中国省际农村劳动力流动空间格局的演化特征及驱动因素

doi:10.3969/j.issn.1004-9479.2025.06.20230705

摘要/Abstract

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

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

李子成, 王珏, 王稳妮. 中国省际农村劳动力流动空间格局的演化特征及驱动因素[J]. 世界地理研究, 2025, 34(6): 120-134.

Zicheng LI, Jue WANG, Wenni WANG. Evolutionary characteristics and driving factors of spatial patterns of interprovincial rural labour mobility in China[J]. World Regional Studies, 2025, 34(6): 120-134.

图/表 7

参考文献 41

1	SJAASTAD L. The costs and returns of human migration. Journal of Political Economy,1962,70(5):80-93.
2	PIORE M. Birds of passage: Migrant Labor In Industrial Societies.Cambridge:Cambridge University Press,1979.
3	STARK O, BLOOM D. The new economics of labor migration. American Economic Review,1985,75(2):173-178.
4	KRUGMAN P. Increasing returns and economic geography. Journal of Political Economy,1991,99(3):483-499.
5	BHAGAT B. Socioeconomic determinants of temporary labour migration in India. Asian Population Studies,2013,9(2):175-195.
6	BARBOSA H, BARTHELEMY M, GHOSHAL G,et al. Human mobility: Models and applications. Physics Reports,2018(734):1-74.
7	CHEN C. Analysis of the driving factors of U.S.domestic population mobility.Physica A: Statistical Mechanics and its Applications,2020,539(C):122984-122984.
8	BIN D. Research on network patterns and influencing factors of population flow and migration in the Yangtze River Delta Urban Agglomeration. Sustainability,2020,12(17):6803-6803.
9	廖宏斌.农地产权制度变迁与农村劳动力流动:一个纵向考察与分析.四川大学学报(哲学社会科学版),2021(04):73-80.
	LIAO H.The change of rural property rights and rural labor mobility:A longitudinal investigation and analysis. Journal of Sichuan University (Philosophy and Social Science Edition),2021(04):73-80.
10	张琛,孔祥智.农村劳动力流动的演变历程、趋势与政策建议.中国特色社会主义研究,2022(03):31-38.
	ZHANG C, KONG X.Evolution of rural labour mobility, trends and policy recommendations.Socialism with Chinese Characteristics Studies,2022(03):31-38.
11	陈钊,陆铭.从分割到融合:城乡经济增长与社会和谐的政治经济学.经济研究,2008(01):21-32.
	CHEN Z, LU M.From segmentation to integration: The political economy of Urban-Rural economic growth and social harmony.Economic Research,2008(01):21-32.
12	张军涛,游斌,朱悦.农村劳动力流动对城乡二元经济结构转化的影响——基于经济增长中介效应的分析.经济问题探索,2021(06):125-137.
	ZHANG J, YOU B, ZHU Y.The influence of mobility of rural labor on the transformation of urban: rural dual economic structure: Based on the intermediary effect of economic growth.Inquiry Into Economic Issues,2021(06):125-137.
13	谭林,崔静.农村劳动力流动与三次产业结构调整的内生性研究.中国农业资源与区划,2017,38(03):14-19.
	TAN L, CUI J.The endogenous nature of the rural labor force flow and the adjustment of the three industrial structure.Journal of China Agricultural Resources and Regional Planning,2017,38(03):14-19.
14	苗海民,朱俊峰.从乡土中国到城乡中国——农村劳动力选择性流动抑制了土地流转吗?.世界经济文汇,2021(06):72-95.
	MIAO H, ZHU J.From rural China to urban and rural China: Does the selective movement of rural labor inhibit land transfer?.World Economic Papers,2021(06):72-95.
15	尹志超,刘泰星,张诚.农村劳动力流动对家庭储蓄率的影响.中国工业经济,2020(01):24-42.
	YIN Z, LIU T, ZHANG C. The influence of rural labor migration on household saving rate.China Industrial Economics,2020(01):24-42.
