IDEAS home Printed from https://ideas.repec.org/a/prs/ecstat/estat_0336-1454_2016_num_486_1_10693.html

Les changements d’organisation du travail dans les entreprises : quelles conséquences sur les accidents du travail des salariés ?

Author

Listed:
  • Damien Euzénat
  • Meradj Mortezapouraghdam

Abstract

[fre] Cet article étudie l’influence de l’introduction, au sein des entreprises, des nouvelles formes d’organisation du travail, souvent inspirées du toyotisme (production en juste‑à‑temps, équipes autonomes de travail…) sur le risque d’accidents du travail des salariés. Dans ce but, il estime, sur l’enquête française COI (Changements organisationnels et informatisation) dédiée à ce sujet et appariée avec les données administratives sur les accidents du travail, un modèle de données de comptage en panel à effets fixes sur les années 2003 et 2006. L’utilisation d’un tel modèle garantit que les résultats ne sont pas biaisés par l’hétérogénéité inobservée et fixe dans le temps des entreprises. L’article conclut que les changements de pratiques organisationnelles sont corrélés avec le risque d’accidents du travail, mais que cette corrélation reste modeste. Ainsi, l’obtention de la norme ISO 9001 est associée à une réduction des accidents du travail, mais seulement dans les entreprises de 200 salariés ou plus. Ce résultat suggère que la formalisation du processus de production encouragée par la norme améliore la sécurité de l’entreprise à condition que celle‑ci l’analyse. Or, les grandes entreprises disposent sans doute de plus de moyens pour entreprendre cette analyse. En outre, l’obtention d’ISO 9001 dans les petites entreprises est peut‑être plus souvent imposée par un donneur d’ordre ; de ce fait, ces entreprises sont plus susceptibles de considérer les outils proposés par la norme comme de simples formalités pour l’obtenir, sans chercher à en tirer profit. L’article conclut aussi que la mise en place de l’analyse de la valeur (qui désigne un ensemble de méthodes formalisées de résolutions de problèmes susceptibles d’intervenir dans le cadre du processus de production) est associée à une réduction des accidents du travail. Ce résultat tendrait à confirmer que ces méthodes sont efficaces.

Suggested Citation

  • Damien Euzénat & Meradj Mortezapouraghdam, 2016. "Les changements d’organisation du travail dans les entreprises : quelles conséquences sur les accidents du travail des salariés ?," Économie et Statistique, Programme National Persée, vol. 486(1), pages 129-147.
    • Handle: RePEc:prs:ecstat:estat_0336-1454_2016_num_486_1_10693
    DOI: 10.3406/estat.2016.10693
    Note: DOI:10.3406/estat.2016.10693
    as

