A Social Network Approach to Financial Inclusion

Kathleen (not taking second module)

Title

“Influencing the Influencers: Identifying Key Individuals and Organizations in the Financial Inclusion Space”

Background

Dr. Donald Kaberuka, President of the African Development Bank, gave a lecture this week at Fletcher titled “Inclusive Growth: Insuring Prosperity Reaches Africa’s Bottom of the Pyramid.” Last spring, the Institute for Business in the Global Context and The MasterCard Center for Inclusive Growth sponsored The Inclusive City conference. Financial inclusion and inclusive growth are popular buzzwords in development today, but what links the two concepts in practice?

Financial inclusion writ large encompasses much more than its origins in microcredit. The Center for Financial Inclusion defines it as “access to a full suite of quality financial services, provided at affordable prices, in a convenient manner, and with dignity for the clients…delivered by a range of providers.” The path of financial inclusion provides a stabilizing force for communities, while also increasing opportunities for inclusive macroeconomic growth, in a virtuous cycle. As Dr. Kaberuka said, “We know what needs to be done, it’s a question of political will.” Kaberuka also suggested that the private sector has several roles to play, first and foremost as an engine of steady, predictable growth, but also as a responsible provider of financial services and a partner for practical policy reform.

This summer, I interned at a relatively new corporate think tank, focused on the issue of inclusive growth. I spent a fair amount of time developing a data set on the key influencers in the financial inclusion space, as part of my organization’s strategic planning process. A new think tank, they faced the challenge of defining and understanding their audience. Who did they need to reach, what message did they want to send, and how should they disseminate that message to maximize its impact? As I envisioned it, social network analysis would help answer these questions, so I tried to structure my data collection accordingly.

Research Questions

To help inform marketing strategy, the relevant question is, can we identify who are the key individuals who create linkages across issues, geographies, organizations, sectors, and industries, and what useful patterns do those linkages follow?  For example, who is working on branchless or retail banking, and do any of those individuals or organizations also have expertise in mobile? Where geographically are they concentrated? Who among them is the “best” connected, and what in general do the connection patterns look like? Where are there structural gaps? Is no one in Africa researching social grants? Do different sectors focus on different core competencies, or is there overlap? Identifying structural gaps is key to defining the think tank’s research agenda, whereas issue clusters could be useful to set conference themes.

Data

To begin to answer those questions, I created a matrix of individuals and organizations across sectors and industries that touch on topics related to inclusive growth. I cast a wide net to maximize possibilities for cross-sector and cross-issue collaboration. I also derived a relative influencer “score” based on whether individuals were published, active on social media, frequent conference speakers, award-winners, or affiliated with multiple institutes or initiatives. I grouped and consolidated core competencies based on current trends in financial inclusion, to identify each individual’s areas of expertise. For example, issues or competencies included entrepreneurship, youth, mobile, finance, public private partnerships, policymaking and regulatory reform, micro-insurance, branchless banking, etc.

The data in its current form would still need to be cleaned before it could be imported to UCINET. I assigned numeric values for regions, issues, sectors, and relative rankings, but for example, when one individual had expertise in multiple issues, multiple issue networks might need to be uploaded separately, otherwise I’m not sure how to create one master file that would be analyzable. Also, the information captured in the relative ranking could be further codified for an additional level of analysis - for example, which individuals are linked by being alumni of the same fellowship, or sit on the same Board?

Network Measures

There are several important network measures to look at here. Whole network measures such as density and distance would give a sense of how cohesive the network itself is, which would be interesting due to the fact that the network is a patchwork of disparate fields pulled together by virtue of some relation to inclusive growth, so it may not be that dense. Newman Girvan, factions, and ego networks would help me analyze the structures and strengths of sub-groups. Centrality measures such as betweenness, eigenvector, closeness, and in/out degree would help me understand how those groups are connected.

Conclusion

Individuals with high betweenness would be good to facilitate public private partnerships, coordinate research projects, or collaborate on program implementation. High in/out degree and eigenvector individuals would be VIP conference guests due to their behind the scenes powerbroker status. Whereas those with key “closeness” scores would be good to target for message dissemination, marketing white paper publications and conference results.

I did not initially include social networks as one of the core competencies relevant to researchers and practitioners of financial inclusion, but it is fast becoming an area of fruitful inquiry, whereby social networks themselves have been suggested as a potential engine of inclusion. CGAP is an excellent source for further reading on this and all topics in financial inclusion. (http://www.cgap.org/blog/killer-apps-china-social-networks-and-financial-inclusion)