of employmentMauritius: 48.7% of urban population, 14.9% of employmentMalawi: 38% of urban population, 59.9% of employmentMalaysia: 0.2% of urban population, 21.9% of employmentNamibia: 41.4% of urban population, 29.9% of employmentNiger: 70.4% of urban population, 83.7% of employmentNigeria: 48.5% of urban population, 66.5% of employmentNicaragua: 5.52% of urban population, 38.9% of employmentNetherlands: 0% of urban population, 13.4% of employmentNorway: 0% of urban population, 4.17% of employmentNepal: 40.1% of urban population, 77% of employmentNew Zealand: 0% of urban population, 12.6% of employmentOman: 0% of urban population, 3.81% of employmentPakistan: 56% of urban population, 55.5% of employmentPanama: 16.3% of urban population, 37.9% of employmentPeru: 45.1% of urban population, 47.5% of employmentPhilippines: 35.9% of urban population, 33.6% of employmentPapua New Guinea: 22.3% of urban population, 71.2% of employmentPoland: 4.22% of urban population, 15% of employmentPortugal: 0.059% of urban population, 9.66% of employmentParaguay: 15.1% of urban population, 36.1% of employmentWest Bank and Gaza: 19.5% of urban population, 18.4% of employmentQatar: 0% of urban population, 0.456% of employmentRomania: 2.52% of urban population, 13% of employmentRussian Federation: 2.62% of urban population, 4.93% of employmentRwanda: 38.3% of urban population, 39.4% of employmentSaudi Arabia: 0.55% of urban population, 1.66% of employmentSudan: 73.7% of urban population, 39.7% of employmentSenegal: 46.4% of urban population, 63% of employmentSingapore: 0% of urban population, 9.3% of employmentSolomon Islands: 1.95% of urban population, 68.7% of employmentSierra Leone: 49.3% of urban population, 86% of employmentEl Salvador: 16.5% of urban population, 30.8% of employmentSomalia, Fed. Rep.: 48.7% of urban population, 46% of employmentSerbia: 1.42% of urban population, 23.9% of employmentSao Tome and Principe: 82.4% of urban population, 35% of employmentSuriname: 15.8% of urban population, 18.4% of employmentSlovak Republic: 0.066% of urban population, 11.8% of employmentSlovenia: 0.527% of urban population, 10.6% of employmentSweden: 0.336% of urban population, 6.87% of employmentEswatini: 17% of urban population, 28.4% of employmentSyrian Arab Republic: 41.1% of urban population, 38.9% of employmentChad: 82% of urban population, 90.2% of employmentTogo: 38.5% of urban population, 75% of employmentThailand: 2% of urban population, 48% of employmentTajikistan: 0.05% of urban population, 28% of employmentTurkmenistan: 8.38% of urban population, 28.3% of employmentTimor-Leste: 33.9% of urban population, 58.6% of employmentTonga: 0.308% of urban population, 33.7% of employmentTrinidad and Tobago: 8.63% of urban population, 21.2% of employmentTunisia: 7.64% of urban population, 17.9% of employmentTurkiye: 14.1% of urban population, 23.9% of employmentTanzania: 70.1% of urban population, 83.7% of employmentUganda: 52.7% of urban population, 74.4% of employmentUkraine: 1.1% of urban population, 19% of employmentUruguay: 1.3% of urban population, 24.4% of employmentUnited States: 0.0932% of urban population, 3.81% of employmentUzbekistan: 7.1% of urban population, 33.3% of employmentSt. Vincent and the Grenadines: 2.76% of urban population, 22% of employmentVirgin Islands (U.S.): 1.1% of urban population, 5.47% of employmentViet Nam: 32.5% of urban population, 51.4% of employmentVanuatu: 3.06% of urban population, 58% of employmentSamoa: 34.6% of urban population, 41.6% of employmentSouth Africa: 24.2% of urban population, 11.7% of employmentZambia: 48.3% of urban population, 70.2% of employmentZimbabwe: 54.9% of urban population, 68.2% of employmentAREBFABHRGNQINDMLIQATSDNSLBSTPTCDVUT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7761.2e-35171
By rank (Spearman), not adjusted+0.8053.5e-40171
Standard (Pearson), adjusted for national income+0.3862.0e-7171
By rank (Spearman), adjusted for national income+0.3764.3e-7171

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 171 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: +0.390 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.367 (without TJK) to +0.419 (without STP). 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: STP (+0.033).

