جستجوی مقالات مرتبط با کلیدواژه « صنایع کارخانه‎ای » در نشریات گروه « اقتصاد »

The main purpose of this study is to investigate of economies of scale and thier evolutions in Iran’s manufacturing industries. Economies of scale are the cost advantages that companies achieve due to operational scale, and reducing the cost per unit of product increases the economies of scale. In this paper, economies of scale in the Iranian industries have been studied and analyzed with an approach based on econometric models and estimation of a translog cost function for the period from  2011 to 2019 in terms of two-digit ISIC codes. The results showed that the trend of scale elasticity in terms of the cost in Iran’s manufacturing industries had always been less than one for 19 years. Such a result confirms that Iranian industries have been almost within the range of economies of scale in the last two decades and have not yet left this range. Indeed, the Iranian manufacturing industries have not yet been able to grab the advantage of economies of scale and from this view, they still have small sizes and scales. Also, most of the country's large and capital-intensive industries have relatively large scales (compared to Iran’s economy) and their further development requires entry into export markets. in contrast, the labor-intensive industries in the country are mostly small-scale. This indicates that developing and expanding the size of firms through promoting the mentioned industries and resolving production problems and obstacles leads to greater prosperity and better utilization of economies of scale.

The main goal of this research is to evaluate and analyze the condition of economies of scale in Iran's manufacturing sector and examine its developments using the econometric methodology with the Translog cost function approach.The findings of this research show that Iran's industries have not yet used the benefits of their economies of scale during the past two decades, and from this point of view, they still have small scales. The ratio of added value to the number of enterprises and the ratio of real investment to the number of enterprises as indicators of the scale of production in the 1390s compared to the 1380s has become smaller. In other words, the scale of production in Iran's manufacturing sector has decreased during the last decade, and this is a confirmation of a long-term stagnation in Iran's industrial sector. Also, the results of the investigation at the level of two-digit ISIC codes also shows that the industries of food products, production of tobacco and tobacco products, production of clothing, production of leather and related products, production of other means of transportation, and production of furniture in They have moved to reduce the scale. On the other hand, the industries of production of various types of beverages, production of paper and paper products, printing and reproduction of recorded media, production of chemicals and chemical products, and production of drugs and chemical and herbal medicinal products have clearly moved towards increasing scales. The most important policy recommendation of this study is predicting the price and access to raw materials, access to financial resources, reforming the ownership and management structure of large industrial companies by moving towards the private sector and especially market development for domestic industrial products and the possibility of accessing markets. The external goal is to increase industrial exports along with increasing competitiveness in global markets

Economic complexity is one of the criteria for measuring the knowledge-based economy of a country. Several studies have shown that improving the economic complexity index leads to sustainable economic growth and improved welfare. Therefore, policy makers tend to place the development and progress of the country in the path of knowledge-based economy and production of complex products. But the channel of influence of complexity on the economy of a country is questionable. Identifying this channel can lead to the focus of development policies. The hypothesis investigated in this research is the effectiveness of economic complexity on the field of factory activities. In the literature review, it was found that the relationship between these two variables is not predetermined. To test the hypothesis, a statistical sample including 46 countries has been selected for a period of 31 years ending in 2020. The PMG model has been used for data processing. The result of the estimation of the model shows that the increase in the added value of factory industries is one of the consequences of the improvement of economic complexity. This finding can help to set development policies.

The production of all goods and services is often associated with unwanted side output, which neglecting it in the calculation of the output can cause a significant distortion in the calculation of productivity This article, using the estimation of Malquist--Lionberger and Malmquist indexes, has investigated the difference in the results of attention to the undesirable output in calculating the productivity of 24 ISIC two-digit code activity fields during the period of 2011-2018. . The comparisons show that there is a significant gap between the productivity score with regard to adverse conditions and productivity regardless of adverse conditions. The results of the test confirm the existence of the considered significant difference.Therefore, it statistically makes and considering unfavorable is more reliable and can be more appropriate in this context. The average productivity of the manufacturing sector without taking into account the undesirable result was equal to 0.895, while with this result, the productivity of the same sector reached 0.908. This result shows the importance of paying attention to the side output for accurate measurement of corporate productivity

