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A machine-learning history of English caselaw and legal ideas prior to the Industrial Revolution I: generating and interpreting the estimates

Published online by Cambridge University Press:  05 August 2020

Peter Grajzl  and
Peter Murrell
Peter Grajzl
Affiliation:
Department of Economics, The Williams School of Commerce, Economics, and Politics, Washington and Lee University, Lexington, VA, USA CESifo, Munich, Germany
Peter Murrell*
Affiliation:
Department of Economics, University of Maryland, College Park, MD20742, USA
*
*Corresponding author. Email: pmurrell@umd.edu
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Abstract

The history of England's institutions has long informed research on comparative economic development. Yet to date, there exists no quantitative evidence on a core aspect of England's institutional evolution, that embodied in the accumulated decisions of English courts. Focusing on the two centuries before the Industrial Revolution, we generate and analyze the first quantitative estimates of the development of English caselaw and its associated legal ideas. We achieve this in two companion papers. In this, the first of the pair, we build a comprehensive corpus of 52,949 reports of cases heard in England's high courts before 1765. Estimating a 100-topic structural topic model, we name and interpret all topics, each of which reflects a distinctive aspect of English legal thought. We produce time series of the estimated topic prevalences. To interpret the topic timelines, we develop a tractable model of the evolution of legal-cultural ideas and their prominence in case reports. In the companion paper, we will illustrate with multiple applications the usefulness of the large amount of new information generated by our approach.

Keywords

CaselawEnglish historyidea diffusioninstitutional developmentmachine learningC8N00K10Z10P10
Type
Research Article
Information
Journal of Institutional Economics , Volume 17 , Issue 1 , February 2021 , pp. 1 - 19
Copyright
Copyright © Millennium Economics Ltd 2020

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