placestats

Equality score vs. Informal employment

Fades after adjusting for income

Across 132 countries, Equality score and Informal employment rise and fall together at r = -0.62 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.13, which could easily be chance (q = 0.34, 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, equality score is worse by 0.972 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.328 typical gaps.

Link before adjusting (r)-0.621p = 2.1e-15 · 132 countries
After adjusting for national income-0.131p = 0.135 · likely range [-0.30, +0.04]
p, allowing for regions0.274standard error 0.107 · 6 regions
Check for luck (q)0.341across 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.

Equality score · typical gaps (standard deviations)link with income +0.64 · high
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

Equality score · typical gaps (standard deviations) →Informal employment · % of employment →-2.511.060.63%98.5%Angola: -1.52 typical gaps (standard deviations), 95.3% of employmentArgentina: -0.0236 typical gaps (standard deviations), 51.6% of employmentArmenia: 0.843 typical gaps (standard deviations), 76.1% of employmentAustria: 0.632 typical gaps (standard deviations), 1.32% of employmentBurundi: -1.63 typical gaps (standard deviations), 98% of employmentBelgium: 0.895 typical gaps (standard deviations), 1.37% of employmentBenin: -0.24 typical gaps (standard deviations), 96.3% of employmentBurkina Faso: -0.796 typical gaps (standard deviations), 93.8% of employmentBangladesh: 0.51 typical gaps (standard deviations), 84% of employmentBulgaria: 0.143 typical gaps (standard deviations), 1.2% of employmentBosnia and Herzegovina: 0.689 typical gaps (standard deviations), 16.6% of employmentBolivia: 0.0341 typical gaps (standard deviations), 83.9% of employmentBrazil: -0.529 typical gaps (standard deviations), 35.6% of employmentBarbados: 0.435 typical gaps (standard deviations), 61.8% of employmentBotswana: -1.27 typical gaps (standard deviations), 75.6% of employmentSwitzerland: 0.491 typical gaps (standard deviations), 1.48% of employmentChile: -0.0426 typical gaps (standard deviations), 26.4% of employmentCote d'Ivoire: -0.126 typical gaps (standard deviations), 92.1% of employmentCameroon: -0.674 typical gaps (standard deviations), 87.1% of employmentCongo, Dem. Rep.: -2.33 typical gaps (standard deviations), 96.3% of employmentColombia: -0.906 typical gaps (standard deviations), 56% of employmentComoros: 0.565 typical gaps (standard deviations), 88.8% of employmentCabo Verde: -0.374 typical gaps (standard deviations), 60.6% of employmentCosta Rica: -0.196 typical gaps (standard deviations), 36.1% of employmentCyprus: 0.611 typical gaps (standard deviations), 1.68% of employmentCzechia: 0.961 typical gaps (standard deviations), 2.26% of employmentGermany: 0.484 typical gaps (standard deviations), 2.49% of employmentDjibouti: -0.607 typical gaps (standard deviations), 47.6% of employmentDenmark: 0.709 typical gaps (standard deviations), 2.82% of employmentDominican Republic: 0.177 typical gaps (standard deviations), 54.7% of employmentEcuador: -0.286 typical gaps (standard deviations), 68.5% of employmentEgypt, Arab Rep.: 0.764 typical gaps (standard deviations), 68.6% of employmentSpain: 0.498 typical gaps (standard deviations), 2.2% of employmentEstonia: 0.666 typical gaps (standard deviations), 2.4% of employmentEthiopia: -0.344 typical gaps (standard deviations), 77.7% of employmentFinland: 0.858 typical gaps (standard deviations), 2.19% of employmentFiji: 0.553 typical gaps (standard deviations), 45.7% of employmentFrance: 0.608 typical gaps (standard deviations), 3.15% of employmentUnited Kingdom: 0.566 typical gaps (standard deviations), 6.51% of employmentGeorgia: 0.382 typical gaps (standard deviations), 51.5% of employmentGhana: -1.07 typical gaps (standard deviations), 78% of employmentGambia, The: -0.358 typical gaps (standard deviations), 83.9% of employmentGuinea-Bissau: -0.509 typical gaps (standard deviations), 94.8% of employmentGreece: 0.503 typical gaps (standard deviations), 3.58% of employmentGrenada: -0.0989 typical gaps (standard deviations), 48% of employmentGuatemala: -0.409 typical gaps (standard deviations), 75.9% of employmentHonduras: -0.592 typical gaps (standard deviations), 82.6% of employmentCroatia: 0.701 typical gaps (standard deviations), 1.17% of employmentHaiti: -0.964 typical gaps (standard deviations), 91.6% of employmentHungary: 0.656 typical gaps (standard deviations), 1.31% of employmentIndonesia: 0.358 typical gaps (standard deviations), 81% of employmentIndia: 0.836 typical gaps (standard deviations), 88.1% of employmentIreland: 0.769 typical gaps (standard deviations), 1.9% of employmentIraq: 0.713 typical gaps (standard deviations), 67.6% of employmentIceland: 0.895 typical gaps (standard deviations), 1.97% of employmentItaly: 0.444 typical gaps (standard deviations), 3.42% of employmentJamaica: 0.11 typical gaps (standard deviations), 54.6% of employmentJordan: 0.499 typical gaps (standard deviations), 55.1% of employmentKenya: -0.947 typical gaps (standard deviations), 86.5% of employmentKyrgyz Republic: 0.801 typical