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    Chapter Sizing & Allocation in Labour Market: business strategies and multivariate analysis

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    Author(s)
    Marletta, Andrea cc
    Language
    English
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    Abstract
    In Labour Market, the issue of Sizing and Allocation is a discussed problem. In this study, this topic has been considered from a statistical point of view. Indeed, the choice to change the number of your team of employees in a business context needs a very accurate analysis. If for example a firm decides to launch a new product on the market, it could be necessary to recruit new resources. The proposed statistical approach aims to give some hints about the total number of employees analysing the features of the existing market and the territorial geography. From a statistical point of two techniques of multivariate analysis have been presented as exploratory tools. In the application, a Principal Component Analysis has been used to investigate the business environment after some qualitative interviews to the board of the company. In a second step, some different scenarios have been proposed to determine the exact number of new resources using a data hybridization technique including internal and external sources. Finally, the allocation of the new hired of the scenarios on the Italian territory has been achieved thanks to the construction of a territorial potential index.
    URI
    https://library.oapen.org/handle/20.500.12657/58233
    Keywords
    Labour Market; Multivariate analysis; Number of employees
    DOI
    10.36253/978-88-5518-461-8.23
    ISBN
    9788855184618, 9788855184618
    Publisher
    Firenze University Press
    Publisher website
    https://www.fupress.com/
    Publication date and place
    Florence, 2021
    Series
    Proceedings e report, 132
    Classification
    Social research & statistics
    Pages
    4
    Rights
    https://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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