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

Subject Areas : Regional Planning

Ghasem Ramezanpour Nargesi ¹ , Negin Fallah Haghighi ² , Gholamhossein Abdollahzadeh ³ , Zeinab Sharifi ⁴

1 - Associate Professor, Department of Technology Development Studies (DTDS), Iranian Research Organization for Science and Technology (IROST), Tehran, Iran.
2 - Associate Professor, Department of Technology Development Studies (DTDS), Iranian Research Organization for Science and Technology (IROST), Tehran, Iran.
3 - Associated Professor, Department of Agricultural Extension and Education, Faculty of Agricultural Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
4 - Ph.D. in Agricultural Development, Faculty of Agriculture, Yasouj University, Yasouj, Iran.

Received: 2021-07-04 Accepted : 2021-11-12 Published : 2023-11-22

Keywords: inequality, Economic Indicators, Unbalanced Development, Composite Indicator,

Abstract :

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.
Extended Abstract
 
Introduction
According to the World Bank, more than 13% of the world's population lives in developing regions. In developing countries, including Iran, there are regional inequalities and economic gaps between regions (Jafari et al., 2021: 61). In fact, one of the prominent features of spatial development in Iran is regional inequalities that will cause heterogeneous and unbalanced growth between regions (Maleki et al., 2018: 54). Considering the importance of studying economic inequalities between provinces and geographical areas for better planning and management in order to achieve economic development, the first step is to study the current situation of the community. Composite indicators have been widely used to compare regional function (Henninger, 1998: 3; UNDP, 1999: 6; Nardo et al., 2005: 9). However, being poorly formulated or misinterpreted, the results of composite indicators will lead to incorrect policy-making (Abdollahzadeh et al., 2015: 2). Accordingly, the present study focuses on preventing the deletion or loss of information during the composite indicator construction process using TOPSIS, factor analysis and taxonomy methods, as well as cluster analysis to analyze spatial imbalances of economic development in Iran. Consequently, the hypotheses were in the following:

It seems that the distribution of economic development indicators in the provinces of Iran is unequal.
It seems that there is a spatial imbalance in the level of economic development in the provinces of Iran.
It seems that the pattern of spatial distribution of economic development among the provinces of Iran is balanced.

 
Methodology
This research is an applied one in terms of purpose and descriptive-analytical in terms of nature and method. The statistical population consisted of 31 provinces of Iran and according to the type of available information (results of the general population and housing census in 2018 and the results of statistical yearbooks in 2018), 30 economic indicators have been used. In this study, first, the inequality of each indicator was calculated applying the coefficient of variation and Williamson coefficient. Then the principal component analysis method was employed to weight the indicators. Finally, factor analysis, numerical taxonomy and TOPSIS methods were used to construct a composite indicator of provincial development. Cluster analysis for leveling has also been applied in this research. The coefficient of variation shows how the data are distributed in a geographical area and is calculated to compare the distribution of two or more attributes so that the high value indicates the inequality coefficient in the distribution of indicators (Jafari et al., 2021: 65). In TOPSIS technique, the M alternatives are evaluated through N indicators. In general, this technique is based on the concept that the selected alternative should have the shortest distance to the positive ideal (A+) and the maximum distance to the negative ideal (A-) (Maleki et al., 2018: 10) .Taxonomy analysis is used for various classifications in science, the specific type of which is numerical taxonomy. Numerical taxonomy is applied to evaluate the similarities between taxonomic units and to classify those elements into taxonomic groups. 
 
Results and Discussion
The results showed that the highest inequality are in the indicators including "the share of production in the service sector of the province out of production of the country's services", "the share of production in the mining sector of the province (without oil) out of production of the country's production " and "the share of GDP without oil", respectively. In contrast, the lowest inequality is in the "provincial indicator of business space of the province 2018", "male economic participation rate" and "inflation rate (percentage)", respectively. The findings of the principal component analysis method to weigh the indicators also indicated that the indicators including "the share of production in the industrial sector of the province out of production of the country's industry", "the share of GDP without oil" and "provincial share of value added in industrial workshops with 50 to 99 employees in the country" have the highest weight, respectively, whereas the indicators of "per capita production of livestock products"," male unemployment rate " and "male economic participation rate" have the lowest weight. Thus, hypothesis 1 based on unequal distribution of economic development indicators in the provinces of Iran was supported. Three methods of factor analysis, numerical taxonomy and TOPSIS were used for calculating the composite indicator. The results of all three methods are largely similar. According to all three methods, the provinces of Tehran, Isfahan, Bushehr, Khuzestan had the best situation, respectively, whereas the three provinces of South Khorasan, Chaharmahal and Bakhtiari and Ilam had the lowest rank in all three methods. In general, the provinces that had a larger share in the production of industry and services, were in a higher position in terms of the economic development, while the provinces that had a small share in the production of industry and GDP without oil were in a lower position. Therefore, hypothesis 2 based on spatial imbalance in the level of economic development in the provinces of Iran was supported. The result of cluster analysis to classify the level of economic development of the provinces reveals four clusters or levels of provinces. In the first level, only Tehran province is located, which indicates that there is a great distance between Tehran province and other provinces in terms of economic development. The second level of development includes two provinces (Isfahan and Bushehr), the third level consists of 11 provinces, mainly situated in the center and north of the country, and finally the fourth level includes 17 provinces, which are mostly located in the border areas and some in the center of Iran. Thus, hypothesis 3 based on the pattern of balanced spatial distribution of economic development among the provinces of Iran was rejected.
 
Conclusion
Based on the results of this research, the distribution of economic development indicators in the provinces of Iran was unequal. Therefore, hypothesis 1 was supported. In addition, the provinces of Tehran, Isfahan, Bushehr, Khuzestan provinces have the best situation, respectively, and the provinces of South Khorasan, Charmahal and Bakhtiari and Ilam have the lowest rank. Thus, hypothesis 2 was supported. These findings are consistent with the results of Jafari et al (2021) and Zarabi and Shahivandi (2010), which identified Tehran, Isfahan as developed and Charmahal and Bakhtiari and Ilam as deprived in terms of economic development indicators. Furthermore, the results of leveling using cluster analysis showed that there is a gap and inequality between the provinces of the country in terms of economic indicators. Therefore, hypothesis 3 was rejected. Hence, it can be inferred that the core-periphery model has been formed in Iran. In other words, economic development poles have been formed in Iran, which have a higher level of economic development, and the farther a region is from these poles of economic development, the lower is the level of economic development. Accordingly, some suggestions have been recommended.

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