EFWP6 «Accelerators, Networks and Venture Capital Financing»
Carolina Dams, Virginia Sarria-Allende, Ricardo Pasquini
April 2017
We explore the effects that accelerator programs have on the social capital of startup teams, and on the effects that this has on enhancing subsequent startups’ performance. Using a unique dataset built from AngelList, Seed-DB, Crunchbase and LinkedIn, and using different estimates of social capital, we show that Accelerator Programs enhances the probability of receiving VC financing, and that this effect is more pronounced in startups with better prior networks. Our results also show a tendency of startups with higher social capital participating with higher probability in Accelerator Programs.
EFWP5 «Accelerated Female Entrepreneurs and their Access to Venture Capital»
Carolina Dams, Virginia Sarria Allende, Ricardo Pasquini, Gabriela Robiolo
November 2016
Equity financing is an important source of funding for startups. Yet, it is not equally available to all entrepreneurs. Even when male and female entrepreneurs found companies at a similar pace, there is a significant gap in equity funding for female entrepreneurs. Accelerators have the potential to help female founders reduce this gap. This paper shows that Accelerator Programs have direct impact in reducing the barriers female founders face when raising equity financing. Our findings confirm that female entrepreneurs that go through Accelerator Programs increase the chances of receiving equity financing by 30%. Additionally, the study shows that the incremental effect is more pronounced for female than for male entrepreneurs.
EFWP4 «Random Network Formation in Entrepreneurial Finance: A Simple Model and Evidence»
Ricardo Pasquini and Virginia Sarria-Allende
August 2016
We propose a simple two-mode random network formation model aimed to mimic the properties of an entrepreneurial finance network, and calibrate it with data of a network of startups, and investors of the entrepreneurial finance setting in California. In the model some investors match startups at random, and some others find about investment opportunities by invitations from investors they are connected with. This model helps explains features of the observed network such as its degree distribution, average distance and diameter, and clustering.
EFWP3 «The Performance of Multilateral Development Banks in Venture Capital Funds»
Carolina Dams, Virginia Sarria Allende, María José Murcia
July 2016
This chapter examines the performance of Multilateral Development Banks investments in Venture Capital (MDBVC) and compares them to public programs. We used a unique dataset of 437 startups that received Venture Capital investments in 7 countries in Latin America during the period 2000-2014. Consistent with previous studies, we find that private VCs (PVC) lead the ranking; however, startups funded by MDBVC have more chances of receiving subsequent rounds of financing than startups funded by government-sponsored VC funds
EFWP2 «Matching in Entrepreneurial Finance Networks»
Pasquini, Ricardo, Robiolo, Gabriela and Sarria-Allende, Virginia
July 2016
We empirically explore the importance of networks in the match formation of startups and investors. Using a massive network of connections from the entrepreneurial finance setting in California, we estimate a matching model were network distance can both determine the value of a prospective match as well as moderate observable complementarities. We find that distance drives matching value and moderates preferences for experience and education. While we corroborate that there is significant sorting along this preferences in realized matches, our results indicate that connections can potentially outweigh them, emphasizing the role of networks in alleviating matching frictions in these markets.
EFWP1 «The Added-Value of Network Connections in Entrepreneurial Finance»
Ricardo Pasquini, Gabriela Robiolo
July 2016
Entrepreneurs are usually exhorted to attract the best networked investors. We provide further insights into this advice by estimating network effects in the performance of entrepreneurial ventures. We show dimensions that are critical in this estimation, such as the consideration for startups’ connections, common connections among investors, and the decreasing returns to network centrality, and estimate their relative importance. We estimate networks effects using an an original database of connections among startups, investors and individuals with relevant roles in California, collected from web-based sources, resulting in a network of nearly 1 million connections.