Research on the Impact Effect of Data Factor Agglomeration Empowering Industrial Chain Resilience
DOI:
https://doi.org/10.54097/k0zz4263Keywords:
Big data policy, data factor agglomeration, difference-in-differences method, industrial chain resilience.Abstract
This study examines how data factor agglomeration empowers industrial chain resilience, using the establishment of national big data comprehensive pilot zones as a quasi-natural experiment. Based on panel data of 266 Chinese cities from 2009 to 2022, we apply the multi-period difference-in-differences (DID) and double machine learning (DDML) methods. Results show that data factor agglomeration significantly improves industrial chain resilience, mainly through technological innovation, digital infrastructure construction, and industrial structure rationalization. The positive effects are more pronounced in eastern coastal regions, areas with high market, and developed digital economies. These findings provide empirical support for big data policy optimization and industrial chain upgrading.
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