Do Alcohol consumption and Human capital (PWT) go together across countries?

Across 140 countries the link before adjusting for anything is r = +0.53, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.56.

Does the link hold within countries over time, not just across countries?

Comparing each country only with itself across 2000-2020, and setting aside what happened worldwide in each year, the link is -0.013 (141 countries, 2,961 country-years). Using year-to-year changes, it is +0.027.

Does Alcohol consumption cause Human capital (PWT)?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Alcohol consumption vs. Human capital (PWT)

Fades after adjusting for income

Across 140 countries, Alcohol consumption and Human capital (PWT) rise and fall together at r = +0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.31, which could easily be chance (q = 0.080, 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), 2.17 to 3.31 points, alcohol consumption is 3.38 litres per adult per year higher at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 3.22 litres per adult per year.

Link before adjusting (r)+0.528p = 2.1e-11 · 140 countries

After adjusting for national income+0.311p = 0.000 · likely range [+0.15, +0.45]

p, allowing for regions0.001standard error 0.055 · 7 regions

Check for luck (q)0.080across 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.

Alcohol consumption · litres per adult per yearlink with income +0.51 · moderate

Human capital (PWT) · index (1 = no schooling)link with income +0.82 · proxy

“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	+0.528	2.1e-11	140
By rank (Spearman), not adjusted	+0.557	8.7e-13	140
Standard (Pearson), adjusted for national income	+0.311	0.000	140
By rank (Spearman), adjusted for national income	+0.369	7.9e-6	140

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: +0.217 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.283 (without KWT) to +0.344 (without BFA). 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: BFA (+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.

	n	r
East Asia & Pacific	17	+0.42
Europe & Central Asia	40	+0.30
Latin America & Caribbean	23	+0.55
Middle East, North Africa, Afghanistan & Pakistan	16	+0.49
Sub-Saharan Africa	37	+0.16

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	47	-0.02
Middle third	46	+0.52
Richest third	47	+0.46

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	+0.26
Muslims	34	+0.02

By last European coloniser

	n	r
France	20	-0.21
Never a European colony	15	+0.26
Spain	18	+0.71
United Kingdom	45	+0.38

By official ideology

	n	r
Nationalist	19	+0.22
No strong official ideology	97	+0.36

By party system

	n	r
A few parties banned	20	+0.09
No parties banned	103	+0.31

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 = +0.311 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 21years. 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 = -0.013, 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 alcohol consumption moved did not tend to see their human capital (PWT) move with it.

Across countries (adjusted)+0.311for comparison

Within countries-0.065each country vs itself

…and vs the same year elsewhere-0.013q = 0.918 · 141 countries

Year-on-year changes+0.027q = 0.576

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.

2,961 country-years (one country in one year), 141 countries, 2000–2020, about 21 years each on average. Share of the variation that happens within countries over time: 4% for Alcohol consumption, 4% 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): -0.013. 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

	litres per adult per year	index (1 = no schooling)	Years
Romania ROU	16.8	3.35	2020, 2023
Latvia LVA	12.9	3.21	2020, 2023
Lithuania LTU	12.1	3.41	2020, 2023
Czechia CZE	12	3.78	2020, 2023
Germany DEU	11.8	3.71	2020, 2023
Poland POL	11.7	3.49	2020, 2023
Bulgaria BGR	11.6	3.26	2020, 2023
Austria AUT	11.5	3.45	2020, 2023
Uganda UGA	11.3	2.69	2020, 2023
Burkina Faso BFA	11.3	1.38	2020, 2023
Moldova MDA	11.1	3.18	2020, 2023
Tanzania TZA	10.9	1.77	2020, 2023
Luxembourg LUX	10.8	3.47	2020, 2023
Lao PDR LAO	10.8	2.01	2020, 2023
Ireland IRL	10.8	3.27	2020, 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

Alcohol consumption

Total alcohol consumption per capita (litres of pure alcohol, age 15+)

World Health Organization Global Health Observatory · CC BY 4.0 · updated quarterly

Recorded drinking plus an estimate of unrecorded drinking, per person aged fifteen and over. Ends in 2020. No "better" direction is set, and the reason is the strongest fact about this measure on the site. Of everything here, it is what most separates countries by their largest religion. Alcohol is forbidden in Islam, so a low figure in a Muslim-majority country is religious practice showing up in a health statistic. Calling that end "best" would make this site score religious practice, which it refuses to do everywhere else. Every measure of attendance, prayer, marriage and belief here has no "better" direction for exactly that reason. This is the same fact arriving through a different column. The World Health Organization is right that alcohol carries a real burden of disease, and the site does not dispute it. It declines to turn that into a score for a country, when the thing being scored would largely be how many of its people are Muslim.

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 2019–2023. 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 Alcohol consumption and Human capital (PWT)

This page shows that two measures go together. It does not show that Alcohol consumption changes Human capital (PWT), or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.