bu ali sina universityJournal of Applied Economics Studies in Iran2322-253072620180723Formation of Internal Migrant Networks An Economics ApproachFormation of Internal Migrant Networks An Economics Approach127230810.22084/aes.2018.15452.2588FAAmirHabibdoustUniversity of MazandaranZahraAlmeiUniversity of Mazandaran0000-0002-2913-7292Journal Article20180109<strong>Introduction</strong> <br />We are embedded by our social networks. Social networks or connections has had important role in shaping of agents behavior. “While the importance of embeddedness of economic activity in social settings has been fundamental to sociologists for some time, it was largely ignored by economists until the last decade”.(Zenou, 2016). The role of social networks in shaping behavior and formation of networks has received increasing attention recent years. Some central questions in the area of economics of social networks are as follows. How agents are affected by their social networks’ member? How our connections can improve our output? Why there are different shape of networks? Which factors are important in formation of network? Which networks are efficient and stable? Migration, by nature, is a phenomena that is involved in relationship and contacts. In this regards, migration is an attractive area in economics of social network. While there are several economic theory about migration, from theories of initiation and perpetuationofmigration to theories of migration’s welfare effect, some economists has done research about economic, social and cultural assimilation of migrants. In this line of research, formation of migrants’ network is a new open issue. Which factors are important in formation of network of migrants? Which shape of network would emerge in short run and long run? And so on. <br />The paper aims to find essential factors which are important factor in shaping network. Moreover, optimal level of effort migrants to link with central migrant is investigated and developed to heterogeneity condition. To do so, a survey among Ardablian who migrated to Rasht is conducted and run dyadic regression based on theoretical background. <br /><strong>Theoretical Background, Method and data</strong> <br />Social network, as an unofficial institution, can improve our understanding from social and economic phenomena. Generally speaking, there are three scopes of research in the field of social networks. Network effect (also known as games on network), network formation, topology and structure if network are the ongoing area of research. The paper is categorized in the network formation scope. Network formation models are modeled by two different approaches: strategic network formation and stochastic network formation. Obviously, economists are more interested in strategic approach. There are different methods of model of strategic network formation. Table 1 shows a comprehensive classification of strategic network formation models. <br /><strong>Table 1- Strategic network formation models</strong>
<br /> Cooperative and Noncooperative models<br />Farsighted and Myopic models <br />Dynamic and Static models <br />Complete and Imperfect Information model <br /><br />We followed Brueckner (2006) and Epstein and Heizler-Cohen (2016), which is a complete information, myopic and noncooperative model to as a basis for our econometric model. We applied logit method for dyadic data to detect effective factors on network formation. Based on our theoretical model, several variables such as time of arrival, distance at birthplace are used as explanatory variables as well as variables such as age, education and income as proxy for homophily are employed as control variables. <br />Finally, equation (1) estimated, following Fafchamps and Gubert (2007) method to correct standard errors to overcome inference problem. <br /> <br /> <br />L<sub>ij</sub> = a+b<sub>1 </sub>¦z<sub>i</sub>-z<sub>j</sub>¦ +b<sub>2</sub>(z<sub>i</sub>+z<sub>j</sub>)+c ¦d<sub>ij</sub>¦ +u<sub>ij (1)</sub> <br /> <br /> <br />Where z<sub>j </sub>and z<sub>i</sub> , d<sub>ij </sub>and d<sub>ij</sub> are nodes’ (migrants) specification (such as time of arrival), specification of link (such as distance at birthplace) and error term, respectively. L<sub>ij </sub>stands for relationship matrix, including 0 and 1. <br />Moreover, in order to focus on importance of time of arrival, two another form is specified. Equation (2) is a second degree model and equation (3) is a model with dummy variable for different time of arrival. <br />L<sub>ij</sub> = a+b<sub>1 </sub>¦z<sub>i</sub>-z<sub>j</sub>¦ +b<sub>2</sub>(z<sub>i</sub>+z<sub>j</sub>)+c<sub>1</sub> ¦d<sub>ij</sub>¦ +c<sub>1</sub>+d<sub><span style="font-size: 8.33333px;">ij</span></sub><sup><span style="font-size: 8.33333px;">2</span></sup>+u<sub>ij (2)</sub> <br />L<sub>ij</sub> = a+b<sub>1 </sub>¦z<sub>i</sub>-z<sub>j</sub>¦ +b<sub>2</sub>(z<sub>i</sub>+z<sub>j</sub>)+c<sub>1</sub> ¦d<sub>ij</sub>¦ +δD+u<sub>ij (3)</sub> <br />It is noteworthy that, the models are reestimated after adding control variables such as differences in education, age, income as proxy for homophily. In order to gathering data, a purposeful questioner designed and a survey conducted among Ardablian who migrated to Rasht. Finally, 411 members and 400 links are detected. <br />As mentioned before, optimal effort of migrants to making link is investigated. <br />Bi=(P(e<sup><span style="font-size: 8.33333px;">s</span></sup><sub><span style="font-size: 8.33333px;">i,1</span></sub>))u+ P (e<sup>s</sup><sub>i,1</sub>)(∑<span><sup>h=2</sup> P(e<sup>s</sup><sub>h,1</sub>)v)-e<sup>s</sup><sub>i,1 (4)</sub></span> <br />Where is cost or effort in a star network. P(e<sup>s</sup><sub>i,1</sub> ) stands for the intensity (or strength) of the relationship between i and j depends on the investment e. B<sub>j</sub> , u and v stand for beneﬁt or payoff from the friendship, utility of making direct connection and utility of indirect links, respectively. <br />The optimal effort of individual i who is invested in the linking with the central agent can be obtained as follows: <br /> <br />δB<sub>i</sub>⁄δe<sup>s</sup><sub>i,1 = 0 </sub>→δP(e<sup>s</sup><sub>i,1</sub> )⁄δe<sup>s</sup><sub>i,1 =</sub> 1⁄u+ ∑<sup>h=N </sup>P(e<sup>s</sup><sub>i,1</sub> )v (5) <br /><br />In the formation of a star network, the intensity of the agents’ relationships with the central agent increases in every period not with homogenous utility but also with heterogeneity in direct and indirect utility. <br /><strong>Finding</strong> <br />According to our estimations, distance at birthplace (suggesting that migrants who are born in close by locality are more prone to making link), sum of arrival time and differences between time of arrive have explanatory power (Table 5, Panel a). <br />Moreover, according our second model estimation, squared arrival time has significant effect in linking. It means that there is signiﬁcant U-shaped relationship between the probability of linking and time of arrival to destination such that new migrants prone to link with old migrants and also tend to making connection between themselves (Table 5, Panel b). Our final estimation shows that migrants are more likely to connects with their cohort (who migrants less than 5 years) and link with older migrants (who migrated more than 23 years) (Table 5. Panel c).Adding control variable does not have a significant effect on the mentioned results. In other words, although some our control variables are significant, the main finding does not change (Table 6, Panel a, b and c). <br /><strong>Conclusion</strong> <br />Results show that time of arrival, distance at birthplace, age, education and income has significant effect on probability of making link. Furthermore, there is a U shape relationship between probability of making link and time of arrival, which indicates tendency of migrants for linking to cohort migrant also earlier migrants. Another finding which stems from our contribution is that optimal level of effort increases in each period and this finding hold at special level heterogeneity.<strong>Introduction</strong> <br />We are embedded by our social networks. Social networks or connections has had important role in shaping of agents behavior. “While the importance of embeddedness of economic activity in social settings has been fundamental to sociologists for some time, it was largely ignored by economists until the last decade”.