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

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Handbook of Cliometrics

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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.

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Notes

  1. 1.

    This might be the most fundamental difference to the “traditional” humanities with their expertise in dealing with ambiguities. See also the final section.

  2. 2.

    An exception to this observation can be found in the series “Current Research in Digital History” published by the Roy Rosenzweig Center for History and New Media. See https://crdh.rrchnm.org (last access July 12, 2022).

  3. 3.

    Ash and Hansen (2023) provide a similar, though more technical, review article on methods for text analysis in economics. Other helpful introductions to textual analysis are provided by Gentzkow et al. (2019), Grimmer and Stewart (2013), Grimmer et al. (2022).

  4. 4.

    An example of studies that quantify textual sources without the help of digital tools can be found in Whaples (1991). Contrary to the Social Sciences, quantitative text analysis by means of coding-books, which confusingly is sometimes called “Qualitative Text Analysis,” does not seem to have been particularly common in economic history.

  5. 5.

    The most popular model, Latent Dirichlet Allocation (LDA), was introduced by Blei et al. (2003). Readers who are interested in the details of topic modelling are recommended to the introductions by Blei (2012) and Wehrheim (2019a).

  6. 6.

    The difference between pre-defined categorization scheme and categorization by means of a topic model will be further addressed in the section “Outlook and Conclusion.”

  7. 7.

    It goes without saying that topic modelling also requires human intervention, regarding, e.g., data selection, model specification, and evaluation. The central feature of topic models is that the two crucial steps of category building and classification are performed solely by the algorithm.

  8. 8.

    Topic models can be useful also for data collection. For example, the model created for this chapter contains a topic related to Germany (topic 12). I used this topic to identify relevant articles for the chapter “Cliometrics and the Study of German History” by Tobias Jopp and Mark Spoerer in this volume.

  9. 9.

    In the following section, I provide an overview of studies that apply some sort of computational text analysis and that are of potential interest to economic historians. Some of these studies do not address an EH question in the strict sense but still might be useful to future economic historians.

  10. 10.

    See Nicholson for an account on how the digital turn has affected the use of newspapers as a historiographic source.

  11. 11.

    This particularly concerns newspapers published after World War II for which access can be quite expensive if one is interested in full texts, which are necessary for applying methods such as topic modelling.

  12. 12.

    The preprocessing procedure and the model specifications are documented in the Online Appendix.

  13. 13.

    Diverting from this rule, topic 22 was classified as neutral due to its high topic share and its idiosyncratic development (see Online Appendix). This is of course a static picture as topics are defined as EH/Econ only once for the whole period. As we will see below, the affiliation of a topic can change over time.

  14. 14.

    Cf. https://www.aeaweb.org/articles?id=10.1257/000282803321455250 (last access July 15, 2022). It could be revealing to measure the overlap between the classification based on the topic model and JEL-codes. Due to a lack of data, this evaluation could not be carried out yet.

  15. 15.

    As a side note, the JEL-codes of this paper do not contain a N-code. One could argue that papers published in EEH all have an N-code by default, so assigning one explicitly is unnecessary. On the other hand, there are papers published in EEH that come with an explicit N-code. Others, again, come without any JEL-code.

  16. 16.

    As topic modelling is a probabilistic process, one should be eminently cautious in using the data produced by a topic model as input for further quantitative analysis. Repeatedly running the same model with the same parameters yields different results. Also, the results are highly dependable on the parameters, that is, the number of topics, the number of iterations, the hyperparameters alpha and beta, the seed-value, and finally, the preprocessing.

  17. 17.

    That is, the sum of the top five topics per year. Note that these actual five topics vary between years.

  18. 18.

    These thoughts result from my own experience in an interdisciplinary DH project (https://media-sentiment.uni-leipzig.de) as well as on conversations with colleagues with similar experiences.

  19. 19.

    See Ash and Hansen (2023) for a discussion of the use of textual data as input for econometric models.

  20. 20.

    Grimmer et al. (2022), see text analysis as an augmentation for human readers.

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Authors and Affiliations

  1. History Department, University of Regensburg, Regensburg, Germany

    Lino Wehrheim

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  1. Lino Wehrheim
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Correspondence to Lino Wehrheim .

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  1. BETA/CNRS, University of Strasbourg, Institute for Advanced Study, Strasbourg, France

    Claude Diebolt

  2. University of Wisconsin – La Crosse, La Crosse, WI, USA

    Michael Haupert

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Wehrheim, L. (2024). Digital Methods in Economic History: The Case of Computational Text Analysis. In: Diebolt, C., Haupert, M. (eds) Handbook of Cliometrics. Springer, Cham. https://doi.org/10.1007/978-3-031-35583-7_118

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