Analysis of Spatial Imbalances in Iran's Economic Development: Application of Composite Indicator Approach

One of the salient features of Iran's spatial development model is the existence of unbalanced economic development among the provinces of the country which will lead to imbalance growth among regions. The first step for better planning and management in order to achieve balance economic development is to study economic development inequalities among provinces. Therefore, the present study aimed to analyze spatial imbalances of economic development in Iran. This research has been applied in terms of purpose and descriptive-analytical in terms of nature and method. The statistical population consisted of 31 provinces of Iran and the research method was based on combining 30 economic indicators in a final composite indicator. The inequality of each indicator was calculated applying the coefficient of variation and Williamson coefficient, the weight of each indicator was obtained by the principal component analysis method, and the final composite indicators were calculated and the provinces were ranked employing the three methods of TOPSIS, factor analysis and numerical taxonomy. Furthermore, the leveling of the composite indicator was carried out using cluster analysis. Findings showed that the indicators of "the share of production of the province's service sector in the production of the country's services" had the highest inequality whereas "provincial indicator of business space of the province 2018" had the lowest inequality. Furthermore, the indicators including "the share of production in the industrial sector of the province out of production of the country's industry" and "per capita production of livestock products had the highest and lowest weight, respectively. The result of calculating composite indicators revealed that the provinces including Tehran, Isfahan, Bushehr and Khuzestan had the best situation, respectively, and the provinces including South Khorasan, Charmahal and Bakhtiari and Ilam had the lowest rank. In addition, the results of GIS maps and cluster analysis indicate gaps and inequalities between the provinces of the country in terms of economic indicators so that the core-periphery model is established in the process of economic development. Finally, in order to eliminate this inequality and economic development imbalance, some appropriate solutions have been recommended.