(Zenou, 2016). The role of social networks in shaping behavior and formation of networks has received increasing attention recent years. Some central questions in the area of economics of social networks are as follows. How agents are affected by their social networks’ member? How our connections can improve our output? Why there are different shape of networks? Which factors are important in formation of network? Which networks are efficient and stable? Migration, by nature, is a phenomena that is involved in relationship and contacts. In this regards, migration is an attractive area in economics of social network. While there are several economic theory about migration, from theories of initiation and perpetuationofmigration to theories of migration’s welfare effect, some economists has done research about economic, social and cultural assimilation of migrants. In this line of research, formation of migrants’ network is a new open issue. Which factors are important in formation of network of migrants? Which shape of network would emerge in short run and long run? And so on. <br />The paper aims to find essential factors which are important factor in shaping network. Moreover, optimal level of effort migrants to link with central migrant is investigated and developed to heterogeneity condition. To do so, a survey among Ardablian who migrated to Rasht is conducted and run dyadic regression based on theoretical background. <br /><strong>Theoretical Background, Method and data</strong> <br />Social network, as an unofficial institution, can improve our understanding from social and economic phenomena. Generally speaking, there are three scopes of research in the field of social networks. Network effect (also known as games on network), network formation, topology and structure if network are the ongoing area of research. The paper is categorized in the network formation scope. Network formation models are modeled by two different approaches: strategic network formation and stochastic network formation. Obviously, economists are more interested in strategic approach. There are different methods of model of strategic network formation. Table 1 shows a comprehensive classification of strategic network formation models. <br /><strong>Table 1- Strategic network formation models</strong>
<br /> Cooperative and Noncooperative models<br />Farsighted and Myopic models <br />Dynamic and Static models <br />Complete and Imperfect Information model <br /><br />We followed Brueckner (2006) and Epstein and Heizler-Cohen (2016), which is a complete information, myopic and noncooperative model to as a basis for our econometric model. We applied logit method for dyadic data to detect effective factors on network formation. Based on our theoretical model, several variables such as time of arrival, distance at birthplace are used as explanatory variables as well as variables such as age, education and income as proxy for homophily are employed as control variables. <br />Finally, equation (1) estimated, following Fafchamps and Gubert (2007) method to correct standard errors to overcome inference problem. <br /> <br /> <br />L<sub>ij</sub> = a+b<sub>1 </sub>¦z<sub>i</sub>-z<sub>j</sub>¦ +b<sub>2</sub>(z<sub>i</sub>+z<sub>j</sub>)+c ¦d<sub>ij</sub>¦ +u<sub>ij (1)</sub> <br /> <br /> <br />Where z<sub>j </sub>and z<sub>i</sub> , d<sub>ij </sub>and d<sub>ij</sub> are nodes’ (migrants) specification (such as time of arrival), specification of link (such as distance at birthplace) and error term, respectively. L<sub>ij </sub>stands for relationship matrix, including 0 and 1. <br />Moreover, in order to focus on importance of time of arrival, two another form is specified. Equation (2) is a second degree model and equation (3) is a model with dummy variable for different time of arrival. <br />L<sub>ij</sub> = a+b<sub>1 </sub>¦z<sub>i</sub>-z<sub>j</sub>¦ +b<sub>2</sub>(z<sub>i</sub>+z<sub>j</sub>)+c<sub>1</sub> ¦d<sub>ij</sub>¦ +c<sub>1</sub>+d<sub><span style="font-size: 8.33333px;">ij</span></sub><sup><span style="font-size: 8.33333px;">2</span></sup>+u<sub>ij (2)</sub> <br />L<sub>ij</sub> = a+b<sub>1 </sub>¦z<sub>i</sub>-z<sub>j</sub>¦ +b<sub>2</sub>(z<sub>i</sub>+z<sub>j</sub>)+c<sub>1</sub> ¦d<sub>ij</sub>¦ +δD+u<sub>ij (3)</sub> <br />It is noteworthy that, the models are reestimated after adding control variables such as differences in education, age, income as proxy for homophily. In order to gathering data, a purposeful questioner designed and a survey conducted among Ardablian who migrated to Rasht. Finally, 411 members and 400 links are detected. <br />As mentioned before, optimal effort of migrants to making link is investigated. <br />Bi=(P(e<sup><span style="font-size: 8.33333px;">s</span></sup><sub><span style="font-size: 8.33333px;">i,1</span></sub>))u+ P (e<sup>s</sup><sub>i,1</sub>)(∑<span><sup>h=2</sup> P(e<sup>s</sup><sub>h,1</sub>)v)-e<sup>s</sup><sub>i,1 (4)</sub></span> <br />Where is cost or effort in a star network. P(e<sup>s</sup><sub>i,1</sub> ) stands for the intensity (or strength) of the relationship between i and j depends on the investment e. B<sub>j</sub> , u and v stand for beneﬁt or payoff from the friendship, utility of making direct connection and utility of indirect links, respectively. <br />The optimal effort of individual i who is invested in the linking with the central agent can be obtained as follows: <br /> <br />δB<sub>i</sub>⁄δe<sup>s</sup><sub>i,1 = 0 </sub>→δP(e<sup>s</sup><sub>i,1</sub> )⁄δe<sup>s</sup><sub>i,1 =</sub> 1⁄u+ ∑<sup>h=N </sup>P(e<sup>s</sup><sub>i,1</sub> )v (5) <br /><br />In the formation of a star network, the intensity of the agents’ relationships with the central agent increases in every period not with homogenous utility but also with heterogeneity in direct and indirect utility. <br /><strong>Finding</strong> <br />According to our estimations, distance at birthplace (suggesting that migrants who are born in close by locality are more prone to making link), sum of arrival time and differences between time of arrive have explanatory power (Table 5, Panel a). <br />Moreover, according our second model estimation, squared arrival time has significant effect in linking. It means that there is signiﬁcant U-shaped relationship between the probability of linking and time of arrival to destination such that new migrants prone to link with old migrants and also tend to making connection between themselves (Table 5, Panel b). Our final estimation shows that migrants are more likely to connects with their cohort (who migrants less than 5 years) and link with older migrants (who migrated more than 23 years) (Table 5. Panel c).Adding control variable does not have a significant effect on the mentioned results. In other words, although some our control variables are significant, the main finding does not change (Table 6, Panel a, b and c). <br /><strong>Conclusion</strong> <br />Results show that time of arrival, distance at birthplace, age, education and income has significant effect on probability of making link. Furthermore, there is a U shape relationship between probability of making link and time of arrival, which indicates tendency of migrants for linking to cohort migrant also earlier migrants. Another finding which stems from our contribution is that optimal level of effort increases in each period and this finding hold at special level heterogeneity.https://aes.basu.ac.ir/article_2308_0dee7ec7a88c282fd4dcae9eff486881.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253072620180723Ranking of investment projects in construction of Mashhad and studying their problems, especially the worn texture around the holy shrine (using gray method)Ranking of investment projects in construction of Mashhad and studying their problems, especially the worn texture around the holy shrine (using gray method)2950230910.22084/aes.2018.14681.2534FAMaryamRasoulzadehresearcherJavadBaratiACECRJournal Article20171003<strong>Aim:</strong> The reconstruction of urban context in the around of holy shrine in Mashhad has become to one of the main problems of Mashhad City for many years, due to the presence of millions of pilgrims in this city. Attracting investors is one of the important problems to implement the defined investment projects in this reconstruction plan. This research seeks to provide an analysis of the investor's behavior at the microeconomic level, and the reasons for investors' reluctance to participate in these projects and investment problems in this context are examined.