gaps (standard deviations), 57.5% of employmentKiribati: 0.995 typical gaps (standard deviations), 61.5% of employmentKorea, Rep.: 0.545 typical gaps (standard deviations), 29.1% of employmentLao PDR: 0.261 typical gaps (standard deviations), 90.5% of employmentLebanon: 0.249 typical gaps (standard deviations), 55.5% of employmentLiberia: -0.456 typical gaps (standard deviations), 89.3% of employmentSt. Lucia: -0.0751 typical gaps (standard deviations), 28.3% of employmentSri Lanka: 0.202 typical gaps (standard deviations), 67.6% of employmentLesotho: -1.22 typical gaps (standard deviations), 89.1% of employmentLithuania: 0.341 typical gaps (standard deviations), 3.2% of employmentLuxembourg: 0.505 typical gaps (standard deviations), 1.93% of employmentLatvia: 0.474 typical gaps (standard deviations), 2.17% of employmentMoldova: 0.895 typical gaps (standard deviations), 52.2% of employmentMadagascar: -1.46 typical gaps (standard deviations), 96.1% of employmentMaldives: 0.754 typical gaps (standard deviations), 47.3% of employmentMexico: -0.0506 typical gaps (standard deviations), 56.4% of employmentMarshall Islands: 0.344 typical gaps (standard deviations), 32.6% of employmentNorth Macedonia: 0.413 typical gaps (standard deviations), 11.1% of employmentMali: -0.544 typical gaps (standard deviations), 94.4% of employmentMalta: 0.6 typical gaps (standard deviations), 0.63% of employmentMyanmar: 0.408 typical gaps (standard deviations), 80.7% of employmentMongolia: 0.623 typical gaps (standard deviations), 27.1% of employmentMozambique: -2.51 typical gaps (standard deviations), 95.5% of employmentMauritania: 0.336 typical gaps (standard deviations), 89.3% of employmentMauritius: 0.311 typical gaps (standard deviations), 23.4% of employmentMalawi: -1.72 typical gaps (standard deviations), 92.7% of employmentNamibia: -1.55 typical gaps (standard deviations), 55.6% of employmentNiger: -1.01 typical gaps (standard deviations), 98.5% of employmentNigeria: -0.587 typical gaps (standard deviations), 93.1% of employmentNicaragua: -0.386 typical gaps (standard deviations), 81.8% of employmentNetherlands: 0.958 typical gaps (standard deviations), 2.63% of employmentNorway: 0.91 typical gaps (standard deviations), 1.43% of employmentNepal: 0.652 typical gaps (standard deviations), 81.5% of employmentNaoero: 0.326 typical gaps (standard deviations), 47.9% of employmentPakistan: -0.0796 typical gaps (standard deviations), 88.7% of employmentPanama: -0.497 typical gaps (standard deviations), 58.7% of employmentPeru: 0.00275 typical gaps (standard deviations), 70.5% of employmentPoland: 0.795 typical gaps (standard deviations), 8.62% of employmentPortugal: 0.48 typical gaps (standard deviations), 4.73% of employmentParaguay: -0.155 typical gaps (standard deviations), 64.5% of employmentWest Bank and Gaza: 0.292 typical gaps (standard deviations), 48.8% of employmentRomania: 0.708 typical gaps (standard deviations), 2.21% of employmentRussian Federation: 0.539 typical gaps (standard deviations), 19.3% of employmentRwanda: -0.82 typical gaps (standard deviations), 84.3% of employmentSudan: 0.213 typical gaps (standard deviations), 94.2% of employmentSenegal: -0.103 typical gaps (standard deviations), 89.2% of employmentSierra Leone: -0.683 typical gaps (standard deviations), 93.1% of employmentEl Salvador: 0.0329 typical gaps (standard deviations), 64% of employmentSerbia: 0.525 typical gaps (standard deviations), 17.8% of employmentSuriname: 0.129 typical gaps (standard deviations), 50.9% of employmentSlovak Republic: 1.06 typical gaps (standard deviations), 4.37% of employmentSlovenia: 1.02 typical gaps (standard deviations), 0.7% of employmentSweden: 0.734 typical gaps (standard deviations), 2.83% of employmentEswatini: -1.85 typical gaps (standard deviations), 53.3% of employmentSeychelles: 0.575 typical gaps (standard deviations), 14.2% of employmentChad: -0.729 typical gaps (standard deviations), 96.9% of employmentTogo: -0.634 typical gaps (standard deviations), 92.3% of employmentThailand: 0.525 typical gaps (standard deviations), 63.2% of employmentTajikistan: 0.207 typical gaps (standard deviations), 63.6% of employmentTimor-Leste: -0.345 typical gaps (standard deviations), 80.6% of employmentTonga: 0.87 typical gaps (standard deviations), 59.2% of employmentTunisia: 0.484 typical gaps (standard deviations), 36.9% of employmentTurkiye: -0.0777 typical gaps (standard deviations), 27.7% of employmentTuvalu: -0.0403 typical gaps (standard deviations), 20.1% of employmentTanzania: -1.21 typical gaps (standard deviations), 90.8% of employmentUganda: -1.56 typical gaps (standard deviations), 94.5% of employmentUruguay: 0.135 typical gaps (standard deviations), 28.5% of employmentViet Nam: 0.323 typical gaps (standard deviations), 67% of employmentVanuatu: 0.0795 typical gaps (standard deviations), 72.4% of employmentSamoa: 0.0986 typical gaps (standard deviations), 51.5% of employmentSouth Africa: -1.12 typical gaps (standard deviations), 34.8% of employmentZambia: -2.37 typical gaps (standard deviations), 82.7% of employmentZimbabwe: -1.72 typical gaps (standard deviations), 85% of employmentARMCODCOMCZEINDMOZSVKSVNSWZZAFZMB

