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Data-Driven Robust DEA Models for Measuring Operational Efficiency of Endowment Insurance System of Different Provinces in China

Author

Listed:
  • Shaojian Qu

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Can Feng

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Shan Jiang

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Jinpeng Wei

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yuting Xu

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

China is facing an increasingly serious aging problem, which puts forward higher requirements for the smoothness of the endowment insurance system. Accurate evaluation of the efficiency of the system can help the government to find problems and improve the system. Some scholars have used data envelopment analysis (DEA) method to measure the efficiency of endowment insurance system. However, according to the literature, the impact of government policy adjustment and economic shocks on output of the data was ignored. In this study, a robust optimization method is applied to deal with uncertainty. Robust DEA models proposed in this paper are based on three kinds of uncertainty sets. A data-driven robust optimization method is also applied to resolve the over-conservative problem. Compared with the robust DEA method, based on analysis it is found that the data-driven robust DEA method is more flexible and reliable for efficiency estimating strategies. The results of data-driven robust DEA models illustrate that the government should increase its support for the endowment insurance system, especially for the underdeveloped regions.

Suggested Citation

  • Shaojian Qu & Can Feng & Shan Jiang & Jinpeng Wei & Yuting Xu, 2022. "Data-Driven Robust DEA Models for Measuring Operational Efficiency of Endowment Insurance System of Different Provinces in China," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9954-:d:886109
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    References listed on IDEAS

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    Cited by:

    1. Shengqing Chang & Jingjing Ding & Chenpeng Feng & Ruifeng Wang, 2024. "A Hybrid Parallel Processing Strategy for Large-Scale DEA Computation," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2325-2349, June.
    2. Shaojian Qu & Yuting Xu & Ying Ji & Can Feng & Jinpeng Wei & Shan Jiang, 2022. "Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China," Sustainability, MDPI, vol. 14(20), pages 1-26, October.
    3. Liu, Sujiao & Zhu, Mengcheng & Ling, Wenhao, 2023. "Research on the impact of population aging and endowment insurance on household financial asset allocation- Evidence on CFPS data," Finance Research Letters, Elsevier, vol. 54(C).

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