Understanding the impact of trade policy effect uncertainty on firm-level innovation investment: A deep learning approach - CHEN, Daniel. (Institutional Knowledge at Singapore Management University, 2023)
Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique Chinese public firms during the period of 2007-2019. The results of econometric analyses show that firms experiencing higher TPEU tend to reduce innovation investments. Furthermore, this effect is stronger for firms within industries with lower competition, involving more foreign sales, and not owned by the state. Our inferences persist when utilizing the abnormal TPEU derived from a two-stage analysis, and when filtering out other potential confounding effects. We further fortify the causal effect of TPEU by showing its impact on innovation investments was stronger after the outbreak of the ongoing U.S.-China trade war since 2018. These findings help to explain prior mixed findings by demonstrating that policy effect uncertainty, in contrast to policy state uncertainty, exerts a salient influence on firms’ innovation investment decisions, and by highlighting resource dependence factors as important contingencies.