Macroeconomics
Zana Mozaffari; Bakhtiar Javaheri
Abstract
Human capital is a hidden variable. In different economic studies, various proxies have been used as a proxy for human capital, including the average literacy index, the number of graduates or the average number of years of schooling. This study will review the economic literature first, and then the ...
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Human capital is a hidden variable. In different economic studies, various proxies have been used as a proxy for human capital, including the average literacy index, the number of graduates or the average number of years of schooling. This study will review the economic literature first, and then the three pillars of human capital index including education variables, skills and health will be analyzed for the Iranian economy. In addition, by using fuzzy approach and Mamdani Fuzzy Inference System, the human capital index in the Iranian economy during the 1981-2019 period will be estimated. The results of this calculation shows that during the period under study, the human capital index has continuously grown; in 1981, the index was estimated at 0.13 and 0.59 in 2019. On this basis, it can be stated that human capital in the Iranian economy during the 1981 to 2019 period has grown significantly. This accumulation of human capital can be seized in the production processes, leading to increase in production and productivity..
Esfandiar Jahangard; Alireza Naseriborocheni
Abstract
In this paper, we combine the input-output approach with fuzzy clustering using the input-output tables of 2011 and 2006 in order to identify the sectoral weights of the Iranian economy. The outliers were separately clustered in order to eliminate their undesirable effects on clustering. The results ...
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In this paper, we combine the input-output approach with fuzzy clustering using the input-output tables of 2011 and 2006 in order to identify the sectoral weights of the Iranian economy. The outliers were separately clustered in order to eliminate their undesirable effects on clustering. The results show that key economic sectors in the manufacturing and mining (non-metallic minerals, basic metals, machinery and equipment n.e.c., distribution of gaseous fuels through mains, other mining, coke and refined petroleum products and nuclear fuels), in Iran are outliers, hence, need to be separately clustered. Moreover, the key sectors are "crude petroleum and natural gas" - "wholesale and retail, maintenance and repair of motorcycles and related parts and accessories, public administration, land transport and agriculture" for export promotion and job creation, respectively.
Majid Sameti; Mohammad Reza Ghasemy; Horam Osmanpoor
Abstract
Tax capacities and the lack of justice in taxing Iranian provinces are among the authorities’ concerns. Therefore, identifying the tax capacity of provinces is an inevitable necessity. Various methods of econometrics, Input-Output models, the frontier-function model, and fuzzy time series have ...
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Tax capacities and the lack of justice in taxing Iranian provinces are among the authorities’ concerns. Therefore, identifying the tax capacity of provinces is an inevitable necessity. Various methods of econometrics, Input-Output models, the frontier-function model, and fuzzy time series have been used to estimate tax capacity. The main problem with these methods is that they implement the estimation based on the past data. In this study, the application of fuzzy logic control (FLC) method to determine the tax capacity of the Iranian provinces in the year 2011 is one step in solving this problem. The results of the study showed that except for Tehran, there is a potential tax capacity in other provinces of the country. In most of the provinces, tax effort is not only at low level but it also has a high dispersion, which shows that taxation from the provinces has not been based on justice. Also, ability of tax payment of the country can increase.
Ali Saedvandi; Hossein Sadeghi; Zahra Keshavarzi
Volume 18, Issue 56 , October 2013, , Pages 95-122
Abstract
Although depreciation is a crucial factor in economic growth models, little effort has been made to estimate depreciation rates. In this study, we attempt to estimate integrated fuzzy indicators for depreciation rates in 21 comparable developing countries. In the framework of fuzzy logic, first, we combine ...
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Although depreciation is a crucial factor in economic growth models, little effort has been made to estimate depreciation rates. In this study, we attempt to estimate integrated fuzzy indicators for depreciation rates in 21 comparable developing countries. In the framework of fuzzy logic, first, we combine ten related variables to obtain four depreciation indicators, namely human, social, physical, and natural capital. Then the four indicators are combined to obtain an overall depreciation rate. The results indicate that remarkable gap exists among developing countries. The overall depreciation rates are at the highest level in the CIS countries (circa 0/7) and at the lowest level in some of the developing European nations (circa 0/4). Due to lack of information, the exact estimation of a combined depreciation indicator seems impossible for Iran; nevertheless, we estimate a minimum boundary for this country, which indicates the dismal situation of capital preservation in Iran.
