Published May 2021 | Version v1
Journal article

Trade policy uncertainty and corporate innovation evidence from Chinese listed firms in new energy vehicle industry

  • 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.2021.105217

Additional 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.