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

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

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 uncerta...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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