How do data trading centers affect firm innovation: quasi-experimental evidence from China

This study examines the impact of efficient data transactions on firm innovation. We develop a theoretical framework showing that transaction frictions in processed data markets impede the circulation of data bundles and that efficient transaction mechanisms introduced by Data Trading Centres (DTCs) promote firm innovation by reducing data acquisition costs and strengthening learning from external knowledge. Exploiting the staggered establishment of DTCs in China as a quasi-natural experiment and employing a stacked difference-in-differences estimator, we find that DTCs significantly increase firms’ innovation output. Mechanism analyses show that these effects are stronger for firms with greater dependence on externally sourced processed data and for those with greater opportunities for knowledge spillovers, providing direct evidence for the production cost-saving and learning channels predicted by the theoretical model. Beyond increasing innovation quantity, DTCs also enhance the quality and novelty of firms’ innovations by increasing patent citations, broadening patent knowledge breadth, and raising the adjusted citation-based disruptiveness (ACD) index, suggesting that DTCs encourage more original technological exploration.