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Modeling Equity for Allocating Public Resources

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Community-Based Operations Research

Abstract

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.

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References

  • Armony, M., & Ward, A. (2010). Fair dynamic routing in large-scale heterogeneous-server systems. Operations Research, 58(3), 624–637.

    Article  Google Scholar 

  • Atar, R., Shaki, Y., Shwartz, A. (2009) A blind policy for equalizing cumulative idleness. Working paper, Technion–Israeli Institute of Technology, Haifa.

    Google Scholar 

  • Ball, M. O., & Lin, F. L. (1993). A reliability model applied to emergency service vehicle location. Operations Research, 41(1), 18–36.

    Article  Google Scholar 

  • Barnett, A. (1996). Building equal opportunity on firmer footing. ORMS Today 23(4). http://www.lionhrtpub.com/orms/orms-8-96/building.html. Accessed 28 Nov 2010.

  • Bazaraa, M. S., Sherali, H. D., & Shetty, C. M. (1993). Nonlinear programming: theory and algorithms (2nd ed.). New York, NY: Wiley.

    Google Scholar 

  • Bowles, S. (2004). Microeconomics: behavior, institutions, and evolution. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Broome, J. (1982). Equity in risk bearing. Operations Research, 30, 412–414.

    Article  Google Scholar 

  • Camerer, C. (2003). Behavioral game theory. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Caulkins, J. (1996). Color-blind policies are not enough. ORMS Today 23(4). http://www.lionhrtpub.com/orms/orms-8-96/color-blind.html. Accessed 28 Nov 2010.

  • Chankong, V., & Haimes, Y. Y. (1983). Multi-objective decision making. Theory and methodology. Amsterdam: Dover Publications, North-Holland.

    Google Scholar 

  • Chanta, S., Mayorga, M. E., & McLay, L. (2010a). The minimum p-envy location problem: a new model for equitable distribution of emergency resources. Clemson, SC: Clemson University.

    Google Scholar 

  • Chanta, S., Mayorga, M.E., McLay, L. (2010b). Improving emergency service in rural areas without sacrificing coverage to the region: a bi-objective covering location model for EMS systems. Technical report. Clemson, SC: Clemson University.

    Google Scholar 

  • Charnes, A., & Cooper, W. W. (1962). Chance-constrained programming. Management Science, 6, 73–79.

    Article  Google Scholar 

  • Current, J., Daskin, M., & Schilling, D. (2002). Discrete network location models. In Z. Drezner & H. W. Hamacher (Eds.), Facility location: applications and theory (pp. 82–118). Berlin: Springer-Verlag.

    Google Scholar 

  • Diamond, P. (1967). Cardinal welfare, individualistic ethics, and interpersonal comparison of utility: comment. The Journal of Political Economy, 75, 765–766.

    Article  Google Scholar 

  • Erkut, E., Ingolfsson, A., & Erdogan, G. (2008). Ambulance location for maximum survival. Naval Research Logistics, 55(1), 42–55.

    Article  Google Scholar 

  • Erkut, E., Ingolfsson, A., Sim, T., & ErdoÄŸan, G. (2009). Computational comparison of five maximal covering models for locating ambulances. Geographical Analysis, 41, 43–65.

    Article  Google Scholar 

  • Espejo, I., Marin, A., Puerto, J., & Rodriguez-Chia, A. M. (2009). A comparison of formulations and solution methods for the minimum-envy location problem. Computers & Operations Research, 36, 1966–1981.

    Article  Google Scholar 

  • Felder, S., & Brinkmann, H. (2002). Spatial allocation of emergency medical services: minimising the death rate or providing equal access? Regional Science and Urban Economics, 32, 27–45.

    Article  Google Scholar 

  • Ferraro, G. (1998). Cultural anthropology. Belmont, CA: Wadsworth Publishing.

    Google Scholar 

  • Fitch, J. (2005). Response times: myths, measurement and management. Journal of Emergency Medical Services, 9, 46–56.

    Article  Google Scholar 

  • Gopalan, R., Kolluri, K. S., Batta, R., & Karwan, M. H. (1990). Modeling equity of risk in the transportation of hazardous materials. Operations Research, 38(6), 961–973.

    Article  Google Scholar 

  • Green, L. V., & Kolesar, P. J. (2004). Improving emergency responsiveness with management science. Management Science, 50(8), 1001–1014.

    Article  Google Scholar 

  • Henderson, S. G., & Mason, A. J. (2004). Ambulance service planning: simulation and data visualization. In M. L. Brandeau, F. Sainfort, & W. P. Pierskalla (Eds.), Operations research and health care: a handbook of methods and applications (pp. 77–102). Boston, MA: Kluwer Academic.

    Google Scholar 

  • Joshi, K. (1990). An investigation of equity as a determinant of user information satisfaction. Decision Sciences, 21, 786–807.

