Classifying Non-banking Monetary Systems using Web Data
Ariane Tichit (),
Clément Mathonnat () and
Diego Sébastien Landivar
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Clément Mathonnat: CERDI - Centre d'Études et de Recherches sur le Développement International - UdA - Université d'Auvergne - Clermont-Ferrand I - CNRS - Centre National de la Recherche Scientifique
Diego Sébastien Landivar: Business School Clermont - France Business School Clermont - France Business School [France Business School]
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This paper develops a new classification of non-banking currencies projects based on a lexical analysis from web data. The advantage of this method is to derive an endogenous typology of monetary projects, based on how currencies are depicted on the internet. Therefore, it allows to by-pass issues face for now in the literature to uncover a clear classification of non-banking currencies projects from exogenous elements. Our textual corpus consists in 320 web pages, corresponding to 1210 text pages. We first applied to our data a downward hierarchical clustering, which enables us to endogenously derive five different classes, allowing us to operate distinctions not only between non-banking currencies projects, but also between these latter and the standard monetary system. Then, we resorted to a similarity analysis and according to our results; all non-banking currencies projects define themselves in relation to the standard monetary system, with the exception of Local Exchange Trading Systems (LETs).
Keywords: Non-banking money; Text mining; Web data; Downward hierarchical clustering; Similarity analysis (search for similar items in EconPapers)
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Working Paper: Classifying Non-banking Monetary Systems using Web Data (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:halshs-01230219
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