placestats

Extreme poverty rate vs. Informal employment

Fades after adjusting for income

Across 132 countries, Extreme poverty rate and Informal employment rise and fall together at r = +0.65 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.03, which could easily be chance (q = 0.89, 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: sign flips once outliers are trimmed.

Comparing countries across the middle half of informal employment, 16% to 83.9% of employment, extreme poverty rate is worse by 26.6% of population at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 1.63% of population the other way.

Link before adjusting (r)+0.651p = 2.7e-17 · 132 countries
After adjusting for national income-0.029p = 0.744 · likely range [-0.20, +0.14]
p, allowing for regions0.815standard error 0.116 · 6 regions
Check for luck (q)0.893across 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.

Extreme poverty rate · % of populationlink with income -0.83 · proxy
Informal employment · % of employmentlink with income -0.88 · 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

Extreme poverty rate · % of population →Informal employment · % of employment →0%85.3%0.63%98.5%Angola: 39.3% of population, 95.3% of employmentArgentina: 1% of population, 51.6% of employmentArmenia: 0.8% of population, 76.1% of employmentAustria: 0.5% of population, 1.32% of employmentBurundi: 74.2% of population, 98% of employmentBelgium: 0.1% of population, 1.37% of employmentBenin: 27.2% of population, 96.3% of employmentBurkina Faso: 42.1% of population, 93.8% of employmentBangladesh: 5.9% of population, 84% of employmentBulgaria: 1% of population, 1.2% of employmentBosnia and Herzegovina: 0.3% of population, 16.6% of employmentBolivia: 2.1% of population, 83.9% of employmentBrazil: 3% of population, 35.6% of employmentBarbados: 1.7% of population, 61.8% of employmentBotswana: 21.4% of population, 75.6% of employmentSwitzerland: 0.2% of population, 1.48% of employmentChile: 0.4% of population, 26.4% of employmentCote d'Ivoire: 20.8% of population, 92.1% of employmentCameroon: 26.7% of population, 87.1% of employmentCongo, Dem. Rep.: 85.3% of population, 96.3% of employmentColombia: 8.5% of population, 56% of employmentComoros: 5.1% of population, 88.8% of employmentCabo Verde: 14.6% of population, 60.6% of employmentCosta Rica: 0.8% of population, 36.1% of employmentCyprus: 0% of population, 1.68% of employmentCzechia: 0% of population, 2.26% of employmentGermany: 0.7% of population, 2.49% of employmentDjibouti: 25.4% of population, 47.6% of employmentDenmark: 0.4% of population, 2.82% of employmentDominican Republic: 0.8% of population, 54.7% of employmentEcuador: 3.4% of population, 68.5% of employmentEgypt, Arab Rep.: 1.4% of population, 68.6% of employmentSpain: 0.8% of population, 2.2% of employmentEstonia: 0.3% of population, 2.4% of employmentEthiopia: 38.6% of population, 77.7% of employmentFinland: 0.2% of population, 2.19% of employmentFiji: 4.7% of population, 45.7% of employmentFrance: 0.1% of population, 3.15% of employmentUnited Kingdom: 0.4% of population, 6.51% of employmentGeorgia: 4.2% of population, 51.5% of employmentGhana: 39% of population, 78% of employmentGambia, The: 22% of population, 83.9% of employmentGuinea-Bissau: 39.9% of population, 94.8% of employmentGreece: 0.6% of population, 3.58% of employmentGrenada: 0.8% of population, 48% of employmentGuatemala: 9.7% of population, 75.9% of employmentHonduras: 15.7% of population, 82.6% of employmentCroatia: 0.3% of population, 1.17% of employmentHaiti: 40.4% of population, 91.6% of employmentHungary: 0.9% of population, 1.31% of employmentIndonesia: 4% of population, 81% of employmentIndia: 5.3% of population, 88.1% of employmentIreland: 0.1% of population, 1.9% of employmentIraq: 0.5% of population, 67.6% of employmentIceland: 0.1% of population, 1.97% of employmentItaly: 0.9% of population, 3.42% of employmentJamaica: 1.4% of population, 54.6% of employmentJordan: 0.1% of population, 55.1% of employmentKenya: 45.5% of population, 86.5% of employmentKyrgyz Republic: 2.2% of population, 