IDEAS home Printed from https://ideas.repec.org/p/pie/dsedps/2016-209.html
   My bibliography  Save this paper

The Challenge of Measuring Corporate Social Irresponsibility

Author

Listed:
  • Davide Fiaschi
  • Elisa Giuliani
  • Nicola Salvati

Abstract

In this paper, we develop a family of indexes to measure the social irresponsibility of firms. We define corporate social irresponsibility (CSIR) on the basis of firms' alleged involvement in human rights abuses. After a critical appraisal of the existing CSIR raw data and measures/indexes, we take a M-quantile regression approach to develop a family of CSIR indexes that overcome the limitations of existing measures. We apply our methodology to a sample of 380 large publicly-listed firms, observed over the period 2004-2012. Our analysis develops a family of CSIR indexes robust to firms' media exposure, size and industry specificities, and provides a measure of their accuracy..

Suggested Citation

  • Davide Fiaschi & Elisa Giuliani & Nicola Salvati, 2016. "The Challenge of Measuring Corporate Social Irresponsibility," Discussion Papers 2016/209, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    • Handle: RePEc:pie:dsedps:2016/209
    Note: ISSN 2039-1854
    as

    Download full text from publisher

    File URL: https://www.ec.unipi.it/documents/Ricerca/papers/2016-209.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anita Mendiratta & Shveta Singh & Surendra Singh Yadav & Arvind Mahajan, 2023. "Bibliometric and Topic Modeling Analysis of Corporate Social Irresponsibility," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(3), pages 319-339, September.

    More about this item

    Keywords

    Corporate Social Irresponsibility (CSIR); M-quantile regression; CSIR index.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • O50 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pie:dsedps:2016/209. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/dspisit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.