The Allocation of Extremely Scarce Resources: Giving Mental Healthcare a Fair Chance

Abstract

The lack of treatment of people in need of psychological help due to the long waiting lists within mental healthcare has been a topic of public debate for the past decades. With this thesis, rather than focussing on ways to shorten the waiting lists, I aimed to evaluate the methods that could be used to allocate mental healthcare resources. My first analysis was about the current allocation method that is generally applied: waiting lists with a first-come, first-served procedure. I have concluded that this method is not justified when it concerns the allocation of extremely scarce resources, which is the case in MHC. In order to develop a justifiable alternative to the current system, I applied a utilitarian, prioritarian and fairness approach to the MHC case respectively. I started with a utilitarian cost-effectiveness analysis because extreme scarcity requires the prioritisation of efficiency. Due to the conflicts of this approach with egalitarian and fairness principles, I consider this approach to be insufficient. Next I discussed a prioritarianist weighted CEA, to resolve the egalitarian concerns of utilitarianism. Because of the remaining concerns, I proposed the addition of a weighted lottery system. After discussing the necessity of the lottery requirement and possible concerns, I concluded that a weighted lottery with probabilities based on a weighted CEA should account for the most important principles of MHC resource allocation and therefore pose a promising alternative to the current system.