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The Spatiotemporal Elasticity of Age Structure in China’s Interprovincial Migration System

Author

Listed:
  • Yufei Lin

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China)

  • Yingxia Pu

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • Xinyi Zhao

    (Max Planck Institute for Demographic Research, Konrad- Zuse-Str. 1, 18057 Rostock, Germany
    Leverhulme Centre for Demographic Science and Department of Sociology, University of Oxford, Oxford OX1 1JD, UK)

  • Guangqing Chi

    (Department of Agricultural Economics, Sociology, and Education, Pennsylvania State University, University Park, PA 16802, USA)

  • Cui Ye

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China)

Abstract

The supply and demand of labour in the market can often experience profound transformations as a result of an ageing population. This can substantially impact the sustainable development of human society. Since the 1970s, China’s internal migration has continued to increase, but there has been a shift toward an ageing trend since the year 2000. How does the change of age structure interact with socioeconomic development to produce changes in the supply and demand of labour over space and time? This study constructs a spatial dynamic panel data model of interprovincial migration flows in China from 1985 to 2015 in order to quantify the spatiotemporal impacts of age structure on migration. The preliminary results indicate that age structure plays the most important role among regional socioeconomic characteristics of migration, dominated by the large supply, demand, and cross elasticities of labour population. Labour demand and the cross elasticities of total dependency ratio rank second. Comparatively, the total elasticities of regional GDP and wage levels on migration flows are not as significant as expected. This study lays the groundwork for identifying the interaction mechanisms of migration systems and provides important insights on regional sustainable development from the perspective of ageing.

Suggested Citation

  • Yufei Lin & Yingxia Pu & Xinyi Zhao & Guangqing Chi & Cui Ye, 2023. "The Spatiotemporal Elasticity of Age Structure in China’s Interprovincial Migration System," Sustainability, MDPI, vol. 15(10), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8001-:d:1146655
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    References listed on IDEAS

