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Construct a composite indicator based on integrating Common Weight Data Envelopment Analysis and principal component analysis models: An application for finding development degree of provinces in Iran

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  • Omrani, Hashem
  • Valipour, Mahsa
  • Jafari Mamakani, Saeid

Abstract

Balanced development of regions requires the fair distribution of facilities and services. Hence, it is necessary to find and estimate the development degree of regions for policy makers. This paper presents an integrated Common Weight Data Envelopment Analysis (CWDEA) – Principal Component Analysis (PCA) model to find out the development degree of provinces in Iran. First, 131 suitable indicators are selected and then, the indicators are classified in fourteen different classes. In classical DEA model, each Decision Making Unit (DMU) is free to set its weights to reach the efficient frontier. In this paper, to restrict flexibility in indicator weights, development degree of provinces in each class is calculated using CWDEA model. Since, the proposed CWDEA model is not capable of fully ranking of provinces with all indicators, hence, the development degrees generated by CWDEA model are considered as indicators of PCA and the final ranks are obtained using PCA model. The results of proposed CWDEA-PCA model show that Yazd, Semnan and Bushehr are top three provinces in Iran.

Suggested Citation

  • Omrani, Hashem & Valipour, Mahsa & Jafari Mamakani, Saeid, 2019. "Construct a composite indicator based on integrating Common Weight Data Envelopment Analysis and principal component analysis models: An application for finding development degree of provinces in Iran," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:soceps:v:68:y:2019:i:c:s0038012117300022
    DOI: 10.1016/j.seps.2018.02.005
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    1. Giacalone, Massimiliano & Mattera, Raffaele & Nissi, Eugenia, 2022. "Well-being analysis of Italian provinces with spatial principal components," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    2. Goker, Nazli & Karsak, E.Ertugrul, 2021. "Two-stage common weight DEA-Based approach for performance evaluation with imprecise data," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    3. Zeng, Ximei & Zhou, Zhongbao & Gong, Yeming & Liu, Wenbin, 2022. "A data envelopment analysis model integrated with portfolio theory for energy mix adjustment: Evidence in the power industry," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).

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