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

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  • Xingnan Xue
  • Peng Liang
  • Fujing Xue
  • Nan Hu
  • Ling Liu

Abstract

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 innovation behavior when the underlying firms are more financially constrained, and/or when the management is more myopic.

Suggested Citation

  • Xingnan Xue & Peng Liang & Fujing Xue & Nan Hu & Ling Liu, 2024. "Trade policy uncertainty and the patent bubble in China: evidence from machine learning," Asia-Pacific Journal of Accounting & Economics, Taylor & Francis Journals, vol. 31(5), pages 808-829, September.
    • Handle: RePEc:taf:raaexx:v:31:y:2024:i:5:p:808-829
    DOI: 10.1080/16081625.2023.2298934
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    Cited by:

    1. Pang, Silu & Hua, Guihong, 2024. "How does digital tax administration affect R&D manipulation? Evidence from dual machine learning," Technological Forecasting and Social Change, Elsevier, vol. 208(C).

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