Informal employment vs. Productivity (TFP)
Fades after adjusting for income
Across 94 countries, Informal employment and Productivity (TFP) rise and fall together at r = -0.72 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.02, which could easily be chance (q = 0.96, 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 when a single country is removed.
Comparing countries across the middle half of productivity (TFP), 0.451 to 0.78 ratio to the US, informal employment is better by 37.3% of employment at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.771% of employment.
Link before adjusting (r)-0.718p = 4.0e-16 · 94 countries
After adjusting for national income-0.021p = 0.844 · likely range [-0.22, +0.18]
p, allowing for regions0.915standard error 0.184 · 6 regions
Check for luck (q)0.955across 18,622 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
Productivity (TFP) · ratio to the US (US = 1)link with income +0.79 · 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
best-fit line line after adjusting
Four ways to measure the link
| Method | r | p | Countries |
|---|---|---|---|
| Standard (Pearson), not adjusted | -0.718 | 4.0e-16 | 94 |
| By rank (Spearman), not adjusted | -0.741 | 1.4e-17 | 94 |
| Standard (Pearson), adjusted for national income | -0.021 | 0.844 | 94 |
| By rank (Spearman), adjusted for national income | +0.018 | 0.865 | 94 |
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 94 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: +0.697 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.092 (without EGY) to +0.016 (without BRB). 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: EGY (-0.071).
Fragile: this link may not hold up.
- sign flips when a single country is removed
- 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.
| n | r | ||
|---|---|---|---|
| Europe & Central Asia | 37 | +0.29 | |
| Latin America & Caribbean | 19 | -0.22 | |
| Sub-Saharan Africa | 25 | -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.
| n | r | ||
|---|---|---|---|
| Poorest third | 32 | -0.03 | |
| Middle third | 31 | -0.02 | |
| Richest third | 31 | -0.26 |
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.
| n | r | ||
|---|---|---|---|
| Christians | 68 | -0.08 | |
| Muslims | 16 | +0.07 |
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.
| n | r | ||
|---|---|---|---|
| Spain | 16 | -0.51 | |
| United Kingdom | 22 | -0.12 |
By official ideology
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.
| n | r | ||
|---|---|---|---|
| No strong official ideology | 70 | +0.06 |
By party system
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.
| n | r | ||
|---|---|---|---|
| A few parties banned | 13 | +0.57 | |
| No parties banned | 77 | -0.07 |
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 = -0.021 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 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 = +0.107, 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 productivity (TFP) move with it.
Across countries (adjusted)-0.021for comparison
Within countries+0.122each country vs itself
…and vs the same year elsewhere+0.107q = 0.595 · 69 countries
Year-on-year changes-0.072q = 0.259
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.
996 country-years (one country in one year), 69 countries, 2000–2023, about 14 years each on average. Share of the variation that happens within countries over time: 7% for Informal employment, 2% for Productivity (TFP). 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
Same year (lag 0): +0.107. 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 (+0.26 at lag +7) is higher than the same-year one by 0.15. 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 employment | ratio to the US (US = 1) | Years | |
|---|---|---|---|
| Niger NER | 98.5% | 0.352 | 2022, 2023 |
| Burundi BDI | 98% | 0.211 | 2014, 2023 |
| Benin BEN | 96.3% | 0.405 | 2022, 2023 |
| Mozambique MOZ | 95.5% | 0.236 | 2015, 2023 |
| Angola AGO | 95.3% | 0.483 | 2024, 2023 |
| Sudan SDN | 94.2% | 0.352 | 2022, 2023 |
| Burkina Faso BFA | 93.8% | 0.432 | 2024, 2023 |
| Nigeria NGA | 93.1% | 0.268 | 2024, 2023 |
| Sierra Leone SLE | 93.1% | 0.465 | 2018, 2023 |
| Togo TGO | 92.3% | 0.338 | 2022, 2023 |
| Cote d'Ivoire CIV | 92.1% | 0.677 | 2022, 2023 |
| Tanzania TZA | 90.8% | 0.337 | 2024, 2023 |
| Lao PDR LAO | 90.5% | 0.433 | 2022, 2023 |
| Mauritania MRT | 89.3% | 0.634 | 2019, 2023 |
| Senegal SEN | 89.2% | 0.323 | 2024, 2023 |
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)
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table", American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt. Data: Penn World Table version 11.0, Groningen Growth and Development Centre, doi:10.34894/FABVLR.
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.
Productivity (TFP)
Total factor productivity level, compared with the US (Penn World Table)
Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)
Total factor productivity (TFP) is how much a country produces compared with what its workers, their schooling, and its machines and buildings would be expected to produce. It is the part of GDP that those inputs do not account for, such as better technology or better organisation. The US is set to 1 in every year, so 0.5 means a country gets about half as much output from the same inputs as the US. It is worked out from GDP, capital, the number of workers, their schooling and the labor share, so it is linked to each of them by arithmetic. It is left out of the economy score: being built from GDP, it would turn that score back into GDP per person.
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 18,622 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Informal employment and Productivity (TFP)
This page shows that two measures go together. It does not show that Informal employment changes Productivity (TFP), or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Informal employment or Productivity (TFP)
- Informal employment vs Retirement age, men before +0.63 → after adjusting +0.57
- Informal employment vs Population 65+ before -0.87 → after adjusting -0.54
- Informal employment vs Median age before -0.88 → after adjusting -0.51
- Government expense vs Informal employment before -0.52 → after adjusting -0.46
- Informal employment vs PISA maths before -0.81 → after adjusting -0.46
- Informal employment vs Rule of law before -0.78 → after adjusting -0.44
- Fixed broadband vs Informal employment before -0.86 → after adjusting -0.42
- Informal employment vs Mean years of schooling before -0.83 → after adjusting -0.41