Abstract:

Rural labor mobility is a crucial component in building China's "dual-circulation" development paradigm and serves as a key driver for advancing agricultural and rural modernization. Based on interprovincial rural labor migration data from 2006 to 2021 and China's modernization strategic goals for 2035 and 2050, this study employs a modified gravity model to construct an interprovincial rural labor flow matrix. Integrating social network analysis and grey prediction modeling, the findings are as follows: ① The spatial correlation intensity of interprovincial rural labor mobility in China has evolved from a "tripartite divergence" pattern toward a "bipolar divergence" configuration, with a trend toward eventual "monocentric" integration. Jiangsu and Anhui exhibit the highest spatial correlation intensity in rural labor flows.② The overall network density and network efficiency of interprovincial rural labor mobility show fluctuating growth, with network connectivity consistently at 1. The network hierarchy demonstrates a "stepwise" upward trend, though the stability of spatial correlation intensity requires reinforcement. Eastern coastal regions display higher in-degree centrality, serving as primary destinations for labor inflows, while most western provinces show higher out-degree centrality, functioning as source regions. Jiangxi and Guangdong exhibit both betweenness centrality and closeness centrality above the national average, playing pivotal "bridging" roles in cross-provincial labor mobility.③ Cohesive subgroup analysis reveals that with the gradual improvement of transportation infrastructure and labor markets, coupled with economic restructuring, the economic and geographical characteristics of China's interprovincial labor mobility spatial pattern have progressively weakened.④ Factors such as rural income disparities, R&D investment gaps, and fiscal agricultural expenditure difference exert significantly negative effects on the spatial correlation of interprovincial rural labor mobility. In contrast, land transfer variations, rural fixed asset investment differences, and industrial structure disparities demonstrate significantly positive impacts, whereas wage level and population size show no statistically significant influence.

Key words: rural labour mobility, gravity model, social network analysis, MRQAP

摘要：

农村劳动力流动是我国“双循环”新发展格局构建的重要内容，是促进农业农村现代化的重要抓手。基于2006—2021年中国省际农村劳动力跨省流动数据，结合我国2035年、2050年现代化战略目标，运用修正的引力模型构建省际农村劳动力流动矩阵，并采用社会网络分析和灰色预测模型，分析中国省际农村劳动力流动空间格局的演化特征和驱动因素，得出如下结果：①我国省际农村劳动力流动空间关联强度由“三极分化”格局逐渐向“两极分化”格局演变，并趋向于“一元化”格局，江苏和安徽的农村劳动力流动空间关联强度最高；②省际农村劳动力流动整体网络密度与网络效率波动上升，网络关联度均为1，网络等级整体呈“阶梯形”上升特征，空间关联强度的稳定性有待增强。东部沿海地区点入度较高，是劳动力流动的主要流入地；大部分西部省份点出度较高，是劳动力流动的流出地区；江西、广东等地的中间中心度与接近中心度均高于全国平均值，对劳动力跨省流动发挥着主要的桥梁作用；③凝聚子群分析表明，随着交通设施与劳动力市场的逐步完善以及经济结构的转型升级，我国省际农村劳动力流动空间格局的经济和地域特征逐渐减弱；④农村居民收入差异、R&D投入差异和财政支农差异等因素对我国省际农村劳动力流动空间关联性具有显著的负向影响，土地流转差异、农村固定资产投资差异和产业结构差异因素则有显著的正向影响，工资水平差异和人口规模差异无显著影响。

关键词: 农村劳动力流动, 引力模型, 社会网络分析, 多元回归二次指派程序