Abstract: The dilemma of being “large but not strong” in manufacturing and the inefficiency in the service industry have become one of the challenges facing China's industrial transformation and upgrading towards highquality development Over the past decade，a new round of technological revolution and industrial transformation has injected new vitality into productivity development The innovative activities and knowledge dissemination involved in cuttingedge technologies and emerging industries have transcended the realm of traditional productivity reform Especially when data becomes a factor，serving both as means of production and as objects of labor involved in production and transaction processes，it greatly enriches the types of means of production and labor objects，forming a new productive force characterized by the essence of renewing quality and achieving higher quality This results in the coordination and unity of “quantity and quality” in productivity development
New quality productive forces，characterized by the improvement of total factor productivity （TFP），is centered on innovation，where data elements play a crucial role in enhancing both innovation levels and TFP This study takes the nationallevel big data comprehensive pilot zone as a quasinatural experiment Based on panel data of manufacturing and service listed companies in China from 2009 to 2021，a differenceindifferences model is employed to identify the causal effects and mechanisms of data elements on enterprise TFP The research findings indicate that data elements significantly enhance enterprise TFP，with a greater impact on TFP in the service sector compared to the manufacturing sector This conclusion remains robust after a series of robustness checks Mechanism analysis results suggest that data elements mainly exert an indirect impact on enterprise TFP through digital transformation and innovation Moreover，the moderation effects indicate that industrial integration and data elements jointly play a significant role in improving enterprise TFP The effect is not significantly different between moderatelevel and highlevel industrial integration areas，whereas it is not significant in lowlevel industrial integration areas
In order to fully leverage the role of new quality productive forces in enhancing total factor productivity，based on the findings of our research，we propose the following policy recommendationsFirstly，we should vigorously develop new quality productive forces and accelerate the digital transformation of enterprises Secondly，we should accelerate the empowerment of traditional industries with new quality productive forces，promoting their transformation and upgrading through the widespread application of intelligent technologies and green technologies Thirdly，we should adhere to the leadership of scientific and technological innovation in the development of new quality productive forces

Key words: data elements, new quality productive forces, total factor productivity, innovation, industry convergence

摘要： 新质生产力以全要素生产率提升为标志、核心是创新，数据要素对于提升创新水平与全要素生产率具有重要意义。本文将国家级大数据综合试验区试点作为一次准自然实验，基于我国2009—2021年制造业和服务业上市公司面板数据，利用双重差分模型识别数据要素影响企业全要素生产率的因果效应与作用机制。研究发现：数据要素显著提升了企业全要素生产率，且对服务业企业全要素生产率的影响大于制造业企业；经过一系列稳健性检验后上述结论依然成立。机制分析结果表明，数据要素主要通过数字化变革和创新对企业全要素生产率产生间接影响。调节效应表明，产业融合与数据要素会共同发挥作用、提升企业全要素生产率，且在中、高产业融合区域的作用效果没有显著差异，在低产业融合区域的效果不显著。

关键词: 数据要素, 新质生产力, 企业全要素生产率, 创新, 产业融合