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Creative destruction, human capital accumulation, and growth in a digital economy

Published online by Cambridge University Press:  10 November 2023

Lewei Liao ,
Xuezheng Qin ,
Xiaolong Li  and
Liutang Gong
Lewei Liao
Affiliation:
Guanghua School of Management, Peking University, Beijing, China
Xuezheng Qin
Affiliation:
School of Economics, Peking University, Beijing, China
Xiaolong Li*
Affiliation:
School of Modern Post, Beijing University of Posts and Telecommunications, Beijing, China
Liutang Gong
Affiliation:
Guanghua School of Management, Peking University, Beijing, China
*
Corresponding author: Xiaolong Li; Email: tell714@gmail.com
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Abstract

The rapid development of the digital economy has highlighted the crucial role of data in economic growth. This study investigates the impact of two types of innovation on long-term growth by incorporating data into a model of creative destruction and knowledge accumulation. Unlike traditional factors, data exhibit nonrivalry between the two research and development (R&D) sectors, thereby influencing the growth rate of economic outputs simultaneously without interference. Our findings reveal the existence of a balanced growth path (BGP) in both the decentralized economy and the social planner’s economy. In horizontal innovation, data can be transformed into digital knowledge to promote the economic growth [Cong et al. (2021)]. In addition to horizontal innovation, the utilization of data in vertical innovation also enhances the success rate of innovation, with a gradual decrease in per capita data usage on the BGP. Moreover, as agents accumulate human capital, the economy achieves higher output levels, effectively addressing consumer privacy concerns. However, along the transitional path, insufficient data provision by both R&D sectors leads to lower economic growth rates or more intense economic fluctuations, necessitating policy interventions.

Keywords

DataVertical InnovationHuman CapitalPrivacyNonrivalryJ24O33O41
Type
Articles
Information
Macroeconomic Dynamics , First View , pp. 1 - 25
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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Footnotes

We thank Xiaoyong Cui, Chan Wang, Danxia Xie, Longtian Zhang, Pengfei Sun, Junru Chen and the anonymous associate editor and referees for their feedback. All remaining errors are the authors.

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