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<p>In recent years, China’s shadow education sector has undergone dramatic transformation amid rapid expansion and increasingly stringent regulatory policies. Drawing on nationwide registration and deregistration data from 2000 to 2024, this study systematically examines the spatiotemporal dynamics of institutional entry, exit, and survival. By integrating multi-source data—including POIs, transport networks, policy documents, and city rankings<b>—</b>and applying an Accelerated Failure Time (AFT) survival model, the study reveals three key findings: (1) Temporally, institutional entry and exit closely align with policy shifts, with a sharp rise in exits following the “Double Reduction” policy, particularly among Academic Tutoring Institutions; (2) Spatially, the entry and exit of shadow education institutions exhibit pronounced clustering patterns across different developmental stages, with hotspots primarily concentrated in the economically developed eastern regions and gradually diffusing toward central and western China; (3) Institutional survival is shaped by individual characteristics, macro-environmental conditions, and micro-location attributes. Unlike general industries, shadow education is highly sensitive to policy environments, and the effects of influencing factors vary significantly across city tiers. The research in this paper helps to deepen the understanding of the survival risk mechanism of shadow education institutions, provides empirical support for evaluating the implementation effect of the “double reduction” policy, and also provides valuable references for policymakers and industry practitioners.</p>

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Shadow Education Survival in China: Spatiotemporal Patterns and Influencing Factors

  • 期刊论文

  • 发表日期：

    2025年11月22日

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  • Xuexiao Cheng,
  • Zhanfu Luo

摘要

In recent years, China’s shadow education sector has undergone dramatic transformation amid rapid expansion and increasingly stringent regulatory policies. Drawing on nationwide registration and deregistration data from 2000 to 2024, this study systematically examines the spatiotemporal dynamics of institutional entry, exit, and survival. By integrating multi-source data—including POIs, transport networks, policy documents, and city rankings—and applying an Accelerated Failure Time (AFT) survival model, the study reveals three key findings: (1) Temporally, institutional entry and exit closely align with policy shifts, with a sharp rise in exits following the “Double Reduction” policy, particularly among Academic Tutoring Institutions; (2) Spatially, the entry and exit of shadow education institutions exhibit pronounced clustering patterns across different developmental stages, with hotspots primarily concentrated in the economically developed eastern regions and gradually diffusing toward central and western China; (3) Institutional survival is shaped by individual characteristics, macro-environmental conditions, and micro-location attributes. Unlike general industries, shadow education is highly sensitive to policy environments, and the effects of influencing factors vary significantly across city tiers. The research in this paper helps to deepen the understanding of the survival risk mechanism of shadow education institutions, provides empirical support for evaluating the implementation effect of the “double reduction” policy, and also provides valuable references for policymakers and industry practitioners.