57.5% of employmentKiribati: 0.9% of population, 61.5% of employmentKorea, Rep.: 0.1% of population, 29.1% of employmentLao PDR: 7.1% of population, 90.5% of employmentLebanon: 5.8% of population, 55.5% of employmentLiberia: 33.6% of population, 89.3% of employmentSt. Lucia: 0.1% of population, 28.3% of employmentSri Lanka: 2.7% of population, 67.6% of employmentLesotho: 41.9% of population, 89.1% of employmentLithuania: 1.1% of population, 3.2% of employmentLuxembourg: 0.1% of population, 1.93% of employmentLatvia: 0.4% of population, 2.17% of employmentMoldova: 0.1% of population, 52.2% of employmentMadagascar: 69.2% of population, 96.1% of employmentMaldives: 0% of population, 47.3% of employmentMexico: 1.6% of population, 56.4% of employmentMarshall Islands: 2.1% of population, 32.6% of employmentNorth Macedonia: 3.9% of population, 11.1% of employmentMali: 36.1% of population, 94.4% of employmentMalta: 0.4% of population, 0.63% of employmentMyanmar: 10.3% of population, 80.7% of employmentMongolia: 0.4% of population, 27.1% of employmentMozambique: 81.4% of population, 95.5% of employmentMauritania: 10.2% of population, 89.3% of employmentMauritius: 0.5% of population, 23.4% of employmentMalawi: 75.4% of population, 92.7% of employmentNamibia: 22.9% of population, 55.6% of employmentNiger: 60.5% of population, 98.5% of employmentNigeria: 41.8% of population, 93.1% of employmentNicaragua: 6.6% of population, 81.8% of employmentNetherlands: 0.1% of population, 2.63% of employmentNorway: 0.2% of population, 1.43% of employmentNepal: 2.4% of population, 81.5% of employmentNaoero: 9.7% of population, 47.9% of employmentPakistan: 23% of population, 88.7% of employmentPanama: 3.1% of population, 58.7% of employmentPeru: 5.1% of population, 70.5% of employmentPoland: 0.2% of population, 8.62% of employmentPortugal: 0.4% of population, 4.73% of employmentParaguay: 2.1% of population, 64.5% of employmentWest Bank and Gaza: 2.1% of population, 48.8% of employmentRomania: 0.7% of population, 2.21% of employmentRussian Federation: 0.1% of population, 19.3% of employmentRwanda: 38.6% of population, 84.3% of employmentSudan: 10.1% of population, 94.2% of employmentSenegal: 17.9% of population, 89.2% of employmentSierra Leone: 41.5% of population, 93.1% of employmentEl Salvador: 4.6% of population, 64% of employmentSerbia: 1.1% of population, 17.8% of employmentSuriname: 2.2% of population, 50.9% of employmentSlovak Republic: 0.5% of population, 4.37% of employmentSlovenia: 0% of population, 0.7% of employmentSweden: 0.8% of population, 2.83% of employmentEswatini: 44.5% of population, 53.3% of employmentSeychelles: 0.7% of population, 14.2% of employmentChad: 39.5% of population, 96.9% of employmentTogo: 34.7% of population, 92.3% of employmentThailand: 0% of population, 63.2% of employmentTajikistan: 6.1% of population, 63.6% of employmentTimor-Leste: 44% of population, 80.6% of employmentTonga: 0.4% of population, 59.2% of employmentTunisia: 0.7% of population, 36.9% of employmentTurkiye: 0.2% of population, 27.7% of employmentTuvalu: 9% of population, 20.1% of employmentTanzania: 51.3% of population, 90.8% of employmentUganda: 59.8% of population, 94.5% of employmentUruguay: 0.2% of population, 28.5% of employmentViet Nam: 1.6% of population, 67% of employmentVanuatu: 19.5% of population, 72.4% of employmentSamoa: 4.5% of population, 51.5% of employmentSouth Africa: 17.4% of population, 34.8% of employmentZambia: 71.7% of population, 82.7% of employmentZimbabwe: 49.2% of population, 85% of employmentBDIBELBOLCODCOMINDMLTMOZSVNZMB

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6512.7e-17132
By rank (Spearman), not adjusted+0.8212.1e-33132
Standard (Pearson), adjusted for national income-0.0290.744132
By rank (Spearman), adjusted for national income+0.2680.002132

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 132 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: +0.623 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.062 (without TUV) to -0.009 (without MOZ). 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 (-0.033).

