مجله اقتصاد و توسعه منطقه ای
پیاپی 14 (پاییز و زمستان 1396)

The main purpose of this study is the spatial investigation of the industrial structure among Iran’s provinces and the matters that can effect this formed structure. In fact, the underlying spatial structure of industry which shapes the economy is the result of the spatial dependence on the different parts of that economy due to which the centripetal and centrifugal forces come to existence. This study analyses the spatial structure of industry in the Iranian economy. Therefore, the vital questions raising here are how is the spatial structure of the industrial activities in this economy? Whether or not this structure associates with the spatial inequality? What factors cause the formation of such structure in the present industry? And eventually, knowing whether spatial dependence on different provinces of Iran, could be an important channel to construct this structure. These various issues are addressed in this study. Geographers and economists alike have sought to develop indices that capture inequality across industries, time, and space. In the first section, we present the ideal index of spatial concentration i.e., Ellison and Glaeser index (1997). This will allow us to find out how spatial inequalities imply new and specific constraints. Then, the approach that consists of regressing industry-specific indices of spatial concentration on a number of explanatory variables suggested by theoretical models, such as the intensity of increasing returns, the level of trade costs, or the market potential is considered.
In order to answer the questions of this study, Ellison and Glaeser (1997) concentration index has been used to evaluate the industrial spatial structure between the provinces of Iran. As this measurement has been done within the period of 1997-2013 and is based on the two-digit industries (ISIC classified), the effective factors forming the structure of industrial activities which have estimated the spatial econometric model of the time should be identified. The analytical model used in this study is Spatial Autoregressive Panel Data model (SAR Panel Data). An important point is that in spatial regression models each observation corresponds to a location or region. In other words, this model could consider the dependence between the regions or sections. Spatial dependence reflects a situation where values observed at one location or region, say observation i, depends on the values of the neighboring observations at nearby locations. Spatial regression models exploit the complicated dependence structure between observations which represent countries, regions, counties, etc. As a result, the parameter estimates contain a wealth of information on the relationships among the observations or regions. A change in a single observation (region) associated with any given explanatory variable will affect the region itself (a direct impact) which can potentially affect all the other regions indirectly (an indirect impact). In fact, the ability of spatial regression models to capture these interactions represents an important aspect of spatial econometric. An implication of this is that a change in the explanatory variable for a single region (observation) can potentially affect the dependent variable in all other observations (regions). This is of course a logical consequence of our SDM model, since the model takes into account other dependent regions and the explanatory variables. This type of development has wide-ranging implications for the interface of economic theory and econometrics. It suggests that spatial econometric models may be applicable to many situations where they have not previously been employed.
The results of EG indicator show that the spatial structure of industry in the economy is considerably unequal. Thus, based on this inequality, Azarbaijan sharghi, Qazvin, Markazi, and Tehran with values of .044, .051, .052 and .063 for spatial concentration index perspectively, are the most industrialized Provinces. In contrast, Bushehr, Hormozgan, and Ilam with values of .550, .244, and .317 are as the least developed provinces in the industry sector and have only certain industrial activities. In the next phase of the study, results of estimating spatial econometric model show that coefficients of market potential, return to scale, price per square meter of land in the province, and the transportation costs in spatial model, each one is estimated as -.174, -.041, -.023, and.038, respectively. In contrast, the coefficients of the variables of average wages and budget expenses of government is estimated with positive values. The estimation of the rate of dependence coefficient on provinces in spatial model (.430) shows that the centripetal forces in the provinces can dominate the centrifugal forces. In addition, due to existance of spatial dependence, we can identify extensive network of interdependences among provinces and their potentials impact on the structure of industry. Also, we can analyze the province's capacity to absorb changes in any of the mentioned factors that affect the structure. Results from the identified extensive network show that provinces of Tehran, Qom, and Qazvin have the highest coefficient of effectiveness and the highest ability to absorb the changes generated in the explanatory variable. Besides, Sistan and Baluchestan and Hormozgan provinces on average, have the lowest coefficient of effectiveness and the ability to absorb the changes generated in the explanatory variables.

