Esfandiar Jahangard; Jamal Kakaie
Abstract
The COVID-19 pandemic became widespread in most countries of the world in late 2019. In addition to human casualties, it has also affected the economies of countries. According to IMF, the world economic growth in 2020 was -3.5 percent. However, the GDP’s growth rate of the Iran economy for ...
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The COVID-19 pandemic became widespread in most countries of the world in late 2019. In addition to human casualties, it has also affected the economies of countries. According to IMF, the world economic growth in 2020 was -3.5 percent. However, the GDP’s growth rate of the Iran economy for 2020 has been positive and was reported as 1.5 percent, but there is evident that the economy has been affected by the coronavirus. In this paper, we evaluate the effect of the COVID-19 pandemic on the production, employment, and value-added of the economy of Iran by using the hypothetical extraction method of Dietzenbacher & Lahr (2013) and the Input-Output general equilibrium model of Zaytseva (2000). We have used the data from the input-output table of the Iran Central Bank in 2016 and the employment figures of the Statistics Center of Iran. The results show that the production and the value-added of the economy will decrease by 4.3 and 4 percent respectively. Among the value-added components, mixed-income and net operating surplus experienced the highest declining growth rate. Also, about 6.5 percent of the country's employees have been affected directly and indirectly by the Coronavirus.
Elham Shadabfar; Fatemeh Bazzazan; Ali Asghar Banouei
Abstract
The Multi-Regional Input-Output Table (MRIO) provides comprehensive information on the economic statistics of regions, with help of which economic structure of regions and economic relations among them are determined. Since regional tables and statistics data of inter-regional trade, which are ...
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The Multi-Regional Input-Output Table (MRIO) provides comprehensive information on the economic statistics of regions, with help of which economic structure of regions and economic relations among them are determined. Since regional tables and statistics data of inter-regional trade, which are necessary for the construction of multi-regional output tables, are not created by official institutions in Iran, using non-survey methods in regional Input-output literature is the only solution. The main aim of this paper is to provide a nine-zone Input-output table based on the CHARM method in Iran, and to estimate interregional trade. Regional accounts and the national statistical Input-output table in 2011 of Iran’s Statistical Center, have been used as statistical bases. The results of this study indicate that the total value of Interregional trade in Iran is 1000679 billion rials. The highest volume of interregional trade belongs to Khuzestan region is 337658, and the region of southern Alborz with a volume of 242225 and the lowest volume is related to the Azarbaijan region with a volume of 38,283 billion Rials. The largest volume of interregional trade of Iran is in the crude oil and natural gas sector, and then services. The highest volume of interregional trade in the Shomal, Azerbaijani, South-Eastern and Zagros regions are in the agricultural sector, Khuzestan region in the crude oil and natural gas sector, Fars region in the construction of petroleum products and chemicals, the south Alborze and Khorasan regions are in the service sector And the Central region in the manufacturing of metals and electronic and metal products.
Saeed Moshiri; Maryam Parsa; Liela Darougar
Abstract
As a general-purpose technology, information and communication technology (ICT) leads to increasing productivity and economic growth in different sectors. Iran, as a semi-industrialized developing economy, has recently made relatively high level of investment on ICT in different sectors of the economy. ...
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As a general-purpose technology, information and communication technology (ICT) leads to increasing productivity and economic growth in different sectors. Iran, as a semi-industrialized developing economy, has recently made relatively high level of investment on ICT in different sectors of the economy. In this paper, we investigate the relationship between ICT sector and other sectors of the economy using an input-output table. We construct an input-output table with 37 sectors including ICT sector, using the updated IO table for year 2010. ICT goods production comprises 1.8 percent of total goods production and ICT service is 1.32 percent of the total service production. The results show that one unit increase in final demand for information technology (IT) products will increase total output by 1.63 units and one unit increase in final demand for communication technology (CT) products will increase total output by 2.18 units. The greatest impact of ICT in manufacturing sectors will be respectively in food and beverage, basic metal, and chemical products, and in the services sector in the wholesale, retail sale, financial intermediates, and real estate services. We also calculate the Average Propagation Length (APL) of the changes in the ICT final demand. The results indicate that the average propagation length of the changes in the ICT final demand is 1 for the services sector and 2 for the manufacturing industries.
afsane sherkat; Mohammad Jelodari Mamaghani; Ali Asghar Banouei; Ashkan Mokhtary Asl Shouti; Sonia Sabzalizad Honarvar
Volume 15, Issue 56 , April 2015, , Pages 135-160
Abstract
In this paper, we have used four conventional, Adjusted, Generalized, and Adjusted Generalized RAS methods to update input-output Coefficients (IOC). Conventional and Adjusted RAS methods can only update positive and zero cells and are not sensitive to the existing negative cells like net exports and/or ...
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In this paper, we have used four conventional, Adjusted, Generalized, and Adjusted Generalized RAS methods to update input-output Coefficients (IOC). Conventional and Adjusted RAS methods can only update positive and zero cells and are not sensitive to the existing negative cells like net exports and/or net taxes in Input-Output Tables (IOTs). To solve this drawback, analysts have proposed Generalized RAS (GRAS). This method can update positive, zero and negative cells. From the application view point, this method has two limitations: First, it is more focused on numerical examples rather than real IOTs. Second, extending the GRAS to AGRAS has not been attempted yet. The above limitations will lead us to pose the following questions: is it possible to extend Generalized RAS to Adjusted Generalized RAS? And which method has more statistical errors? For this purpose, we have used the two aggregated survey based IOTs of Iran for the years 1996 and 2001. Our findings show that it is possible to extend GRAS to AGRAS. Whit respect to the measurement of statistical errors, the following results have been obtained: first, the statistical errors of GRAS is lower than Conventional and ARAS, and the second statistical errors of AGRAS are much lower than corresponding figures in Conventional, ARAS and GRAS.
Esfandiar Jahangard; Afrouz Azadikhah Jahromi
Volume 13, Issue 51 , January 2014, , Pages 81-111
Abstract
In this paper we identify the production chains of Iranian economy by using average propagation length (APL) index. In our imprical investigation, we have applied the mentioned methodology to the 2000 input-output table of Iran. In order to obtain a clear overview of the production chains in Iran, the ...
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In this paper we identify the production chains of Iranian economy by using average propagation length (APL) index. In our imprical investigation, we have applied the mentioned methodology to the 2000 input-output table of Iran. In order to obtain a clear overview of the production chains in Iran, the table was aggregated to 6 sector level. At first we calculate the backward and forward linkages and APLs, and then we visualize the production chains for Iranian economy. As a second application we have considered the linkages, APLs and production chains at a more detailed level. For this purpose, we have used the 28-sector classification. The results for the backward and forward linkages of each sector indicate that industry and electricity, gas and water supply are known as key sectors. Also it was found that largest average forward APL value belongs to agriculture and mining, and smallest value belongs to services and construction. Likewise, the largest average backward APL value is observed for construction and agriculture, and smallest value oserverd for mining and services. It should also be noted that in both applications mining located at the beginning of the production chain o Iranian economy.
