There is extensive literature on the drivers and inhibitors of foreign aid allocation, yet studies that explicitly apply a network perspective are so far scarce. Our research investigates the drivers of foreign aid allocation at the sectoral level. The major assumption behind foreign aid is that donors allocate aid according to the needs of the recipients in the respective sectors to achieve sustainable development goals. However, aid allocation is a complex phenomenon that is influenced by a variety of factors ranging from self-interest to historical relationships and recipient merits. We argue that using a network perspective to examine foreign aid patterns is essential due to the inherent interdependencies among donors and recipients. To this end, the two-mode exponential random graph model (ERGM) is applied to examine the mechanisms of sectoral aid allocation for the 6 largest sectors by aid volume within the donor-recipient networks over the observed period 2005-2022. The research uses aid data from the OECD Creditor Reporting System (CRS) to construct the networks. We aim to investigate how ties are formed by accounting for the effects of the recipient’s need in the respective sector measured by an indicator of need/performance. We also consider the economic trade volume between donor and recipient to account for donor self-interest, as well as geographical proximity, colonial history, and recipient governance quality. We control for network self-organization and assign attributes for both donors and recipients that capture the economy size, wealth, and population.