<strong>Method:</strong> Interviewing to the experts of the renovation project and inventors has used for selection of the most significant problems of investors in the investment projects around the holy shrine, and the causes has looked at through a questionnaire completed by about 60 investors of this region, and attempted to prioritize capital-investors. To analyze the data from questionnaires and interviews has used the Gray Ranking method.
In researches, the black is represented, as lack of information, but the white is full of information. Therefore, the information that is either incomplete or undetermined is called Grey. Grey system is a system having incomplete information. The Grey number in Grey system shows a number with less complete information. The Grey element represents an element with incomplete information. The Grey relation is the relation with incomplete information. Three terms are the typical symbols and features for Grey system and Grey phenomenon. There are six styles for the theory of Grey system.
(1) Grey generation: in method is data processing to supplement information. It is aimed to process those complicate and tedious data to gain a clear rule that is the whitening of a sequence of numbers. (2) Grey modeling: in style done by step 1 to establish a set of Grey variation equations and Grey differential equations. (3) Grey prediction: By using the Grey model to conduct a qualitative prediction, it is called the whitening of development. (4) Grey decision: A decision is made under imperfect countermeasure and unclear situation that is called the whitening of status. (5) Grey relational analysis: quantify all influences of different factors and their relation; it is called the whitening of factor relation. (6) Grey control: Work on the data of system behavior and search any rules of behavior development to foretaste future’s behavior, the prediction value can be fed back into the system in order to control the system.
This study use fourth method on Grey relational analysis, and apply to project evaluation and selection.
<strong>Finding:</strong> The results showed that investors preferred investment in the construction options in "residential" projects and then investment in "commercial" projects in other urban regions to investment in "commercial-residential buildings the around of holy shrine". The order of their choice is investment in: (1) Residential building, (2) Commercial Projects, (3) projects of commercial-residential the around of holy shrine, (4) Tourism and leisure projects.
It can be analyzed the external factors and macroeconomic policies of the country, such as monetary policy of the Central Bank, which are effective on economic stability and inflation, can be effective in making investment decisions. For example, controlling the rate of credit growth in the private sector is a monetary policy in line with the financial stability of the economy, which it effective on the investor's decision.
<strong>Conclusion:</strong> The most important problems of investing projects in the worn-out texture surrounding the shrine that has caused lack of attractiveness for investment are: "The shift of managers and responsible staff and the lack of stability in the posts assigned to individuals in the offices, long judicial process and the lack of efficient interaction of executive agencies, get high fees for licenses by municipality, and lack cooperation ownerships for owned of land and problems associated with the user change".<strong>Aim:</strong> The reconstruction of urban context in the around of holy shrine in Mashhad has become to one of the main problems of Mashhad City for many years, due to the presence of millions of pilgrims in this city. Attracting investors is one of the important problems to implement the defined investment projects in this reconstruction plan. This research seeks to provide an analysis of the investor's behavior at the microeconomic level, and the reasons for investors' reluctance to participate in these projects and investment problems in this context are examined.
<strong>Method:</strong> Interviewing to the experts of the renovation project and inventors has used for selection of the most significant problems of investors in the investment projects around the holy shrine, and the causes has looked at through a questionnaire completed by about 60 investors of this region, and attempted to prioritize capital-investors. To analyze the data from questionnaires and interviews has used the Gray Ranking method.
In researches, the black is represented, as lack of information, but the white is full of information. Therefore, the information that is either incomplete or undetermined is called Grey. Grey system is a system having incomplete information. The Grey number in Grey system shows a number with less complete information. The Grey element represents an element with incomplete information. The Grey relation is the relation with incomplete information. Three terms are the typical symbols and features for Grey system and Grey phenomenon. There are six styles for the theory of Grey system.
(1) Grey generation: in method is data processing to supplement information. It is aimed to process those complicate and tedious data to gain a clear rule that is the whitening of a sequence of numbers. (2) Grey modeling: in style done by step 1 to establish a set of Grey variation equations and Grey differential equations. (3) Grey prediction: By using the Grey model to conduct a qualitative prediction, it is called the whitening of development. (4) Grey decision: A decision is made under imperfect countermeasure and unclear situation that is called the whitening of status. (5) Grey relational analysis: quantify all influences of different factors and their relation; it is called the whitening of factor relation. (6) Grey control: Work on the data of system behavior and search any rules of behavior development to foretaste future’s behavior, the prediction value can be fed back into the system in order to control the system.
This study use fourth method on Grey relational analysis, and apply to project evaluation and selection.
<strong>Finding:</strong> The results showed that investors preferred investment in the construction options in "residential" projects and then investment in "commercial" projects in other urban regions to investment in "commercial-residential buildings the around of holy shrine". The order of their choice is investment in: (1) Residential building, (2) Commercial Projects, (3) projects of commercial-residential the around of holy shrine, (4) Tourism and leisure projects.
It can be analyzed the external factors and macroeconomic policies of the country, such as monetary policy of the Central Bank, which are effective on economic stability and inflation, can be effective in making investment decisions. For example, controlling the rate of credit growth in the private sector is a monetary policy in line with the financial stability of the economy, which it effective on the investor's decision.