Since the end of the Second World War, international trade has grown faster than the world production in nearly a year. In this period, trade among the developed nations has increased much faster than trade in general accounting for an increasing proportion of total trade. Balassa (1966) and Grubel (1967, 1970) demonstrated the importance of simultaneous increase by all countries regarding their exports of most industries. The pioneering studies of the gravity model were realized by Tinbergen (1962) and Pöyhönen (1963). The gravity model is analogous to Newton's law of gravity, where the state gravity between two objects is directly related to each other and inversely related to distance. Anderson's (1979) and Deardorff (1998) have considered that the gravitational equation helps explain the pattern of international trade.
In the 1980’s, Romer (1986, 1990), and Lucas (1988) studied the endogenous growth models. Endogenous growth theories identify a number of channels that affects growth, such as productivity, human capital, and openness. Most of the studies show that trade and economic growth are positively correlated.
When economic geography was born in 1990’s some authors as in Krugman (1993) explained the relationship between North and South considering the mobility between the countries. This process involves trade flows, migration, and direct foreign investment.
In last years, a number of gravity models have been applied to explain the bilateral trade flows (Egger 2002; Serlenga & Shin 2007; Faustino & Leitão, 2008).
The objective of this paper is to examine the pattern of Iran trade by adopting an argument gravity model. The manuscript uses a panel data approach. This study analyses the link between gravity model and Iran trade. The manuscript considers the determinants of Iran and and D8 countries within the years of 2006 to 2015. This study uses country-specific characteristics (per capita income, market size, geographical distance, and factor endowments).The structure of the paper is a follows.
This model is analogous to Newton’s law of gravity, which states that the gravity between two objects is directly related to their masses and inversely related to their distance.
Where Fij denotes the flow from country i to country j. Yi and Yj are the economic sizes of the two countries, usually measured as the gross domestic product (GDP), or per-capita GDP. Dij is the distance between the countries. G is a gravitational constant.
In order to facilitate the econometric estimations, we apply logs the gravity equation (1), hence, we obtain a linear relationship as follows: Where lnG corresponds to the intercept, while , and are elasticity’s.
According to the gravity approach, the trade between the two countries is directly related to their incomes (or per-capita incomes) and inversely related to the distance between them.
Since the pioneering studies of Tinbergen (1962), Pöyhönen (1963), Anderson (1979), Pagoulatos and Sorensen (1975), Caves (1981), Toh (1982), Krugman, (1997), and Badinger and Breuss (2008) the geographic distance has been an important determinant of trade. The distance can be analyzed in terms of geography, culture, language, and adjacency (Border). Rauch (1999) and Eichengree and Irwin (1998) emphasize the importance of border and common language.
Anderson (1979) introduced the product differentiation by country of origin assumption. A few years later Bergstrand (1985), Egger (2002) and Grossman and Helpman (2005) used the income per capita to specify the supply side of economies.
Usually geographic distance measures the cost of transport. According to the literature there is an increase in the flow of trade if the transportation costs decrease. The theoretical predictions show a negative correlation between distance and the trade. Balassa (1966), Balassa and Bauwens (1987), Stone and Lee (1995), Clark and Stanley (2003), and Badinger and Breuss (2008) found a negative sign between geographical distance and trade.
The empirical model uses the dummy variables to the cultural distance, language, and to the border.
The similarities of the countries encourage bilateral trade. Frankel et al. (1998) and Papazolou et al. (2006) demonstrate the importance of these qualitative variables to analyze the regional trading agreements (RTAs).
Balassa (1966) and Balassa and Bauwens (1987) found a positive sign. The empirical studies show that gravity models utilize gravitational factors as in volume of trade, capital follows, and migration (Baltagi et al., 2003; Faustino & Leitão, 2008; Kabir & Salin, 2010; Leitão & Faustino, 2009, 2010; Serlenga & Shin, 2007; Skabic & Orlic, 2007; White, 2009).
Where is bilateral trade (exports plus imports), is a set of explanatory variables.
All variables are in the logarithm form; is the unobserved time-invariant specific effects; captures a common deterministic trend; e is a random disturbance assumed to be normal, and identical distributed (IID) with .
