Analysis reveals distinct spatial aging trends in Shenzhen, depending on Hukou status, highlighting policy needs.
Objective As China’s youngest city with immigrants constituting 66% of its population, Shenzhen is paradoxically facing accelerated and compressed demographic aging. Although its overall aging rate remains below the international aging society threshold, the city confronts unique challenges as a migrant receiving metropolis without domestic precedents. Through a novel perspective of the Hukou system (i.e., the household registration system), this research reveals distinct spatial patterns of the older populations with different Hukou types. The findings aim to inform policy responses to Shenzhen’s upcoming concentrated aging transitions. Methods This study integrates administrative-level statistical data with nighttime light data, spatially disaggregated to a 1*1 km grid resolution. Employing spatial autocorrelation analysis, we identify spatial patterns of older populations. The spatial Durbin model is further applied to quantify both direct and spatial spillover effects of economic, social welfare, and natural environmental factors on the two aging cohorts. Results Local Hukou holders show a “west to east rising” aging trend, while older population without Shenzhen’s Hukou exhibit a “south high, north low” pattern. Both groups demonstrate strong positive spatial spillover effects within their respective Hukou type. Key influencing factors differ between the groups, with local Hukou older adults prioritizing environmental quality, while non-local Hukou older adults are more closely linked to economic indicators. Conclusion Distinct spatial patterns emerge between Hukou-registered and migrant older populations, with three influence categories exhibiting divergent mechanisms across these Hukou-defined cohorts. These dual disparities necessitate tailored policy interventions addressing institutionalized aging inequalities in Shenzhen, and also offering insights for other rapidly urbanizing cities with similar demographic structures.
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Luo et al. (2025) studied this question.
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