<strong>Conclusion:</strong> The most important problems of investing projects in the worn-out texture surrounding the shrine that has caused lack of attractiveness for investment are: "The shift of managers and responsible staff and the lack of stability in the posts assigned to individuals in the offices, long judicial process and the lack of efficient interaction of executive agencies, get high fees for licenses by municipality, and lack cooperation ownerships for owned of land and problems associated with the user change".https://aes.basu.ac.ir/article_2309_5140a56592e22ddb415ef1fcc3d30f5d.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253072620180723The comparison of habit formation of urban and rural households for food and non-food goods: using Envelope theorem and Euler equations approachThe comparison of habit formation of urban and rural households for food and non-food goods: using Envelope theorem and Euler equations approach5169231010.22084/aes.2018.14397.2517FARezaRoshanJournal Article20170820<strong>1. Introduction</strong> <br />It is important for policy makers and planners to study the consumer behavior of households and to understand the role of consumer habits in shaping their consumption pattern for consumption of various food and non-food commodity groups. Since researchers believe that habits can play an important role in consumer behavior of individuals and households, they, along with structural parameters of the utility function such as risk aversion, Elasticity of Intertemporal Substitution (EIS), are also considered habits formation of consumers as one of the parameters of the time separation utility function. Duesenberry (1949) believed that the consumption of each individual consumer was not independent of the consumption of others, and consumer preferences were determined not only on the basis of the absolute level of his expenditures, but his relative consumption of the rest of society and past consumption of the individual also influenced his consumption behavior. Therefore, the habit formation of household’s consumption shows their consumption behavior and it is an important factor in the utility function. In present research, consumption of past periods has been introduced as an indicator of the habits in the consumer utility function. In fact, the main questions that the research seeks to answer are: <br />How is the consumption habit of urban households for eating food compared to rural households? How is the pattern of consumption habits of urban households for non-food commodities compared to rural households? The formation of the consumption habits of urban households for food is more powerful or non-food commodities? How is the pattern of consumption habits of urban households for non-food commodities compared to rural households? <br /> <br /> <strong>2. Materials and Methods</strong> <br />Since the introduction of the permanent hypothesis (Friedman, 1957) and the consumption life cycle (Modigliani and Bromberg, 1954), the concept of consumption smoothing has been widely used to explain household behavior. Hall (1978), in his paper, modeled the total consumption dynamics by extracting Euler's equation from the first-order condition of the optimization problem of consumer choice. The Euler equation has been widely used in economic literature to estimate the parameters of the risk-free utility. This approach reflects the fact that consumers are also interested in determining their current consumption of their past use or consumption of others (Dayton, 1992, 16). In models where consumption habits have been introduced in the household utility function, the utility depends on the level of consumption and accumulation of habits, which are measured by the past periods of the average consumption (fakhre hoseini, 2015, 69). This means that the habits of people are formed over a long period of time. In general, habits of consumption can be divided into two types of external and internal. External habits, presented by researchers like Abel 1990, Campbell and Cochran 1999, Galli 1994, Auer 2013), suggest that household preferences are based on total consumption lags. On the other hand, the formation of the habits of consumption is based on the assumption that household consumption depends on the consumption of their own past periods (Reader & Hill, 1973; Sandrasans, 1989; Constantinides, 1990, Dreyer& Schneider& Smith, 2013, Kwan, Leung, & Dong, 2015). <br />In the following, we Model the formation of consuming habits and extract the Euler equation. <br />Assume that each household tries to select at time t in order to maximize the following expression (Carroll, 2000, 69): <br />max E<sub>t</sub> [∑<sup>T</sup><sub>s=t </sub>β<sup>s-t </sup>u(c<sub>s</sub> , h<sub>s</sub>)] (1)<br /><sub> </sub> <br />Which β is the time preferences factor, <em>h</em> is accumulations of habits, and E is the expectation operator. Suppose that the constraints that the above maximization problem faces are: <br />x<sub>t+1</sub>= R [x<sub>t</sub>-c<sub>t</sub>]+ y<sub>t+1</sub> (2) <br />h<sub>t+1</sub>=h<sub>t</sub>+λ(c<sub>t</sub>-h<sub>t</sub>) (3)<br /> <br /> Which R is the risk-free interest rate, y, the income of the workforce in the period t,x is the cash of the household (the total amount of resources available to spend in period t). The Bellman equation corresponding to the above equations is defined as: <br /> v<sub>t</sub>(x<sub>t</sub>, h<sub>t</sub>)=max <sub>{</sub>c<sub>t}</sub> u(c<sub>t</sub>,h<sub>t</sub>)+βE<sub>t</sub>[v<sub>t+1</sub>(x<sub>t+1</sub>, h<sub>t+1</sub>)] (4)<br />The first-order optimization condition for Equation (4) relative to expenditures consumed by is: <br />0=u<sup><span style="font-size: 8.33333px;">c</span></sup><sub><span style="font-size: 8.33333px;">t</span></sub>+βE<sub>t</sub>(λv<sup><span style="font-size: 8.33333px;">h</span></sup><sub>t+1</sub> - Rv<sup>x</sup><sub>t+1</sub>) (5) <br />uct=βE<sub>t</sub> (Rv<sup>x</sup><sub>t+1</sub>- λv<sup>h</sup><sub>t+1</sub>) (6) <br />That λ is a parameter of "consumption habits". To obtain the Euler equation, we use the equations (1) to (6) and envelope theorem. After some calculations, we have: <br />u<sup>c</sup><sub>t</sub>+ β[λv<sup>h</sup><sub>t+1</sub>+ (1-λ)u<sup>c</sup><sub>t+1</sub>]=Rβ[u<sup>c</sup><sub>t+1+</sub>β(λv<sup>h</sup><sub>t+1</sub>(1-λ)u<sup>c</sup><sub>t+2</sub>] (7) <br /><br />In fact, (7) is Euler's equation for maximizing the problem of present article. <br />If we assume that the formation of habits is such that the level of habits in the period t is equal to the consumption of the previous period. That means: <br />h<sub>t</sub>= c<sub>t-1 (8)</sub><br /> <br />In this case, equation (7) is simplified as follows: <br />u<sup>c</sup><sub>t</sub>+βu<sup>h</sup><sub>t+1</sub>=Rβ[u<sup>c</sup><sub>t+1</sub>+βu<sup>h</sup><sub>t+2</sub>] (9)<br /> <br />Suppose that, like Melbourne (1988), for the utility function, we consider the following form: <br />u (c , h) = v (c - αh) (10)<br /> <br />Therefore: <br />u<sup>c</sup> = v’ , uh = -αv’ (11)<br /> <br />Replacement (11) in (9) gives: <br />v’<sub>t</sub>-αβv’<sub>t-1</sub>=Rβ(v’<sub>t+1</sub>-αβv’<sub>t+2</sub>] (12)<br /> <br />The stable solution corresponding to the first root (12) under the fixed asset yields is: <br />v’<sub>t </sub>= RβE<sub>t</sub> (v’<sub>t+1</sub>) or 1= RβE<sub>t</sub> (v’<sub>t+1</sub>⁄v’<sub>t</sub>) (13)<br /> <br />Now if we assume that the utility function is in the form:(z=z<sup>1-ρ</sup>/1-ρ), then (z)=z<sup>-ρ</sup>. Therefore, according to (13), we will have: <br />1=RβE<sub>t</sub> (z<sub>t+1</sub>⁄z<sub>t</sub>)<sup>-ρ</sup> (14)<br /> <br />Considering relations (8) and (9), yield: z<sub>t</sub>=c<sub>t</sub>-αc<sub>t-1</sub> , So: <br />1= RβE<sub>t</sub> (c<sub>t+1</sub>-αc<sub>t</sub>⁄c<sub>t</sub>- αc<sub>t-1</sub>)<sup>-ρ</sup> (15) <br />1= RβE<sub>t</sub> ((c<sub>t+1/</sub>c<sub>t</sub>)/1-αc<sub>t-1/</sub>c<sub>t</sub>)<sup>-ρ</sup> (16)<br /> <br />The parameters of nonlinear Euler equation (16) are estimated using generalized moments (GMM) and suitable instrument variables. <br /> <br /><strong>3. Empirical Results and