数据财政的前景研究：企业需求视角

Abstract

The digital economy has propelled frontier institutional explorations into data finance, thereby bringing the demand for public data into focus. Given that the realization of data value is premised on human capital matching, this paper examines enterprises' demand for public data from the unique perspective of hiring decisions. Theoretical analysis indicates that as local governments grant public data transaction authorization, enterprises will expand the relative share of data analysts in total employment, an effect that exhibits heterogeneity across different operational models. Drawing on big data from recruitment platforms, empirical findings reveal that the implementation of authorized operation of public data by local governments significantly increases enterprises' hiring scale for data analysis roles. An anatomy of the authorization clauses demonstrates that the unified authorization model is optimal, and the state-owned agency model is preferable. Regarding corporate hiring dynamics, the proportional increase in recruitment for high-skilled positions surpasses that for low-skilled ones, with wages exhibiting a similar pattern; furthermore, employers prioritize work experience over educational credentials. The reform has also driven an increase in the relevant revenues of local governments. From the perspective of the data analysis value chain, the policy yields no significant impact on upstream roles but exerts a positive effect on downstream ones. The expanded recruitment for data-related roles is accompanied by the downsizing of other R&D positions, reflecting a structural adjustment in R&D focus. This paper aims to extend the feasibility study of data finance from conceptual exploration to concrete analysis. It holds significant implications for advancing fiscal system innovation in the digital economy era and strengthening the mechanisms for the sustainability of local public finance.