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

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. •We calculate the development degrees of provinces in different classes based on common weight DEA (CWDEA) model.•We classify the indicators into 14 different classes.•The scores generated by CWDEA model are considered as indicators of PCA model.•We rank the provinces based on the PCA model.