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

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Abstract

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.

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Fig. 1
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Source: Reproduced with permission from Blei (2012a)

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Fig. 8

Sources: Author’s own computations, Diebolt and Haupert (2018)

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Notes

  1. Questions on the future of economic history were discussed on a special panel at the 75th anniversary of the Economic History Association. See Journal of Economic History, Volume 75 Issue 4.

  2. For an assessment of the status quo in digital history, see the white paper “Digital History and Argument”, the Arguing with Digital History working group, Roy Rosenzweig Center for History and New Media.

  3. Jockers (2013, p. 123) calls them the “mother of all collocation tools”.

  4. As the literature review shows, there are several economists who have recently become aware of them.

  5. Scott Weingart blog gives a helpful overview of blogs on topic modeling. See http://www.scottbot.net/HIAL/index.html@p=19113.html. Although this overview may be somewhat outdated, it is still a good starting point for scholars who are unfamiliar with topic modeling.

  6. See Blei et al. (2003), Blei and Lafferty (2009), Griffiths and Steyvers (2004), and Steyvers and Griffiths (2007) for formal descriptions.

  7. Describing the origins of topic modeling in the context of digital humanities is relatively challenging as this touches on several disciplines which all have different histories. For example, the ‘history of humanities computing’ can be traced back to Father Roberto Busa, who indexed the work of Thomas Aquinas in the late 1940 s. See Hockey (2004) and Jockers (2013). For a brief description of the recent development of topic models, see Lüdering and Winker (2016).

  8. There have been several extensions of the original model covering different assumption of LDA Blei (Blei 2012a, pp. 82–84). In the following, the terms LDA and topic model will be used synonymously.

  9. Abstracts and keywords pose their own problems. With large collections, even the reading of abstracts can become too time-consuming. However, keywords may be too vague to be useful.

  10. In this example, the word table could be accompanied by words like chair, tablecloth, or leg in the first topic, while in the second it could be words like column, row, or cell.

  11. This is why topic models are also called generative models. See Steyvers and Griffiths (2007, p. 427).

  12. More precisely, this nonzero probability follows from estimating the topic shares using Gibbs sampling, also used in this paper (see below). The share of topic k in document d is approximated by \( \hat{\theta }_{d,k} = \frac{{n_{d,k} + \alpha }}{{N_{d} + K\alpha }} \) with \( n_{d,k} \) being the number of times document d uses topic \( k \) and \( N_{d} \) equaling the total number of words in document d. Including the Dirichlet parameter \( \alpha \) results in \( \hat{\theta }_{d,k} \) always being nonzero. See Boyd-Graber et al. (2017, pp. 15–16) and Griffiths and Steyvers (2004, pp. 5229–30).

  13. The following description is inspired by a lecture given by Mimno (2012b) and Ted Underwood description of topic modeling on his blog (available at https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/). For a technical description, see Griffiths and Steyvers (2004) and Steyvers and Griffiths (2007).

  14. There is a trade-off between topic coherence and the time it takes to train the model. Finding many topics in large corpora can keep the computer busy for hours.

  15. This parameter is sometimes called “hyperparameter”.

  16. One step in topic modeling frequently consists of removing capitalization (see below). In the following, words are kept in lower case if they constitute a topic.

  17. There are also attempts to automatically assign labels to topics. See Lau et al. (2011).

  18. For example, in a German newspaper corpus analyzed in a different project, one topic consisted mainly of modal verbs.

  19. Some algorithms operate with a term-document matrix, which is a transposed DTM.

  20. In general, the word vectors as well as TM can contain absolute word counts (occurrences) or relative word counts (term frequency or tf–idf. See below.). The conversion of text files into word vectors or a DTM can be carried out within the different topic model applications (see below) or independently from topic modeling. Tools are, e.g., the tm (text mining) package for R developed by Feinerer (2017) or programs like RapidMiner.

  21. The dimension of this DTM will be reduced by removing certain words. See below.

  22. Ostensibly, a tokenizer is a computer program which cuts sentences into pieces called “tokens”, based on predefined rules. For example, the sentence “Mr. Smith’s mother is seventy-nine years old, but she doesn’t look her age.” could be tokenized most simply by cutting at each whitespace and punctuation mark:“Mr|Smith|s|mother|is|seventy|nine|years|old|but|she|doesn|t|look|her|age”. This simple rule could be modified, for example, so that it does not split numbers, to delete every “s” following an apostrophe, or to leave common expression like doesn’t intact.

  23. For an extension relaxing the bag of words assumption, see Wallach (2006).

  24. For tools to carry out these steps, see Graham et al. (2016). For a discussion of the effect of stopword removal, see Schofield et al. (2017).

  25. For a general discussion of texts as data for economic research, see Gentzkow et al. (2017).

  26. OCR mistakes can build their own topic, see Jockers (2013).

  27. See, for example, McFarland et al. (2013).

  28. To name just two examples, Miller (2013) uses a Chinese corpus and Heiberger and Koss (2018) use documents written in German.

  29. This statement is only based on impressions based on work conducted in a different project and has not been verified empirically.