The combined data used within the period of 2006 to 2015 for each of the commodity groups HS28 (inorganic chemical products), HS29 (organic chemical products), and HS38 (various products of the chemical industry) with a large database and in a minimum method ordinary squares, fixed effects, and random effects are estimated. The results of the study showed that the explanatory power of the model was high for all the three groups of products and the volume of trade of the HS28 commodity group with regard to the GDP of the exporters and the GDP of the importers,.Meanwhile, the size, economic dimensions, and the per capita income have significant direct effects. Trade imbalance and distance have a significant but inverse effect on the business flow of the countries under study.

Underestimation of this high-demand services in today's world has resulted in the non-optimal allocation of resources and incorrect management and planning. In this research, focusing on Chalidareh Tourism Complex in Mashhad, a finite-horizon bayesian dynamic pricing model has been used to determine the extent of willingness to pay for non-market regional natural resources. In so doing, based on Chen and Wu (2016), Gamma and Two-Point priors with exponential and normal WTP (Willingness to Pay) distribution have been used. The results showed that the average WTP for general exploitation of this complex is within the extent of 12230 IRR (as minimum) in Gamma prior and exponential distribution and 45270 IRR (as maximum) in the Two-Point prior and exponential distribution. Also, the average of WTP is 28750 IRR, while the WTP is 10623 IRR in non-Bayesian approach, which is lower than any of Bayesian estimations. Therefore, the application of Finite-Horizon Bayesian Dynamic Pricing (FHBD) algorithm in dynamic pricing can be an appropriate way to determine the threshold amount of WTP for the exploitation of natural resources.
An important insight from the literature on dynamic pricing is that the optimal selling price of such products depends on the remaining inventory and the length of the remaining selling season (see e.g., Gallego & Van Ryzin, 1994). The optimal decision is, thus, not to use a single price but a collection of prices: one for each combination of the remaining inventory and the length of the remaining selling season. To determine these optimal prices it is essential to know the relation between the demand and the selling price. In most literature from the 1990s on dynamic pricing, it is assumed that this relation is known to the seller, but in practice, the exact information on the consumer behavior is generally not available. It is, therefore, not surprising that the review on dynamic pricing by Bitran and Caldentey (2003) mentions dynamic pricing with demand learning as an important future research direction. The presence of digital sales data enables a data-driven approach of dynamic pricing, where the selling firm not only determines optimal prices, but also learns how changing prices affects the demand. Ideally, this learning will eventually lead to optimal pricing decisions.
Theoretical Framework: In this paper, we focus on the dynamic pricing problem of selling a limited amount of inventory over a short selling horizon. In this regard, dedicating a certain number of periods for exploratory experimenting may be costly due to the limited time and inventory. Instead, a simultaneous optimization of pricing and learning is desired, which can be achieved by formulating the problem as a Bayesian dynamic program. However, computing the optimal policy for the dynamic program can be difficult, if not intractable due to the high dimensionality. Moreover, the binary customer choice model described above gives a rise to a two-sided censoring effect, that is, the observation of the customer’s WTP is censored either from the left or from the right side by the posted price. Because no simple conjugate prior distribution exists under the two-sided censoring (Braden & Freimer, 1991), one cannot resort to the conjugate prior technique to reduce the problem dimensionality.
Consider a finite-horizon dynamic pricing problem for a single product. Inventory replenishment is not possible during the selling horizon, and the terminal value at the end of the horizon is zero. At the beginning of each period, given the available inventory quantity q, the seller determines the unit price p for the product. The goal is to maximize the expected total revenue over the finite horizon. Specifically, we divide the finite selling horizon into T periods to guarantee that there is one customer arrival in each period (e.g., Broder & Rusmevichientong, 2012; Talluri & Van Ryzin, 2004). Time periods are indexed in reverse order, with the first selling period being period T and the last period being period 1. The customer arriving in period t has WTP Xt, which is a random draw from an i.i.d. distribution with a continuous density f (x|θ), where x ≥ 0 is the actual WTP and θ ∈ Θ is an unknown parameter of the distribution. At the beginning of period t, the seller has a prior belief concerning the value of θ, denoted by πt (θ). For the ease of exposition, we assume that Θ is a continuous set and that πt (θ) is a density over this set. When Θ is a discrete set, all our analysis will carry through by treating πt (θ) as a probability mass function. We shall use πt (θ) and πt (θ) interchangeably and suppress the subscript t whenever appropriate within the context.