16	韩佳丽.深度贫困地区农村劳动力流动减贫的理论逻辑与实践路径.云南民族大学学报(哲学社会科学版),2020,37(04):92-102.
	HAN J.Theoretical logic and practical path of rural labor mobility for poverty alleviation inimpoverished areas.Journal of Yunnan Nationalities University: Social Sciences,2020,37(04):92-102.
17	曾永明,钟子康,刘厚莲.网络视角下中国跨省人口流动格局跃迁及驱动机制:1991—2020年.中国人口·资源与环境,2023,33(03):160-170.
	ZENG Y, ZHONG Z, LIU H.China's interprovincial population mobility patterns and driving mechanisms from the network perspective:1991-2020.Chinese Journal of Population,Resources and Environment,2023,33(03):160-170.
18	尹志超,刘泰星,严雨.劳动力流动能否缓解农户流动性约束——基于社会网络视角的实证分析.中国农村经济,2021(07):65-83.
	YIN Z, LIU T, YAN Y.Can labor migration alleviate the constraints of farmers' mobility? An empirical analysis from the perspective of social network.Chinese Rural Economy,2021(07):65-83.
19	张子贤,孙伯驰.公共服务供给效率对城乡收入差距的影响及其提升路径.求索,2022(01):152-164.
	SUN Z, SUN B.The Impact of public service supply efficiency on the rural-urban income gap and its path to enhancement.Seeker,2022(01):152-164.
20	韩佳丽.贫困地区农村劳动力流动减贫的路径比较研究.中国软科学,2019(12):43-52.
	HAN J. A comparative study on the paths of rural labor mobility for poverty reduction in poor areas. China Soft Science,2019(12):43-52.
21	王西玉,崔传义,赵阳, 等.中国二元结构下的农村劳动力流动及其政策选择.管理世界,2000(05):61-69.
	WANG X, CUI C, ZHAO Y, et al. Rural labour mobility and its policy options under China's dual structure.Management World,2000(05):61-69.
22	刘玉.中国流动人口的时空特征及其发展态势.中国人口·资源与环境,2008(01):139-144.
	LIU Y. The spatio-temporal distribution of floating population and its development tendency in China.Chinese Journal of Population,Resources and Environment,2008(01):139-144.
23	薛彩霞,王录仓,常飞.中国城市流动人口时空特征及影响因素.地域研究与开发,2020,39(02):157-162.
	XUE C, WANG L, CHANG F.Spatio-temporal characteristics and influencing factors of urban floating population in China.Areal Research and Development,2020,39(02):157-162.
24	鲁永刚,张凯.地理距离、方言文化与劳动力空间流动.统计研究,2019,36(03):88-99.
	LU Y, ZHANG K.Geographic distance,dialect difference,and spatial labor mobility.Statistical Research,2019,36(03):88-99.
25	王怡,周晓唯.基于社会网络分析法的脱贫人口空间关联特征研究.财经问题研究,2019(10):19-27.
	WANG Y, ZHOU X.Study on the spatial correlation characteristics of the population out of poverty based on the social network analysis method.Research on Financial and Economic Issues,2019(10):19-27.
26	陈典,郑晓冬,上官霜月, 等.社会网络对流动人口社会融入的影响——基于中国劳动力动态调查的实证分析.南方人口,2021,36(06):16-30.
	CHEN D, ZHENG X, SHANGGUAN S, et al.The impact of social network on social inclusion of the floating population: An empirical analysis based on China labor-force dynamic survey.South China Population,2021,36(06):16-30.
27	臧玉珠,周生路,周兵兵, 等.1995—2010年中国省际人口迁移态势与空间格局演变——基于社会网络分析的视角.人文地理,2016,31(04):112-118.
	ZANG Y, ZHOU S, ZHOU B, et al.The Volume changes and spatial pattern dynamics of China's interprovincial migration: A perspective of social network analysis.Human Geography,2016,31(04):112-118.
28	汤兆云,张憬玄.新生代农民工的社会网络和社会融合——基于2014年流动人口动态监测调查江苏省数据的分析.江苏社会科学,2017(05):8-15.
	TANG Z, ZHANG J.Social networks and social integration of the new generation of migrant workers - An analysis based on the 2014 migrant population dynamic monitoring survey data in Jiangsu Province.Jiangsu Social Sciences,2017(05):8-15.