Discussion </strong> <br />In this paper is estimated the coefficient of habit formation for the cost of food and nonfood goods of urban and rural households in Iran during 1357-1394, using Euler equations GMM approach. Household’s consumption expenditure data is located at the Iranian Statistics Center, the results of the survey on household expenditure and household income. To further acquaint with the average annual costs of nonfood goods for a rural household (crnok), the average annual costs of food and tobacco consumption for rural household (crk), the average annual costs of nonfood goods for a urban household (cunok), the average annual costs of food goods for a urban household (cuk), they are presented in Table 1: <br /> <br />Table 1: Consumption expenditures of food and nonfood goods for urban and rural households in Iran <br /> <br /> <strong>Consumption expenditures Variable Mean Median Min Max</strong> <br /><span> Nonfood(urban household) <span>cunok <span>35475367 <span>35475367 <span>307990 <span>200000000</span></span></span></span></span></span> <br /><span> food(urban household) <span>cuk <span>11866378 <span>86637811 <span>175217 <span>62431000</span></span></span></span></span></span> <br /><span><span><span><span><span><span><span> Nonfood(rural household) <span>crnok <span>17181566 <span>4167900 <span>108888 <span>89205000</span></span></span></span></span></span></span></span></span></span></span></span> <br /><span><span><span><span><span><span><span><span><span><span><span><span><span> food(rural household) cuk <span>11677346 <span>3578014 <span>125977 <span>57778000</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span> <br />Source: Iranian Statistics Center and research calculates <br /> <br />According to the table 1, we can say that the average consumption expenditure of urban and rural households for food goods is approximately equal, but nonfood consumption costs for each urban household is more than twice of a rural household. <br /> <br /><strong>3.1 Calculation of the coefficient of formation of consumption habits for all kinds of consumption expenditures of urban and rural households</strong> <br />For estimating of coefficients of habit formation for consumption expenditures In Euler equation (16), we apply GMM approach. So, it is necessary that all of the variables that mentioned to be stationary. Hence, we applied ADF test for this mean and we ensured that all of the variables are stationary. After that, we used GMM method for estimating coefficient of habit formation <br /> for all kinds of consumption expenditures of Iranian urban and rural households using equation (16). Results are presented in table (2): <br /> <br />Table 2: The results of calculating the coefficient of formation of consumption habits or α and relative risk aversion coefficient ρ using the formula of mole (16) and method GMM <br /><span><strong>Consumption expenditures α ρ <em>J</em> <em>J<sup>*</sup>=N*J</em></strong> </span> <br /><span><span> Nonfood(urban household)</span> <span>1.5(0.04) <span>1.73(0.042) </span></span> <span>0.00014 <span>0.0049</span></span></span> <br /><span><span> food(urban household)</span> <span>1.41(0.00) <span>1.7(0.014) <span>0.015 <span>0.54</span></span></span></span></span> <br /><span><span><span><span><span><span> Nonfood(rural household) <span>1.62(0.00) <span>3.98(0.08) <span>0.021 <span>0.74</span></span></span></span></span></span></span></span></span></span> <br /><span><span><span><span><span><span><span><span><span><span><span> food(rural household) <span>1.01(0.00) <span>0.81(0.001) <span>0.017 <span>0.56</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span> <br /> *numbers in parentheses () are p-values for t statistic and <span>x</span><sup>2</sup><span> <sub>1,%5</sub></span>. <br /> <br />Hansen’s J-statistic indicates the validity of all the models and instrument variables used in them, because:<strong> </strong> <br /> <br /> J<sup>*</sup>=N*J=35*0.00014=0.0049<χ<sup>2</sup><sub>r-l,%5=x<sup>2</sup> 1,%5</sub>=3.841 <br /> J<sup>*</sup>=N*J=36*0.015=0.54<χ<sup>2</sup><sub>r-l,%5=x<sup>2</sup> 1,%5</sub>=3.841 <br /> J<sup>*</sup>=N*J=34*0.021=0.74<χ<sup>2</sup><sub>r-l,%5=x<sup>2</sup> 1,%5</sub>=3.841 <br /> J<sup>*</sup>=N*J=33*0.017=0.056<χ<sup>2</sup><sub>r-l,%5=x<sup>2</sup> 1,%5</sub>=3.841 <br /><strong> </strong> <br />It is worth noting that the kernel of all equations is Bartlett, and the fixed-bandwidth is selected from the Newey West. <br /> <br /><strong>4. Conclusion</strong> <br />The results of the research showed that the role of habits in the utility function of urban and rural households is significant and important for both groups, and consumption of past periods of households has an impact on the consumption of their current period; So, the coefficient of formation of consumption habits for rural household food expenditure are more than this coefficient among rural households. On the other hand, the coefficient of habits formation for urban households for non-food is about 40% more than that for rural households. On the other hand, the results of this study indicate that the relative risk aversion for consumption of rural household goods is more than this factor for urban households. So, the tendency of rural households to consume food in the present time is more than the desire of urban households. While for non-food commodity groups, relative risk aversion is higher for urban households than rural households. In other words, urban households tend to be more likely to use non-food commodities than rural households at the current time, and are less likely to postpone consumption of these types of goods. Also, descriptive statistics indicate that during survey period, the average expenditure of non food goods for every urban household is more than twice that of rural household. While the average consumption expenditure of urban and rural households for consumer goods is approximately equal. In sum, the findings of this study showed that the formation of habits in the consumption pattern of Iranian urban and rural households has a significant place.<strong>1. Introduction</strong> <br />It is important for policy makers and planners to study the consumer behavior of households and to understand the role of consumer habits in shaping their consumption pattern for consumption of various food and non-food commodity groups. Since researchers believe that habits can play an important role in consumer behavior of individuals and households, they, along with structural parameters of the utility function such as risk aversion, Elasticity of Intertemporal Substitution (EIS), are also considered habits formation of consumers as one of the parameters of the time separation utility function. Duesenberry (1949) believed that the consumption of each individual consumer was not independent of the consumption of others, and consumer preferences were determined not only on the basis of the absolute level of his expenditures, but his relative consumption of the rest of society and past consumption of the individual also influenced his consumption behavior. Therefore, the habit formation of household’s consumption shows their consumption behavior and it is an important factor in the utility function. In present research, consumption of past periods has been introduced as an indicator of the habits in the consumer utility function. In fact, the main questions that the research seeks to answer are: <br />How is the consumption habit of urban households for eating food compared to rural households? How is the pattern of consumption habits of urban households for non-food commodities compared to rural households? The formation of the consumption habits of urban households for food is more powerful or non-food commodities? How is the pattern of consumption habits of urban households for non-food commodities compared to rural households? <br /> <br /> <strong>2. Materials and Methods</strong> <br />Since the introduction of the permanent hypothesis (Friedman, 1957) and the consumption life cycle (Modigliani and Bromberg, 1954), the concept of consumption smoothing has been widely used to