A Finite-Horizon Bayesian Dynamic Pricing Model base on Chen and Wu (2016), Gamma and Two-Point priors with exponential and normal WTP distribution have been used. Results showed that the average WTP for general exploitation of this complex is within the extent of 12230 IRR (as minimum) in Gamma prior and exponential distribution and 45270 IRR (as maximum) in Two-Point prior and exponential distribution. Also, the average is 28750, while the WTP is 10623 IRR in non-Bayesian approach, which is lower than all the Bayesian estimation results. Therefore, the application of FHBD algorithm in dynamic pricing can be an appropriate way to determine the threshold amount of WTP for the exploitation of natural resources.
Conclusions & Suggestions: In sum, we study the Bayesian dynamic pricing problem under two-sided censoring with a short time horizon and limited inventory. Upon comparing it with the exact-observation system, we found that having better information always improves the revenue performance, while the optimal price under the exact-observation system can be either higher and lower than that under the two-sided censoring system. When comparing the above two systems with the no-learning system, we discover a surprising result that learning can bring a negative value when the inventory is scarce due to the biased learning effect. A derivative approximation heuristic is then devised to numerically solve the two-sided censoring problem. We further develop a performance bound to compare our proposed heuristic with other benchmark heuristics. Numerical experiments demonstrate that our heuristic consistently outperforms others and is robust with respect to WTP distributions. The two-sided censoring effect in our problem is a result of the binary customer choice model. When a customer faces a choice among multiple products, a more general choice model that surveys in the substitution effect is needed.

One of the most important challenges in the Iranian labor market is the phenomenon of education-job mismatch, which is reflected as the inequality between the educated labor force and the workforce needed in the labor market. This is despite the fact that in recent decades, according to the country's macro policies, too many resources have been allocated to human resources training, especially at higher education level. The presence of such conditions for the employment of higher education graduates and the lack of due attention to the need for acquiring job skills in the labor market have resulted in a higher unemployment rate for the higher education graduates than for other groups. The most important outcome of this phenomenon is the education-job mismatch.
Education-job mismatch appears in two forms in society: vertical mismatch and horizontal mismatch. Vertical mismatch means the level of education or skill of higher education graduates is more or less than the needs of the society. For example, the country needs workers with bachelor's degree, but the workforce may have higher or lower levels of education. Horizontal mismatch means the field of education or skills of workforce does not correspond to the labor market needs. For example, the country needs agriculture-related graduates, while job seekers are educated in other fields. Among the two types of mismatch mentioned, this paper chose to examine the vertical mismatch between education and occupation. It also intended to measure the scope of the mismatch in some cities of the country and to identify some of the most important factors affecting it.
The present paper pursues two main goals. The first one is to measure the mismatch of education and job and the second is to identify the factors affecting it. For this purpose, the paper takes two general steps. In the first step, the research variables are calculated using the data obtained from the Ian’s Population and Housing census. In the second step, the effect of independent variables on the dependent variable, education-job mismatch, is analyzed using an econometric model. It should be noted that econometric models are estimated annually for individual cities of the country.
This paper uses one of the three methods of measuring mismatch: job Analysis, realized matches and self-assessment. Each of these methods has inherent limitations and difficulties, and none has absolute superiority over the other methods. The choice of method depends on the nature and characteristics of the available data. Considering all available data, this paper uses job analysis method. This method makes a relationship between job needs and education levels using ISCO and ISCED classifications.
Thus, having calculated the mismatch by job analysis method, the effects of independent variables on the dependent variable is estimated using the spatial econometric model. The research variables are: Education-Job Mismatch (EJM), Relative Supply of Educated Labor Force (RSEL), Relative Demand of Educated Labor Force (RDEL), Regional Specialization Index (RS), Standardized Unemployment Rate (SU), and share of older (50–70 years old) (X1), female (X2), and rural (X3) workers.