Evaluating the energy demand of the industrial sector is important and necessary for policy making in order to increase production and improve efficiency in this sector. The purpose of this study is to estimate the demand for major energy consumption in the industrial sector, including natural gas energy, electric energy, and petroleum products energy (gas oil and mazut) in the form of a system of equations during the period 1988-2019. The results of this research, based on seemingly unrelated regression equations, show that the demand for natural gas and oil products is inelastic, and natural gas energy input is substituted by electricity and oil product energy is substituted by natural gas. Also, the increase in the added value of the industrial sector has a positive and significant effect on the demand for natural gas energy and petroleum products. In addition, the increase in the ratio of capital to labor in the industrial sector is accompanied by a decrease in the demand for natural gas energy and petroleum products and an increase in the demand for electric energy. In addition, the increase in the ratio of capital to labor in the industrial sector has led to a decrease in the demand for natural gas energy and petroleum products and an increase in the demand for electric energy.

In this paper, the effect of formed habits on consumer behavior was investigated by separating the three effects of generation, age and time (year) by generating a quasi-hybrid data set using the Dayton method. For this purpose, in the field of generations, 14 generations were studied from 1305 to 1995. Concerning time (year), this research investigates over 30 years from 1986 to 2020. The age of the subjects in this study ranged from 16 to 72 years. The results of the analysis of generation effects show that consumption expenditures are increasing with younger generations. The results of the study of the effect of time show the increase of households' consumption expenditure during the years 1986 to 2020 and the somewhat constant trend of the years 1995 to 2015. Results of the effects of age on consumption expenditure pattern confirmed the life cycle pattern.

The purpose of this article is to evaluate the trend and the factors that determining the growth of the total factor productivity (TFP) in the manufacturing industries in the provinces of Iran using a stochastic frontier approach in the period of 2011-2019. First, the stochastic frontier translog production function was estimated by the exponential true fixed effects (ETFE) method. Second, the TFP components were measured includes technical progress, technical efficiency changes and scale efficiency. The results show that the average TFP growth of provinces manufacturing industries was 31.1% annually, which technical progress and technical efficiency changes decline and scale effects improve the TFP. In the study period at first the total factor productivity growth increases in 2012 and 2013 and then decreases in 2014 and 2015. In the years 2016 to 2019, the growth of TFP has increased again. The technical progress has had a negative growth in all years, but its trend has been accompanied by improvement in the study period. Among the provinces, Ardebil, Kurdistan and Hormozgan experienced the highest TFP growth and Tehran, Sistan and Baluchistan and Mazandaran experienced the lowest TFP growth. According to the findings, manufacturing industries need to use advanced technologies along with re-allocation of resources.

Economic growth is influenced by two factors including the accumulation of production factors and the increase in efficiency. Efficiency is one of the important issues of the economy. Although its improvement is necessary for all countries, it is of double importance in developing countries, due to the lack of superior technology and more waste of resources and production inputs. Since the industry sector constitutes a high share of the gross domestic product and is of great importance due to its previous and subsequent connections with other economic sectors, the improvement of efficiency in this sector can lead to an increase in employment, production, and income in the entire economy. The industry sector is one of the significant energy-consuming sectors in every country, and the improvement of energy efficiency as one of the important policy tools plays an essential role in the growth of the industry. Therefore, this study examines the technical efficiency and scale of energy consumption of the industries in the provinces of the country. To this end, the non-parametric method of Data Envelopment Analysis (DEA) is used. Also, the issue of targeting energy subsidies in 2019 has had a significant impact on the cost of energy consumption in the industrial sector of the country. On this basis, the present study analyzes the technical efficiency of energy in the years after the implementation of this law. Considering the difference in the share of energy consumption according to the type of energy carriers at the industry level, we have considered four leading energy carriers for the calculation of efficiency.

