Intelligent Development and the Reshaping of Human Capital Allocation: A Systemic Perspective

The advent of intelligent development in the economic system has brought about revolutionary innovations that have raised scholars' expectations for improving the misallocation of human capital. With the availability of observational data, improved simulations, and advances in algorithms, resources can be managed in an efficient and intelligent manner to meet various needs (Wang et al., 2018; Zhao et al., 2021). Enterprises can identify their human resource needs and target labor positions more effectively, resulting in cost and time advantages (Yuan et al., 2022). Machine learning has effectively addressed the problem of incomplete information and externalities, eliminating information islands and breaking down barriers that hinder the free flow of information and resources (Bo, 2020). From a systemic perspective, the disruption of the labor market by intelligent development represents a reshaping of the human capital demand structure. The application of intelligent manufacturing has increased job skill requirements and further accelerated the polarization of employment, necessitating workers to either adjust their jobs to both ends of the skill requirements spectrum or extend their educational attainment and participate in new skills training to improve their skills, both of which may improve the misallocation of human capital (Acemoglu and Restrepo, 2018; Balsmeier and Woerter, 2019; Dixon et al., 2021). Jaimovich (2011) argued that the lack of industrial categories hinders the effective classification of talent, while innovative activities can expand the range of productive activities and enable agents to better match activities according to heterogeneous skills, demonstrating the spillover effects of 'job innovation' and 'career innovation' brought about by intelligence. Furthermore, the intelligent application of enterprises considers the optimal structure and composition of factors that form the maximum productivity, improving the matching degree between production factors and reducing factor misallocation (Ballestar et al., 2022).