IDEAS home Printed from https://ideas.repec.org/p/drm/wpaper/2020-4.html
   My bibliography  Save this paper

Covid-19 : analyse spatiale de l’influence des facteurs socio-économiques sur la prévalence et les conséquences de l’épidémie dans les départements français

Author

Listed:
  • Nadine Levratto
  • Mounir Amdaoud
  • Giuseppe Arcuri

Abstract

This paper analyses the socio-economic determinants of hospitalizations and death rates related to Covid-19 on the one hand, and the excess mortality observed this year compared to previous ones, on the other. It proposes a territorial approach to these questions thanks to the use of data calculated at the French departments level. The exploratory spatial analysis carried out reveals the heterogeneity and spatial autocorrelation of the disease and its consequences. The use of spatial econometric models, then, allows us to highlight the influence of demographic density, social inequalities, part of blue-collars in the active population and emergency services on the studied phenomena.

Suggested Citation

  • Nadine Levratto & Mounir Amdaoud & Giuseppe Arcuri, 2020. "Covid-19 : analyse spatiale de l’influence des facteurs socio-économiques sur la prévalence et les conséquences de l’épidémie dans les départements français," EconomiX Working Papers 2020-4, University of Paris Nanterre, EconomiX.
  • Handle: RePEc:drm:wpaper:2020-4
    as

    Download full text from publisher

    File URL: https://economix.fr/pdf/dt/2020/WP_EcoX_2020-4.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Mounir Amdaoud & Giuseppe Arcuri & Nadine Levratto, 2021. "Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(4), pages 629-642, June.

    More about this item

    Keywords

    Covid-19; local variables; spatial analysis;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

    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:drm:wpaper:2020-4. 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: Valerie Mignon (email available below). General contact details of provider: https://edirc.repec.org/data/modemfr.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.