Abstract
While the study of high-growth firms focuses predominantly on venture-financed startups, the majority of IPOs and acquisitions are achieved without venture capital. This paper uses a predictive analytics approach to shed light on these "missing" growth firms. To do so, we first estimate a (non-causal) model of the likelihood of receiving venture capital within the population of all business registrants, and then use the predictions from that model to estimate the probability of a successful growth outcome (either an IPO or significant acquisition) among the population of firms that did not raise venture capital. We find striking evidence that observables at birth that predict the ability to attract venture capital are also highly predictive of growth within the non-VC sample. For example, firms that do not receive VC funding but that experience a significant growth outcome are much more likely to have received formal intellectual property protection within a year of founding, are less likely to have named the firm after the founders, and more likely to have registered in Delaware. We then use our estimates of "VC-likelihood" to perform a fine-grained matching between firms that are born with identical observables, but only differ in whether they will receive venture capital or not. This allows us to study the process of selection into VC and to estimate an upper bound on the returns to venture capital: While a naive comparison of the probability of growth between venture-backed and non-venture-backed firms implies nearly a 500X increase in the probability of an exit, after matching, VC-backed firms are only 5 times more likely to grow than comparable, non-VC-funded firms. Our findings highlight that contrary to a case-based literature emphasizing the differences between firms that grow with and without venture capital, our predictive analytics approach on the full population of firms suggests that firms with growth potential -- irrespective of future funding source -- are much more similar to each other than they are to the overall population of new businesses.