We examine an emerging phenomenon that talented employees leave successful entrepreneurial firms to join less mature start-ups. Using proprietary person-level data and private firm data, we find that the presence of these “serial venture employees” positively predicts their new employers’ future success in terms of exit likelihoods, size growth, venture capital financing, and innovation productivity. Such predictive power is more likely driven by a screening/matching channel rather than venture labor’s nurturing role. Our paper sheds light on an underexplored pattern of inter-firm labor flow, which provides a nonfinancial yet value-relevant signal about private firms for investors and stakeholders.

How do venture capitalists (VCs) make investment decisions? Using a large administrative data set on French entrepreneurs that contains VC-backed as well as non-VC-backed firms, we use algorithmic predictions of new ventures’ performance to identify the most promising ventures. We find that VCs invest in some firms that perform predictably poorly and pass on others that perform predictably well. Consistent with models of stereotypical thinking, we show that VCs select entrepreneurs whose characteristics are representative of the most successful entrepreneurs (i.e., characteristics that occur more frequently among the best performing entrepreneurs relative to the other ones). Although VCs rely on accurate stereotypes, they make prediction errors as they exaggerate some representative features of success in their selection of entrepreneurs (e.g., male, highly educated, Paris-based, and high-tech entrepreneurs). Overall, algorithmic decision aids show promise to broaden the scope of VCs’ investments and founder diversity.

We estimate the unobservable effort that firms put into boosting their efficiency. Identification comes from a model in which firms accumulate capital but also choose a flow of effort that controls efficiency period by period. Model estimates show that, for all cohorts and industries, young firms choose relatively more growth and old firms choose more efficiency. Amongst young firms, higher capital growth predicts higher markups in the long-term, but increases the risk of not surviving into maturity. Our model estimates help explain the priced firms’ exposures to the profitability and in- vestment risk factors of the investment CAPM.