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A Macroscope of English Print Culture, 1530-1700, Applied to the Coevolution of Ideas on Religion, Science, and Institutions

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  • Peter Grajzl
  • Peter Murrell

Abstract

We combine unsupervised machine-learning and econometric methods to examine cultural change in 16th- and 17th-century England. A machine-learning digest synthesizes the content of 57,863 texts comprising 83 million words into 110 topics. The topics include the expected, such as Natural Philosophy, and the unexpected, such as Baconian Theology. Using the data generated via machine-learning we then study facets of England's cultural history. Timelines suggest that religious and political discourse gradually became more scholarly over time and economic topics more prominent. The epistemology associated with Bacon was present in theological debates already in the 16th century. Estimating a VAR, we explore the coevolution of ideas on religion, science, and institutions. Innovations in religious ideas induced strong responses in the other two domains. Revolutions did not spur debates on institutions nor did the founding of the Royal Society markedly elevate attention to science.

Suggested Citation

  • Peter Grajzl & Peter Murrell, 2023. "A Macroscope of English Print Culture, 1530-1700, Applied to the Coevolution of Ideas on Religion, Science, and Institutions," CESifo Working Paper Series 10325, CESifo.
    • Handle: RePEc:ces:ceswps:_10325
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    More about this item

    Keywords

    cultural history; England; machine-learning; text-as-data; coevolution; VAR;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • Z10 - Other Special Topics - - Cultural Economics - - - General
    • N00 - Economic History - - General - - - General
    • P10 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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