, Volume 13, Issue 1, pp 83–125 | Cite as

Economic history goes digital: topic modeling the Journal of Economic History

  • Lino WehrheimEmail author
Original Paper


Digitization and computer science have established a completely new set of methods with which to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, i.e., statistical algorithms that automatically infer the content from large collections of texts. In this article, I present an introduction to topic modeling and give an initial review of the research using topic models. I illustrate their capacity by applying them to 2675 articles published in the Journal of Economic History between 1941 and 2016. By comparing the results to traditional research on the JEH and to recent studies on the cliometric revolution, I aim to demonstrate how topic models can enrich economic historians’ methodological toolboxes.


Economic history Topic models Latent Dirichlet allocation Cliometrics Digitization Methodology 

JEL Classification

A12 C18 N01 



I am grateful to Claude Diebolt and Michael Haupert for generously sharing their data, Robert Whaples and Ann Carlos for insights concerning the JEH, and two anonymous referees for invaluable comments and suggestions on the manuscript. I thank Manuel Burghardt for patiently answering my technical questions on topic modeling, and Mark Spoerer, Tobias Jopp, and Katrin Kandlbinder for their continued support. Finally, I am very grateful to the participants in the research seminar in economic history as well as the lecture series on Digital Humanities at Universität Regensburg.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of History, Economic and Social History, Department of EconomicsUniversity of RegensburgRegensburgGermany

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