placestats

Informal employment vs. Physicians

Holds up after adjusting for income

Across 140 countries, Informal employment and Physicians rise and fall together at r = -0.82 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.43 (q = 0.031, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate negative link remains.

Comparing countries across the middle half of informal employment, 16% to 83.9% of employment, physicians is worse by 2.84 per 1,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 1.78 per 1,000 people.

Link before adjusting (r)-0.823p = 9.3e-36 · 140 countries
After adjusting for national income-0.430p = 1.2e-7 · likely range [-0.56, -0.28]
p, allowing for regions0.002standard error 0.073 · 6 regions
Check for luck (q)0.031across 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
Physicians · per 1,000 peoplelink with income +0.85 · 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

Informal employment · % of employment →Physicians · per 1,000 people →0.63%98.5%0.0386.6Afghanistan: 86.1% of employment, 0.317 per 1,000 peopleAngola: 95.3% of employment, 0.244 per 1,000 peopleArgentina: 51.6% of employment, 5.11 per 1,000 peopleArmenia: 76.1% of employment, 3.36 per 1,000 peopleAntigua and Barbuda: 12.8% of employment, 2.92 per 1,000 peopleAustria: 1.32% of employment, 5.51 per 1,000 peopleBurundi: 98% of employment, 0.078 per 1,000 peopleBelgium: 1.37% of employment, 3.57 per 1,000 peopleBenin: 96.3% of employment, 0.216 per 1,000 peopleBurkina Faso: 93.8% of employment, 0.148 per 1,000 peopleBangladesh: 84% of employment, 0.722 per 1,000 peopleBulgaria: 1.2% of employment, 4.33 per 1,000 peopleBahamas, The: 18.3% of employment, 1.9 per 1,000 peopleBosnia and Herzegovina: 16.6% of employment, 2.58 per 1,000 peopleBolivia: 83.9% of employment, 1.28 per 1,000 peopleBrazil: 35.6% of employment, 2.36 per 1,000 peopleBarbados: 61.8% of employment, 2.96 per 1,000 peopleBrunei Darussalam: 21.4% of employment, 1.89 per 1,000 peopleBotswana: 75.6% of employment, 0.379 per 1,000 peopleSwitzerland: 1.48% of employment, 4.48 per 1,000 peopleChile: 26.4% of employment, 3.33 per 1,000 peopleCote d'Ivoire: 92.1% of employment, 0.166 per 1,000 peopleCameroon: 87.1% of employment, 0.136 per 1,000 peopleCongo, Dem. Rep.: 96.3% of employment, 0.208 per 1,000 peopleColombia: 56% of employment, 2.54 per 1,000 peopleComoros: 88.8% of employment, 0.424 per 1,000 peopleCabo Verde: 60.6% of employment, 0.73 per 1,000 peopleCosta Rica: 36.1% of employment, 2.69 per 1,000 peopleCyprus: 1.68% of employment, 3.56 per 1,000 peopleCzechia: 2.26% of employment, 4.35 per 1,000 peopleGermany: 2.49% of employment, 4.53 per 1,000 peopleDjibouti: 47.6% of employment, 0.211 per 1,000 peopleDenmark: 2.82% of employment, 4.5 per 1,000 peopleDominican Republic: 54.7% of employment, 2.43 per 1,000 peopleEcuador: 68.5% of employment, 2.31 per 1,000 peopleEgypt, Arab Rep.: 68.6% of employment, 0.671 per 1,000 peopleSpain: 2.2% of employment, 4.29 per 1,000 peopleEstonia: 2.4% of employment, 3.47 per 1,000 peopleEthiopia: 77.7% of employment, 0.143 per 1,000 peopleFinland: 2.19% of employment, 3.61 per 1,000 peopleFiji: 45.7% of employment, 0.813 per 1,000 peopleFrance: 3.15% of employment, 3.28 per 1,000 peopleUnited Kingdom: 6.51% of employment, 3.3 per 1,000 peopleGeorgia: 51.5% of employment, 5.64 per 1,000 peopleGhana: 78% of employment, 0.266 per 1,000 peopleGambia, The: 83.9% of employment, 0.09 per 1,000 peopleGuinea-Bissau: 94.8% of employment, 0.252 per 1,000 peopleGreece: 3.58% of employment, 6.58 per 1,000 peopleGrenada: 48% of employment, 1.38 per 1,000 peopleGuatemala: 75.9% of employment, 1.28 per 1,000 peopleGuyana: 51% of employment, 1.39 per 1,000 peopleHonduras: 82.6% of employment, 0.489 per 1,000 peopleCroatia: 1.17% of employment, 3.91 per 1,000 peopleHaiti: 91.6% of employment, 0.292 per 1,000 peopleHungary: 1.31% of employment, 3.46 per 1,000 peopleIndonesia: 81% of employment, 0.524 per 1,000 peopleIndia: 88.1% of employment, 0.723 per 1,000 peopleIreland: 1.9% of employment, 3.88 per 1,000 peopleIraq: 67.6% of employment, 1.02 per 1,000 peopleIceland: 1.97% of employment, 4.37 per 1,000 peopleItaly: 3.42% of