A Network Analysis of Development Finance Institutions and Private Co-Investment in African Agrifood Systems

Authors

  • Alexander Poon Department of Computational and Data Sciences, George Mason University, Fairfax, VA
  • Kunal Chevuri Department of Computational and Data Sciences, George Mason University, Fairfax, VA
  • Aaron D'Souza Department of Computational and Data Sciences, George Mason University, Fairfax, VA
  • Vrinda Shah Department of Computational and Data Sciences, George Mason University, Fairfax, VA
  • Avinash Kunala Department of Computational and Data Sciences, George Mason University, Fairfax, VA
  • Elaina Wi Department of Computational and Data Sciences, George Mason University, Fairfax, VA
  • Linh Dieu Quach Department of Computational and Data Sciences, George Mason University, Fairfax, VA
  • Yousef Malik Department of Computational and Data Sciences, George Mason University, Fairfax, VA
  • Adam Malik Department of Computational and Data Sciences, George Mason University, Fairfax, VA
  • Ron Mahabir Department of Computational and Data Sciences, George Mason University, Fairfax, VA
  • Maction Komwa Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA
  • Olga Gkountouna Department of Computational and Data Sciences, George Mason University, Fairfax, VA

DOI:

https://doi.org/10.13021/jssr2026.5678

Abstract

Blended finance policy operates on the assumption that development finance institutions (DFIs) crowd in private capital. However, existing evidence primarily measures the overall volume of co-financing associated with DFI investment rather than identifying whom DFIs actually invest alongside. To address this gap, we provide what is, to our knowledge, the first network-based test of this assumption for African agrifood systems. Using a hand-verified dataset of 131 African agrifood co-investment deals (2015-2025) assembled from six sources, we construct a co-investment network of 347 investors and 1,188 co-investment ties and test whether DFIs occupy bridging positions between public and private capital. DFIs exhibit mean betweenness centrality more than three times that of other investors (p = 0.016), and 62.3% of DFI ties in the network's main component connect outward to non-DFI investors. Crucially, this effect is concentrated in just three institutions: FMO, the International Finance Corporation, and British International Investment. Excluding them eliminates the DFI advantage entirely (p = 0.113). The pattern also varies geographically and sectorally. Bridging is strong in West Africa, but absent in East and Southern Africa, and technology-enabled agrifood segment businesses attract two to three times more co-investors than primary production. These findings demonstrate that crowding in is an institution-specific capability rather than a categorical property of development finance. Consequently, blended-finance mandates and evaluations should focus on the catalytic performance of individual institutions rather than assuming performance across all DFIs.

Published

2026-09-24

Issue

Section

College of Science: Department of Computational and Data Sciences