Discussion and The results of this study suggest that due to the different reasons such as economic, social, and political conditions on the one hand and labor market conditions and labor force characteristics on the other hand, the country has faced job-occupation mismatch in different cities in 1996, 2006, and 2011. This phenomenon occurred due to the lack of educated labor force in the labor market and the shortage of supply and excess demand for labor in 1996. However, in 2006, this shortage declined, as the willingness to higher education increased. Finally, in 2011, educated labor force increased significantly and the education-job mismatch resulted in the excess supply and the shortage of demand. In other words, this phenomenon emerged as undereducation in 1996 and overeducation in 2006 and 2011.
Also, the results of spatial econometric model demonstrated that RSEL had significant negative effects on EJM in 1996 and 2006 and insignificant positive effects in 2011. However, RDEL had a positive and insignificant effect on EJM in 1996 and 2006 and significant negative effects in 2011. Other independent variables had positive effects on the education-job mismatch.
In the present paper, using the data obtained from Iranian Population and Housing Census in the years 1996, 2006, and 2011, the education-job mismatch was calculated in the cities of the country and the affecting factors were identified. The results of the research indicate that if the society faces with a shortage of educated workforce, the relative supply of educated labor will reduce the mismatch and the relative demand of educated labor will increases it. Also, when the country faces a surplus of educated workforce, the relative supply of educated labor increases the mismatch and the relative demand of educated labor reduces it. This is while the number of higher education graduates is increasing rapidly leading to the greater education-job mismatch. To resolve the problem and to reduce the mismatch, it is important to improve communication between universities and educational centers with the labor market, and to remove the barriers to job creation, investment in technology and skill sectors, and workforce mobility between different jobs, occupations and geographical areas.

Economic balance and equilibrium along with using different potentials of human resources and natural endowment of each region can be considered as one of the manifestations of sustainable development in each country. Although this topic is relevant to all sectors of the economy, it is becoming more increasingly important in Iran especially in the industrial sector. However, the evidence suggests that, in spite of the entrance of a large number of new firms in this sector, many have already existed in the early years. However, based on the existing studies, among the factors influencing the life duration of economic firms, the firm's location has a great importance. Therefore, this study aims to investigate the impact of location on the life duration of the new entrance firms in the textile industry as one of the largest industries in Iran. The data of this study were obtained from the Iranian Statistics Center and tested by probit regression method. The results indicate a significant effect of location on the life duration of new firms in textile industries in Iran which is in favor of industrialized provinces compared to the others. The differentiation of the impact of the location on the firm's life duration in the group of homogeneous provinces in terms of development or underdevelopment is also visible only in the developed provinces. From the policy viewpoint, these findings assert to the need of paying attention to the location factor in the establishment of the firm at the general level and even among the homogeneous provinces in terms of the level of industrial development according to the type of firm and its activity (especially in the textile industry). With the onset of the activity of an economic firm, its life can be affected by many factors. The place of the firm, as an unchangeable factor, is an important one. Nevertheless, this subject has been less considered in the economic geography literature of Iran. Therefore, given the importance of the industry sector as the basis of development and textile industry as one of the main industries in Iran, this paper is organized in order to investigate the impact of the location of new entrance firms on the life duration of textile firms.
Theoretical Fundamentals: The industrial economics literature considers the structural determinants of the life duration of an effective firm. Based on the literature in this area, it is assumed that at least the firm size, capital intensity, innovative activities, firm technology, the number of firms in the industry, and the rate of entrance are the indicators of the competition level. While Audretsch (1994) has been the first who focused on the firm's location dimension, the impact of the firm's location on the entrance of firms and the continuation of their activities is an important and undeniable subject which aims to explain the locatinal patterns of entry and the exit of the firms. The place where the firm is located consists of several factors which can be distinguished from other factors such as where the other firms are located which in turn may affect the individual performance of each firm. The theory of regional inbound growth is one of the doctrines of this field which assumes that due to the mobility of capital and labor, the locational differences resulting from technology will disappear in the long run. New geo-economic theories have explained the persistent regional differences. Accordingly, only places with skilled labor have the ability to use technology. Based on the spatial margins to profitability theory, a firm chooses a location where it can maximize its profit. On the other hand, the dynamics in a firm's economic area can change the location of a firm change over time to become a desirable location. In this area, there are also two theories namely inner-city incubator and filtering-down. According to the first theory, the rates of entry in large urban areas are higher due to the external costs from supplier and customer proximity as well as the speed of information flows. The theory of filtering-down in large urban areas also believes that after the evolution of the products and the approach of the firm to the standard level, the firm's natural tendency is to avoid the risky environments and to settle in the suburbs with less complications. Therefore, the performance of firm after entry will always be affected by the size and economic density of the place where they are located. Hence, the existing theories have tried to examine the impact of the location on the firm's performance including the exit (survival).