    Article  Google Scholar 

  • Keeney, R. (1980). Equity and public risk. Operations Research, 28, 527–534.

    Article  Google Scholar 

  • Keeney, R., & Winkler, R. (1985). Modelling effectiveness-equity trade-offs in public service delivery systems. Operations Research, 33, 955–970.

    Article  Google Scholar 

  • Kozanidis, G. (2009). Solving the linear multiple choice knapsack problem with two objectives: profit and equity. Computational Optimization and Applications, 43(2), 261–294.

    Article  Google Scholar 

  • Larsen, M. P., Eisenberg, M. S., Cummins, R. O., & Hallstrom, A. P. (1993). Predicting survival from out-of-hospital cardiac arrest: a graphic model. Annals of Emergency Medicine, 22, 1652–1658.

    Article  Google Scholar 

  • Larson, R. C. (1987). Perspectives on queues: social justice and the psychology of queueing. Operations Research, 35(6), 895–905.

    Article  Google Scholar 

  • Larson, R. C., & Odoni, A. R. (1981). Urban operations research. New Jersey: Prentice-Hall.

    Google Scholar 

  • Mandell, M. (1991). Modelling effectiveness-equity trade-offs in public service delivery systems. Management Science, 37, 467–482.

    Article  Google Scholar 

  • Marianov, V., & Serra, D. (2002). Location problems in the public sector. In Z. Drezner & H. W. Hamacher (Eds.), Facility location: applications and theory (pp. 119–150). Berlin: Springer-Verlag.

    Google Scholar 

  • Marsh, M., & Schilling, D. (1994). Equity measurement in facility location analysis: a review and framework. European Journal of Operational Research, 74, 1–17.

    Article  Google Scholar 

  • McLay, L.A. (2011). Emergency medical service systems that improve patient survivability. In: Encyclopedia of operations research. Wiley, Hoboken, NJ (to appear)

    Google Scholar 

  • McLay, L., & Mayorga, M. (2010). Evaluating emergency medical service performance measures. Health Care Management Science, 13(2), 124–136.

    Article  Google Scholar 

  • Rajagopalan, H., Saydam, C., Setzler, H., Sharer, E. (2011). Decision making for emergency medical services. In: M. Johnson (Ed.), Community-based operations research: decision modeling for local impact and diverse populations. New York: Springer, pp. 293–316.

    Google Scholar 

  • Rawls, J. (1999). A theory of justice. Cambridge, MA: Belknap Press of Harvard University Press.

    Google Scholar 

  • ReVelle, C., & Hogan, K. (1989). The maximum availability location problem. Transportation Science, 23(3), 192–200.

    Article  Google Scholar 

  • Sampson, S. E. (2006). Optimization of volunteer labor assignments. Journal of Operations Management, 24, 363–377.

    Article  Google Scholar 

  • Sarin, R. (1985). Measuring equity in public risk. Operations Research, 33, 210–217.

    Article  Google Scholar 

  • Savas, E. (1969). Simulation and cost-effectiveness analysis of New York’s emergency ambulance service. Management Science, 15, B608–B627.

    Article  Google Scholar 

  • Savas, E. (1978). On equity in providing public services. Management Science, 24, 800–808.

    Article  Google Scholar 

  • Stiell, I., Wells, G., & Field, B. (1999). Improved out-of-hospital cardiac arrest survival through the inexpensive optimization of an existing defibrillation program: OPALS study phase II. Ontario prehospital advanced life support. The New England Journal of Medicine, 35, 647–656.

    Google Scholar 

  • Stone, D. (2002). Policy paradox: the art of political decision making. New York, NY: W. W. Norton & Company.

    Google Scholar 

  • Studnek, J., & Fernandez, A. R. (2007). Non-urgent is no fun. Journal of the Emergency Medical Services, 32(10), 38.

    Article  Google Scholar 

  • Tseytlin, Y. (2007). Queueing systems with heterogeneous servers: improving patients’ flow in hospitals. M.Sc. Research Proposal. The Faculty of Industrial Engineering and Management Technion – Israel Institute of Technology

    Google Scholar 

  • Waaelwijn, R. A., de Vos, R., Tijssen, J. G. P., & Koster, R. W. (2001). Survival models for out-of-hospital cardiopulmonary resuscitation from the perspectives of the bystander, the first responder, and the paramedic. Resuscitation, 51, 113–122.

    Article  Google Scholar 

  • Winston, W., & Venkataramanan, M. (2003). Introduction to mathematical programming. Canada: Thomson-Brooks/Cole.

    Google Scholar 

Download references

Acknowledgments

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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Correspondence to Philip D. Leclerc .

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Leclerc, P.D., McLay, L.A., Mayorga, M.E. (2012). Modeling Equity for Allocating Public Resources. In: Johnson, M. (eds) Community-Based Operations Research. International Series in Operations Research & Management Science, vol 167. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0806-2_4

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