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Showing 4 results for Manufacturing Industries

Dr Alimorad Sharifi, Dr Karim Azarbaijani, Dr Iraj Kazemi, Aboozar Shakeri,
Volume 1, Issue 1 (10-2010)
Abstract

Industrial energy demand analysis has always been one of the leading fields of research in economics. This issue is more critical in the case of developing countries especially those with transition experiences. In this paper, third generation of dynamic factor demand models for the Iranian manufacturing industries is estimated to analyze the speed of adjustment in factor demands. Data which is used in this study is an Iranian industrial plant based on two-digit international classification code during 1374-1386. The translog functional form is used as model specification. The main findings are the complementary relation between energy carriers, electricity, and capital and low adjustment speed of capital stock. In Iranian manufacturing industries, demand for energy carriers and capital, with expansion of manufacturing activities and technological change has increased, while the demand for labor has decreased.
Somayeh Azami, Sajedeh Jalilian, Maryam Ahmadi,
Volume 7, Issue 25 (10-2016)
Abstract

The current study is an attempt to estimate markup and return to scale of 19 two-digit ISIC manufacturing industries of Iran, simultaneously, in accordance to Solow Residual and Structural approach, during the period 1995-2007. Based on Solow Residual approach, the neoclassical assumption of constant return to scale is approved within 95% of manufacturing industries; however in 84% of industries the price was higher than marginal cost significantly. Based on structural approach, 53% of manufacturing industries of Iran are experiencing increasing return to scale significantly; however, in 79% of industries, the price is higher than marginal cost. According to the criteria share of the cost of inputs in income as a theoretic criteria for return to scale-markup ratio, in 53% of cases, structural approach estimates this ratio closer to the reality.


Mahmod Mahmodzadeh, Mehdi Fathabadi,
Volume 7, Issue 26 (12-2016)
Abstract

The aim of this paper is decomposition of total factor productivity (TFP) growth to four factors technological progress, technical efficiency, allocative efficiency, scale effects in 21 manufacturing industries, using a panel data technique, during 2000-2011.Findings show that the production elasticity related to labor and capital is o.57 and 0.13, respectively and economy of scale is less than unit. Also, results indicate that productivity growth is positive only in 8 industries that include electronics, communications, paper, medical and optical industries. The decomposition reveals that, TP has been the main driving force of productivity growth- especially in chemical, non-metal mineral, primary metal, motor vehicles, trailers and semi-trailers- while negative efficiency changes, allocative efficiency and scales effects observed in certain industries have contributed to reduce average productivity growth.


Somayeh Azami, Latif Poor-Karimi, Sahar Sadri,
Volume 9, Issue 31 (3-2018)
Abstract

The purpose of this study is to evaluate environmental productivity changes in Iranian manufacturing industries, with two-digit ISIC codes, during 2003-2014. For this purpose, Meta-frontier Non-radial Malmquist CO_2 emission Performance Index (MNMCPI) is used. This index considers technological heterogeneities of industries. Empirical results indicate that, during 2003-2014, MNMCPI has grown, on average; the highest growth rate belongs to industries with medium technology. Also, all three indices of EC, BPC and TGC, as MNMCPI components, experienced growth, on average. TGC has the greatest impact in industries with medium technology while BPC has the greatest impact in industries with high and low technology. In general, BPC had the greatest effect on MNMCPI growth.The highest growth rate in EC index is observed in industries with low technology and the highest growth rates in BPC index, which shows the effect of innovation, and in TGC index are observed in industries with medium technology. Therefore, based on TGC index, industries with medium technology level are leading technological industries. Rregression analysis shows that energy intensity has a negative and significant effect and R&D has a positive significant effect on MNMCPI.


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