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Classifying Non-Bank Currency Systems Using Web Data

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  • Ariane Tichit

    () (CERDI - Centre d'Études et de Recherches sur le Développement International - Clermont Auvergne - UCA - Université Clermont Auvergne - CNRS - Centre National de la Recherche Scientifique)

  • Clément Mathonnat

    () (CERDI - Centre d'Études et de Recherches sur le Développement International - Clermont Auvergne - UCA - Université Clermont Auvergne - CNRS - Centre National de la Recherche Scientifique)

  • Diego Landivar

    (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)

Abstract

This paper develops a new classification of non-bank currency systems based on a lexical analysis from French-language web data in order to derive an endogenous typology of monetary projects, based on how these currencies are depicted on the internet. The advantage of this method is that it bypasses problematic issues currently found in the literature to uncover a clear classification of non-bank currency systems from exogenous elements. Our textual corpus consists of 320 web pages, corresponding to 1,210 text pages. We first apply a downward hierarchical clustering (DHC) to our data, which enables us to endogenously derive five different classes and make distinctions, not only between non-bank currency system but between these and the standard monetary system. Next, we perform a similarity analysis. Our results show that all non-bank currency systems define themselves in relation to the standard monetary system, with the exception of Local Exchange Trading Systems (LETS). KEYWORDS non-bank money, text mining, web data, downward hierarchical clustering, similarity analysis. ACKNOWLEDGEMENTS This article was prepared as part of the Research-Action program in Social Innovation financed by the Region of Auvergne. The usual disclaimers apply. We are very grateful to Jerome Blanc and Vincent Mazenod for their support.

Suggested Citation

  • Ariane Tichit & Clément Mathonnat & Diego Landivar, 2016. "Classifying Non-Bank Currency Systems Using Web Data," Post-Print hal-01995950, HAL.
  • Handle: RePEc:hal:journl:hal-01995950
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01995950
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    1. Michael S Evans, 2009. "Zelizer's Theory of Money and the Case of Local Currencies," Environment and Planning A, , vol. 41(5), pages 1026-1041, May.
    2. Seyfang, Gill & Longhurst, Noel, 2013. "Growing green money? Mapping community currencies for sustainable development," Ecological Economics, Elsevier, vol. 86(C), pages 65-77.
    3. Michael S Evans, 2014. "A Computational Approach to Qualitative Analysis in Large Textual Datasets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    4. Jérôme Blanc, 2011. "Classifying "CCs": Community, complementary and local currencies' types and generations," Post-Print halshs-00583587, HAL.
    5. Jérôme Blanc, 2013. "Penser la pluralité des monnaies à partir de Polanyi : un essai de typologie," Post-Print halshs-00828095, HAL.
    6. Jérôme Blanc & Marie Fare, 2012. "Chartes et comités d'agrément dans les dispositifs de monnaies citoyennes," Working Papers halshs-00758172, HAL.
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