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 & Pacific23+0.40
Europe & Central Asia48+0.60
Latin America & Caribbean26+0.65
Middle East, North Africa, Afghanistan & Pakistan21+0.63
Sub-Saharan Africa45+0.33

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 third57+0.47
Middle third57+0.21
Richest third57+0.46

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
Christians103+0.37
Muslims49+0.51

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
France25+0.55
Never a European colony21+0.64
Spain19+0.31
United Kingdom52+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 = +0.386 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 = +0.015, 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 slum share of urban population moved did not tend to see their vulnerable employment move with it.

Across countries (adjusted)+0.386for comparison
Within countries+0.262each country vs itself
…and vs the same year elsewhere+0.015q = 0.899 · 161 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.

1,841 country-years (one country in one year), 161 countries, 2000–2022, about 11 years each on average. Share of the variation that happens within countries over time: 1% for Slum share of urban population, 9% for Vulnerable employment. 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

0.10−0.1-100+10years Vulnerable employment trails Slum share of urban population →lag -10: r = -0.021 (n = 1,682)lag -9: r = -0.019 (n = 1,841)lag -8: r = -0.017 (n = 1,841)lag -7: r = -0.021 (n = 1,841)lag -6: r = -0.021 (n = 1,841)lag -5: r = -0.016 (n = 1,841)lag -4: r = -0.011 (n = 1,841)lag -3: r = 0.001 (n = 1,841)lag -2: r = 0.010 (n = 1,841)lag -1: r = 0.016 (n = 1,841)lag +0: r = 0.015 (n = 1,841)lag +1: r = 0.026 (n = 1,840)lag +2: r = 0.033 (n = 1,838)lag +3: r = 0.047 (n = 1,836)lag +4: r = 0.034 (n = 1,671)lag +5: r = 0.045 (n = 1,669)lag +6: r = 0.033 (n = 1,519)lag +7: r = 0.050 (n = 1,517)lag +8: r = 0.035 (n = 1,365)lag +9: r = 0.049 (n = 1,363)lag +10: r = 0.033 (n = 1,209)

Same year (lag 0): +0.015. 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.

The data


% of urban population
% of employment		Years
Mali MLI92.5%75.8%2022, 2025
Burkina Faso BFA87.9%80.6%2022, 2025
Sao Tome and Principe STP82.4%35%2022, 2025
Chad TCD82%90.2%2022, 2025
Congo, Dem. Rep. COD78.4%81.5%2022, 2025
Congo, Rep. COG75.3%75.9%2022, 2025
Sudan SDN73.7%39.7%2022
Afghanistan AFG71.6%81.8%2022, 2025
Niger NER70.4%83.7%2022, 2025
Tanzania TZA70.1%83.7%2022, 2025
Central African Republic CAF68.9%79.4%2022, 2025
Madagascar MDG65.7%84.4%2022, 2025
Equatorial Guinea GNQ64.7%23.1%2022, 2025
Ethiopia ETH64.3%84.1%2022, 2025
Benin BEN64%78.5%2022, 2025

World Bank, World Development Indicators
World Bank, World Development Indicators

Sources

Slum share of urban population

Population living in slums (% of urban population)

UN-Habitat · CC BY 4.0 · updated quarterly

Vulnerable employment

Vulnerable employment, total (% of total employment)

International Labour Organization · CC BY 4.0 · updated quarterly

Self-employed workers and unpaid family workers, who have no employer and usually no protection. The ILO estimates it with a model for every country and year, which is why it is complete where surveys are not.

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 7 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 Slum share of urban population and Vulnerable employment

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

More links with Slum share of urban population or Vulnerable employment