Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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