The research steps include defining an appropriate model for calculating technical efficiency, determining the type of DEA model in terms of input or output-oriented and the type of efficiency with regard to scale, collecting, calculating the technical efficiency and the scale of the industrial sector of each province, and finally analyzing the results. Data envelopment analysis (DEA) is a non-parametric linear programming method for evaluating the efficiency of decision-making units (DMU). The main advantage of this method compared to parametric methods such as the stochastic boundary function is that the shape of the distribution function and the production relations do not create a limit for it. In addition to technical efficiency, scale efficiency can be obtained for all units by calculating the ratio of technical efficiency in the state of constant efficiency to technical efficiency in the state of variable efficiency. The researchers used the input-oriented multi-stage DEA model with six inputs and one output to determine the technical efficiency and the energy consumption scale efficiency of the industrial sector in 31 provinces. In this model, the variables of labor force (people), formation of real fixed capital (million rials), consumption of natural gas, diesel, fuel oil and electricity (barrels equivalent of crude oil) in the industry sector of each province are the  inputs, and the actual output of the industry sector (million rials) in each province is considered as the output.

The results of technical and scale efficiency scores of industries in the provinces analyzed with the multi-stage DEA model during the years 2011-2019 show that, in 2011 (the beginning of the subsidies targeting), only active manufacturing industries in the provinces of Isfahan, Ilam, Bushehr, Tehran, Khorasan Razavi, North Khorasan, Khuzestan, Sistan and Baluchistan, Kermanshah, Kohgiluyeh and Boyer Ahmad, Gilan, Markazi, and Hormozgan were technically efficient. The inefficiency was higher in Lorestan, Golestan, Yazd, and West Azerbaijan provinces. Also, over time, when the effect of the increase in the price of energy carriers became more evident in the subsidy targeting law, the technical efficiency was relatively improved in most of the provinces. The noteworthy point here is that, among the efficient provinces, Tehran, Bushehr, Khuzestan, and Hormozgan have had the highest share of energy consumption. Ilam, Sistan and Baluchistan, Kahgiluyeh and Boyar Ahmad provinces are among the provinces with the lowest energy shares. The industrial province of Yazd, despite having being the fourth place in energy consumption in the industry sector (about 7 percent share) after West Azarbaijan Province, has the lowest average technical efficiency score. This shows that, in this province, planning is required for extensive changes in various sectors of industry in order to increase the efficiency in Energy consumption. The evaluation of the efficiency of the scale of the industries in the country's provinces shows that, in 2014 and the beginning of the law of targeting the subsidies, the industry sector of the provinces of Isfahan, Bushehr, Tehran, Sistan and Baluchistan, Fars, Kermanshah, Gilan, Markazi, Hormozgan worked on an optimal scale, but the other provinces had inefficient scales. Among the inefficient provinces, the intensity of scale inefficiency in Ilam Province was higher than that in the other provinces, which indicates that the size of its production organization is not optimal and it can move towards an efficient scale by changing the size. This is despite the fact that, after the year 2019, a relative improvement in the scale efficiency score occurred for most of the provinces. In the last year, active manufacturing industries in the provinces of Ardabil, Alborz, Bushehr, Tehran, Khuzestan, Sistan and Baluchistan, Kurdistan, Kermanshah, Markazi, Hormozgan and Yazdbenefited from the efficiency of the scale.

Conclusion and Policy Implications: 

In DEA models, for each inefficient unit, an efficient unit or a combination of two or more efficient units is introduced as a reference unit. In this regard, each inefficient unit should be compared with an efficient unit to reach the efficiency limit. Therefore, the reference unit should be similar in size and structure to the inefficient units that measure it. In this regard, the seven provinces of Ilam, Bushehr, Tehran, Khuzestan, Sistan and Baluchistan, Kohgiluyeh Boyer Ahmad and Hormozgan are considered as reference units for the other provinces of the same level in terms of the size and structure of the industry to improve efficiency. In terms of policy-making, it is suggested that the technical efficiency score of the provinces be supported by the government and its projects for the industry sector through the allocation of low-interest loans, industrial subsidies, tax exemptions, etc.

