<p>This study examines the innovative aspects of Chinese provinces, focusing on the effects of the “Belt and Road” Initiative (BRI) on growth and innovation, influenced by factors such as research and development, patents, human capital and technology market transactions across provincial economies, using annual data from 2010 to 2023. The analysis utilizes advanced panel data estimators that include the Pooled Mean Group (PMG) and Augmented Mean Group (AMG) and produces robust outcomes in the presence of cross-sectional dependence and slope heterogeneity. Additionally, Dumitrescu and Hurlin panel causality is employed to ascertain the relationship among the variables. PMG estimations indicate a positive and significant correlation between innovation input and output index factors in both the long and short run. AMG estimations yield positive and significant results in the long term; however, the short-term estimations present mixed outcomes. Finally, this study recommends some policy implications with an emphasis on modernizing local industries and promoting new and light manufacturing sectors endorsing economic policies, such as tax breaks and financial incentives that stimulate technological innovation and bring transformation in economies. Funding for branding, international trade, technological advancements and invention should be given top priority by government. Moreover, public service platforms should foster cooperation and expansion.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Bridging innovation and growth: the Belt and Road Initiative’s impact on Chinese provincial economies

  • Preethu Rahman,
  • Mohammad Musa

摘要

This study examines the innovative aspects of Chinese provinces, focusing on the effects of the “Belt and Road” Initiative (BRI) on growth and innovation, influenced by factors such as research and development, patents, human capital and technology market transactions across provincial economies, using annual data from 2010 to 2023. The analysis utilizes advanced panel data estimators that include the Pooled Mean Group (PMG) and Augmented Mean Group (AMG) and produces robust outcomes in the presence of cross-sectional dependence and slope heterogeneity. Additionally, Dumitrescu and Hurlin panel causality is employed to ascertain the relationship among the variables. PMG estimations indicate a positive and significant correlation between innovation input and output index factors in both the long and short run. AMG estimations yield positive and significant results in the long term; however, the short-term estimations present mixed outcomes. Finally, this study recommends some policy implications with an emphasis on modernizing local industries and promoting new and light manufacturing sectors endorsing economic policies, such as tax breaks and financial incentives that stimulate technological innovation and bring transformation in economies. Funding for branding, international trade, technological advancements and invention should be given top priority by government. Moreover, public service platforms should foster cooperation and expansion.