نشریه اقتصاد و تجارت نوین
سال پانزدهم شماره 2 (پیاپی 47، تابستان 1399)

The main objective of this study is to examine the effects of non-oil exports and foreign direct investment on employment, in a unified framework, by reviewing the information of 14 MENA member states in 2010-2018 using the Spatial econometrics  model. The explanatory variables studied in this study include exports, the degree of trade openness, gross domestic product, oil export, fixed capital and real wages, which were examined directly and indirectly and in general. The results of this study showed that exports and Foreign Direct Investment as important variables of this research have a direct and indirect role in increasing the employment of MENA member states. But the degree of trade openness has not had a positive impact on employment. In addition, real and fixed wages have been able to increase the employment of the countries under study. The variable oil export have had a negative impact on employment, which is a negative predicted oil export and according to empirical views. According to the results it is recommended to increase employment, improve international trade and reducing crude oil sales

One of the new approaches to purchasing and procuring goods and materials in the supply chain is the use of reverse auction. With the rapid and ever-expanding development of information technology and the Internet around the world, the use of Internet platforms for this type of procure has also been taken into account and has created online reverse auction method. In this paper, a new framework for the online reverse auction process is provided that takes both sides of the procurement process (buyer and seller). The proposed auction process is a multi-attribute semi-sealed multi-round online reverse auction. In this process, an online market-maker, with the prediction of the buyer scoring function, facilitates the seller's bidding process. To fit the function, a multi-layer perceptron neural network model is used. In this case, in addition to hiding the seller's scoring function, information is provided to sellers to improve the bid. Also, the methods of scoring by the buyer are defined Additive, Multiplicative and risk aversion, which is based on the theory of perspective. Within this framework, sellers improve their bids in each round using an optimization model. By simulating the auction process, the proposed framework was evaluated in comparison with an open auction, taking into account seller scoring criteria, seller profits, and number of auction rounds

According to the importance of clean energies in the reduction of greenhouse gas emission and environmental damage, in the present study, the impact of renewable energy consumption on economic growth in the D-8 group countries was investigated over the period of 1990 to 2014. According to the panel stationary property of the variables, the panel autoregressive distributed lag method (Panel ARDL) of mean group (MG), pooled mean group (PMG), and dynamic fixed effect (DFE) estimators were investigated. The empirical results implied that the pooled mean group model emerged as the most efficient among the three estimators. The results of causality tests indicate that there is a bidirectional causality relationship among clean energy (renewable), non-renewable energy, and economic growth in D-8 group countries. The results show that controlling for the other variables, a 1% rise in clean energy causes is likely to cause a 0.15% increase in economic growth in the long-run. Also, the results showed that in the long-run, a 1% increase in non-renewable energy will decrease the economic growth of D-8 group countries by 0.12%. Also, empirical results indicate that investment, employment and technology have a significant and positive effect on economic growth in long run. According to the results of present study, clean energy production and consumption must be increased in the D-8 countries

The behavior of Opec’s main exporter such as Saudi Arabia has been the most important problem of Iran and other oil importer or exporter countries. Excess capacity in oil production of Saudi Arabia let them, in respect to other OPEC's members such as Iran, have different behavior in normal and interruptions. In this paper, we use seasonal data in the 2000-2018 period. We modified griffin’s model and estimated it by the ARDL method and investigate the export’s behavior of Saudi Arabia in normal and interruptions periods. We decomposed the other OPEC variable into four different states to evaluate Saudi Arabia's responses in these states. Empirical results show that Saudi Arabia's exports in the normal periods (stability in oil markets) are coordinate and symmetric but in interruptions periods (wars, sanctions,…) is not coordinate and asymmetric with the rest of OPEC countries. also if other OPEC counties have a cumulative increase in oil exports, Saudi Arabia responds in normal and interruptions periods are symmetric (its response is equal in export quantity) but not coordinate (its response is in opposite direction, so export coefficient is negative). This negative coefficient shows that Saudi Arabia's responses in interruptions periods have a stabilizing effect on oil markets. also if other OPEC counties have a cumulative decrease in oil exports, Saudi Arabia's response in normal and interruptions periods is not only asymmetric (its response is not equal in export quantity) but also not coordinate(its response is in opposite direction, so export coefficient is negative). Here also a negative coefficient shows that Saudi Arabia's responses in interruptions periods have a stabilizing effect on oil markets. But the empirical result of griffin's model (1985) says that an export cut by other OPEC, Saudi Arabia's response to these four states is similar.

Knowledge development and accumulation are important sources of economic growth and development and Learning is one of the effective factors in the development and accumulation of knowledge. Learning reduces the average cost over time along with increasing cumulative production. The chemical industry plays an important role in the country's economy due to its high value added, product diversity and employment. The objective of this study is to analyze learning by doing in Iran's chemical industry at the level of four-digit ISIC codes using panel data during the period 2011 to 2015. For this purpose, the learning curve was estimated using the panel data technique, assuming a constant return to scale. The finding indicate that learning has been achieved in Iran's chemical industry and the slope of the learning curve, ie the effect of learning in all sub-sectors of Iran's industry, as expected, is negative and equal to 0/86. The results indicate that the learning effect is different in the sub-sectors of Iran's chemical industry, but it is close to the average learning intensity.

Iran has more than 33 trillion cubic meters of conventional natural gas reserves and 157 billion barrels of crude oil reserves. Therefore, it has a good potential and comparative advantage for the development of the petrochemical industry with the aim of completing the oil and gas value chain. According to statistical data in 2017-2018, these products have obtained 27% of the total value added of the industrial sector and 51% of the total exports of Iran. Considering the importance of this sub-sector of industry in the Iranian economy, this study examines the comparative advantage of four commodity codes for the production of refined petroleum products, the production of basic chemicals, the production of other chemical products and the production of raw plastics and synthetic rubber using the opportunity cost method of domestic resources in the period 2000 to 2017. According to the research results, in general, from 2000 to 2010, the comparative advantage of these goods has decreased, but from 2010 to 2017, the trend of comparative advantage has been increasing. Also, based on the results of the cost of non-tradable resources and the cost of wages until 2010 had the largest increase in the cost of production of these products and from 2010 to 2017 the increase in the exchange rate had the greatest impact on increasing the comparative advantage.