Published online by Cambridge University Press: 15 November 2021
The absence of observable innovation data for a firm often leads us to exclude or classify these firms as non-innovators. We assess the reliability of six methods for dealing with unreported innovation using several different counterfactuals for firms without reported R&D or patents. These tests reveal that excluding firms without observable innovation or imputing them as zero innovators and including a dummy variable can lead to biased parameter estimates for observed innovation and other explanatory variables. Excluding firms without patents is especially problematic, leading to false-positive results in empirical tests. Our tests suggest using multiple imputation to handle unreported innovation.
The simulation code and R&D imputed data are available at www.wendunwang.weebly.com/research.html. An early version of this article circulated under the title “Missing Innovation around the World.” We appreciate discussions and advice from an anonymous referee, Renee Adams, Sumit Agarwal, Benito Arrunda, Richard Boylan, Lora Dimitrova, Bronwyn Hall, Jarrad Harford (the editor) Gilles Hilary, Yael Hochberg, Qin Li, Gustavo Manso, Jiaming Mao, Naci Mocan, Randall Morck, Ivan Png, Wenlan Qian, Amit Seru, Vijay Singal, Noah Stoffman, Xuan Tian, and Rosemarie Ziedonis. We are grateful to seminar participants at City University of Hong Kong, Hong Kong Polytechnic University, Louisiana State University, Maastricht University, the National University of Singapore, Rotterdam School of Management, Temple University, the University of Queensland, the University of South Carolina, the University of Toronto, Virginia Tech, Wilfrid Laurier University, University of Technology Sydney, and the 2020 Price College of Business (PCOB) Valuation & Accounting for Intangible Assets Workshop. Koh acknowledges financial support from the ESSEC Foundation and CY Initiative of Excellence.