Data and To do this study, the data was collected from a census of workshops of ten employees most of which were from the Iranian Statistics Center with existing constraints (such as the limitation of the study period and the duration of pursuit of firms) to study the life duration of the newly established firms in the textile industry of Iran during the period of 1996-2005. The probit regression model has been used to estimate the influence of factors affecting the life duration of firms. This model has been tested in two stages. In the first stage, the intended provinces are divided into three developed, less developed, and undeveloped groups based on the level of industrial development, and the effects of these three groups have been tested. In the second stage, the effect of location on the life duration of firms in three separate sections is investigated. Besides, among homogeneous provinces each section has been tested in terms of level of development as well.
Also, the firms with smaller size at the beginning of their activity would experience the lower life duration. Making production in a non-optimal size can be considered as one of the reasons for this event. Therefore, according to the findings of this study, it is necessary to determine the industrial location appropriate to each activity from the initial conditions for starting activities in each industry by considering the region of the country. Although, the negligence of this issue will facilitat the firm entry but this increases the probability of firm exit from activity and leads to the loss of resources. The results of this study indicate that fistly, among the industrial developed provinces, less developed, and underdeveloped industrial regions, the newly established firms that have started their activity in the textile industry and in non-industrialized regions are more likely to survive than other regions. According to the existing literature on this area and the economic conditions of Iran, this is because of some reasons such as the provision of special state facilities to start economic activities in order to establish a relative income balance and to create economic prosperity in these areas, lack of sever competition, relatively cheap human resources, and ultimately favorable conditions for confirming the theoretical foundations of the margin of profitability.
Secondly, there is no significant difference in the life duration of the newly established firms in the textile industry when the impact of location on the life duration of firms between industrialized provinces is investigated.
Thirdly, the comparison of life duration of the newly established firms in the textile industry in the less developed provinces indicates that the firms located in East Azarbayjan survive more than other provinces. Fourthly, among the developed industrial provinces, new textile firms located in Tehran and Markazi have lower life duration, while new textile firms located in Qazvin have more life duration. Fifthly, in all models, in addition to the place impact, other variables affecting the firm's life duration have been also tested in four groups namely firm, employees, expenditures, and industry characteristics. All findings indicate a positive and significant effect of private ownership, labor productivity, technology and advanced education, and the negative and significant impact of the virtual variable on the size of small firms. Therefore, in addition to the impact of the location, new firms in the textile industry with the private sector, higher productivity, and technology enjoys from higher survival probability.

The high volatility of food prices is a concern to the general public and policy makers because such price movements are a deterrent to increased agricultural productivity and they tend to intensify inflationary pressures (Karagbo, 2005, p. 205). In recent years, the rising trend in global oil and food prices has been a source of interest to researchers to explore the relationship between these two variables; thus, many studies have been focused on the relationship between these two variables (Alghalith, 2010; Baffes, 2007; Headey & Fan, 2008). In this regard, the main purpose of this paper is to investigate the impact of the oil price shocks on the food price in the oil exporting (including Iran, Russia, Saudi Arabic, Norway, Venezuela, Kuwait, and Nigeria) as well as the oil importing countries (including UAS, Japan, Chin, South Korea, France, India, and Spain).
Theoretical frame work: The rapid increase of commodity price causes tension in most countries, regardless of whether they are among the developed or developing countries. Oil and food prices have a strong impact on macroeconomic variables.
