This study assesses the operational efficiency of medical insurance funds in 13 prefecture-level cities in Jiangsu Province, aiming to identify regional disparities and analyze key influencing factors, in order to provide policy insights for optimizing fund allocation and promoting balanced regional development. Using data envelopment analysis (DEA) with three input and two output indicators, the efficiency levels of medical insurance funds in each city were measured, and regional differences were compared. The rank-sum ratio method was further employed to adjust indicators and calculate a comprehensive efficiency score. Regression analysis was then conducted to examine influencing factors, including medical insurance payment reform coverage and population aging, while addressing potential endogeneity. Results reveal a clear regional pattern in efficiency: southern Jiangsu > central Jiangsu > northern Jiangsu. Cities in southern Jiangsu exhibit relatively high comprehensive efficiency, though predominantly with decreasing returns to scale, whereas cities in northern Jiangsu show lower efficiency, mainly due to pure technical inefficiency, while still maintaining increasing returns to scale. Regression results indicate that DEA comprehensive efficiency and payment reform coverage have significant positive effects on fund efficiency, while population aging exerts a negative impact. In conclusion, regional disparities in medical insurance fund efficiency in Jiangsu are primarily attributable to uneven resource allocation, varied progress in payment reform, and challenges posed by population aging. Policy recommendations should focus on optimizing resource distribution, advancing payment reforms, and addressing aging-related pressures.

Qiaoling Xu, Libin Zeng, Xusheng Zhang. Efficiency Evaluation of Medical Insurance Fund Operation in Prefecture-Level Cities of Jiangsu Province Based on DEA-RSR. Academic Journal of Humanities & Social Sciences (2026), Vol. 9, Issue 4: 25-31. https://doi.org/10.25236/AJHSS.2026.090404.

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