数据要素驱动农业企业新质生产力发展——理论机制与实证检验

Data Elements Driving the Development of New Quality Productivity in Agricultural Enterprises—Theoretical Mechanisms and Empirical Tests

摘要: 在数字经济与农业现代化深度融合的背景下，如何破解农业生产“靠天吃饭”的资源约束，培育新质生产力，成为农业强国建设的关键议题。作为新型生产要素，数据被视为优化农业价值链的核心引擎，但其微观驱动机制及边界条件尚不明晰。基于此，本文以2012~2024年沪深A股农业上市企业为样本，构建“数据要素–企业动态能力–新质生产力”框架，从信息不对称缓解、生物性资产管理优化、自然风险抵御增强三条路径阐释其驱动机制，实证检验数据要素的驱动效应及其作用机理。研究发现：(1) 数据要素应用显著促进了农业企业新质生产力提升，该效应经多重稳健性检验依然成立；(2) 动态能力在这一过程中发挥了部分中介作用，揭示了其转化路径；(3) 企业ESG表现正向调节数据要素的驱动效应，高ESG表现企业的数据边际生产力提升50%以上；(4) 异质性分析表明，数据要素驱动作用在非国有企业和h核心农业生产领域更为显著，其中畜牧业驱动效应尤为突出，种植业因生产周期长且当前样本规模有限，效应尚不显著。本文为农业数字化政策设计和企业数据战略制定提供了微观实证依据。

Abstract: Against the backdrop of the deep integration of digital economy and agricultural modernization, addressing the resource constraints arising from agriculture’s reliance on weather and cultivating new quality productive forces (NQPF) has emerged as a central challenge in China’s pursuit of agricultural strength. As a novel factor of production, data is regarded as the core engine for optimizing the agricultural value chain; however, its micro-level driving mechanisms and boundary conditions remain underexplored. Drawing on data from agricultural listed companies on the Shanghai and Shenzhen A-share markets over the period 2012~2024, this study constructs a theoretical framework linking “data elements - enterprise dynamic capabilities - new quality productive forces.” It elucidates three specific driving pathways—reducing information asymmetry, optimizing biological asset management, and enhancing resilience against natural risks—and empirically tests the driving effect of data elements and its underlying mechanisms. The findings reveal that: (1) Data element application significantly promotes the development of NQPF in agricultural enterprises, and this effect remains valid after multiple robustness checks; (2) Enterprise dynamic capability plays a partial mediating role, revealing its transformation pathway; (3) ESG performance positively moderates this promoting effect, with the marginal productivity of data increasing by more than 50% in enterprises with high ESG performance; (4) The driving effect is more pronounced in non-state-owned enterprises and core agricultural production sectors. Notably, the effect is particularly strong in the livestock industry, whereas the effect in the crop farming sector is not statistically significant, likely due to longer production cycles and limited sample size among listed companies. These findings offer micro-level empirical evidence for agricultural digitalization policy design and enterprise data strategy formulation.