The main purpose of this article is a comparative analysis of the optimal level of production capacity of the country's top factories. In this article, we compare five different industries that are leading industries in the field of production. These industries are: (chemical, base metals, petroleum products, gold and jewelry, stone), in order to determine the optimal level of production capacity in the top factory industries with the two-digit code ISIC.Rev.2 and ISIC.Rev.3 from The translog cost function was used. Production efficiency is the equivalence of scale traction or cost traction (inverse of scale traction) with one. To obtain the optimal level of production, first from the equation of scale elasticity or cost elasticity with one, a production level was obtained which naturally satisfies the first condition, then the second condition in production was obtained for research purposes. The results of comparative analysis of top industries showed that the production capacity in the chemical industry was the amount of optimal production capacity equal to 598647 million rials. The average real output value of each industrial enterprise in the chemical industry production sector is 57042.36 million rials per year, it seems that the production companies in this sector have produced more than their capacity. And have made the most of their optimal capacity. Capacity utilization here was estimated at 185%. The amount of optimal production capacity of petroleum products is equal to 34921.6 million rials. The average value of real output of each industrial enterprise in the production of petroleum products is 39821.51 million rials per year, it seems that the enterprises of this sector have used their capacity in a more efficient way. Capacity utilization was estimated at 164%. The actual production level of basic metals was 57421.2 million rials per year, which indicates the minimum point of minimization of the total average cost function. The average value of real output of each industrial enterprise in the production of basic metals is 61235.2 Rials per year. This indicates that companies in this group of industries in the country use about 33.7% of their nominal capacity. Which is much less than the optimal value is required. The nominal production capacity of the gold and jewelry industries is about 59%. Production capacity is increasing. The amount of optimal production capacity in this industry was equal to 57846.3 million Rials. The average value of the actual output of each industrial enterprise in the production of gold and jewelry industries is 65432.2 million Rials per year, it seems that manufacturing enterprises in this sector of their capacity They used it in a better way. Capacity utilization here was estimated at 174%. The nominal production capacity of the stone industry is about 41%. The amount of optimal production capacity in this industry was equal to 26741.4 million rials. The average value of real output of each industrial enterprise in the stone industry production sector is 31254.6 million rials per year, it seems that the manufacturing enterprises in this sector have not used their capacity in a more optimal way. Capacity utilization was estimated at 78% in the analysis.

Regarding the importance of knowledge-based capital in the modern economy, this paper aims to measure this kind of capital in Iran’s manufacturing sector using the exploratory factor analysis method. Data include active firms in Iran’s manufacturing sector at the 2-digit ISIC level over the period 2002-2018. The results indicate the existence of a factor in the manufacturing firms with a positive and increasing trend of knowledge-based capital accumulation. The findings also show that the accumulation of this type of capital in Iran’s manufacturing sector is in the early stages of development and highly depends on the literate labor force.

Air pollution concerns, climate change, and sustainable development necessitate the discussion of the dynamics of energy intensity. Nowadays, energy convergence is widely used as a tool for considering the dynamics of energy intensity. The energy intensity convergence in manufacturing industries is suitable to assess whether there is a knowledge spillover between manufacturing industries and whether government policies have been effective for reducing energy intensity in manufacturing industries. The purpose of the study is to examine energy intensity convergence in Iranian manufacturing industries. To do that, we collected data from nine manufacturing industries from 1995 to 2015 and employed the generalized method of moments in panel data (GMM) technique. The results of the model estimation show that there is  convergence of energy intensity in the manufacturing industries.

According to empirical evidence, the consequences of supportive policies will not necessarily lead to efficient industries, optimal allocation of resources, international competition, and optimal use of opportunities. Inappropriate or suboptimal use of the supportive policies in manufacturing industries may change the direction of scarce resources and the relative prices in the society which may lead to price distortion and some monopoly power in the country's industries.  In this study, using the Hall-Rager method, we analyzed the effect of supportive policies on price deviation in the Iranian manufacturing industries. To do this we used the Herfindahl index, the simple rate of tariff, and Implicit nominal support index as independent variables and the price deviation in the manufacturing industries of the country as a dependent variable. The model as a dynamic panel data has been estimated with the generalized method of moments for 92 industries based on the four-digit ISIC code during 2004-2014. The results showed that between the years 2004 to 2014, the highest level of simple tariff rate was for the shoes and clothes industry with 100 percent. The cement and plaster industry had the most implicit support amongst the  92 studied industries. According to the estimated models, centralized industrial structure, implicit nominal support, tariff rate, and barrier entry intensity have had a positive and significant effect on the formation of price deviation in Iran's manufacturing industries.