After the oil boom in 1970s, studies on the oil price shock attracted the attention of many researchers because it was one of the most effective causes of a universal economic slowdown, especially in oil importing countries (Alom et al., 2011). Despite numerous researches showing the impact of the oil price shock on local or/and global economy, less studies have illustrated the role of the oil price shock in food prices. Results and outcome were mixed with different explanations. These explanations can be approximately divided into two categories: Demand side and supply side factors. The demand side factors considered the fundamental force of rising food prices. As a result, increasing population, rapid economic growth, rising consumption, and rapid increase in the production of biofuel and ethanol, etc. led to the increase in the aggregate demand on agriculture commodities which finally caused food prices to increase. Supply-side factors were also cited highlighting higher agricultural prices. Among others, slow growth in agricultural production, soaring crude oil prices, and droughts were more pronounced supply-side explanations (Abdlaziz et al., 2016).
Feedstock demand for biofuel and its relation to the oil price seemed to keep their roles in determining the recent behavior of agricultural product prices and were considered as some of the important factors of agricultural commodity demand and agricultural prices. Other studies indicated the relationship between food and oil prices as the factors of rising production of biofuels (Gilbert, 2010). Baffes (2007), Yu et al. (2006), and Zhang and Reed (2008) tried to investigate the dynamics of oil and agricultural commodity interconnection. Yu et al. (2006) could not discover any effect of oil prices on the edible oil prices (sunfower oil, olive oil, and palm oil) and the agricultural raw material prices index, respectively. Zhang and Reed (2008) also found that oil price fluctuations did not affect different types of agricultural commodities’ prices in China like corn, soy meal, and pork. Similar results were found by Nazlioglu and Soytas (2010) inTurkey. Moreover, Nazlioglu (2011) concluded that there is no linear causality between the oil price and agricultural commodities but found a non-linear relation between the oil and food prices.
Baffes and Dennis (2013) found evidence of a strong impact of the oil price change on the food price index. However, he suggested that individual commodity prices should be analyzed separately. In a more recent study, Ibrahim (2015) pointed that there exists a long-run relation between the oil price increase and the food price, while the long-term oil price reduction and the food price is non-existent. Baffes and Haniotis (2010) found that the highest pass-through from energy prices to non-energy prices exists for fertilizer which is specifically followed by agriculture in general.While detecting the impact of petroleum prices on food prices, exchange rate movements have a great role and its importance cannot be ignored. Nazlioglu and Soytas (2012) explained the relationship between the oil price and the 24 world agricultural commodity prices in panel setting. They found strong evidence of transmission from world oil price to agricultural commodity prices, on one side and a positive impact of the weak dollar on food prices on the another side.
In this paper, in order to estimate the asymmetric effects of oil shocks on the food price index in the selected oil exporting and oil importing countries, we used the Karagbo (2005) paper model, which includes foreign exchange and monetary and trading policies by adjusting, including, and adding variables of positive and negative oil prices shocks, logarithmically and in the form of panel data as follows: (1)
In Eq. (1), countries are denoted by the subscript i and the subscript t denotes the time period (1995-2014). Other variables are defined as follows: Ln: natural logarithm; fpi: food price index (2005=100); y: income per capita; exr: real exchange rate; m2: The volume of liquidity; open: The degree of the economic openness index by the ratio of the sum of exports and imports to the gross domestic product; dfp: Food Production Index (2005=100); oilp: positive shocks of oil price; oilp-: negative shocks of oil price.
In order to estimate the model, first, positive and negative oil price shocks have been extracted by using Granger and Yoon (2002) method. Then the panel co-integration tests confirmed the asymmetric long-run equilibrium relationship. In the end, the non-linear Panel Mean Group (PMG) approach was used to measure the asymmetric effects.
The findings show that, positive (negative) shocks of oil price, increased (decreased) the food price index in both the oil exporting and oil importing countries and the impact of the positive shocks is more than the negative shocks (confirm the effects of asymmetric). Also, the effect of positive shocks (negative) in oil-exporting countries is more (less) than oil-importing countries. According to the other results, monetary policy in oil-exporting countries and exchange policy in oil-importing countries have the most influence on the food price index.
