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Measurement and anticipation of territorial vulnerability to offshoring risks : An analysis on sectoral data for France

Author

Listed:
  • Hugues Jennequin

    (LASTA - Laboratoire d'Analyse des Sociétés, Transformations et Adaptations - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université)

  • Luis Miotti

    (CEPN - Centre d'Economie de l'Université Paris Nord - UP13 - Université Paris 13 - USPC - Université Sorbonne Paris Cité - CNRS - Centre National de la Recherche Scientifique)

  • El Mouhoub Mouhoud

    (LEDa - Laboratoire d'Economie de Dauphine - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres)

Abstract

While economic studies generally conclude that there is little impact from offshoring at the macroeconomic level, offshoring generates significant asymmetric shocks at the local level, which it is important to accurately anticipate. This is what this study is about, with the construction of an original indicator of territories' vulnerability to the risks of offshoring manufacturing activity. Firstly, the factors of vulnerability are identified at a fine‑grained level of activity. Using principal‑component analysis at the sector level, four types of manufacturing industry sectors are brought out according to their potential for offshoring, which is a function of the characteristics of their jobs and their content in routine tasks, and product characteristics.Then, following the approach Aubert and Sillard (2005) implemented on data by establishment, an index of actual offshoring at the sector level is estimated. This makes it possible both to characterise the risk of offshoring in the four main types of sectors and to measure the economic vulnerability of employment zones.

Suggested Citation

  • Hugues Jennequin & Luis Miotti & El Mouhoub Mouhoud, 2018. "Measurement and anticipation of territorial vulnerability to offshoring risks : An analysis on sectoral data for France," Post-Print hal-01874289, HAL.
    • Handle: RePEc:hal:journl:hal-01874289
    DOI: 10.24187/ecostat.2017.497d.1934
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