نشریه پژوهش های اقتصاد صنعتی
پیاپی 15 (بهار 1400)

The purpose of this paper is to investigate the effect of the industry sector tariff rate changes on the Iranian households’ welfare under the conditions of the resistance economy. To achieve this goal, based on Samadi et al.'s (2019) survey, the computable general equilibrium model of Decallo et al. (2013) has been adjusted. After making the adjustments, using the adjusted social accounting matrix of 2011 and GAMS software, the equations were solved and analyzed in the form of three scenarios. The findings show that in the first scenario, without assuming the implementation of the notified policies of the resistance economy, the reduction of tariff rate in all sectors of industry causes the level of households’ welfare will become negative. In the second scenario, assuming the implementation of the notified policies of the resistance economy, welfare in all sectors of industry except medium-tech industries is positive and its trend is descending in all sectors except high-tech industries. In the third scenario, assuming the strengthening of social and institutional factors, welfare in all sectors is positive and its trend is upward in medium and high-tech industries and downward in low-tech industries. Therefore, the reduction of tariff rate is recommended only in the high-tech industries if the notified policies of resistance economy are implemented.

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.

Many factors affect the exit of firms from the industry sector. In this regard, the role of macroeconomic factors is very important due to their impact on the growth environment and firm activity. Hence, the purpose of this research is to study the macroeconomic factors’ influence on the exit of manufacturing firms in the provinces of Iran (ISIC codes: 15-37) during the years 1996-2014 by using the spatial panel econometrics method. The results confirm the impacts of inflation, population, economic growth, and industrialization index of each province's economy on the exit of firms in the province and its adjacent provinces. The exit of firms in each province has also a negative impact on the exit of the firms in the adjacent provinces. Considering the role of macroeconomic factors on the exit of firms from the province and adjacent provinces, it is recommended to implement the regional policies which are in regard to the synergy of industrial production within and between provinces.

Small and medium industries are some of the most important economic sectors in any country. These industries, usually transforming to business incubator by economies of agglomeration in industrial centers, while large industries are becoming growth poles based on economies of scale. In the regional economics framework, SMEs affect the geographic space and large industries affect the economic space. While small industries by relying on the diffusion effects play their role in industrial development and economic growth as a business incubator. The present article is examining and modeling two issues in the field of small and medium industries’ activities effects: the diffusion process modeling and the modeling of how information transmission (or similarly, technology and innovation). Both issues can be brought up in the wave theories. Among two given regions, differences between the type and function of industries can usually make the difference between the waves. considering that each industry has its own preceding and following effects, each region also proportion to the type of established industries in it can have preceding and following effects on the other regions in geographic space and economic space. For the diffusion process model, the approach of gravity model, and for the process pattern and how information transmission, a hierarchical system is used. In both methods, the results are shown in form of the diffusion models in general and under specific conditions. The extracted theoretical functions indicate how the regions interact based on the types of established industries in them. The effects of the considered models show that the SMEs can have significant effects on the surrounding regions around via sending permanent innovation waves, information, and other development waves.

The purpose of this study is to investigate the relation between the shareholder basic and the investment return volatilities of the industrial companies listed on the Tehran Stock Exchange. In this study, one hundred companies active in the Tehran Stock Exchange which have had a relatively higher level of liquidity, were selected as a sample over a 6 year- period (2014-2019). In this regard, a panel data regression model was used. According to the existence of heteroscedasticity problem in the estimated results, the GLS method was used for estimating the model’s parameters. Also, based on the iterative fixed effect test and Hausman test, the random effects model was preferable to the fixed effects model. Hence, a panel regression model was formulated by random effects. The results show that by increasing the number of the large shareholders of the industrial companies and their ownership percentage, two effects are created. The first effect is related to reducing the herding behavior of the large shareholders and subsequently related to reducing the return volatility level of the industrial companies. The second effect is related to the share price volatilities which have been arisen from the trading effects of the large shareholders. Also, by increasing the number of the large shareholders and their ownership percentage, gradually the impact of the trading market of the large shareholders on the companies return volatilities superior to the impact of the more informed shareholders.

The manufacture of machinery and equipment has one of the largest production value chains among different industries and the products of these industries are widely used as intermediate goods in other industries. In this paper, the production function, Average elasticity, marginal elasticity, substitution elasticity, and elasticities of types of energy for the nine selected activities of the machinery and equipment industries have been estimated by using the Translog production function and seasonal statistics and information of the years 2002(2)-2019 (1).The results show that the marginal elasticities of labor, capital stock, and energy are positive for all the production activities. The average elasticities of employment and energy in three activities the manufacture of pumps, compressors, taps, and valves (2813), manufacture of bearings, gears, gearing, and driving elements (2814), manufacture of machinery for textile, apparel, and leather production (2826) are more than the other activities. The manufacture of engines and turbines except aircraft, vehicle, and cycle engines (2811), manufacture of machinery for metallurgy (2823), and manufacture of the other special-purpose machinery (2829), have the most average production elasticity with respect to the capital stock and energy, respectively. In the manufacture of bearings, gears, gearing, and driving elements (2814), manufacture of agricultural and forestry machinery (2821), manufacture of machinery for mining, quarrying, and construction (2824), and manufacture of machinery for textile, apparel, and leather production (2826), the estimated coefficients for the substitution elasticities between capital stock and employment as well as the substitution elasticities between capital stock and energy are positive that it indicates the inputs are substitute, In the manufacture of pumps, compressors, taps, and valves (2813) and the manufacture of the other special-purpose machinery (2829), there is a substitution relationship between only the two inputs of capital stock and employment. Substitution relations between energy and employment for the three activities of manufacture of bearings, gears, gearing, and driving elements (2814), manufacture of engines and turbines, except aircraft, vehicle, and cycle engines (2811), and manufacture of machinery for metallurgy (2823) has been observed.