Modeling Equity for Allocating Public Resources

  • Philip D. Leclerc
  • Laura A. McLay
  • Maria E. Mayorga
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 167)


Equity and fairness constitute central concerns in many disciplines, and their relevance to the allocation of public resources is undeniable. However, measures of equity used in the studies of public resource allocation are frequently ad hoc, and no standard measure of equity or process for selecting a measure of equity has emerged. Nevertheless, a burgeoning literature exists that systematically considers how best to model equity with perspectives from many disciplines. The goal of this chapter is to review, synthesize, and critically evaluate key contributions to modeling equity for allocating resources in public service systems. This chapter provides a useful guide to the central issues in the modeling of equity and fairness for operations researchers that reflects a broad, multidisciplinary perspective. Throughout the discussion, the planning and provision of Emergency Medical Services (EMS) resources are used as a microcosm of public services allocation problems, and equity modeling issues are illustrated through problems arising in EMS.


Resource Allocation Emergency Medical Service Gini Coefficient Post Equity Equity Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The multidisciplinary ideas for this chapter came about during the second author’s participation in the Enabling the Next Generation of Hazards Researchers fellowship program. Support of this fellowship program by the National Science Foundation is gratefully acknowledged. This material is based on the work supported by the US Department of Homeland Security under Grant Award Number 2008-DN-077-ARI001-02 and the US Department of the Army under Grant Award Number W911NF-10-1-0176. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the US Department of Homeland Security or the US Department of the Army. The authors wish to thank the editor and the two anonymous referees for their helpful comments and suggestions, which has resulted in a significantly improved manuscript.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Philip D. Leclerc
    • 1
  • Laura A. McLay
    • 1
  • Maria E. Mayorga
    • 2
  1. 1.Department of Statistical Sciences & Operations ResearchVirginia Commonwealth UniversityRichmondUSA
  2. 2.Department of Industrial EngineeringClemson UniversityClemsonUSA

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