explain household behavior. Hall (1978), in his paper, modeled the total consumption dynamics by extracting Euler's equation from the first-order condition of the optimization problem of consumer choice. The Euler equation has been widely used in economic literature to estimate the parameters of the risk-free utility. This approach reflects the fact that consumers are also interested in determining their current consumption of their past use or consumption of others (Dayton, 1992, 16). In models where consumption habits have been introduced in the household utility function, the utility depends on the level of consumption and accumulation of habits, which are measured by the past periods of the average consumption (fakhre hoseini, 2015, 69). This means that the habits of people are formed over a long period of time. In general, habits of consumption can be divided into two types of external and internal. External habits, presented by researchers like Abel 1990, Campbell and Cochran 1999, Galli 1994, Auer 2013), suggest that household preferences are based on total consumption lags. On the other hand, the formation of the habits of consumption is based on the assumption that household consumption depends on the consumption of their own past periods (Reader & Hill, 1973; Sandrasans, 1989; Constantinides, 1990, Dreyer& Schneider& Smith, 2013, Kwan, Leung, & Dong, 2015). <br />In the following, we Model the formation of consuming habits and extract the Euler equation. <br />Assume that each household tries to select at time t in order to maximize the following expression (Carroll, 2000, 69): <br />max E<sub>t</sub> [∑<sup>T</sup><sub>s=t </sub>β<sup>s-t </sup>u(c<sub>s</sub> , h<sub>s</sub>)] (1)<br /><sub> </sub> <br />Which β is the time preferences factor, <em>h</em> is accumulations of habits, and E is the expectation operator. Suppose that the constraints that the above maximization problem faces are: <br />x<sub>t+1</sub>= R [x<sub>t</sub>-c<sub>t</sub>]+ y<sub>t+1</sub> (2) <br />h<sub>t+1</sub>=h<sub>t</sub>+λ(c<sub>t</sub>-h<sub>t</sub>) (3)<br /> <br /> Which R is the risk-free interest rate, y, the income of the workforce in the period t,x is the cash of the household (the total amount of resources available to spend in period t). The Bellman equation corresponding to the above equations is defined as: <br /> v<sub>t</sub>(x<sub>t</sub>, h<sub>t</sub>)=max <sub>{</sub>c<sub>t}</sub> u(c<sub>t</sub>,h<sub>t</sub>)+βE<sub>t</sub>[v<sub>t+1</sub>(x<sub>t+1</sub>, h<sub>t+1</sub>)] (4)<br />The first-order optimization condition for Equation (4) relative to expenditures consumed by is: <br />0=u<sup><span style="font-size: 8.33333px;">c</span></sup><sub><span style="font-size: 8.33333px;">t</span></sub>+βE<sub>t</sub>(λv<sup><span style="font-size: 8.33333px;">h</span></sup><sub>t+1</sub> - Rv<sup>x</sup><sub>t+1</sub>) (5) <br />uct=βE<sub>t</sub> (Rv<sup>x</sup><sub>t+1</sub>- λv<sup>h</sup><sub>t+1</sub>) (6) <br />That λ is a parameter of "consumption habits". To obtain the Euler equation, we use the equations (1) to (6) and envelope theorem. After some calculations, we have: <br />u<sup>c</sup><sub>t</sub>+ β[λv<sup>h</sup><sub>t+1</sub>+ (1-λ)u<sup>c</sup><sub>t+1</sub>]=Rβ[u<sup>c</sup><sub>t+1+</sub>β(λv<sup>h</sup><sub>t+1</sub>(1-λ)u<sup>c</sup><sub>t+2</sub>] (7) <br /><br />In fact, (7) is Euler's equation for maximizing the problem of present article. <br />If we assume that the formation of habits is such that the level of habits in the period t is equal to the consumption of the previous period. That means: <br />h<sub>t</sub>= c<sub>t-1 (8)</sub><br /> <br />In this case, equation (7) is simplified as follows: <br />u<sup>c</sup><sub>t</sub>+βu<sup>h</sup><sub>t+1</sub>=Rβ[u<sup>c</sup><sub>t+1</sub>+βu<sup>h</sup><sub>t+2</sub>] (9)<br /> <br />Suppose that, like Melbourne (1988), for the utility function, we consider the following form: <br />u (c , h) = v (c - αh) (10)<br /> <br />Therefore: <br />u<sup>c</sup> = v’ , uh = -αv’ (11)<br /> <br />Replacement (11) in (9) gives: <br />v’<sub>t</sub>-αβv’<sub>t-1</sub>=Rβ(v’<sub>t+1</sub>-αβv’<sub>t+2</sub>] (12)<br /> <br />The stable solution corresponding to the first root (12) under the fixed asset yields is: <br />v’<sub>t </sub>= RβE<sub>t</sub> (v’<sub>t+1</sub>) or 1= RβE<sub>t</sub> (v’<sub>t+1</sub>⁄v’<sub>t</sub>) (13)<br /> <br />Now if we assume that the utility function is in the form:(z=z<sup>1-ρ</sup>/1-ρ), then (z)=z<sup>-ρ</sup>. Therefore, according to (13), we will have: <br />1=RβE<sub>t</sub> (z<sub>t+1</sub>⁄z<sub>t</sub>)<sup>-ρ</sup> (14)<br /> <br />Considering relations (8) and (9), yield: z<sub>t</sub>=c<sub>t</sub>-αc<sub>t-1</sub> , So: <br />1= RβE<sub>t</sub> (c<sub>t+1</sub>-αc<sub>t</sub>⁄c<sub>t</sub>- αc<sub>t-1</sub>)<sup>-ρ</sup> (15) <br />1= RβE<sub>t</sub> ((c<sub>t+1/</sub>c<sub>t</sub>)/1-αc<sub>t-1/</sub>c<sub>t</sub>)<sup>-ρ</sup> (16)<br /> <br />The parameters of nonlinear Euler equation (16) are estimated using generalized moments (GMM) and suitable instrument variables. <br /> <br /><strong>3. Empirical Results and Discussion </strong> <br />In this paper is estimated the coefficient of habit formation for the cost of food and nonfood goods of urban and rural households in Iran during 1357-1394, using Euler equations GMM approach. Household’s consumption expenditure data is located at the Iranian Statistics Center, the results of the survey on household expenditure and household income. To further acquaint with the average annual costs of nonfood goods for a rural household (crnok), the average annual costs of food and tobacco consumption for rural household (crk), the average annual costs of nonfood goods for a urban household (cunok), the average annual costs of food goods for a urban household (cuk), they are presented in Table 1: <br /> <br />Table 1: Consumption expenditures of food and nonfood goods for urban and rural households in Iran <br /> <br /> <strong>Consumption expenditures Variable Mean Median Min Max</strong> <br /><span> Nonfood(urban household) <span>cunok <span>35475367 <span>35475367 <span>307990 <span>200000000</span></span></span></span></span></span> <br /><span> food(urban household) <span>cuk <span>11866378 <span>86637811 <span>175217 <span>62431000</span></span></span></span></span></span> <br /><span><span><span><span><span><span><span> Nonfood(rural household) <span>crnok <span>17181566 <span>4167900 <span>108888 <span>89205000</span></span></span></span></span></span></span></span></span></span></span></span> <br /><span><span><span><span><span><span><span><span><span><span><span><span><span> food(rural household) cuk <span>11677346 <span>3578014 <span>125977 <span>57778000</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span> <br />Source: Iranian Statistics Center and research calculates <br /> <br />According to the table 1, we can say that the average consumption expenditure of urban and rural households for food goods is approximately equal, but nonfood consumption costs for each urban household is more than twice of a rural household. <br /> <br /><strong>3.1 Calculation of the coefficient of formation of consumption habits for all kinds of consumption expenditures of urban and rural households</strong> <br />For estimating of coefficients of habit formation for consumption expenditures In Euler equation (16), we apply GMM approach. So, it is necessary that all of the variables that mentioned to be stationary. Hence, we applied ADF test for this mean and we ensured that all of the variables are stationary. After that, we used GMM method for estimating coefficient of habit formation <br /> for all kinds of consumption expenditures of Iranian urban and rural households using equation (16). Results are presented in table (2): <br /> <br />Table 2: The results of calculating the coefficient of formation of consumption habits or α and relative risk aversion coefficient ρ using the formula of mole (16) and method GMM <br /><span><strong>Consumption expenditures α ρ <em>J</em> <em>J<sup>*</sup>=N*J</em></strong> </span> <br /><span><span> Nonfood(urban household)</span> <span>1.5(0.04) <span>1.73(0.042) </span></span> <span>0.00014 <span>0.0049</span></span></span> <br /><span><span> food(urban household)</span> <span>1.41(0.00) <span>1.7(0.014) <span>0.015 <span>0.54</span></span></span></span></span> <br /><span><span><span><span><span><span> Nonfood(rural household) <span>1.62(0.00) <span>3.98(0.08) <span>0.021 <span>0.74</span></span></span></span></span></span></span></span></span></span> <br /><span><span><span><span><span><span><span><span><span><span><span> food(rural household) <span>1.01(0.00) <span>0.81(0.001) <span>0.017 <span>0.56</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span> <br /> *numbers in parentheses () are p-values for t statistic and <span>x</span><sup>2</sup><span> <sub>1,%5</sub></span>. <br /> <br />Hansen’s J-statistic indicates the validity of all the models and instrument variables used in them, because:<strong> </strong> <br /> <br /> J<sup>*</sup>=N*J=35*0.00014=0.0049<χ<sup>2</sup><sub>r-l,%5=x<sup>2</sup> 1,%5</sub>=3.841 <br /> J<sup>*</sup>=N*J=36*0.015=0.54<χ<sup>2</sup><sub>r-l,%5=x<sup>2</sup> 1,%5</sub>=3.841 <br /> J<sup>*</sup>=N*J=34*0.021=0.74<χ<sup>2</sup><sub>r-l,%5=x<sup>2</sup> 1,%5</sub>=3.841 <br /> J<sup>*</sup>=N*J=33*0.017=0.056<χ<sup>2</sup><sub>r-l,%5=x<sup>2</sup> 1,%5</sub>=3.841 <br /><strong> </strong> <br />It is worth noting that the kernel of all equations is Bartlett, and the fixed-bandwidth is selected from the Newey West. <br /> <br /><strong>4. Conclusion</strong> <br />The results of the research showed that the role of habits in the utility function of urban and rural households is significant and important for both groups, and consumption of past periods of households has an impact on the consumption of their current period; So, the coefficient of formation of consumption habits for rural household food expenditure are more than this coefficient among rural households. On the other hand, the coefficient of habits formation for urban households for non-food is about 40% more than that for rural households. On the other hand, the results of this study indicate that the relative risk aversion for consumption of rural household goods is more than this factor for urban households. So, the tendency of rural households to consume food in the present time is more than the desire of urban households. While for non-food commodity groups, relative risk aversion is higher for urban households than rural households. In other words, urban households tend to be more likely to use non-food commodities than rural households at the current time, and are less likely to postpone consumption of these types of goods. Also, descriptive statistics indicate that during survey period, the average expenditure of non food goods for every urban household is more than twice that of rural household. While the average consumption expenditure of urban and rural households for consumer goods is approximately equal. In sum, the findings of this study showed that the formation of habits in the consumption pattern of Iranian urban and rural households has a significant place.https://aes.basu.ac.ir/article_2310_ec12aa5a11bf497cefba7ffce1e59b19.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253072620180723Effect of Financial Development and Business Cycles on Bank Risk in IranEffect of Financial Development and Business Cycles on Bank Risk in Iran7187231110.22084/aes.2018.14179.2498FAMansourZaranezhadMasoudKhodapanahAssistant Professor in EconomicsNiloofarKhadiviGraduant student in EconomicsJournal Article20170720Banks as the financial institutions of the economy have a significant role in the financial intermediation. Therefore, savings, investment, production, employment and growth in the national economy are affected by the decisions and proceedings of the banks. Lending credits have been always considered as the main function of the banks and the change of the economic circumstances can affect the banking risk. As the repayment of the borrowers may decrease over time, the main issues that all banks usually face with are credit risk and non-performing loans. One of the most serious challenges that the country’s banking system is facing with in recent years has been the increasing amount of uncollectible bank debt and non-performing loans. Banks in Iran have too much non-collectible corporate debt on their books. This is due to the bank-centered financial and monetary markets of the country, and the most significant role of banks in creating credit and the liquidity in the country. At present, most of the banks in Iran are facing with lack of resources which has greatly been reduced their ability to finance the projects and boost banking resources. This has led to an increase in bank bankruptcy probability. Considering the above mentioned factors, identifying the factors affecting bank credit risk is very important. Business cycles and financial development are considered as two main factors which affect the credit risk and non-performing loans. Business cycles are a kind of systematic fluctuations in macroeconomic activities of countries that are often emerged by firm’s business activities. A cycle with a period of economic prosperity, which simultaneously occurs in various economic activities, begins and ends with a period of stagnation and contraction. These changes are repeated over and over again, and varying from one year to several years. Therefore, the credit risk of banks is closely related to business cycles, because during the recession, the bank leverage is low, and it is high during the boom period. During the economic boom, banks offer more facilities to businesses and reduce new loans in times of economic downturn. The other main factor affecting the credit risk and non-performing loans is financial development. Financial markets in which debt and financial assets are exchanged are channels for saving that are influenced by technical and general changes, as well as changes in laws and regulations. The set of factors, policies and institutions that lead to deep and extensive access to financial services will lead to financial development. The development of financial markets, with a positive impact on economic activity, will increase the productivity of financial services, risk management, allocation of capital and resource mobilization. Financial markets can play an important role in economic growth, since financial efficiency transfers capital from savers to borrowers and directs resources to productive and profitable investment projects. Of course, the development of financial markets for the economy involves opportunities and threats, because on the one hand, by optimizing the allocation of capital, firms increase the opportunity for economic growth, but on the other hand it can risk the financial system. Therefore, the purpose of the current study was to investigate the effects of financial development and business cycles on the credit risk of the banks for a selection of Iranian banks during the period from 2006-2014 using panel data approach and Eviews software. The model of the research is the modified model of Vithessonthi and Tongurai (2016). The integration and cointegration tests showed that the variables are integrated and there are a significant long-run equilibrium relationship among these variables. The results of the study using panel data and fixed-effects model indicated that financial development and business cycles have negative effects on the credit risk of the banks.Banks as the financial institutions of the economy have a significant role in the financial intermediation. Therefore, savings, investment, production, employment and growth in the national economy are affected by the decisions and proceedings of the banks. Lending credits have been always considered as the main function of the banks and the change of the economic circumstances can affect the banking risk. As the repayment of the borrowers may decrease over time, the main issues that all banks usually face with are credit risk and non-performing loans. One of the most serious challenges that the country’s banking system is facing with in recent years has been the increasing amount of uncollectible bank debt