Conclusions & Suggestions: Based on the results, it can be said that the vulnerability of oil exporting countries is higher than the oil importing countries in confronting with oil shocks from the direction food price index. Also, monetary policy in oil-exporting countries and exchange policy in oil-importing countries have the most influence on the food price index. Accordingly, controlling the volume of liquidity in oil-exporting countries and controlling the exchange rates in oil-importing countries could have an important impact on the food price reductions in these countries.

Considering the limited financial and investment resources governing the country's economy, the proper planning for the work of certain investments is one of the measures that must be taken by the countries’ economic authorities.
Theoretical frame work: Determination of priority activities in the province's industrial investment optimum return and optimal allocation of capital in the field of optimization of the establishment of industries is considered as an effective step (Javier, 2009). Capital is one of the main factors of commerce and the greatest means of gaining profit. Capital, in the broadest sense of the word, is the economic commodity that potentially or actually produces economic goods. Capital may be either legal cash (cash) or illegal (property, privilege, or practical) (Mahdavi, 2005, p. 22). Investment can be done inductively, externally, independently, and directly. For example, a household's investment in the securities of the company is a direct investment. Investments can be made in various fields (such as investment in the industrial, agricultural, housing and fixed assets sectors, and bourse, etc.). In other words, investment means a change in the volume of capital over a period, and investment is in fact the formation of capital (Rai & Talangi, 2004, p. 153). The border capacity in West Azerbaijan has created a space where joint industrial and mineral investment with neighboring countries is possible in this province. Besides, the geographic location of West Azerbaijan province and its border features with the three foreign countries can in fact provide the opportunities and benefits for the other provinces as well. Investors do not have to design, plan and take necessary measures, especially in the private sector, so in other similar points and therefore the priority of industries has a particular importance.
This is an applied research based on the method of collecting information and data in a descriptive and experimental way through the survey method. The study population in this research can be defined at two levels. In order to identify the effective indicators in determining the priorities of industrial investment in West Azerbaijan and to exploit the importance and the interdependence of the indicators, experts and decision makers of investment affairs of the industry, mining, and trade organization of West Azerbaijan are taken into account. In this research, the following tools have been used to collect information.
A: Documents related to Industrial Activities Assessment Indicators (ISIC) at the Iranian Statistics Center.
B: Paired matrix questionnaires are based on a 9-point Likert scale in which the parity scale is between indices, sub-indices, and finally variables, these questionnaires are: 1- The maturity comparison matrix inventory of effective indicators in determining the priority of industrial investment.
2) Indicator grading questionnaire
The data were obtained from the comparison of the pair values as well as the statistical data of the indices of each of the industries by means of the hierarchical analysis of the model through the selection of Expert Choice software. According to the research goal, in the framework of the operation of determining the priorities of industrial investment in West Azerbaijan -by comparing the pair of determinants and influential indices in industrial investment, as well as the comparative comparison of industrial activities based on the two-digit ISIC code for each- the indicators are investigated using the Analytical Hierarchy Model and Expert Choice Software.
Conclusions and Suggestions: Finally, the data from the paired comparisons showed that the multi-index scale has affected the determination of the priorities of the commercial industry in West Azerbaijan with an acceptable rate of inconsistency (0.02). Labor productivity indices, compensation, indices yield share of the shares of the workshops for each activity from the whole factories, the manpower production index, the concentration of profit, the ratio of efficiency, the indicator of the ratio of trainee professionals from the total staff, the investment index, and the index of production of employees from the total staff with the highest and least importance in determining investment priorities Industrial are in West Azerbaijan province. After the formation of the matrix of paired comparisons, the effective indicators in determining the investment priorities of West Azerbaijan and determining the index gradient as well as calculation of indices by comparing the values of the pair of activities in terms of indicators, the last activities in order to determine the priorities of industrial investment West Azerbaijan province in the mode of synthesis of distribution based on all indicators is calculated individually and in general respectively industrial activity of refinery coke production, production of materials and chemical products, production of basic metals, production of other non-metallic minerals and materials industries Food and Drinking First to Fifth Priorities. Other industrial activities of the province in order to allocate industrial investment, industrial and furniture manufacturing, wood and wood products, furniture and artifacts production, printing and reproduction industries, and printing and reproduction of media and recycling of metals and metals non-metallic is the first priority in comparison with other industrial activities of the province.