Six million refugees have been living in camps in 2021 due to multiple armed conflicts worldwide. Regulations often impede refugees’ integration into host countries; thus, refugees have to seek help from humanitarian organizations (HOs). HOs traditionally provide in-kind (e.g., food) assistance and now offer cash (monetary assistance) that refugees can spend at local retail stores. However, cash assistance can be exploited by local retailers’ market power, which challenges HOs’ mission of helping refugees while doing no harm to host communities. Completely informed by field research in three refugee camps in northwestern Greece, we analyze the trade-off between in-kind and cash assistance from the perspective of an HO. We propose two cash assistance policies, implementable by a partnership between the HO and the local government, to curb the retailer’s market power and ensure that the refugees, the local residents, and the retailer are better off than if only in-kind assistance is provided. We use field research to define our research setting and support our main modeling assumptions and parameters. Then, we use a game-theoretical model to analyze the interactions among multiple stakeholders in an ecosystem consisting of an HO, refugees, a monopolistic retailer, local residents, and a local government. We demonstrate the effectiveness of our proposed cash assistance policies that benefit refugees and local residents while ensuring the retailer’s profitability. In particular, a price-dependent cash assistance (PDCA) policy aligns the incentives between the retailer and the HO-government partnership. This new policy for cash assistance acts as a lever for the retailer to voluntarily set desirable prices, which benefit both refugees and their host community. We provide tools and implementable policies that guide HOs to improve their budget allocation between in-kind and cash assistance for refugees living in areas where local market power exists. Moreover, we clearly outline the roles of HOs and the local government in a partnership for cash assistance to refugees.

US nonprofits declare three types of expenses in their IRS 990 forms: program spending to meet their beneficiaries' needs; fundraising spending to raise donations; administration spending to build and maintain capacity. Charity watchdogs expect nonprofits to prioritize program over other expenses. We study when such expectations may be counterproductive. We characterize the optimal budget allocations to program, fundraising, and administration using a two-period model, which also includes the nonprofit’s capacity, return on program (the net value of program to beneficiaries) and uncertain future needs of beneficiaries. The optimal allocation depends on the nonprofit's capacity. At high capacity, the nonprofit should spend on administration just enough to maintain its existing capacity. At moderate capacity, the nonprofit should still just maintain its existing capacity, but also limit its program spending, so that it has money to fundraise a budget that will use up its capacity. At low capacity, the nonprofit should increase administration spending to expand its future capacity. We use public data to compare our model’s prescriptions with the actual budget allocations of a leading network of foodbanks. Our case study indicates that the foodbank network underspends on administration and fundraising, which triggers a phenomenon known in the nonprofit sector as the starvation cycle. We conduct a sensitivity analysis focused on the return on program. At higher values of return, the gap between actual and prescribed allocations shrinks. Perhaps this indicates that the nonprofit's allocations are based on high estimates of return. An important implication is that, when setting expectations for a nonprofit, watchdogs should be mindful of the nonprofit's capacity and return on program.

This study examines empirically the fleet composition of a large international humanitarian organization (HO) that responds to natural disasters and armed conflicts. We focus on understanding how the HO configures its fleet and specifically decides on renting or subcontracting transportation to deliver services to its beneficiaries. Transportation expenses are one of the largest expenses for the organization. We use thirteen-year panel data on transportation and personnel (local and expatriate) expenses of twenty-four country offices of this organization to build a fixed-effect model. We combine our proprietary data with publicly available data on natural disasters and armed conflicts.