Data elements and innovation dynamics: evidence from China’s national big data comprehensive pilot

Data-driven innovation is transforming manufacturing systems. This study examines the effects of China’s big data comprehensive pilot (BDCP) zone policy on firm innovation, using the panel dataset of 2,393 manufacturing firms from 2010 to 2023. Employing a time-varying difference-in-differences (DID) framework as our baseline model, we estimate how BDCP influences innovation, distinguishing between firms’ patent applications and granted patents. Then, we apply the Callaway and Sant’Anna DID (CSDID) estimator to demonstrate how this policy drives innovation dynamics, accounting for variation in treatment timing and temporal dynamics. Results suggest that BDCP fosters both patent metrics, where patent applications appear to be more sensitive to the policy incentive. After robustness checks, our heterogeneity analysis further reveals systematic differences in treatment effects across firm characteristics including ownership type, technology development, and factor intensity. By integrating timeliness and substantive validity within the two interconnected patent metrics, this study conceptualizes innovation as a dynamic transition process from patent application to grant.