employment, 4.19 per 1,000 peopleJamaica: 54.6% of employment, 0.46 per 1,000 peopleJordan: 55.1% of employment, 2.85 per 1,000 peopleKenya: 86.5% of employment, 0.289 per 1,000 peopleKyrgyz Republic: 57.5% of employment, 1.57 per 1,000 peopleCambodia: 89.4% of employment, 0.211 per 1,000 peopleKiribati: 61.5% of employment, 0.193 per 1,000 peopleKorea, Rep.: 29.1% of employment, 2.61 per 1,000 peopleLao PDR: 90.5% of employment, 0.331 per 1,000 peopleLebanon: 55.5% of employment, 2.68 per 1,000 peopleLiberia: 89.3% of employment, 0.178 per 1,000 peopleSt. Lucia: 28.3% of employment, 4.23 per 1,000 peopleSri Lanka: 67.6% of employment, 1.14 per 1,000 peopleLesotho: 89.1% of employment, 0.235 per 1,000 peopleLithuania: 3.2% of employment, 4.47 per 1,000 peopleLuxembourg: 1.93% of employment, 2.98 per 1,000 peopleLatvia: 2.17% of employment, 3.4 per 1,000 peopleMoldova: 52.2% of employment, 4.02 per 1,000 peopleMadagascar: 96.1% of employment, 0.172 per 1,000 peopleMaldives: 47.3% of employment, 2.23 per 1,000 peopleMexico: 56.4% of employment, 2.59 per 1,000 peopleMarshall Islands: 32.6% of employment, 0.467 per 1,000 peopleNorth Macedonia: 11.1% of employment, 2.94 per 1,000 peopleMali: 94.4% of employment, 0.188 per 1,000 peopleMalta: 0.63% of employment, 4.51 per 1,000 peopleMyanmar: 80.7% of employment, 0.757 per 1,000 peopleMongolia: 27.1% of employment, 4.13 per 1,000 peopleMozambique: 95.5% of employment, 0.175 per 1,000 peopleMauritania: 89.3% of employment, 0.261 per 1,000 peopleMauritius: 23.4% of employment, 1.44 per 1,000 peopleMalawi: 92.7% of employment, 0.054 per 1,000 peopleNamibia: 55.6% of employment, 0.553 per 1,000 peopleNiger: 98.5% of employment, 0.038 per 1,000 peopleNigeria: 93.1% of employment, 0.38 per 1,000 peopleNicaragua: 81.8% of employment, 0.682 per 1,000 peopleNetherlands: 2.63% of employment, 3.88 per 1,000 peopleNorway: 1.43% of employment, 4.97 per 1,000 peopleNepal: 81.5% of employment, 1.01 per 1,000 peopleNaoero: 47.9% of employment, 1.27 per 1,000 peoplePakistan: 88.7% of employment, 1.16 per 1,000 peoplePanama: 58.7% of employment, 1.63 per 1,000 peoplePeru: 70.5% of employment, 1.69 per 1,000 peoplePalau: 7.27% of employment, 1.81 per 1,000 peoplePoland: 8.62% of employment, 4.03 per 1,000 peoplePortugal: 4.73% of employment, 5.85 per 1,000 peopleParaguay: 64.5% of employment, 3.89 per 1,000 peopleWest Bank and Gaza: 48.8% of employment, 2.17 per 1,000 peopleRomania: 2.21% of employment, 3.63 per 1,000 peopleRussian Federation: 19.3% of employment, 5.11 per 1,000 peopleRwanda: 84.3% of employment, 0.09 per 1,000 peopleSudan: 94.2% of employment, 0.25 per 1,000 peopleSenegal: 89.2% of employment, 0.109 per 1,000 peopleSierra Leone: 93.1% of employment, 0.134 per 1,000 peopleEl Salvador: 64% of employment, 1.62 per 1,000 peopleSomalia, Fed. Rep.: 81.9% of employment, 0.048 per 1,000 peopleSerbia: 17.8% of employment, 3.1 per 1,000 peopleSuriname: 50.9% of employment, 1.35 per 1,000 peopleSlovak Republic: 4.37% of employment, 3.7 per 1,000 peopleSlovenia: 0.7% of employment, 3.37 per 1,000 peopleSweden: 2.83% of employment, 4.41 per 1,000 peopleEswatini: 53.3% of employment, 0.557 per 1,000 peopleSeychelles: 14.2% of employment, 6.6 per 1,000 peopleChad: 96.9% of employment, 0.085 per 1,000 peopleTogo: 92.3% of employment, 0.08 per 1,000 peopleThailand: 63.2% of employment, 0.541 per 1,000 peopleTajikistan: 63.6% of employment, 1.87 per 1,000 peopleTimor-Leste: 80.6% of employment, 0.752 per 1,000 peopleTonga: 59.2% of employment, 1.01 per 1,000 peopleTunisia: 36.9% of employment, 1.31 per 1,000 peopleTurkiye: 27.7% of employment, 2.24 per 1,000 peopleTuvalu: 20.1% of employment, 1.35 per 1,000 peopleTanzania: 90.8% of employment, 0.134 per 1,000 peopleUganda: 94.5% of employment, 0.191 per 1,000 peopleUruguay: 28.5% of employment, 4.67 per 1,000 peopleViet Nam: 67% of employment, 1.11 per 1,000 peopleVanuatu: 72.4% of employment, 0.164 per 1,000 peopleSamoa: 51.5% of employment, 0.561 per 1,000 peopleSouth Africa: 34.8% of employment, 0.794 per 1,000 peopleZambia: 82.7% of employment, 0.324 per 1,000 peopleZimbabwe: 85% of employment, 0.136 per 1,000 peopleARGBDIGEOGRCMHLNERPLWPRTSYCTCDTUV

