Blending philanthropic and private capital has enormous potential, but it is also fraught with the complexities and risks that come with injecting donor capital and donor programming into market systems. To maximize leverage and minimize market distortions, it is critical for donors to not only understand how to structure transactions, but also where in the local finance ecosystem different forms of philanthropic capital can be best utilized.

Most market analyses look at trends in terms of growth within different sectors, policy changes, and gaps in different types and sizes of financing; however, this type of analysis misses insights into the relational network of funds, individual investors, financial institutions, entrepreneurs, and intermediaries.

LINC conducted research on the Venture Capital industry in the U.S. Our research shows that both fund and portfolio company survival is heavily influenced by position in this relational network, and this effect is likely magnified in developing world countries that lack strong formal channels for deal pipeline. To effectively blend capital, donors must be able to identify gaps in the ecosystem that offer the highest-value leverage points and ensure that donor interventions are additional rather than distortive.

Network analysis offers a powerful tool to map the formal and informal on-the-ground relationships that make up the finance ecosystem to identify the highest-potential points for donor investment or programming.

Potential ways to use network analysis to maximize effectiveness of donor dollars for blended finance activities include:

• Analyze and quantify the overall structure of a targeted finance network – creating a visualization of the overall network, including the clusters of actors that tend to work closely together on transactions and information sharing.
• Identify critical players and key gaps – using algorithms that quantify different measures of influence, identify the specific ecosystem players that serve critical needs, such as the “bridge” organizations between different clusters of investors, as well as the overall gaps in the system that no actor is filling.
• Propose specific leverage points for philanthropic capital – based on the key players and gaps identified in the network, layering on qualitative analysis of the constraints faced by these key players and work with partners to propose specific blended finance structures that could precisely target the actors and areas in the system most in need of diverse types of donor support to catalyze private investment.

To concretely demonstrate the potential for network analysis to identify leverage points for blended capital, LINC analyzed data on 3,962 impact investment deals closed since 2004, as cataloged by ImpactSpace (available at https://impactspace.com/investments). In this example, using the data available:

• All investors funding a given portfolio company were considered as “co-investors” with one another, creating 1,382 distinct co-investor pairs from 432 total investors.
• The network of these co-investors was plotted, with each node (sphere) representing a distinct investment company.
• Each line between nodes represents at least one co-investment between two investment companies.
• The width of the line indicates the number of co-investments.
• The size of a node indicates that investment companies’ relative importance in the investor network.
  • Specifically, this importance is quantified using an algorithm that assesses to what extent a given node is the most efficient path between any two other nodes in the network, known in network science as “betweenness centrality.”

The network map reveals a few clear clusters, each representing groups of investors that tend to invest in the same companies. To better understand the nature of the organizations in these clusters color coding was applied:

• Members of the Aspen Network for Development Entrepreneurs (ANDE) were coded and colored in green.
  • Light green lines represent co-investment pairs with one ANDE member.
  • Dark green line represent co-investment pairs of two ANDE members.

The largest and densest cluster of investors in the middle of the network map turns out to be heavily made up of ANDE members and similar organizations, and thus can be characterized as the “Development Core” of investors relatively well-acquainted with USAID and other donors through ANDE and similar fora.

There is a notably distinct but similarly dense cluster of investors in the bottom right corner of the map.

• ANDE members are not evident in this cluster.
• These organizations are predominately finance-first, Silicon-Valley based investors, with a tight network of investments in innovative technology companies working for the greater good.
• This cluster includes investors such as Andreessen Horowitz, DBL Investors, SJF Investors, Owl Ventures, Chan Zuckerberg Initiative, Imprint Capital, Reach Capital, and General Catalyst Partners.

Clearly, this cluster represents enormous potential if linked to the more international development-oriented investors, but there are relatively few existing instances of direct co-investment between these groups.

However, the network analysis allows us to identify those organizations that are investing both with the “Development Core” cluster and the Silicon Valley cluster, and that therefore represent extremely strong opportunities for engagement to bridge this gap. These “Bridgers” and their co-investment connections are highlighted in red in Figure 1, with the most significant investment bridgers shown more closely and labeled in Figure 2 below:

Network analysis can be paired with a wide range of other approaches to improve up-front design and dynamically monitor change through network feedback loops.

• If deployed iteratively alongside a technical assistance (TA) mechanism focused on early-stage enterprises, network analysis could:
  • be used to monitor the extent to which the TA is leading to improved connections between entrepreneurs and investors; and
  • allow the TA provider to adjust the model to increase the quantity and quality of these connections.
• If deployed alongside an investor matchmaking or transaction advisory intervention, network analysis could:
  • be used to monitor how and to what extent the intervention is improving the efficiency and effectiveness of deal identification.

This research begins to identify specific opportunities to pair network analysis with innovative interventions that together will maximize the effectiveness of donor dollars.