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<p>This study examines the spatio-temporal heterogeneity impacts of digital talent mobility on economic growth across five major Chinese urban agglomerations. Based on panel data analysis using the Geographically and Temporally Neural Network Weighted Regression (GTNNWR) model, our findings show first, overall digital talent flow has sustained positive effects in advanced regions. Second, intra-regional flows generate more stable growth than inter-regional flows. Third, while inflows promote growth, outflows reduce innovation in developed areas but ease labor imbalances in periphery cities. As a policy implication, this study advocates region-specific talent strategies to optimize mobility and support balanced development.</p>

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Spatio-temporal heterogeneity effect of digital talent mobility on economic growth: evidence from urban agglomerations in China

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • Yuanfang Chen,
  • Wee-Yeap Lau,
  • Lim-Thye Goh

摘要

This study examines the spatio-temporal heterogeneity impacts of digital talent mobility on economic growth across five major Chinese urban agglomerations. Based on panel data analysis using the Geographically and Temporally Neural Network Weighted Regression (GTNNWR) model, our findings show first, overall digital talent flow has sustained positive effects in advanced regions. Second, intra-regional flows generate more stable growth than inter-regional flows. Third, while inflows promote growth, outflows reduce innovation in developed areas but ease labor imbalances in periphery cities. As a policy implication, this study advocates region-specific talent strategies to optimize mobility and support balanced development.