29	李天籽,陆铭俊.中国人口流动网络特征及影响因素研究——基于腾讯位置大数据的分析.当代经济管理,2022,44(02):1-9.
	LI T, LU M.Research on the characteristics and influencing factors of China's population flow network —Based on tencent location big data analysis.Contemp Econ Manag,2022,44(02):1-9.
30	王婧,刘奔腾,李裕瑞.京津冀人口时空变化特征及其影响因素.地理研究,2018,37(09):1802-1817.
	WANG J, LIU B, LI Y.Spatial-temporal characteristics and influencing factors of population distribution and floatingchanges in Beijing-tianjinhebei region.Geographical Research,2018,37(09):1802-1817.
31	温锋华,古恒宇,许志斌.中国省际人口迁移网络结构及时空演化.经济地理,2022,42(06):13-21.
	WEN F, GU H, XU Z.Network structure and spatiotemporal evolution of China s interprovincial migration.Economic Geography,2022,42(06):13-21.
32	朱孟珏,李芳.1985—2015年中国省际人口迁移网络特征.地理科学进展,2017,36(11):1368-1379.
	ZHU M, LI F.Spatial characteristics of China's interprovincial migration network during 1985-2015.Progress in Geography,2017,36(11):1368-1379.
33	朱虎啸,欧向军,李自鹏, 等.长三角地区城市空间相互作用的多维分析.地理与地理信息科学,2022,38(04):50-57.
	ZHU H,OU X, LI Z, et al.Multidimensional analysis of urban spatial interaction in the Yangtze River Delta.Geography and Geo-information Science,2022,38(04):50-57.
34	邵小彧,翁宗源,苗青松, 等.区域绿色技术创新产出网络演化及要素分析——来自长江经济带城市群的证据.地理与地理信息科学,2022,38(04):40-49.
	SHAO X, WEN Z, MIAO Q, et al.Evolution and element analysis of regional green technology innovation output network: Evidence from the urban agglomeration of the Yangtze River Economic Belt.Geography and Geo-information Science,2022,38(04):40-49.
35	郭连成,左云.中国与欧亚经济联盟国家的贸易效率及潜力研究——基于随机前沿引力模型的分析.经济问题探索,2021(03):100-110.
	GUO L, ZUO Y.Research on trading efficiency between China and countries of eurasian economic union and potentials: Based on stochastic frontier gravity model.Inquiry Into Economic Issues,2021(03):100-110.
36	黄宗晔,杨静.方言对省际人口迁移的影响.人口研究,2020,44(04):89-101.
	HUANG Z, YANG J.The impact of dialect on inter-provincial migration in China.Population Research,2020,44(04):89-101.
37	杨桂元,吴齐,涂洋.中国省际碳排放的空间关联及其影响因素研究——基于社会网络分析方法.商业经济与管理,2016(04):56-68.
38	刘华军,彭莹,裴延峰, 等.全要素生产率是否已经成为中国地区经济差距的决定力量?.财经研究,2018,44(06):50-63.
	LIU H, PENG Y, PEI Y, et al.Has the total factor productivity become the determinant of regional economic disparity in China.Journal of Finance and Economics,2018,44(06):50-63.
39	盛广耀.中国省际人口流动网络的演化及其解释.中国人口·资源与环境,2018,28(11):1-9.
	SHENG G.Study on the evolution and explanation of inter-provincial population flow network in China.Chinese Journal of Population,Resources and Environment,2018,28(11):1-9.
40	尹建军,胡静,黄宇瑄.长江中游城市群城市韧性时空演化特征及动态预测研究.长江流域资源与环境,2023,32(11):2312-2325.
	YIN J, HU J, HUANG Y.Spatial-temporal evolution characteristics and dynamic prediction of urban resilience in urban agglomerations in middle reaches of Yangtze River, Resources and Environment in The Yangtze Basin, 2023,32(11):2312-2325.
41	吴丽君,朱宇,颜俊,等. "一带一路"沿线国家或地区间人口迁移的空间格局及其演化特征.世界地理研究,2022,31(02):249-258.
	WU L, ZHU Y, YAN J, et al.A study on the spatial patterns and their evolution of international migration among countries orareas along the Belt and Road.World Regional Studies,2022,31(02):249-258.