  30. The authors also show how their model can be used for machine translations by generating bilingual lexica. For further applications of topic models in machine translation, see Eidelman et al. (2012) and Zhao and Xing (2007).

  31. I thank one of the anonymous referees for this intriguing question.

  32. For example, translations of scientific publications are similar to the parliament proceedings used in Mimno et al. (2009) in that they are rather direct. However, there can be quite considerable differences in translations of novels. Furthermore, some aspects of a text may become “lost in translation”, as, for example, the German differentiation between the formal form of address Sie and the informal Du is lost in the English translation you. It appears unlikely, though, that issue of this kind pose serious problems to topic modeling.

  33. JEL codes are used by Abramitzky (2015), McCloskey (1976), and Whaples (2002). Another example of a comparable way of text coding can be found in the financial literature on sentiment following Tetlock (2007), which basically measures the tone of texts by counting negative and positive words based on predefined dictionaries.

  34. This should not create the impression that manual coding is regarded as being futile. On the contrary, there are many cases in which it is completely appropriate, and studies like Whaples (2002) as well as professionals’ reliance on human coding, e.g., in media analysis, build a strong case for manual coding. Still, the advantages of one method are best illustrated when compared with the shortcomings of another, and in times of almost unlimited availability of textual sources, automatic methods like topic modeling will probably prevail.

  35. Although there may be some overlaps, this should not be confused with priming as it is understood in psychology.

  36. Of course, even the decision to use any type of quantitative representation of texts is based on the conviction that this can contribute something to our research. It could be that, for example, economic historians affiliated with history departments find this a less useful approach. Naturally, this argument holds true for the use of topic models as well.

  37. For further explanation of the confirmation bias, see, e.g., Oswald and Grosjean (2004).

  38. Of course, the preprocessing steps applied in topic modeling, such as the choice to remove certain stop words, can be regarded as being a priori decisions by the researcher that influence—and thus potentially bias—the output.

  39. For instance, applications of topic models for the use of databases are explored by JSTOR One example is the “text analyzer”, an online tool which identifies documents in the JSTOR database that are similar to a search document in terms of topics. See http://www.jstor.org/analyze/.

  40. Running one model on the 2675 documents of this paper took approximately 35 min using an ordinary computer.

  41. There is no exact lower limit to the number of documents, but experience shows that it takes at least about two hundred scientific paper-sized documents to produce meaningful topics. If the corpus consists of a few long documents, such as books, these documents can be split, e.g., into single chapters. See Jockers (2013). The documents must not be too short either. For example, the length of a single tweet would not be sufficient in finding any meaningful topics, so in this case, several tweets can be aggregated, e.g., on a daily basis. See Lüdering and Tillmann (2016).

  42. The analysis of textual sentiment, i.e., the tone of documents, is a second major approach in text mining, which is used particularly in finance and financial economics. For example, pessimism expressed in financial newspapers is found to influence stock returns and trading volume, see, e.g., Tetlock (2007) and García (2013). An example for a combination of topic modeling and sentiment analysis is given by Nguyen and Shirai (2015).

  43. The Kullback–Leibler divergence (or distance) between two probability distributions p and q is defined as \( {\text{KLD}}\left( {p,q} \right) = \frac{1}{2}\left[ {D\left( {p,q} \right) + D\left( {q,p} \right)} \right] \) with \( D\left( {p,q} \right) = \mathop \sum \nolimits_{j = 1}^{T} p_{j} \log_{2} \frac{{p_{j} }}{{q_{j} }} \). The Jensen–Shannon divergence is defined as \( {\text{JSD}}\left( {p,q} \right) = \frac{1}{2}\left[ {D\left( {p,\frac{p + q}{2}} \right) + D\left( {q,\frac{p + q}{2}} \right)} \right] \). Both measures are equal to zero when p and q are completely identical and, with higher divergence, their value approaches one. See Steyvers and Griffiths (2007).

  44. Available at http://dsl.richmond.edu/dispatch/pages/home.

  45. In finance, there appears to be an affinity toward text as data, which can be traced back to Tetlock (2007), who was the first to use textual analysis in order to measure market sentiment.

  46. That is, all articles published in regular and Task issues except regular book reviews and dissertation summaries, see Whaples (1991).

  47. In the first trials, almost all topics contained the word Cambridge. Other cities that occur in the final topics were not found to appear regularly in bibliographical references except in combination with “university press”.

  48. As ‘york’ and ‘cent’ occurred in several early topics, it became clear that in fact New York and per cent was meant. Thus, this step was taken for reasons of clarity and esthetics.

  49. The stopwords can be received upon request. For sample one, the database consists of 1728 documents.

  50. If a stemmer had been used, these words would have been collapsed into japan.

  51. Depicting every topic as a word cloud would exceed the available space of this article.

  52. Footnote 14 in Walters and Walters (1944) may serve as an example: “[…] Parish to John Craig, March 1, 1806, to Villaneuva, March 18, 1806, to Robert and John Oliver, October 29, 1806, in Parish LB, I, 239, 290, 291; II, 5.”

  53. These subjects are based on JEL codes. See Whaples (1991, pp. 289–90).

  54. Of course, this assignment is somewhat subjective, but it is no more subjective than assigning pages to subjects by hand.

