CREAT: Census Research Exploration and Analysis Tool

On the Lifecycle Dynamics of Venture-Capital- and Non-Venture-Capital-Financed Firms

May 2008

Working Paper Number:

CES-08-13

Abstract

We use a new data set that tracks U.S. firms from their birth over two decades to understand the life cycle dynamics and outcomes (both successes and failures) of VC- and non-VC financed firms. We first ask to what market-wide and firm-level characteristics venture capitalists respond in choosing to make their investments and how this differs for firms financed solely by non-VC sources of entrepreneurial capital. We then ask what are the eventual differences in outcomes for firms that receive VC financing relative to non-VC-financed firms. Our findings suggest that VCs follow public market signals similar to other investors and typically invest largely in young firms, with potential for large scale being an important criterion. The main way that VC financed firms differ from matched non-VC financed firms, is they demonstrate remarkably larger scale both for successful and failed firms, at every point of the firms' life cycle. They grow more rapidly, but we see little difference in profitability measures at times of exit. We further examine a number of hypotheses relating to VC-financed firms' failure. We find that VC-financed firms' cumulative failure rates are lower than non-VC-financed firms but the story is nuanced. VC appears initially 'patient' in that VC-financed firms are less likely to fail in the first five years but conditional on surviving past this point become more likely to fail relative to non-VC-financed firms. We perform a number of robustness checks and find that VC does not appear to have more stringent survival thresholds nor do VC-financed firm failures appear to be disguised as acquisitions nor do particular kinds of VC firms seem to be driving our results. Overall, our analysis supports the view that VC is 'patient' capital relative to other non-VC sources of entrepreneurial capital in the early part of firms' lifecycles and that an important criterion for receiving VC investment is potential for large scale, rather than level of profitability, prior to exit.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
profitability, investment, company, acquisition, entrepreneurial, financial, investing, venture, entrepreneur, finance, investor, financing, acquirer, profitable, funding, fund, invest

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Standard Statistical Establishment List, Standard Industrial Classification, Longitudinal Research Database, Center for Economic Studies, Ordinary Least Squares, Columbia University, Longitudinal Business Database, Initial Public Offering, Washington University, Economic Census, Wholesale Trade, Duke University

Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the capture of semantic meaning in a way that relates to the context of words within the document. The model learns to associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as document classification, clustering, and similarity detection by preserving the order and structure of words. The document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

The 10 most similar working papers to the working paper 'On the Lifecycle Dynamics of Venture-Capital- and Non-Venture-Capital-Financed Firms' are listed below in order of similarity.