年份	网络密度	网络关联度	网络等级	网络效率
2006	0.224	1.000	0.188	0.685
2007	0.224	1.000	0.188	0.685
2008	0.225	1.000	0.188	0.690
2009	0.232	1.000	0.188	0.670
2010	0.229	1.000	0.188	0.675
2011	0.231	1.000	0.188	0.672
2012	0.233	1.000	0.188	0.668
2013	0.231	1.000	0.242	0.672
2014	0.224	1.000	0.242	0.687
2015	0.224	1.000	0.242	0.685
2016	0.233	1.000	0.342	0.680
2017	0.229	1.000	0.342	0.690
2018	0.236	1.000	0.342	0.670
2019	0.218	1.000	0.242	0.700
2020	0.221	1.000	0.344	0.700
2021	0.222	1.000	0.343	0.692
2035	0.198	1.000	0.388	0.722
2050	0.178	1.000	0.550	0.761

年份	网络密度	网络关联度	网络等级	网络效率
2006	0.224	1.000	0.188	0.685
2007	0.224	1.000	0.188	0.685
2008	0.225	1.000	0.188	0.690
2009	0.232	1.000	0.188	0.670
2010	0.229	1.000	0.188	0.675
2011	0.231	1.000	0.188	0.672
2012	0.233	1.000	0.188	0.668
2013	0.231	1.000	0.242	0.672
2014	0.224	1.000	0.242	0.687
2015	0.224	1.000	0.242	0.685
2016	0.233	1.000	0.342	0.680
2017	0.229	1.000	0.342	0.690
2018	0.236	1.000	0.342	0.670
2019	0.218	1.000	0.242	0.700
2020	0.221	1.000	0.344	0.700
2021	0.222	1.000	0.343	0.692
2035	0.198	1.000	0.388	0.722
2050	0.178	1.000	0.550	0.761

城市	点度中心度				中间中心度		接近中心度
城市	点出度	排序	点入度	排序	中心度	排序	点出度	排序	点入度	排序
安徽	2.000	29	5.000	13	0.111	24	11.284	30	51.786	12
北京	5.000	25	14.000	7	79.209	4	12.185	24	56.863	11
福建	10.000	1	16.000	3	109.923	2	13.004	9	64.444	5
甘肃	10.000	1	4.000	14	43.791	8	13.364	7	26.606	22
广东	8.000	4	15.000	4	122.811	1	12.832	10	61.702	6
广西	6.000	12	3.000	16	69.143	6	12.719	11	42.647	16
贵州	6.000	12	2.000	18	23.545	13	12.719	11	39.726	17
海南	6.000	12	0.000	25	0.000	25	14.428	5	3.333	25
河北	5.000	25	11.000	8	40.415	9	11.694	28	60.417	8
河南	5.000	25	9.000	11	20.947	14	12.340	18	58.000	10
黑龙江	8.000	4	0.000	25	0.000	25	14.573	3	3.333	25
湖北	6.000	12	10.000	10	19.500	15	12.236	22	59.184	9
湖南	5.000	25	3.000	16	2.846	18	12.288	20	43.939	15
吉林	8.000	4	0.000	25	0.000	25	14.573	3	3.333	25
江苏	6.000	12	15.000	4	29.299	10	11.983	27	67.442	3
江西	6.000	12	17.000	2	106.952	3	12.554	16	70.732	2
辽宁	7.000	10	2.000	18	12.492	17	12.609	15	47.541	14
内蒙古	6.000	12	2.000	18	1.333	20	12.288	20	27.885	21
宁夏	8.000	4	0.000	25	0.000	25	15.263	1	3.333	25
青海	8.000	4	0.000	25	0.000	25	15.183	2	3.333	25
山东	6.000	12	11.000	8	26.678	12	12.236	22	61.702	6
山西	8.000	4	2.000	18	28.717	11	12.393	17	37.179	19
陕西	9.000	3	1.000	23	0.699	22	13.303	8	21.168	24
上海	6.000	12	15.000	4	14.778	16	12.134	26	65.909	4
四川	6.000	12	2.000	18	1.465	19	12.719	11	26.126	23
天津	2.000	29	6.000	12	0.500	23	11.328	29	47.541	13
新疆	6.000	12	0.000	25	0.000	25	14.428	5	3.333	25
云南	6.000	12	1.000	23	1.277	21	12.340	18	38.667	18
浙江	7.000	10	23.000	1	54.419	7	12.185	24	82.857	1
重庆	6.000	12	4.000	14	79.146	5	12.719	11	33.333	20
均值	6.433		6.433		29.667		12.863		40.446