Fragile: this link may not hold up.
  • sign flips once outliers are trimmed
  • 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 & Pacific16+0.26
Europe & Central Asia40+0.07
Latin America & Caribbean23-0.10
Sub-Saharan Africa39-0.00

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 third44+0.31
Middle third44+0.14
Richest third44+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
Christians88-0.12
Muslims28+0.32

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
France18-0.28
Spain16+0.04
United Kingdom38+0.23

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 = +0.115 after dropping 8 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 26years. 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 = -0.072, 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 extreme poverty rate moved did not tend to see their informal employment move with it.

Across countries (adjusted)-0.029for comparison
Within countries+0.284each country vs itself
…and vs the same year elsewhere-0.072q = 0.713 · 59 countries
Year-on-year changes+0.051q = 0.781
The direction flips. Comparing each country only with itself gives +0.28. Also taking out what happened to the whole world each year gives -0.07. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.
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.

860 country-years (one country in one year), 59 countries, 2000–2025, about 15 years each on average. Share of the variation that happens within countries over time: 2% for Extreme poverty rate, 21% for Informal 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

0.30−0.3-100+10years Informal employment trails Extreme poverty rate →lag -10: r = 0.114 (n = 320)lag -9: r = 0.092 (n = 373)lag -8: r = 0.038 (n = 428)lag -7: r = 0.029 (n = 477)lag -6: r = -0.026 (n = 541)lag -5: r = -0.021 (n = 605)lag -4: r = 0.079 (n = 656)lag -3: r = 0.155 (n = 708)lag -2: r = 0.045 (n = 752)lag -1: r = -0.024 (n = 801)lag +0: r = -0.072 (n = 860)lag +1: r = -0.038 (n = 891)lag +2: r = -0.113 (n = 895)lag +3: r = -0.155 (n = 890)lag +4: r = -0.244 (n = 887)lag +5: r = -0.179 (n = 873)lag +6: r = -0.251 (n = 845)lag +7: r = -0.170 (n = 830)lag +8: r = -0.107 (n = 800)lag +9: r = -0.099 (n = 777)lag +10: r = -0.080 (n = 743)

Same year (lag 0): -0.072. 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 largest value on this curve (-0.25 at lag +6) is higher than the same-year one by 0.18. That gap is not a finding. It is the largest of 21 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.

The data


% of population
% of employment		Years
Congo, Dem. Rep. COD85.3%96.3%2020
Mozambique MOZ81.4%95.5%2022, 2015
Malawi MWI75.4%92.7%2019, 2024
Burundi BDI74.2%98%2020, 2014
Zambia ZMB71.7%82.7%2022, 2024
Madagascar MDG69.2%96.1%2021, 2022
Niger NER60.5%98.5%2021, 2022
Uganda UGA59.8%94.5%2019, 2021
Tanzania TZA51.3%90.8%2018, 2024
Zimbabwe ZWE49.2%85%2019, 2024
Kenya KEN45.5%86.5%2022, 2019
Eswatini SWZ44.5%53.3%2016, 2023
Timor-Leste TLS44%80.6%2014, 2021
Burkina Faso BFA42.1%93.8%2021, 2024
Lesotho LSO41.9%89.1%2017, 2024

World Bank, World Development Indicators
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)

Sources

Extreme poverty rate

Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population)

World Bank · CC BY 4.0 · updated quarterly

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.

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 Extreme poverty rate and Informal employment

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

More links with Extreme poverty rate or Informal employment