收稿日期: 2023-03-20
修回日期: 2023-05-18
网络出版日期: 2024-01-05
基金资助
国家社会科学基金项目(18XMZ059);国家社会科学基金项目(20BMZ149)
Spatiotemporal pattern and dynamic evolution of rural non-farm employment: A case of Qinghai Province
Received date: 2023-03-20
Revised date: 2023-05-18
Online published: 2024-01-05
为了准确把握县域尺度下青海省乡村非农就业水平的时空格局及动态演进趋势，促进共同富裕的实现，基于2010—2020年青海省43个县域单元的面板数据，借助全局趋势分析、标准差椭圆、Moran’s I指数等方法揭示青海省乡村非农就业水平的时空演变特征，并进一步采用Kernel密度估计、马尔科夫链考察其动态演进趋势。结果表明：（1） 青海省乡村非农就业水平总体呈现出波动上升趋势，根据增长速度划分为快速增长阶段和缓慢增长阶段。（2） 在空间分布格局上，空间分布整体表现为“东、西部高中部低”“北高南低”，空间格局呈现出由“正东—正西”分布向“偏东北—偏西南”方向偏移趋势。（3） 在空间相关性上，乡村非农就业水平存在显著的空间正自相关性，“高-高”集聚和“低-低”集聚的板块特征显著。（4） 在分布动态演进上，乡村非农就业水平存在稳定的俱乐部趋同现象，乡村非农就业水平在发展过程中存在“空间溢出”效应。
高福鑫 , 赵玲 , 魏琼 . 乡村非农就业水平的时空格局及动态演进——以青海省为例[J]. 干旱区地理, 2023 , 46(12) : 2111 -2119 . DOI: 10.12118/j.issn.1000-6060.2023.123
This study aims to comprehensively understand the spatiotemporal patterns and dynamic evolution of rural nonfarm employment in Qinghai Province of China at the county level, contributing to the realization of “common prosperity”. Leveraging panel data spanning 2010 to 2020 from 43 county-level units in Qinghai Province, China, we employ global trend analysis, standard deviation ellipse, and Moran’s I index to unveil the spatiotemporal evolution characteristics of rural nonfarm employment from both static and dynamic perspectives. In addition, we use kernel density estimation and Markov chain to explore the dynamic evolution characteristics. Our findings indicate the following key insights: (1) The overall level of rural nonfarm employment exhibits a fluctuating upward trend, marked by distinct stages of rapid and slow growth driven by robust economic development and effective employment policies. (2) Spatially, the distribution pattern is characterized by a “high in the eastern and western, low in the middle” trend, with a noticeable shift from “due east-due west” to “northeast-southwest”. (3) Spatial correlation analysis reveals that neighboring counties, sharing similar resource endowments, nonfarm industry development concepts, and interactive effects in employment policy formulation, exhibit significant positive spatial autocorrelation. Notably, “high-high” and “low-low” agglomerations are prominent. (4) Dynamic evolution, as evidenced by kernel density estimation, indicates a narrowing of regional differences in rural nonfarm employment across Qinghai Province. Despite stable “club convergence” phenomena due to terrain conditions, resource distribution, and economic development disparities among the 43 counties, spatial factors play a crucial role that cannot be overlooked in enhancing rural nonfarm employment. The development process also reflects a noteworthy “spatial spillover” phenomenon in Qinghai Province.
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