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Arrêts maladie : comprendre les disparités départementales

Author

Listed:
  • Mohamed Ali Ben Halima

    () (IRDES institut for research and information in health economics)

  • Thierry Debrand

    () (IRDES institut for research and information in health economics)

  • Camille Regaert

    () (IRDES institut for research and information in health economics)

Abstract

L’objectif de cet article est de comprendre les disparités interdépartementales en termes d’arrêts maladie. Nous utilisons la base de données Hygie, construite à partir de la fusion de différents fihiers administratifs de salariés du secteur privé en France en 2005, qui permet de prendre en considération : les relations « employeurs/employés », l’impact des caractéristiques des entreprises sur la santé de leurs employés mais aussi les interactions entre la santé et le travail. Après avoir rappelé les différents déterminants, entre effet de composition et effet de contexte, des arrêts maladie et leur importance pour comprendre les différences géographiques, nous menons une analyse empirique en trois temps : une analyse descriptive pour mettre en évidence les différences interdépartementales, une analyse multivariée pour mettre en avant les facteurs explicatifs de la probabilité d’être en arrêt maladie et enfn une analyse des déterminants des différences entre les départements.Nos différentes modélisations explicitent une grande partie des disparités interdépartementales. Les effets de composition et les effets de contexte constituent approximativement la moitié de l’écart absolu et les deux tiers de l’erreur quadratique moyenne. Ce sont les variables décrivant l’offre médicale (densité d’omnipraticiens), les contrôles de l’Assurance maladie et l’âge d’entrée sur le marché du travail qui permettent le plus d’expliquer les disparités interdépartementales en matière d’arrêts maladie. Contrairement à d’autres variables de composition ou de contexte qui ont soit une inertie d’évolution temporelle certaine (par exemple : le taux de natalité, le secteur industriel,…), soit des variables pour lesquelles la politique publique de santé a peu d’effets (par exemple : politique de rémunération des entreprises, taux de chômage), le pourcentage d’arrêts de travail contrôlés et la densité d’omnipraticiens sont déjà des leviers importants des politiques de santé. Notre recherche montre qu’ils pourraient être utilisés comme des instruments d’une politique publique visant à la réduction des disparités géographiques.

Suggested Citation

  • Mohamed Ali Ben Halima & Thierry Debrand & Camille Regaert, 2011. "Arrêts maladie : comprendre les disparités départementales," Working Papers DT39, IRDES institut for research and information in health economics, revised Feb 2011.
  • Handle: RePEc:irh:wpaper:dt39
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    File URL: http://www.irdes.fr/EspaceRecherche/DocumentsDeTravail/DT39ArretsMaladieComprendreDisparitesDepartement.pdf
    File Function: First version, 2011
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    References listed on IDEAS

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    Cited by:

    1. Ben Halima Mohamed Ali & Regaert Camille, 2013. "Duration of Sick Leave, Income and Health Insurance: Evidence from French French linked employer-employee data," Economics Bulletin, AccessEcon, vol. 33(1), pages 46-55.
    2. Mohamed Ali Ben Halima & Thierry Debrand & Camille Regaert, 2012. "Sick Leaves: Understanding Disparities Between French Departments," Working Papers DT50, IRDES institut for research and information in health economics, revised Oct 2012.

    More about this item

    Keywords

    Arrêt maladie; Disparités géographiques; Effet de contexte; Effet de composition; Absentéisme.;

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J29 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Other
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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