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Workplace accidents and self-organized criticality

Author

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  • Mauro, John C.
  • Diehl, Brett
  • Marcellin, Richard F.
  • Vaughn, Daniel J.

Abstract

The occurrence of workplace accidents is described within the context of self-organized criticality, a theory from statistical physics that governs a wide range of phenomena across physics, biology, geosciences, economics, and the social sciences. Workplace accident data from the U.S. Bureau of Labor Statistics reveal a power-law relationship between the number of accidents and their severity as measured by the number of days lost from work. This power-law scaling is indicative of workplace accidents being governed by self-organized criticality, suggesting that nearly all workplace accidents have a common underlying cause, independent of their severity. Such power-law scaling is found for all labor categories documented by the U.S. Bureau of Labor Statistics. Our results provide scientific support for the Heinrich accident triangle, with the practical implication that suppressing the rate of severe accidents requires changing the attitude toward workplace safety in general. By creating a culture that values safety, empowers individuals, and strives to continuously improve, accident rates can be suppressed across the full range of severities.

Suggested Citation

  • Mauro, John C. & Diehl, Brett & Marcellin, Richard F. & Vaughn, Daniel J., 2018. "Workplace accidents and self-organized criticality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 284-289.
    • Handle: RePEc:eee:phsmap:v:506:y:2018:i:c:p:284-289
    DOI: 10.1016/j.physa.2018.04.064
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    References listed on IDEAS

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    1. Picchio, Matteo & van Ours, Jan, 2016. "Temporary Jobs and the Severity of Workplace Accidents," Other publications TiSEM 0dfc3a41-4239-4132-b415-4, Tilburg University, School of Economics and Management.
    2. Turcotte, D.L. & Malamud, B.D. & Morein, G. & Newman, W.I., 1999. "An inverse-cascade model for self-organized critical behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 268(3), pages 629-643.
    3. Phillips, J.C., 2013. "Self-organized criticality and color vision: A guide to water–protein landscape evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 468-473.
    4. Bruce Malamud & Donald Turcotte, 1999. "Self-Organized Criticality Applied to Natural Hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 20(2), pages 93-116, November.
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    Cited by:

    1. Doss, Karan & Hanshew, Alissa S. & Mauro, John C., 2020. "Signatures of criticality in mining accidents and recurrent neural network forecasting model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).

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