  55. Except for Canada, which shares a topic with other countries (Topic 20), every country analyzed in Whaples (1991) is comprised of a separate topic. These countries are Britain (33), France (35), Italy (16), Germany (26), Japan (1), Russia/Soviet Union (29), and the United States (9).

  56. These words most probably stem from bibliographical references, which often remained untranslated.

  57. A direct comparison of the results in Whaples (2002) as in sample one could have been carried out as well but was relinquished due to space restrictions.

  58. The 25th subject in Whaples (2002) is the residual “Other”.

  59. The peak of 1960 can be attributed mainly to the Task issue containing a nice punchline: the article with the highest share of topic 20 is Goodrich (1960), which discusses how the use of quantitative methods affects economic history.

  60. The use of annual means is of course prone to outliers. If one is interested in the long-term development, a moving average would probably be more appropriate. However, the outliers could be what we are looking for if we are interested in identifying special events.

  61. The same continuity holds true at a 5 and 20% threshold.

  62. The correlation matrix is available upon request.

  63. See Arun et al. (2010), Cao et al. (2009), Deveaud et al. (2014), and Griffiths and Steyvers (2004) for detailed explanations of each metric. It is important to note that these metrics only deliver the optimal number of topics from a technical point of view. In the end, the optimal K depends on the research question.

  64. Computing these metrics takes a considerable amount of time. For the articles in sample 2, it took 4 days per metric on a standard computer. It is therefore necessary to retain the span of Ks at a manageable level, resulting in a coarse span of topics. It is important to note that these metrics only deliver the optimal number of topics in a technical sense. In the end, the optimal K depends on the research question.

  65. See https://cran.r-project.org/web/packages/ldatuning/ldatuning.pdf.

  66. Due to space restrictions, the topics are not presented here; they are available upon request.

  67. For a comprehensive history of cliometrics see Haupert (2016) and the cited literature.

  68. The econometric language topics exhibit almost identical shares in both samples indicating that they are relatively congruent. The descriptive topics exhibit a degree of difference, because, in sample 2 this topic appears to be less coherent than it does in sample 1.

  69. Diebolt and Haupert (2018) count equations, tables, and graphs per page. See Fig. 8.

  70. Until 1996, papers presented at the annual meetings of the Economic History Association were published in a fourth issue, which was devoted to the “Tasks of Economic History”, see Diebolt and Haupert (2018, p. 22) and Margo (2018, p. 12).

  71. The journals analyzed in his study are: the American Economic Review, Explorations in Economic History, the Journal of Economic History, the Industrial and Labor Relations Review, and the Journal of Human Resources.

  72. Margo (2018) uses an index based on the terms regression, logit, probit, maximum likelihood, coefficient, and standard error.

  73. Another candidate for words and expressions that characterize econometric language are the indices and glossaries of econometric textbooks, which we use in an ongoing project. This provides the advantage that levels of methodological advancement can be differentiated between by using indices from introductory and advanced textbooks.

  74. This touches on the issue of changes in the use of language discussed earlier.

  75. To cite just one example: with a share of 32%, Kuznets' (1952) study on US national income before 1870 is among the papers with the highest share of the descriptive topic 6.

  76. I thank one anonymous referee for the remark concerning the fact that this distinction between quantitative (in the sense of mere counting) and econometric approaches was made already by early cliometricians. See, e.g., McCloskey (1978, 1987).

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Acknowledgements

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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  1. Department of History, Economic and Social History, Department of Economics, University of Regensburg, Universitätsstraße 31, 93040, Regensburg, Germany

    Lino Wehrheim

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Appendices

Appendix 1

See Fig. 9.

Fig. 9
figure 9figure 9

Topic development of sample 2. Asterisks mark labels used in Whaples (2002); annual means. Source: See text

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Appendix 2

See Fig. 10.

Fig. 10
figure 10

Optimal number of topics. Measures are normalized with 0 (1) referring to the series’ minimum (maximum). For the measures proposed by Arun et al. (2010) and Cao et al. (2009), the optimal number of topics can be found at the minimum, for Griffiths and Steyvers (2004) it is the maximum. Measures (Arun et al. 2010) and Cao et al. (2009) indicate that the optimal number of topics lies between 60 and 80, while Griffiths and Steyvers (2004) is somewhat ambiguous. Still, as the line of Griffiths and Steyvers (2004) levels off between 60 and 80, the latter seems to be a plausible compromise. Deveaud et al. (2014) is not computed due to computational limitations. Source: See text

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Appendix 3: Excluding task issues

A topic model with 25 topics is applied on all articles published between 1941 and 2016 excluding Task issues as identified by Diebolt and Haupert (2018) which reduces the corpus from 2675 to 1885 documents. Again, the topic model identifies two topics which can be interpreted as representing quantitative methods. The 15 most probable words of the quantitative topics are shown in Table 4.

Table 4 Quantitative topics without Task issues
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Wehrheim, L. Economic history goes digital: topic modeling the Journal of Economic History. Cliometrica 13, 83–125 (2019). https://doi.org/10.1007/s11698-018-0171-7

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