Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/298606 
Authors: 
Year of Publication: 
2024
Series/Report no.: 
EHES Working Paper No. 258
Publisher: 
European Historical Economics Society (EHES), s.l.
Abstract: 
Medieval and Early-Modern business correspondence between European companies constitutes a rich source of economic, business, and trade information in that the writing of letters was the very instrument through which merchants ordered and organized the shipments of goods, and performed financial operations. While a comprehensive analysis of such material enables scholars to re-construct the supply chains and sales of various goods, as well as identify the trading networks in the Europe, much of the archival sources have not undergone any systematic and quantitative analysis. In this paper we develop a new holistic and quantitative approach for analysing the entire outgoing, and so far unexploited, correspondence of a major Renaissance merchantbank - the Saminiati & Guasconi company of Florence - for the first years of its activity. After digitization of the letters, we employ an AI-based HTR model on the Transkribus platform and perform an automated-text analysis over the HTR-model's output. For each letter (6,376 epistles) thisresultsin the identification of the addressee (446 merchants), their place of residence (65 towns), and the traded goods (27 main goods). The approach developed arguably provides a best-practice methodology for the quantitative treatment of medieval and early-modern merchant letters and the use of the derived historical text as data
Subjects: 
HTR
Machine Learning
Text Analysis
Merchant Letters
JEL: 
N00
N01
C80
C88
C89
Document Type: 
Working Paper

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