Social Optima of Need-based Transfers

Need based transfers (NBTs) are actions to compensate losses after disasters. They are based on shared values and reflect the understanding of the random nature of disasters. To study them, agent based simulations are performed whereby individuals that fall below a threshold (e.g. welfare threshold) will receive help from a single individual who is rich enough to help within a network of connections. An NBT policy acts as risk pooling and its rules have a comprehensive impact on a whole community and its economy: It is found that for short time horizons, optimizing the survival rate of a community is similar to a cutting-stock optimization problem, leading to a policy that closely matches need and giving ability between a recipient and a donor. However, in the long-term this policy leads to the growth of a highly vulnerable subgroup of the population and thus ultimately to low survival rates. It is shown that, on such a long time horizon, a policy that asks the richest potential donor leads to a log-normal wealth distribution that becomes optimal for the survival rate. When individuals make decisions how to build up an insurance network, it is shown that an optimal network has low variance in the node degrees leading to equal sharing of the risk and benefit of such an NBT insurance relationship. Finally, a quasi-equilibrium simulation model is derived that allows for the study of time-dependent and slowly evolving NBT-economies and policies.