Classifying non-bank currency systems using web data
Ariane Tichit*, Clément Mathonnat*, Diego Landivar**
* Clermont University, Auvergne University, CNRS, UMR 6587, CERDI, F-63009 Clermont Fd. Email: firstname.lastname@example.org; Clement.MATHONNAT@udamail.fr; ** ESC Clermont, 63000 Clermont-Fd. Email: email@example.com.
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 by-passes 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 method to our data, which enables us to endogenously derive five different classes and make distinctions among non-bank currency system and 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.
non-bank money, text mining, web data, downward hierarchical clustering, similarity analysis
To cite this article: Tichit, A., Mathonnat, C., and Landivar, D. (2016) ‘Classifying non-bank currency systems using web data’ International Journal of Community Currency Research 20 (Summer) 24-40 <www.ijccr.net> ISSN 1325-9547. http://dx.doi.org/10.15133/j.ijccr.2016.002