The value added of manufacturing activities has positive effects and side effects on the national economy. Improving productivity, increasing the complexity of economy and strengthening capital accumulation are among these consequences. Therefore, in some studies, the role of manufacturing has been interpreted as a growth engine. In addition, improving the economic conditions and per capita incomes usually increase the share of household expenditures in high-tech industrial products. This will increase the demand for factory products and possibly increase the activity of manufactures. The main purpose of this study is to investigate the relationship between the value added induced by the activity of manufacturing industries and economic growth among different countries using PVAR model. The limitation of having a consistent set of data has reduced the number of sample countries to 21. The data belongs to the years 1990-2020. The average share of manufacturing in the total production of the selected countries is 15% and the average economic growth in these countries is about 2%. The result of the study, the stability of which has also been confirmed, does not rule out the existence of a simultaneous and reciprocal relationship between the two main variables of the research. In case of a positive 10% shock in the value added of manufacturing, the economic growth of the selected countries of this study will increase by 1% after 7 years and in case of a positive 10% shock in the economic growth, the activity of manufacturing will increase by about 0.5% after 7 years. In other words, the effect of manufacturing on the economic growth is greater than the effect of the economic growth on factory activity.

The purpose of this study is to investigate the factors affecting the use of production capacity in Iran's manufacturing industries. Using the statistical information of Iran's manufacturing industries with international two-digit ISIC codes during the years 1381-96 and through the cost-transfer function, the minimum cost point as a level of cost corresponding to the optimal production or nominal capacity of different manufacturing companies Iran was estimated and the average ratio of real production to production with nominal capacity was calculated as a percentage of production capacity utilization for different industries. The results of the study show that with the exception of the petrochemical industry, other industries face little use of production capacity. On average, the country's industries use less than 50% of their capacity. This ratio is lower in some industries such as base metals (28.7%), paper (29.6%) and wood and wood products (24.1%) and in other industries except non-metallic mineral products (2 / 84%) is between 41% and 51%, which is very small in total.

This article seeks to identify the factors affecting employment in Iran's manufacturing industries during the period 1996-2015. Employment is one of the main channels affecting the performance of the economy, the changes of which will affect production, imports, exports, competition, and investment.Considering that the manufacturing industry plays an important role in creating value-added and job creation in the country's economy, this issue has caused every part of the country's manufacturing industry to be associated with high diversity. In recent years, factory industries have experienced many fluctuations due to the domestic and international conditions governing the country's economy, which has caused the major industries of the country to operate with less than production capacity, despite the high installed capacity.  

Employment and factors affecting macroeconomic factors creating new job opportunities and increasing employment is one of the most important issues for economic planners and policymakers. Identifying the factors affecting employment is one of the most important issues in the Iranian economy.  

In the long run and in the classical model of labor demand, the wage variable has an important role in determining the equilibrium level of employment; in addition, the variables of firm production are also effective in determining the level of employment. On the other hand, in the long run, the main factors affecting the demand for employment in the manufacturing industry are wage, output, and capital utilization variables, which can be replaced by capital stock, which is also interpreted as the substitution effects of scale. To extract the labor demand function, there are several methods by which labor demand is extracted under the same conditions. In this study, to extract the labor demand in 22 industrial sectors with 10 employees and more, we use the method presented by Varian (1978). In this method, the labor demand function can be minimized by reducing the cost function to the production function.  

The purpose of this paper is to analyze the factors affecting employment in the manufacturing industry by dividing the two-digit ISIC codes by dividing the two-digit ISIC codes by 10 employees and moreover, the period 1996-2015 using a generalized panel data approach and Generalized Least Squares (GLS) estimator. The most important factor in the development of employment in manufacturing industries is the increase in production, the realization of which requires the coordination of monetary, fiscal, foreign exchange and trade policies, and extensive interaction with the global economy and attracting foreign direct investment. The results show that the elasticity of employment demand with respect to wages, production, and capital stock are -0.22, 0.58, and 0.12, respectively. Considering that one of the effective factors in increasing employment in Iran's manufacturing industries are increasing production, it is recommended that the government place more emphasis on combating the smuggling of goods and developing a competitive environment to expand industrial activities and ultimately increase demand for industrial products. Increase industrial production and thereby increase labor demand. Considering the positive effect of capital stock on employment in the manufacturing industries, it is recommended to adopt policies to increase employment, which will lead to increased investment in factory industries to increase employment by increasing capital stock.

