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Optimizing public sector resources: A centralized DEA model for effective allocation of shared inputs

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  • Lin, Sheng-Wei
  • Lu, Wen-Min

Abstract

This study presents a novel centralized Data Envelopment Analysis (DEA) model with shared inputs to optimize the allocation of public sector resources and enhance management efficiency. Recognizing the need for a comprehensive approach to public sector performance evaluation, the study integrates the strengths of the centralized DEA framework and the two-stage DEA model with shared inputs. The centralized DEA model shifts the focus from individual decision-making units to optimizing resources across the entire public sector system. This allows for the reallocation of shared inputs among decision making units, leading to potential efficiency gains and improved overall performance. Incorporating shared inputs within the centralized structure enables a more nuanced understanding of the interdependencies and interactions between distinct functions and stages within the public sector. The empirical application of the proposed model in the context of public sector management and cultural subsidies provides valuable insights. The findings highlight inefficiency and offer guidance for policymakers and administrators on optimizing shared resource use. The centralized DEA model with shared inputs serves as a practical decision-support tool, informing the development of targeted policies and strategies to enhance the efficiency and effectiveness of public service delivery, particularly in resource-constrained environments. This research contributes to public sector performance evaluation's theoretical and methodological advancement, offering a comprehensive framework for resource optimization and improved management practices.

Suggested Citation

  • Lin, Sheng-Wei & Lu, Wen-Min, 2024. "Optimizing public sector resources: A centralized DEA model for effective allocation of shared inputs," Socio-Economic Planning Sciences, Elsevier, vol. 96(C).
    • Handle: RePEc:eee:soceps:v:96:y:2024:i:c:s0038012124002945
    DOI: 10.1016/j.seps.2024.102094
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