Trade policy uncertainty and corporate innovation evidence from Chinese listed firms in new energy vehicle industry
Creators
- 1. School of Economics and Management, North China Electric Power University, Beijing 102206 (China)
- 2. School of Business,Qingdao University, Qingdao 266071, Shandong Province (China)
Description
Highlights: • Uncertainty concerning trade policy is positively associated with more R&D and patents. • Government subsidy mitigates the strength of the relation between trade policy uncertainty and corporate innovation. • Managerial ownership mitigates the strength of the relation between trade policy uncertainty and corporate innovation. Using a sample of Chinese listed firms in new energy vehicle industry over the period of 2007 to 2018, we examine the impact of trade policy uncertainty on corporate innovation. We find that uncertainty concerning trade policy is positively associated with more R&D and patents. Moreover, government subsidy and managerial ownership mitigate the strength of the relation between trade policy uncertainty and corporate innovation. Our conclusions are robust to a variety of sensitivity tests and we use two-stage least squares (2SLS) instrumental variable approach to address the potential endogeneity concerns.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.eneco.2021.105217Additional details
Identifiers
- DOI
- 10.1016/j.eneco.2021.105217;
- PII
- S0140988321001225;
Publishing Information
- Journal Title
- Energy Economics
- Journal Volume
- 97
- Journal Page Range
- vp.
- ISSN
- 0140-9883
- CODEN
- EECODR
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 53107776
- Subject category
- S29: ENERGY PLANNING, POLICY AND ECONOMY;
- Descriptors DEI
- AUTOMOTIVE INDUSTRY; ENERGY POLICY; FINANCIAL INCENTIVES; LEAST SQUARE FIT; OWNERSHIP; PATENTS; SENSITIVITY; TRADE; VEHICLES
- Descriptors DEC
- DOCUMENT TYPES; GOVERNMENT POLICIES; INDUSTRY; MATHEMATICAL SOLUTIONS; MAXIMUM-LIKELIHOOD FIT; NUMERICAL SOLUTION
Optional Information
- Copyright
- Copyright (c) 2021 Elsevier B.V. All rights reserved.