Spatial-temporal analysis of the relationship between interprovincial migration and economic development in China: data from a bivariative autocorrelation study (2000 –2024)

Authors

  • Feilong Zhang Belarus State Economic University, 26 Partyzanski Avenue, Minsk 220070, Belarus

Keywords:

population migration, economic development, spatial analysis, regional agglomeration, Moran’s I, LISA, China
Supporting Agencies
The research was carried out with the financial support of the China Scholarship Council (grant No. 201708150031).

Abstract

Although interregional migration is a key factor in the development of China’s regional economies, existing studies often neglect its dynamic spatial dependence. This study quantitatively assesses the spatiotemporal relationship between interregional migration and economic development in 31 provinces of China from 2000 to 2024. By using bivariate spatial autoregressive analysis (global Moran’s I and local spatial autocorrelation index (LISA)) on provincial-level data from the National Bureau of Statistics of China, the impact of migration on GRP growth patterns is characterised. The results of the analysis allow us to identify three stages of regional agglomeration evolution: 1) the initial stage of extreme concentration of economic activity and migration flows in the eastern coastal regions (2000 –2009); 2) the phase of their partial rebalancing towards the western provinces (2010 –2019); 3) the final stage of policy-induced multipolar redistribution (2020 –2024). Different spatial regimes are also found, characterised by negative autocorrelation during the period of decentralisation (2005 –2014) and the emergence of clusters in new agglomeration hubs (2015 –2019).

Author Biography

  • Feilong Zhang, Belarus State Economic University, 26 Partyzanski Avenue, Minsk 220070, Belarus

    postgraduate student at the department of national economy and public administration, faculty of economics and management

References

  1. Lee ES. A theory of migration. Demography. 1966;3(1):47–57. DOI: 10.2307/2060063.
  2. Krugman P. Increasing returns and economic geography. Journal of Political Economy. 1991;99(3):483–499. DOI: 10.1086/261763.
  3. Chan KW. Migration and development in China: trends, geography and current issues. Migration and Development. 2012;1(2):187–205. DOI: 10.1080/21632324.2012.739316.
  4. Fan CC. Interprovincial migration, population redistribution, and regional development in China: 1990 and 2000 census comparisons. Professional Geographer. 2005;57(2):295–311. DOI: 10.1111/j.0033-0124.2005.00479.x.
  5. Anselin L. Local indicators of spatial association – LISA. Geographical Analysis. 1995;27(2):93–115. DOI: 10.1111/j.1538-4632.1995.tb00338.x.
  6. Cai F, Wang D. Migration as marketization: what can we learn from China’s 2000 census data? China Review. 2003;3(2):73–93.
  7. Moretti E. Local labor markets. Handbook of Labor Economics. 2011;4B:1237–1313. DOI: 10.1016/S0169-7218(11)02412-9.
  8. Чжан Фэйлун. Анализ регионального развития в КНР с помощью инструментария пространственной автокорреляции. Вестник Витебского государственного технологического университета. 2023;1:152–160. DOI: 10.24412/2079-7958-2023-1-152-160.
  9. Lee S-I. Developing a bivariate spatial association measure. An integration of Pearson’s r and Moran’s I. Journal of Geographical Systems. 2001;3:369–385. DOI: 10.1007/s101090100064.
  10. Чжан Фэйлун. Обзор стратегических направлений совершенствования государственной региональной политики Китая в период скоординированного развития. В: Егоров АВ, редактор. Научные труды Белорусского государственного экономического университета. Выпуск 16. Минск: Колорград; 2023. с. 511–517. EDN: FWBXDA.
  11. Чжан Фэйлун. Скоординированное развитие городских агломераций как фактор экономической безопасности регионов Китая. В: Анофриков СП, редактор. Актуальные проблемы экономической безопасности государства и бизнеса. Материалы III Международной научно-практической конференции; 25–26 апреля 2024 г.; Новосибирск, Россия. Часть 1. Новосибирск: Новосибирский государственный университет экономики и управления; 2024. с. 425–432. EDN: KZDRVS.
  12. Tobler WR. A computer movie simulating urban growth in the Detroit region. Economic Geography. 1970;46:234–240. DOI: 10.2307/143141.
  13. Anselin L. A local indicator of multivariate spatial association, extending Geary’s c. Geographical Analysis. 2019;51(2):133–150. DOI: 10.1111/gean.12164.
  14. Anselin L, Xun Li. Tobler’s Law in a multivariate world. Geographical Analysis. 2020;52(4):494–510. DOI: 10.1111/gean.12237.
  15. Lee S-I. A generalized randomization approach to local measures of spatial association. Geographical Analysis. 2009;41(2):221–248. DOI: 10.1111/j.1538-4632.2009.00749.x.

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Published

2025-12-31

Issue

No. 2 (2025): Journal of the Belarusian State University. Economics

Section

O. Economic Development, Innovation, Technological Change, and Growth

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How to Cite

[1]
Zhang, F. 2025. Spatial-temporal analysis of the relationship between interprovincial migration and economic development in China: data from a bivariative autocorrelation study (2000 –2024). Journal of the Belarusian State University. Economics. 2 (Dec. 2025), 97–104.