    Download full text from publisher

    File URL: https://doi.org/10.3406/estat.2016.10693
    Download Restriction: no
    File URL: https://www.persee.fr/doc/estat_0336-1454_2016_num_486_1_10693
    Download Restriction: no
    File URL: https://libkey.io/10.3406/estat.2016.10693?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David I. Levine & Michael W. Toffel, 2010. "Quality Management and Job Quality: How the ISO 9001 Standard for Quality Management Systems Affects Employees and Employers," Management Science, INFORMS, vol. 56(6), pages 978-996, June.
    2. Paul Osterman, 2000. "Work Reorganization in an Era of Restructuring: Trends in Diffusion and Effects on Employee Welfare," ILR Review, Cornell University, ILR School, vol. 53(2), pages 179-196, January.
    3. Paul Osterman, 1994. "How Common is Workplace Transformation and Who Adopts it?," ILR Review, Cornell University, ILR School, vol. 47(2), pages 173-188, January.
    4. David Fairris & Mark Brenner, 2001. "Workplace Transformation and the Rise in Cumulative Trauma Disorders: Is There a Connection?," Journal of Labor Research, Transaction Publishers, vol. 22(1), pages 15-28, January.
    5. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, December.
    6. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    7. White, Halbert, 1983. "Corrigendum [Maximum Likelihood Estimation of Misspecified Models]," Econometrica, Econometric Society, vol. 51(2), pages 513-513, March.
    8. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    9. Lee, Lung-Fei, 1986. "Specification Test for Poisson Regression Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 27(3), pages 689-706, October.
    10. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Ben Halima, Mohamed Ali & Greenan, Nathalie & Lanfranchi, Joseph, 2023. "Getting sick for profit? The impact of cumulative ICT and management changes on long term sickness absence," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 659-688.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gabriele Fiorentini & Enrique Sentana, 2021. "Specification tests for non‐Gaussian maximum likelihood estimators," Quantitative Economics, Econometric Society, vol. 12(3), pages 683-742, July.
    2. Li, Xun-Jian & Sun, Yuan & Tian, Guo-Liang & Liang, Jiajuan & Shi, Jianhua, 2023. "Mean regression model for the zero-truncated Poisson distribution and its generalization," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    3. Baye, Michael & GATTI, RUPERT J & Kattuman, Paul & Morgan, John, 2004. "Estimating Firm-Level Demand at a Price Comparison Site: Accounting for Shoppers and the Number of Competitors," Competition Policy Center, Working Paper Series qt923692d1, Competition Policy Center, Institute for Business and Economic Research, UC Berkeley.
    4. Miguel A. Delgado & Thomas J. Kniesner, 1997. "Count Data Models With Variance Of Unknown Form: An Application To A Hedonic Model Of Worker Absenteeism," The Review of Economics and Statistics, MIT Press, vol. 79(1), pages 41-49, February.
    5. Dionne, Georges & Artis, Manuel & Guillen, Montserrat, 1996. "Count data models for a credit scoring system," Journal of Empirical Finance, Elsevier, vol. 3(3), pages 303-325, September.
    6. Creel, Michael D. & Loomis, John B., 1989. "Theoretical and Empirical Advantages of Truncated Count Data Estimators for Analysis of Deer Hunting in California," WAEA/ WFEA Conference Archive (1929-1995) 245039, Western Agricultural Economics Association.
    7. KENNETH C. LAND & PATRICIA L. McCALL & DANIEL S. NAGIN, 1996. "A Comparison of Poisson, Negative Binomial, and Semiparametric Mixed Poisson Regression Models," Sociological Methods & Research, , vol. 24(4), pages 387-442, May.
    8. Mello, Marco & Moscelli, Giuseppe, 2022. "Voting, contagion and the trade-off between public health and political rights: Quasi-experimental evidence from the Italian 2020 polls," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 1025-1052.
    9. Michael R. Baye & J. Rupert J. Gatti & Paul Kattuman & John Morgan, 2009. "Clicks, Discontinuities, and Firm Demand Online," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 18(4), pages 935-975, December.
    10. Kevin E. Staub & Rainer Winkelmann, 2013. "Consistent Estimation Of Zero‐Inflated Count Models," Health Economics, John Wiley & Sons, Ltd., vol. 22(6), pages 673-686, June.
    11. Agostino Capponi & Zhaonan Qu, 2025. "Handling Sparse Non-negative Data in Finance," Papers 2509.01478, arXiv.org.
    12. Gary King, 1989. "A Seemingly Unrelated Poisson Regression Model," Sociological Methods & Research, , vol. 17(3), pages 235-255, February.
    13. Gurmu, Shiferaw & Rilstone, Paul & Stern, Steven, 1998. "Semiparametric estimation of count regression models1," Journal of Econometrics, Elsevier, vol. 88(1), pages 123-150, November.
    14. Vasiliki Christou & Konstantinos Fokianos, 2014. "Quasi-Likelihood Inference For Negative Binomial Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 55-78, January.
    15. repec:gnv:wpaper:unige:76321 is not listed on IDEAS
    16. Vijverberg, Wim P. & Hasebe, Takuya, 2015. "GTL Regression: A Linear Model with Skewed and Thick-Tailed Disturbances," IZA Discussion Papers 8898, Institute of Labor Economics (IZA).
    17. Tang, Niansheng & Wang, Wenjun, 2019. "Robust estimation of generalized estimating equations with finite mixture correlation matrices and missing covariates at random for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 640-655.
    18. Francisco Blasques & Christian Francq & Sébastien Laurent, 2020. "A New Class of Robust Observation-Driven Models," Tinbergen Institute Discussion Papers 20-073/III, Tinbergen Institute.
    19. Dionne, Georges & Gagne, Robert & Gagnon, Francois & Vanasse, Charles, 1997. "Debt, moral hazard and airline safety An empirical evidence," Journal of Econometrics, Elsevier, vol. 79(2), pages 379-402, August.
    20. Wooldridge, Jeffrey M., 2020. "On the consistency of the logistic quasi-MLE under conditional symmetry," Economics Letters, Elsevier, vol. 194(C).
    21. Moeltner, Klaus, 2003. "Addressing aggregation bias in zonal recreation models," Journal of Environmental Economics and Management, Elsevier, vol. 45(1), pages 128-144, January.

    More about this item

    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:prs:ecstat:estat_0336-1454_2016_num_486_1_10693. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Equipe PERSEE (email available below). General contact details of provider: https://www.persee.fr/collection/estat .

    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.