Budgeting in International Humanitarian Organizations

Abstract

Problem definition: International humanitarian organizations (IHOs) prepare a detailed annual allocation plan for operations that will be conducted in the countries they serve. The annual plan is strongly affected by the available financial budget. The budget of IHOs is derived from donations, which are typically limited, uncertain, and to a large extent earmarked for specific countries or programs. These factors, together with the specific utility function of IHOs, render budgeting for IHOs a challenging managerial problem. In this paper, we develop an approach to optimize budget allocation plans for each country of operations. Academic relevance: The current research provides a better understanding of the budgeting problem in IHOs, given the increasing interest of the operations management community for nonprofit operations. Methodology: We model the problem as a two-stage stochastic optimization model with a concave utility function, and identify a number of analytical properties for the problem. We develop an efficient generalized Benders decomposition algorithm as well as a fast heuristic.
Results: Using data from the International Committee of the Red Cross (ICRC), our results indicate 21.3% improvement in the IHO’s utility by adopting stochastic programming instead of the expected value solution. Moreover, our solution approach is computationally more efficient than other approaches.
Managerial insights: Our analysis highlights the importance of nonearmarked donations for the overall performance of IHOs. We also find that putting pressure on IHOs to fulfill the targeted missions (e.g., by donors or media) results in a lower beneficiaries’ welfare. Moreover, the IHOs benefit from negative correlation among donations. Finally, our findings indicate that if donors allow the IHO to allocate unused earmarked donations to other delegations, the performance of IHO improves significantly.

Keywords: International humanitarian organization; Earmarked donations; Budgeting; Nonlinear stochastic programming; Generalized Benders decomposition