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Questioning the implication of the utility-maximization assumption for the estimation of deprivation cost functions after disasters

Publicado en

  • International Journal of Production Economics

Resumen

  • Deprivation cost functions (DCFs) allow for the quantification of human suffering after disasters strike. Commonly, DCF estimation methods assume that affected individuals aim to maximize their wellbeing while making rational decisions. However, after disasters, people are often under stress and pressure, possibly traumatized, and they can adopt behaviors that are neither compensatory nor utility based. This paper questions the use of Random Utility Maximization (RUM) to estimate DCFs and compares its results with a Random Regret Minimization (RRM) approach, and a combined method that considers both Regret- and Utility-based decision rules. DCFs for multiple supplies are estimated using stated preference data from two case studies: Colombia and Ecuador. The results suggest that deprivation cost estimations yield significantly different valuations depending on the heuristic used to explain choice behavior, with the DCFs estimated using RUM having higher cost values for a time window shorter than 48 h. This suggests that for longer time windows, RRM is the approach that should be used. Moreover, results show that deprivation costs are context-dependent, and should not be transferred directly. Finally, it is shown that DCFs for multiple commodities can be added separately in the same objective function when planning relief distribution operations. This research is the first attempt to consider different choice heuristics for estimating deprivation cost functions, and it is the first to compare data from multiple locations. The implications of these findings provide disaster managers and planners with new challenges and research directions to improve relief distribution plans.

fecha de publicación

  • 2022

Líneas de investigación

  • Choice heuristics
  • Deprivation costs
  • Humanitarian logistics
  • Random regret minimization
  • Random utility maximization

Volumen

  • 247