The land of “oenoeuros”. Wine as the source of regional inflation: the peculiar case of Rioja

Abstract

How can wine affect the economy of a wine-producing region? Can it produce effects like those experienced in oil-producing countries? Those are the questions addressed in this paper, using Rioja as a case study. During the millennium period, while a structural change was taking place in Rioja wine cluster, Rioja's inflationary pattern also varied from its historical behaviour. While having less inflation than the Spanish average in the 1980s and early 1990s, the Rioja region became the most inflationary Spanish region in the millennium period. Simultaneously, the Riojan wine cluster enjoyed higher recognition in global markets, which encouraged new investments, the growth of the surface under vines and both a higher productivity and higher prices. This paper argues that the unprecedented growth of vinegrowing incomes in the period 1995-2007 determined the consumption made by grape producers' households, and consequently, they also influenced price dynamics in Rioja.

The EMPRISES pan-European Framework

“…The tools might also include such components as fuzzy logic and probabilistic analysis (for predictive analysis, trends and situation assessment) (Kim & Bishu, 2006;PR-OWL, 2012), Conceptual Graphs for (Criminal) Transaction Modelling (identification of key agents, resources and facilitators for SOEC) (Jedrzejek, Falkowski, & Bak, 2009;Du, Song, & Munro, 2006;Mifflin, Boner, Godfrey, & Skokan, 2004), Formal Concept Analysis (FCA) for pattern finding (modus operandi and indicator analysis, threat detection, taxonomy visualisation, predictive analysis) (Snášel, Horák, & Abraham, 2008;Kirda, 2010;Thonnard, 2011), Social Network Analysis (SNA) (for the detection and analysis of OC groups and OC activity) (McNally & Alston, 2006;Fox, 2012;SAS, 2009), extending SNA with FCA: Formal Conceptual Network Analysis (to provide SNA with enhanced capabilities for analysis of OC group-group interaction and OC hierarchies, extending CGs with FCA: CG-FCA (identification of incomplete transactions, supply chains and transactional hierarchies, identification of missing agents in transitions) as described earlier (Andrews & Polovina, 2011), linked data analysis (for detecting financial pathways and supply and economic food chains) (Larreina, 2007), Fuzzy Cognitive Mapping (FCM) (for determining weighted cause-and-effect relations and actions) (Carvalho & Tomè, 1999) and Machine Learning (for diagnostic analysis of suspected SOEC activity and economic impact) (Schrodt, 1995).…”