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Although venture capitalists (VCs) can choose from thousands of potential syndicate partners, many co-syndicate with small groups of preferred partners. We term these groups “VC communities.” We apply computational methods from the physical sciences to 3 decades of syndication data to identify these communities. We find that communities comprise VCs that are similar in age, connectedness, and functional style but undifferentiated in spatial location. Machine-learning tools classify communities into 3 groups roughly ordered by their age and reach. Community VC financing is associated with faster maturation and greater innovation, especially for early-stage firms without an innovation history.

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Financial intermediaries worldwide are seeking mechanisms for participating in micro lending.We consider a simple model where a bank may use informed "local capitalists" as intermediaries for on-lending. But the availability of multiple credit sources provides borrowers with an incentive to default voluntarily, making the bank's on-lending mechanism a non-starter. We explore whether a coalition of local capitalists, effectively limiting borrower's opportunity for defaulting multiple times, might be sufficient to facilitate on-lending. Instead, we find that a monopoly moneylender with superior enforcement technology can out-compete the local capitalist coalition if the moneylender also enjoys the smallest transactions costs of lending. We show that a credible competitive threat to the monopoly moneylender can only arise if the local capitalist coalition can also be made cost-effective either by direct subsidies or by measures such as standardization, economies of scale and implementation of best practices. We argue that Franchising is one potential mechanism that could deliver both cost-efficiencies as well as ability for local capitalists to form a coalition. JEL Classification: G21

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