地理学报 ›› 2022, Vol. 77 ›› Issue (10): 2409-2425.doi: 10.11821/dlxb202210001

• 人口地理 •    下一篇

1990—2015年亚洲内部人口迁移格局及影响因素

刘晔1,2,3(), 王晓歌1,2,3, 管靖4, 古恒宇5()   

  1. 1.中山大学地理科学与规划学院,广州 510006
    2.广东省城市化与地理环境空间模拟重点实验室,广州 510006
    3.广东省公共安全与灾害工程技术研究中心,广州 510006
    4.中国科学院地理科学与资源研究所,北京 100101
    5.香港中文大学地理与资源管理学系,香港 999077
  • 收稿日期:2021-11-15 修回日期:2022-05-21 出版日期:2022-10-25 发布日期:2022-12-25
  • 通讯作者: 古恒宇(1994-), 男, 广东广州人, 博士, 副研究员, 研究方向为人口迁移与区域发展、城市计算与城市空间治理。E-mail: henry.gu@pku.edu.cn
  • 作者简介:刘晔(1986-), 男, 广东广州人, 教授, 博士生导师, 研究方向为人口地理、健康地理和城市地理。E-mail: liuye25@mail.sysu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41930646);中山大学中央高校基本科研业务费专项资金(22qntd2001)

Spatial pattern and determinants of international migration flows in Asia, 1990-2015

LIU Ye1,2,3(), WANG Xiaoge1,2,3, GUAN Jing4, GU Hengyu5()   

  1. 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
    2. Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510006, China
    3. Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510006, China
    4. Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China
    5. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong 999077, China
  • Received:2021-11-15 Revised:2022-05-21 Published:2022-10-25 Online:2022-12-25
  • Supported by:
    National Natural Science Foundation of China(41930646);The Fundamental Research Funds for the Central Universities Sun Yat-sen University(22qntd2001)

摘要:

把握亚洲内部跨国/地区人口迁移规律,有助于新时期中国制定合理的国际移民政策,推动“一带一路”倡议迈向高质量发展。本文基于1990—2015年国际双边移民流量数据,运用社会网络分析和空间滤波面板负二项引力模型,阐明亚洲内部跨国/地区人口迁移的时空格局与驱动因素。研究发现:① 1990—2000年亚洲主要迁移流集中在西亚、南亚和东南亚各区域内部,且大多发生在邻国之间,2000—2015年出现多条横跨上述三大区域的大规模迁移流;② 人口迁移网络强度相对较低,联系紧密程度先升后降;③ 政治不稳定与战乱冲突是人口迁移的重要推动力,经济发展水平差异和国民收入差异是重要驱动力,多维邻近因素(经济邻近和文化邻近)也起到一定的推动作用;④ 25年间,经济差异的影响先增后减;政治不稳定一直是人口迁移的主要影响因素,且对人口迁出的影响更大;进出口贸易发展在部分时期积极促进人口流动,留学吸引力影响呈波动趋势;⑤ 非经济和结构性力量在亚洲内部跨国/地区人口迁移过程中起到决定性作用。

关键词: 跨国/地区人口迁移, 空间格局, 驱动因素, 社会网络分析, 空间滤波, 负二项面板引力模型

Abstract:

Understanding the characteristics of transnational migration in Asia is beneficial for China to formulate a reasonable international migration policy in the new era and promote the high-quality development of the Belt and Road Initiative. Based on the data of bilateral international migration flows from 1990 to 2015, this study tries to clarify the temporal and spatial patterns and influencing factors of Asian transnational migration by using social network analysis and spatial filtering panel negative binomial gravity model (ESF). The results are listed as follows: First, the main migration flows in Asia from 1990 to 2000 were concentrated in West Asia, South Asia and Southeast Asia, and most of them occurred between the adjacent countries and regions. From 2000 to 2015, there was a number of large-scale migration flows across the above three subregions. Second, the scale of the migration network in Asia is relatively low, and the degree of closeness between the Asian countries has increased first and then weakened over time. Third, political instability and war conflicts of the origin were important driving forces for transnational migration in Asia. Besides, the differences of economic development and national income between countries were the crucial driving forces, while multi-dimensional proximity factors played an important role in promoting transnational migration. Fourth, from 1990 to 2015, the effect of economic differences on transnational migration increased first and then weakened. In addition, political instability has always been the main influencing factors for migration. Besides, the development of import trade has positively promoted transnational migration, and the attractiveness of studying abroad has a fluctuating effect on transnational migration. Finally, non-economic and structural impacts were the most important factors influencing the transnational migration in Asia.

Key words: transnational migration, spatial pattern, driving factor, social network analysis, spatial filtering, negative binomial panel gravity model