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Digital Methods in Economic History: The Case of Computational Text Analysis

In: Handbook of Cliometrics

Author

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  • Lino Wehrheim

    (University of Regensburg)

Abstract

In the last two decades, there has been a considerable increase in the supply of digital resources available to economic historians. At the same time, scholars have started to use innovative methods and technologies to study these digital sources. In this chapter, I will focus on one of these approaches – computational text analysis (CTA), also known as text mining – that has a great potential for economic historians. Firstly, I will provide an overview of examples of CTA that are relevant to economic historians, illustrating certain trends that have emerged so far. Secondly, to give a hands-on example of this kind of approach, I conduct a case study in which I apply a certain type of CTA, that is, topic-modelling, to a corpus of more than 17,000 research articles published in ten international economics and economic history journals since 1949. Covering flagship journals that represent the wide range of both fields, such as The American Economic Review, The Economic History Review, The Journal of Economic History, and The Journal of Economic Literature, I quantitatively compare the similarity of economics and economic history in terms of their research topics. Finally, I give a brief outlook on digital methods beyond the limits of CTA as well as some general reflections on the use of digital methods in our field.

Suggested Citation

  • Lino Wehrheim, 2024. "Digital Methods in Economic History: The Case of Computational Text Analysis," Springer Books, in: Claude Diebolt & Michael Haupert (ed.), Handbook of Cliometrics, edition 3, pages 2661-2688, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-35583-7_118
    DOI: 10.1007/978-3-031-35583-7_118
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