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Community Enterprises: Snapshots from Italy

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  • Massimo Albanese

    (Department of Economics and Business Studies, University of Genoa, Italy)

Abstract

Community enterprises are not a new type of organisation, but in recent years they have attracted increasing interest for their role in various areas, particularly in depleted contexts. Numerous studies analysed these enterprises in their natural context, but national and international overviews are lacking. We, therefore, do not know how many community enterprises there are, in which sectors they predominantly operate, and so on. Due to the scarcity of data, it is very difficult to provide a picture of community enterprises at the international level, whereas it is more feasible to represent these realities at the national level. Although Italian community enterprises are organisations that are not easily identifiable in the available databases, this paper aims to provide some snapshots to sketch the phenomenon in Italy. The work has a heterogeneous audience because it is of interest to scholars, practitioners, and policymakers. It has implications from a research and practical point of view, as it develops a way to identify Italian community enterprises and provides valuable elements for understanding the efficacy of specific policies and actions, and a piece of knowledge to build a picture of community enterprises.

Suggested Citation

  • Massimo Albanese, 2022. "Community Enterprises: Snapshots from Italy," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 8, ejes_v8_i.
  • Handle: RePEc:eur:ejesjr:342
    DOI: 10.26417/999ibx74
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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Zeileis, Achim & Grothendieck, Gabor, 2005. "zoo: S3 Infrastructure for Regular and Irregular Time Series," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i06).
    3. Regina Kaiser & Agustín Maravall, 1999. "Seasonal Outliers in Time Series," Working Papers 9915, Banco de España.
    4. Stephen W. Hartman, 2013. "The WTO, the Doha Round Impasse, PTAs, and FTAs/RTAs," The International Trade Journal, Taylor & Francis Journals, vol. 27(5), pages 411-430, December.
    5. Maravall, Agustín, 1999. "Seasonal outliers in time series," DES - Working Papers. Statistics and Econometrics. WS 6333, Universidad Carlos III de Madrid. Departamento de Estadística.
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