Employment in agriculture vs. Human capital (PWT)
Fades after adjusting for income
Across 142 countries, Employment in agriculture and Human capital (PWT) 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 -0.19, which could easily be chance (q = 0.22, 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.
Comparing countries across the middle half of human capital (PWT), 2.2 to 3.31 points, employment in agriculture is 26% of employment lower at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 5.53% of employment.
Link before adjusting (r)-0.777p = 5.8e-30 · 142 countries
After adjusting for national income-0.189p = 0.025 · likely range [-0.34, -0.02]
p, allowing for regions0.099standard error 0.097 · 7 regions
Check for luck (q)0.219across 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.
Employment in agriculture · % of employmentlink with income -0.86 · proxy
Human capital (PWT) · index (1 = no schooling)link with income +0.82 · 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
best-fit line line after adjusting
Four ways to measure the link
| Method | r | p | Countries |
|---|---|---|---|
| Standard (Pearson), not adjusted | -0.777 | 5.8e-30 | 142 |
| By rank (Spearman), not adjusted | -0.776 | 9.0e-30 | 142 |
| Standard (Pearson), adjusted for national income | -0.189 | 0.025 | 142 |
| By rank (Spearman), adjusted for national income | -0.150 | 0.075 | 142 |
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 142 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: +0.588 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.219 (without MDA) to -0.158 (without KGZ). 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: KGZ (+0.031).
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 | ||
|---|---|---|---|
| East Asia & Pacific | 19 | -0.50 | |
| Europe & Central Asia | 40 | -0.26 | |
| Latin America & Caribbean | 23 | -0.47 | |
| Middle East, North Africa, Afghanistan & Pakistan | 16 | -0.48 | |
| Sub-Saharan Africa | 37 | +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.
| n | r | ||
|---|---|---|---|
| Poorest third | 48 | -0.20 | |
| Middle third | 47 | +0.04 | |
| Richest third | 47 | -0.17 |
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 | 87 | -0.11 | |
| Muslims | 34 | -0.37 |
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 | ||
|---|---|---|---|
| France | 20 | -0.49 | |
| Never a European colony | 15 | -0.33 | |
| Spain | 18 | -0.25 | |
| United Kingdom | 45 | +0.07 |
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 | ||
|---|---|---|---|
| Nationalist | 20 | -0.18 | |
| No strong official ideology | 97 | -0.18 |
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 | 20 | -0.22 | |
| No parties banned | 103 | -0.11 |
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 = -0.194 after dropping 1 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 33years. 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 = -0.052, 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 employment in agriculture moved did not tend to see their human capital (PWT) move with it.
Across countries (adjusted)-0.189for comparison
Within countries-0.582each country vs itself
…and vs the same year elsewhere-0.052q = 0.610 · 144 countries
Year-on-year changes+0.009q = 0.822
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.
4,749 country-years (one country in one year), 144 countries, 1991–2023, about 33 years each on average. Share of the variation that happens within countries over time: 9% for Employment in agriculture, 5% for Human capital (PWT). 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): -0.052. 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 employment | index (1 = no schooling) | Years | |
|---|---|---|---|
| Burundi BDI | 85.3% | 1.48 | 2025, 2023 |
| Mozambique MOZ | 73% | 1.29 | 2025, 2023 |
| Niger NER | 73% | 1.26 | 2025, 2023 |
| Madagascar MDG | 69.2% | 1.72 | 2025, 2023 |
| Lao PDR LAO | 68.6% | 2.01 | 2025, 2023 |
| Central African Republic CAF | 66.1% | 1.6 | 2025, 2023 |
| Malawi MWI | 65% | 2.14 | 2025, 2023 |
| Uganda UGA | 64.9% | 2.69 | 2025, 2023 |
| Tanzania TZA | 64.6% | 1.77 | 2025, 2023 |
| Mali MLI | 63% | 1.48 | 2025, 2023 |
| Ethiopia ETH | 60.1% | 1.54 | 2025, 2023 |
| Congo, Dem. Rep. COD | 58.5% | 1.83 | 2025, 2023 |
| Zambia ZMB | 54.5% | 2.77 | 2025, 2023 |
| Zimbabwe ZWE | 54.3% | 2.82 | 2025, 2023 |
| Angola AGO | 51.9% | 1.54 | 2025, 2023 |
World Bank, World Development Indicators
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
Employment in agriculture
Employment in agriculture (% of total employment)
International Labour Organization · CC BY 4.0 · updated quarterly
Estimated with a model by the ILO. The classic measure of a country moving from farming to industry and services. It is one of the measures most closely linked to income on the site. No "better" direction is set, because a farming country is not doing badly by farming.
Human capital (PWT)
Human capital index, from years of schooling (Penn World Table)
Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)
"Human capital" means the skills and knowledge workers bring to their jobs. This index measures it from schooling alone. It starts from how many years adults spent in school, then counts the early years for more than the later ones, following studies of how much higher pay tends to be with each extra year. A score of 1 means no schooling; the US is about 3.8. It uses the same schooling figures as mean years of schooling, so the two are close by construction. For that reason it is left out of the education score, which already counts years of schooling.
How this was computed
Data used: most recent value in 2010-2025; values come from 2021–2025. 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 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 Employment in agriculture and Human capital (PWT)
This page shows that two measures go together. It does not show that Employment in agriculture changes Human capital (PWT), or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Employment in agriculture or Human capital (PWT)
- Human capital (PWT) vs One-person households before +0.72 → after adjusting +0.42
- Human capital (PWT) vs Population 65+ before +0.76 → after adjusting +0.41
- Basic water access vs Employment in agriculture before -0.79 → after adjusting -0.40
- Child stunting vs Employment in agriculture before +0.78 → after adjusting +0.40
- Electricity access vs Employment in agriculture before -0.77 → after adjusting -0.39
- Basic sanitation vs Employment in agriculture before -0.79 → after adjusting -0.38
- Human capital (PWT) vs PISA maths before +0.70 → after adjusting +0.38
- Adolescent fertility vs Employment in agriculture before +0.77 → after adjusting +0.38