智慧农业政策实施的新质劳动者需求效应研究——基于生成式人工智能大数据挖掘的经验证据

Abstract

As a crucial carrier for building up China's strength in agriculture driven by digital-intelligent technologies, smart agriculture is profoundly reshaping the structure of agricultural labor demand. Utilizing 12,189 local government policy documents and data on over 21 million online job vacancies from A-share listed companies from 2014 to 2022, this paper employs generative AI large language models to identify enterprises' demand for new quality labor. From the perspective of technology diffusion, it constructs a theoretical framework of "external policy incentives–internal resource allocation–emerging task creation" to systematically examine the impact mechanism and structural characteristics of smart agriculture policy implementation on the demand for new quality labor in digital enterprises and agricultural enterprises. The findings indicate that the implementation of smart agriculture policies significantly increases the demand for new quality labor in enterprises. The primary mechanism is that policy implementation guides enterprises to adjust internal resource allocation, creating new job tasks that integrate digital-intelligent technologies with agricultural production. The demand effect varies significantly across policy types and digital-intelligent infrastructure endowments. Structural analysis from the perspective of the "talent chain" reveals that digital enterprises exhibit a "pagoda-shaped" labor demand structure characterized by deep collaboration across the entire chain, while agricultural enterprises present a "spindle-shaped" structure dominated by midstream high-efficiency workers.