Deleting Unreported Innovation

Journal of Financial and Quantitative Analysis (JFQA), Forthcoming

62 Pages Posted: 4 Mar 2021

See all articles by Ping-Sheng Koh

Ping-Sheng Koh

ESSEC Business School

David M. Reeb

National University of Singapore

Elvira Sojli

UNSW Australia Business School, School of Banking and Finance

Wing Wah Tham

University of New South Wales (UNSW)

Wendun Wang

Erasmus University Rotterdam (EUR) - Department of Econometrics

Date Written: February 27, 2021

Abstract

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.

Keywords: Bias, Listwise Deletion, Innovation, Measuring Innovation, Multiple Imputation, Non-patenting Firms, Unreported R&D, Patents

Suggested Citation

Koh, Ping-Sheng and Reeb, David M. and Sojli, Elvira and Tham, Wing Wah and Wang, Wendun, Deleting Unreported Innovation (February 27, 2021). Journal of Financial and Quantitative Analysis (JFQA), Forthcoming, Available at SSRN: https://ssrn.com/abstract=3794441

Ping-Sheng Koh

ESSEC Business School ( email )

5 Nepal Park
139408
Singapore
+65 6413 9737 (Phone)

David M. Reeb

National University of Singapore ( email )

Mochtar Riady Building
15 Kent Ridge Drive
Singapore, 119245
Singapore

HOME PAGE: http://www.davidreeb.net

Elvira Sojli (Contact Author)

UNSW Australia Business School, School of Banking and Finance ( email )

Sydney, NSW 2052
Australia

Wing Wah Tham

University of New South Wales (UNSW) ( email )

Kensington
High St
Sydney, NSW 2052
Australia

Wendun Wang

Erasmus University Rotterdam (EUR) - Department of Econometrics ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

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