Abstract
How do VCs select startups to fund over multiple rounds? To study this question, I develop a dynamic two-sided matching model of VC funding. Using a hand-collected database including both VC-funded and non-VC-funded startups, I estimate the joint determinants of investment selection and the effects of post- investment influence in a Bayesian framework. The results show that selection depends on startups’ quality and VCs’ influence – VCs may choose to invest in a startup with lower quality if their subsequent impact is large. Importantly, previously funded startups are of higher quality and thus are more likely to get additional funding. A simulation experiment shows that initial random differences in startups can magnify significantly under the joint effects of the selection and influence of VC funding.
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