城市	点度中心度				中间中心度		接近中心度
城市	点出度	排序	点入度	排序	中心度	排序	点出度	排序	点入度	排序
安徽	2.000	29	5.000	13	0.111	24	11.284	30	51.786	12
北京	5.000	25	14.000	7	79.209	4	12.185	24	56.863	11
福建	10.000	1	16.000	3	109.923	2	13.004	9	64.444	5
甘肃	10.000	1	4.000	14	43.791	8	13.364	7	26.606	22
广东	8.000	4	15.000	4	122.811	1	12.832	10	61.702	6
广西	6.000	12	3.000	16	69.143	6	12.719	11	42.647	16
贵州	6.000	12	2.000	18	23.545	13	12.719	11	39.726	17
海南	6.000	12	0.000	25	0.000	25	14.428	5	3.333	25
河北	5.000	25	11.000	8	40.415	9	11.694	28	60.417	8
河南	5.000	25	9.000	11	20.947	14	12.340	18	58.000	10
黑龙江	8.000	4	0.000	25	0.000	25	14.573	3	3.333	25
湖北	6.000	12	10.000	10	19.500	15	12.236	22	59.184	9
湖南	5.000	25	3.000	16	2.846	18	12.288	20	43.939	15
吉林	8.000	4	0.000	25	0.000	25	14.573	3	3.333	25
江苏	6.000	12	15.000	4	29.299	10	11.983	27	67.442	3
江西	6.000	12	17.000	2	106.952	3	12.554	16	70.732	2
辽宁	7.000	10	2.000	18	12.492	17	12.609	15	47.541	14
内蒙古	6.000	12	2.000	18	1.333	20	12.288	20	27.885	21
宁夏	8.000	4	0.000	25	0.000	25	15.263	1	3.333	25
青海	8.000	4	0.000	25	0.000	25	15.183	2	3.333	25
山东	6.000	12	11.000	8	26.678	12	12.236	22	61.702	6
山西	8.000	4	2.000	18	28.717	11	12.393	17	37.179	19
陕西	9.000	3	1.000	23	0.699	22	13.303	8	21.168	24
上海	6.000	12	15.000	4	14.778	16	12.134	26	65.909	4
四川	6.000	12	2.000	18	1.465	19	12.719	11	26.126	23
天津	2.000	29	6.000	12	0.500	23	11.328	29	47.541	13
新疆	6.000	12	0.000	25	0.000	25	14.428	5	3.333	25
云南	6.000	12	1.000	23	1.277	21	12.340	18	38.667	18
浙江	7.000	10	23.000	1	54.419	7	12.185	24	82.857	1
重庆	6.000	12	4.000	14	79.146	5	12.719	11	33.333	20
均值	6.433		6.433		29.667		12.863		40.446

变量	相关系数	平均值	标准差	最大值	最小值	P≥0	P≤0
RI	-0.044^*	-0.000	0.029	0.083	-0.088	0.914	0.086
LT	0.007	-0.000	0.028	0.098	-0.101	0.374	0.626
RD	-0.064^**	-0.000	0.028	0.084	-0.085	0.972	0.028
FAI	0.022	-0.000	0.029	0.087	-0.082	0.219	0.781
FSA	0.022	0.000	0.028	0.084	-0.082	0.228	0.772
PZ	-0.010	-0.001	0.028	0.080	-0.081	0.629	0.371
W	-0.027^*	-0.000	0.029	0.097	-0.088	0.906	0.094
IS	-0.006	-0.000	0.028	0.107	-0.107	0.637	0.363
SA	-0.108^***	0.000	0.045	0.161	-0.096	1.000	0.000