Data trading platforms and total factor productivity: Insights from biased technical progress

Abstract

This paper treats the establishment of data trading platforms as a quasi-natural experiment and employs the Staggered Difference-in-Differences model to examine its impact on enterprises total factor productivity (TFP). The results show that the construction of data trading platforms significantly improves enterprise TFP. Mechanism analysis indicates that this effect is achieved through the promotion of skill-biased and capital-biased technical progress within enterprises. Heterogeneity analysis indicates that the impact is stronger in enterprises with labor-intensive characteristics, higher digital transition, lower market competition, a high-tech orientation, more developed traditional factor market, or lower economic policy uncertainty. Further analysis shows that the impact of data trading platform construction on enterprise TFP exhibits spatial spillover effects in neighboring regions. This paper provides policy implications for improving the construction of data trading platforms.