Abstract Patents and citations are powerful tools increasingly used in financial economics (and management research more broadly) to understand innovation. Biases may result, however, from the interactions between the truncation of patents and citations and the changing composition of inventors. When aggregated at the firm level, these patent and citation biases can survive popular adjustment methods and are correlated with firm characteristics. These issues can lead to problematic inferences. We provide an actionable checklist to avoid biased inferences and also suggest machine learning as a potential new way to address these problems. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online

Using previously unexplored custodial data, we examine alternative investment vehicles (AVs) in private equity (PE) funds over the last four decades. By 2017, AVs reached 40% of all PE commitments. Average AV performance matches the PE market, but underperforms the main funds of the partnerships sponsoring the AVs. Limited partners (LPs) with better past performance invest in AVs with better average performance, even after conditioning on the general partners’ (GPs’) past records. This result is largely driven by preferential access of top LPs to top AVs. Returns in PE increasingly depend on the match between GPs and LPs and both parties’ outside options.