    as
    1. Jianfa Shen, 2015. "Explaining Interregional Migration Changes in China, 1985-2000, Using a Decomposition Approach," Regional Studies, Taylor & Francis Journals, vol. 49(7), pages 1176-1192, July.
    2. Christian Dustmann & Tommaso Frattini & Caroline Halls, 2010. "Assessing the Fiscal Costs and Benefits of A8 Migration to the UK," Fiscal Studies, Institute for Fiscal Studies, vol. 31(1), pages 1-41, March.
    3. Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.
    4. James Mitchell & Nigel Pain & Rebecca Riley, 2011. "The drivers of international migration to the UK: A panel‐based Bayesian model averaging approach," Economic Journal, Royal Economic Society, vol. 121(557), pages 1398-1444, December.
    5. Bianca Biagi & Alessandra Faggian & Isha Rajbhandari & Viktor A. Venhorst, 2018. "Editorial: New Frontiers in Interregional Migration Research," Advances in Spatial Science, in: Bianca Biagi & Alessandra Faggian & Isha Rajbhandari & Viktor A. Venhorst (ed.), New Frontiers in Interregional Migration Research, chapter 0, pages 1-18, Springer.
    6. Anzelika Zaiceva, 2014. "The impact of aging on the scale of migration," IZA World of Labor, Institute of Labor Economics (IZA), pages 1-99, November.
    7. Fatih Karahan & Serena Rhee, 2014. "Population aging, migration spillovers, and the decline in interstate migration," Staff Reports 699, Federal Reserve Bank of New York.
    8. Roberto Patuelli & Peter Nijkamp, 2016. "Travel motivations of seniors," Tourism Economics, , vol. 22(4), pages 847-862, August.
    9. David E. BLOOM & Jocelyn E. FINLAY, 2009. "Demographic Change and Economic Growth in Asia," Asian Economic Policy Review, Japan Center for Economic Research, vol. 4(1), pages 45-64, June.
    10. James P. LeSage & Manfred M. Fischer, 2016. "Spatial Regression-Based Model Specifications for Exogenous and Endogenous Spatial Interaction," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 15-36, Springer.
    11. Manfred M. Fischer & James P. LeSage, 2020. "Network dependence in multi-indexed data on international trade flows," Journal of Spatial Econometrics, Springer, vol. 1(1), pages 1-26, December.
    12. Klaus Nowotny & Dieter Pennerstorfer, 2019. "Network migration: do neighbouring regions matter?," Regional Studies, Taylor & Francis Journals, vol. 53(1), pages 107-117, January.
    13. Wei, Zheng & Hao, Rui, 2010. "Demographic structure and economic growth: Evidence from China," Journal of Comparative Economics, Elsevier, vol. 38(4), pages 472-491, December.
    14. Adriana Ana Maria Davidescu & Vasile Alecsandru Strat & Raluca Mariana Grosu & Ion-Daniel Zgura, 2017. "Determinants of Romanians' Migration within the European Union: Static and Dynamic Panel Gravity Approaches," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 19(46), pages 621-621, August.
    15. Hou, Feng & Picot, Garnett, 2014. "Annual Levels of Immigration and Immigrant Entry Earnings in Canada," Analytical Studies Branch Research Paper Series 2014356e, Statistics Canada, Analytical Studies Branch.
    16. Nickell, Stephen J, 1982. "The Determinants of Equilibrium Unemployment in Britain," Economic Journal, Royal Economic Society, vol. 92(367), pages 555-575, September.
    17. Lloyd, Cynthia B & Niemi, Beth, 1978. "Sex Differences in Labor Supply Elasticity: The Implications of Sectoral Shifts in Demand," American Economic Review, American Economic Association, vol. 68(2), pages 78-83, May.
    18. Daniel A. Griffith & Manfred M. Fischer & James LeSage, 2017. "The spatial autocorrelation problem in spatial interaction modelling: a comparison of two common solutions," Letters in Spatial and Resource Sciences, Springer, vol. 10(1), pages 75-86, March.
    19. William B. Peterman, 2016. "Reconciling Micro And Macro Estimates Of The Frisch Labor Supply Elasticity," Economic Inquiry, Western Economic Association International, vol. 54(1), pages 100-120, January.
    20. James P. LeSage & Christine Thomas-Agnan, 2015. "Interpreting Spatial Econometric Origin-Destination Flow Models," Journal of Regional Science, Wiley Blackwell, vol. 55(2), pages 188-208, March.
    21. David E. Bloom & David Canning & Pia N. Malaney, 1999. "Demographic Change and Economic Growth in Asia," CID Working Papers 15, Center for International Development at Harvard University.
    22. Orn B. Bodvarsson & Jack W. Hou & Kailing Shen, 2016. "Aging and Migration: Micro and Macro Evidence from China," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 11(4), pages 548-580, December.
    23. Feng Hou & Garnett Picot, 2014. "Annual Levels of Immigration and Immigrant Entry Earnings in Canada," Canadian Public Policy, University of Toronto Press, vol. 40(2), pages 166-181, June.
    24. Bianca Biagi & Alessandra Faggian & Isha Rajbhandari & Viktor A. Venhorst (ed.), 2018. "New Frontiers in Interregional Migration Research," Advances in Spatial Science, Springer, number 978-3-319-75886-2, Fall.
    25. Bloom, David E. & Canning, David & Hu, Linlin & Liu, Yuanli & Mahal, Ajay & Yip, Winnie, 2010. "The contribution of population health and demographic change to economic growth in China and India," Journal of Comparative Economics, Elsevier, vol. 38(1), pages 17-33, March.
    26. Timo Mitze, 2012. "Empirical Modelling in Regional Science," Lecture Notes in Economics and Mathematical Systems, Springer, edition 127, number 978-3-642-22901-5, December.
    27. Høgni Kalsø Hansen & Ditte Håkonsson Lyngemark & Cecilie Dohlman Weatherall, 2021. "Migration and employment after an economic shock: regional characteristics and migration patterns," Regional Studies, Taylor & Francis Journals, vol. 55(5), pages 907-920, May.
    28. James P. LeSage & R. Kelley Pace, 2008. "Spatial Econometric Modeling Of Origin‐Destination Flows," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 941-967, December.
    29. Timo Mitze, 2016. "On the Mutual Dynamics of Interregional Gross Migration Flows in Space and Time," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 415-439, Springer.
    30. repec:iza:izawol:journl:y:2014:p:99 is not listed on IDEAS
    31. Antonio Golini & Anna Di Bartolomeo, 2009. "The impact of a massive migration flow on the regional population structure: The case of Italy," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 7(1), pages 149-165.
    32. Yingxia Pu & Xinyi Zhao & Guangqing Chi & Jin Zhao & Fanhua Kong, 2019. "A spatial dynamic panel approach to modelling the space-time dynamics of interprovincial migration flows in China," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(31), pages 913-948.
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