Academic human capital in preindustrial Europe was highly concentrated, and its regional balance shifted over several centuries. A new annual series covering 1200 to 1793 shows northern regions, notably the English Realms, Evangelical Germania and the Netherlands, pulling ahead of Occitania, Central Italy, Portugal and Castilla from around 1500. The series measures a documented population of university professors and academy members, not literacy or schooling across the whole population.
What the study measures
The study by Matthew Curtis, David de la Croix, Filippo Manfredini and Mara Vitale appeared in Explorations in Economic History, volume 101 (July 2026), article 101756. It builds annual series of academic human capital for Europe from 1200 to 1793, aggregated at the level of individual cities, present-day countries and historically defined macro-regions.
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The underlying data come from the Repertorium Eruditorum Totius Europae (RETE), which records individual-level information on university professors and members of scientific academies. For each scholar the authors compute a composite index based on publication outcomes, then sum or average those values over time and across places. The working-paper version of the study (2025) describes the population as one for which systematic records survive from the preindustrial period, which is the main reason the series can be built at all.
What “academic human capital” means in this dataset
In this dataset, academic human capital is an index of documented scholarly output and visibility. It is not a count of students taught, a test of reading ability, or a measure of practical skill. A scholar who published widely and was recorded in the sources contributes more than one whose work left little trace.
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That construction has consequences a reader should keep in view. Parts of the index depend on reference sources such as Wikipedia and VIAF, which are updated over time, so entries can change. Survival of publications and later recognition also shape which scholars look prominent, and the index inherits those biases. The authors treat these as features of the measure to be understood, not as errors that invalidate it. The series is best read as a picture of the academic population the sources capture. It cannot stand in for education among the whole population.
How the series is aggregated
The authors offer three levels of geography, and the choice among them changes what a comparison means.
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- City level: activity is attributed to the place where scholars worked, which shows where academic talent clustered.
- Present-day country level: activity is grouped by modern national borders, which suits comparison with national statistics but can blur older political units.
- Macro-region level: the study uses 18 historically informed macro-regions intended to reflect long-term institutional, linguistic, religious and political commonalities that modern borders may hide. Their boundaries are not treated as fixed across the whole period.
When a scholar held several institutional affiliations at once, the paper allocates that scholar’s contribution across them using equal-share rules, so a person is not counted twice.
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The published series come in four types at each geographic level. They answer different questions, so they should not be swapped for one another.
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| Data type | What it captures | Typical question it answers |
|---|---|---|
| Total university human capital | Index value for scholars holding university posts | How much academic output was tied to universities in a place? |
| Number of universities | Count of universities in the unit | How many institutions existed to host that output? |
| Additional human capital from academy-only affiliations | Index value for members whose affiliation was with an academy but not a university | How much activity sat outside universities? |
| Number of academies | Count of scientific academies in the unit | How many academic bodies operated outside the university system? |
Average human capital per university is a ratio of the first and second rows, and it is the figure to use when comparing institutional quality across places. Total human capital is the figure to use when comparing how much academic activity a region produced overall. Comparing a region’s total with another’s per-university average, or mixing university-only and university-plus-academy series, is a common way to produce misleading conclusions.
Why the series stops in 1793
The series ends in 1793 because the authors identify the French Revolution as a decisive break in higher education. This included the abolition of universities and academies in France, and the same policy was extended to neighbouring regions under military conquest. Rather than let that abrupt political upheaval distort long-run trends, the authors leave the final years out of the main analysis.
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Main patterns in the data
The Little Divergence between north and south
The study’s central pattern is a divergence between northern and southern Europe. From about 1500, average human capital per university rose in the English Realms, Evangelical Germania and the Netherlands. The series for Occitania, Central Italy, Portugal and Castilla, by contrast, stagnated over the same period. The authors label this the “Little Divergence” in academic human capital. It is a description of the measured series, and the paper does not claim a single cause for it.
Academies change the picture for France and Occitania
Adding academies alters comparisons. In the authors’ discussion of Francia and Occitania, a university-only view understates the gap, because academy activity was substantial. Parisian academies make a large contribution to the Francia series, so a reader who looks only at universities will see a smaller difference than the full series shows.
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Black Death, Thirty Years’ War and religious divisions
The authors also examine patterns around the Black Death and the Thirty Years’ War, and they compare Catholic and Protestant areas of the Holy Roman Empire. These are presented as patterns in the data. The study does not turn them into causal claims, and readers should treat them as observed associations that the authors explore rather than settled explanations.
Scotland’s trajectory
Scotland follows a distinct path in the series, and the authors discuss it in relation to the Scottish Enlightenment. As with the other patterns, the discussion describes what the series shows and does not establish a causal mechanism.
Academic human capital is not literacy
Academic human capital should not be read as a proxy for literacy or book consumption. The paper notes that the Netherlands could rank especially high on literacy while the English Realms led on the academic-human-capital measure. The two indicators capture different things, and a region can score high on one and differently on the other.
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Related context on scholarly mobility
A separate peer-reviewed study in the Journal of the European Economic Association (2024) examines the medieval and early modern academic market over 1000 to 1800. It draws on a database of about 48,000 scholars and reports that talent concentrated at stronger universities, that better scholars tended to move toward more attractive institutions, and that they were more mobile than others. That work helps explain how academic talent moved and clustered. It uses its own database and method, so it supports the broader picture rather than validating each figure in the Curtis, de la Croix, Manfredini and Vitale series.
Data access and how to cite
The article is open access in the final journal record. The replication package is listed under DOI 10.3886/E247226V1. Check that package directly for its current files and documentation, since the files were not independently inspected for this article. For publication, cite the 2026 Explorations in Economic History article, and identify the 2025 working-paper version when you quote method details or exact wording drawn from it.
Quick Recap
Reading the series responsibly
- Match the geographic level on both sides of any comparison: city, present-day country or macro-region.
- Keep universities and academies separate unless the table or series you are reading combines them.
- Use per-university averages for institutional quality and totals for overall activity.
- Treat the index as a measure of documented academic output, not general education.
- Read the Black Death, war and religious comparisons as patterns in the data, not as causes.
The Bottom Line
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