Information and Communication Technology (ICT) is needed as a powerful technology for process optimization and therefore its impact on intensity of energy consumption has been studied from various perspectives, but the impact of this technology on intensity of energy consumption is ambiguous. Measuring, monitoring and analyzing energy consumption for awareness in saving energy is a key issue based on ICT applications. This study seeks to investigate the impact of information and communication technology by using the Iran's industries by 4-digit ISIC codes, over the period 2007-2017 on energy intensity. Due to the nature of the data, the model has been estimated by GLS method. The results indicate a negative and significant relationship between per capita ICT capital and energy consumption intensity and the relationship between per capita total capital and energy subsidy variable is positive and significant. In this study, the negative and significant effect of firm size on energy intensity has been achieved and the effect of research and development costs intensity on energy intensity is not significant.

Water shortage in Iran is one of the main limiting factors for the development of economic activities in the next decades. Therefore, the main purpose of this study is to estimate the water demand function in Iran's manufacturing industries. For this purpose, the Translog Cost Function has been used. The data used in this research are related to Iran's manufacturing industries during the years 2002 to 2017. The results of model processing show that in all Allen partial elasticity has the correct and expected negative sign. In other words, the inverse relationship between price and demand for water in terms of absolute value is greater than other inputs. In other words, the sensitivity of water input demand to price changes is higher than other inputs and energy, capital and labor inputs are in the next. The absolute value of the numerical value of the price elasticity of all inputs is less than one, therefore it can be said that demand for all inputs is inelastic. The Morishima elasticity of substitution indicate that the tensile ratio of water, labor, energy and capital factors is less than one and all of them have a positive sign. In other words, inputs have a substitution relationship. Based on the research findings and considering that the price elasticity of water input is about one, it seems that price policies will not have an effect on reducing demand and saving water or will be very low.

Estimating the substitution elasticity of imported products is a key parameter for analyzing trade policies, especially trade integration issues. In general, this elasticity represents the intensity of the impact of import demand on domestic demand from the relative change in domestic price to import. In this study, the Armington substitution elasticity's were estimated for 103 industries four-digit ISIC in the period 1371-1396. The data used for domestic manufacturing production are extracted from the Statistical Center of Iran, the domestic and import price indices at the producer level from the Central Bank of Iran, and the trade data from the Islamic Republic of Iran Customs Administration. According to the results; the industries with the highest positive elasticities of substitution, respectively, are the "Manufacture of malt liquors and malt" and the "Manufacture of basic chemicals, except fertilizers and nitrogen Compounds" and Industries with the lowest positive substitution elasticity are, respectively "Tanning and dressing of leather" and "Building and repairing of pleasure and sporting boats except ships". Accordingly, in industries with positive substitution elasticity, it is possible to replace foreign consumption with domestically produced goods. Long-term elasticities are also significantly higher than short-term tensions (6.7 times).

Informal income and pseudo financial statements are formed to tax evasion. In the model, informal sales are a function of company’s real and formal profit. The statistical distribution of corporate profit is a specific beta distribution which is called pert. The reason for selecting manufacture industries in this research is the complex process of converting input into output in these firms which makes it difficult to accurately audit the tax. Hence, the creation of informal sales in these firms seems possible. Parameters of probability density function for the profit ratio were calculated by a sample of Tehran stock member manufactures. The results of the study indicate that the informal income ratio at the 99% confidence level is between 11 to 12 percent of total revenues of the firms studied. JEL Classification:  H26, C15, L52

The complex structure of the conversion of raw material into the product makes it difficult to create a match between the input and the output in the manufacture industry. Such firms can conceal a part of their sales despite the VAT system. The main motivation of the firm in such a move is to evasion from profit tax. The purpose of this study is to model this type of tax evasion. By simulation of the model variables and using the Monte Carlo method, a distribution for tax capacity, official tax and tax evasion were formed. The research finds that 40 percent of government tax revenues in the manufacture industrial sector disappear in the form of tax evasion. The share of calculated tax evasion is related to VAT, which is not imposed on the firm, but because of informal sales, the taxes related to the added value created in the production and distribution sector are not transfer to the government. The sum of the tax evasion value is 4.2 percent of the revenue of the manufacture industries.