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I. INTRODUCTION
The fields of the history and methodology of economics have recently experienced a “quantitative turn.” The quantitative turn designates researchers’ increasing use of tools and methods such as bibliometrics, prosopography, network analysis, topic modeling, and text mining (Claveau and Gingras 2016; Wright 2016; Claveau and Herfeld 2018; Geiger and Kufenko 2018; Svorenčík 2018; Baccini 2020; Edwards 2020).1 Methods for text analysis especially have gained popularity in other fields such as sociology (Evans and Aceves 2016), political science (Grimmer and Stewart 2013), finance (Gupta et al. 2020), and the broader field of the digital humanities.
We adopt in this article a “text as data” perspective, which entails a shift in how researchers consider text. In a recent contribution, Kenneth Benoit explained that “the essence of treating text as data is that it is always transformed into more structured, summary and quantitative data to make it amenable to the familiar tools of data analysis” (Benoit 2020, p. 463).
Economists have notably embraced the text as data approach. An example is the recent article “Text as Data” by Matthew Gentzkow, Bryan Kelly, and Matt Taddy (2019), published in the Journal of Economic Literature. The authors “provide an overview of methods for analyzing text and a survey of current applications in economics and related social sciences” (Gentzkow, Kelly, and Taddy 2019, p. 537). They note, “The rise of text analysis is part of a broader trend toward greater use of machine learning and related statistical methods in economics” (p. 570). In other words, the text as data approach is becoming an influential methodological approach in economics and will also likely become an important new element in the toolbox of historians and methodologists of economics. According to Benoit (2020), the reasons for the growing popularity of the text as data approach are the increase of computational power, the huge volume of texts available in digital format, and the developments of quantitative measures and tools specifically dedicated to text analysis. That historians and methodologists of economics have rarely used text mining can, at first sight, appear as curious; in fact, their research consists in extracting and creating knowledge from what economists produce, which is—at a very fundamental level—text (see also Komine and Shimodaira 2018).
One reason for this...