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.8239.3e-36140
By rank (Spearman), not adjusted-0.8521.1e-40140
Standard (Pearson), adjusted for national income-0.4301.2e-7140
By rank (Spearman), adjusted for national income-0.3502.4e-5140

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: +0.393 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.465 (without GEO) to -0.398 (without GUY). 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: GEO (-0.034).

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 & Pacific19-0.51
Europe & Central Asia40+0.01
Latin America & Caribbean26-0.30
Sub-Saharan Africa40-0.55

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 third47-0.49
Middle third46-0.24
Richest third47-0.37

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
Christians90-0.37
Muslims31-0.63

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
France19-0.46
Never a European colony12-0.15
Spain16-0.36
United Kingdom43-0.44

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 = -0.430 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.

Loading…

Over time, within countries

Everything above compares countries with each other at one moment. This section compares each country only with itself over 24years. 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 = -0.046, 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 physicians move with it.

Across countries (adjusted)-0.430for comparison
Within countries-0.260each country vs itself
…and vs the same year elsewhere-0.046q = 0.769 · 66 countries
Year-on-year changes-0.089q = 0.467
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.

833 country-years (one country in one year), 66 countries, 2000–2023, about 13 years each on average. Share of the variation that happens within countries over time: 1% for Informal employment, 6% for Physicians. 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

0.40−0.4-100+10years Physicians trails Informal employment →lag -10: r = -0.139 (n = 859)lag -9: r = -0.152 (n = 866)lag -8: r = -0.164 (n = 897)lag -7: r = -0.167 (n = 908)lag -6: r = -0.141 (n = 902)lag -5: r = -0.107 (n = 910)lag -4: r = -0.060 (n = 924)lag -3: r = -0.029 (n = 923)lag -2: r = -0.035 (n = 915)lag -1: r = -0.059 (n = 886)lag +0: r = -0.046 (n = 833)lag +1: r = 0.004 (n = 777)lag +2: r = 0.001 (n = 710)lag +3: r = 0.041 (n = 652)lag +4: r = 0.062 (n = 605)lag +5: r = 0.169 (n = 550)lag +6: r = 0.162 (n = 488)lag +7: r = 0.242 (n = 438)lag +8: r = 0.230 (n = 386)lag +9: r = 0.311 (n = 317)lag +10: r = 0.349 (n = 271)

Same year (lag 0): -0.046. 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
per 1,000 people		Years
Niger NER98.5%0.0382022, 2023
Burundi BDI98%0.0782014, 2022
Chad TCD96.9%0.0852018, 2023
Benin BEN96.3%0.2162022, 2023
Congo, Dem. Rep. COD96.3%0.2082020, 2022
Madagascar MDG96.1%0.1722022
Mozambique MOZ95.5%0.1752015, 2022
Angola AGO95.3%0.2442024, 2022
Guinea-Bissau GNB94.8%0.2522022
Uganda UGA94.5%0.1912021, 2022
Mali MLI94.4%0.1882023
Sudan SDN94.2%0.252022, 2017
Burkina Faso BFA93.8%0.1482024, 2022
Nigeria NGA93.1%0.382024, 2023
Sierra Leone SLE93.1%0.1342018, 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.

Physicians

Physicians (per 1,000 people)

World Health Organization · CC BY 4.0 · updated quarterly

Reported irregularly, so the latest value in the time window used is from a different year for different countries. Hospital beds, already on the site, have no "better" direction, since more beds is not plainly better. More doctors is closer to plainly better, so a direction is set on that basis.

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 Physicians

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

More links with Informal employment or Physicians