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6212.1e-15132
By rank (Spearman), not adjusted-0.6672.7e-18132
Standard (Pearson), adjusted for national income-0.1310.135132
By rank (Spearman), adjusted for national income-0.2320.008132

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: +0.489 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.157 (without IND) to -0.101 (without BWA). 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: BWA (+0.030).

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 & Pacific16-0.07
Europe & Central Asia40+0.08
Latin America & Caribbean23+0.06
Sub-Saharan Africa39+0.02

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 third44-0.24
Middle third44-0.02
Richest third44-0.63

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
Christians88-0.25
Muslims28-0.13

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+0.31
Spain16-0.13
United Kingdom38-0.09

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 = -0.184 after dropping 3 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


typical gaps (standard deviations)
% of employment		Years
Slovak Republic SVK1.064.37%2023, 2024
Slovenia SVN1.020.7%2023, 2024
Kiribati KIR0.99561.5%2023
Czechia CZE0.9612.26%2023, 2024
Netherlands NLD0.9582.63%2021, 2024
Norway NOR0.911.43%2023, 2024
Moldova MDA0.89552.2%2023, 2024
Iceland ISL0.8951.97%2019, 2020
Belgium BEL0.8951.37%2023, 2024
Tonga TON0.8759.2%2021, 2023
Finland FIN0.8582.19%2023, 2024
Armenia ARM0.84376.1%2024, 2017
India IND0.83688.1%2022, 2024
Kyrgyz Republic KGZ0.80157.5%2024, 2023
Poland POL0.7958.62%2023, 2024

wdi
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

Equality score

Equality score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 2 measures from 1 publisher. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Extreme poverty rate, Gini index.

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 Equality score and Informal employment

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

More links with Equality score or Informal employment