Trade policy uncertainty and the patent bubble in China: evidence from machine learning

• Main Authors: XUE, Xingnan, LIANG, Peng, XUE, Fujing, HU, Nan, LIU, Ling
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Machine learning; patent bubble; Resource dependence theory; strategic innovation management; Trade policy uncertainty; Asian Studies; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8662
• Institution: Singapore Management University

This paper draws upon resource dependence theory and investigates how trade policy uncertainty affects firm strategic innovation management in China. Adopting a novel machine learning approach called Word2Vec, we construct and validate a measure of firm-level managers’ perceived trade policy uncertainty (TPU). We find that TPU has a positive effect on the number of total patent applications, but this positive effect is totally driven by low-quality patents instead of high-quality patents. Moreover, we document that firms have stronger incentives for such strategic inno-vation behavior when the underlying firms are more financially con-strained, and/or when the management is more myopic.