and non-performing loans. Banks in Iran have too much non-collectible corporate debt on their books. This is due to the bank-centered financial and monetary markets of the country, and the most significant role of banks in creating credit and the liquidity in the country. At present, most of the banks in Iran are facing with lack of resources which has greatly been reduced their ability to finance the projects and boost banking resources. This has led to an increase in bank bankruptcy probability. Considering the above mentioned factors, identifying the factors affecting bank credit risk is very important. Business cycles and financial development are considered as two main factors which affect the credit risk and non-performing loans. Business cycles are a kind of systematic fluctuations in macroeconomic activities of countries that are often emerged by firm’s business activities. A cycle with a period of economic prosperity, which simultaneously occurs in various economic activities, begins and ends with a period of stagnation and contraction. These changes are repeated over and over again, and varying from one year to several years. Therefore, the credit risk of banks is closely related to business cycles, because during the recession, the bank leverage is low, and it is high during the boom period. During the economic boom, banks offer more facilities to businesses and reduce new loans in times of economic downturn. The other main factor affecting the credit risk and non-performing loans is financial development. Financial markets in which debt and financial assets are exchanged are channels for saving that are influenced by technical and general changes, as well as changes in laws and regulations. The set of factors, policies and institutions that lead to deep and extensive access to financial services will lead to financial development. The development of financial markets, with a positive impact on economic activity, will increase the productivity of financial services, risk management, allocation of capital and resource mobilization. Financial markets can play an important role in economic growth, since financial efficiency transfers capital from savers to borrowers and directs resources to productive and profitable investment projects. Of course, the development of financial markets for the economy involves opportunities and threats, because on the one hand, by optimizing the allocation of capital, firms increase the opportunity for economic growth, but on the other hand it can risk the financial system. Therefore, the purpose of the current study was to investigate the effects of financial development and business cycles on the credit risk of the banks for a selection of Iranian banks during the period from 2006-2014 using panel data approach and Eviews software. The model of the research is the modified model of Vithessonthi and Tongurai (2016). The integration and cointegration tests showed that the variables are integrated and there are a significant long-run equilibrium relationship among these variables. The results of the study using panel data and fixed-effects model indicated that financial development and business cycles have negative effects on the credit risk of the banks.https://aes.basu.ac.ir/article_2311_2497f630f4973910dfe4d1f3b4efee6b.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253072620180723Evaluation the Effectiveness of Selected Banks in Iran and its Relationship with Banking Internal and Macroeconomic VariablesEvaluation the Effectiveness of Selected Banks in Iran and its Relationship with Banking Internal and Macroeconomic Variables89114231310.22084/aes.2018.14331.2510FAHosseinAmiriuniversity of kharazmiJournal Article20170809In economy based on the market, the banking system has a very high responsibility, and one of the most important economic components of the country is the banking system. Banks can provide good conditions for investment and increase employment and national production by providing financial capital for the sectors of the economy. Banks can create new job opportunities and better income distribution at the community level. In addition, banks are owners of personal and public property and domestic and foreign exchanges by keeping cash and facilitating its transfer. Also banks as agents of monetary policy, play an important role in economic stability. Considering the role and importance of the banking system in the economy, the fundamental question arises whether the banking system can meet the needs of monetary and banking affairs in the economy or not. In general, factors such as differences in equipping and allocating resources optimally, different constraints in laws and regulations (the ceiling of short-term and long-term facilities, etc.) and the cost of contracting between banks and the people (payroll) causes differences in efficiency and efficiency of various banks. Hence, evaluation of banks and financial institutions efficiency is of particular importance; Because, with the help of process of evaluating efficiency, we will be able to obtain useful and beneficial information on how to effectively manage affairs in order to achieve the goals set. <br />Iran economy in current situation, passes one of the most sensitive and breathtaking times of its life. In such a situation, there is a double burden and heavy duty on financial system, especially the banking industry of the country and making this industry more effective will help the country's economy. This research is an approach that can be used to evaluate Iran's banks dynamically on the basis of several real variables, considering the economic and environmental conditions of Iran, so that the scarce resources of the community would be desirable to be a step towards improving the efficiency of the Iranian banks. <br />In this research, the efficiency of Iranian banks is measured by using data envelopment analysis method and by selecting the input and output variables of the bank. For this purpose, fixed asset variables, operating costs, deposits and equity are used as input variable and net income variables, total income and facilities as output variable. Also, in the next stage, the effect of macroeconomic variables and internal banking variables on efficiency in two models is reviewed. In first model, the interbank variables include the ratio of investment to total assets, the ratio of debt to total assets and bank size, and macroeconomic variables including economic growth, inflation rate, exchange rate and liquidity growth rate. In second model, fluctuations are used instead of exchange rate and inflation variables. <br />Regarding the results of government banks during the years of research has always been inefficient (87%). Among the government banks, Maskan bank was more efficient than other banks, and the bank's efficiency was 97%. Also, private banks during the 10-year period have an efficiency of 94%, whose efficiency is relatively good. Among the private banks, Parsian Bank is the best-performing bank. Finally, three of Maskan, Tejarat and Saderat Banks, which were formerly state-owned banks and now are private banks, have the best efficiency levels, and the average efficiency of these three banks is 98%. Be Among these three banks, Mellat Bank is more efficient than the rest of the banks. <br />In next step, in the form of panel models, the internal and external variables on the banking system efficiency during the period of 1394-1385 are considered in the form of two models. Regarding the results, it can be seen that the economic growth rate in both models has a positive and significant effect on efficiency of banks. Therefore, the conditions of the economic downturn and boom are effective on banks' efficiency, so that increase in the economic growth rate leads to increase the efficiency of the banks and vice versa. Also, the growth rate of liquidity does not have a significant relationship with the efficiency of banks. Inflation and exchange rate variables and their fluctuations have a negative and significant relationship with the efficiency of banks; so that the inflation rate and its fluctuations as well as the exchange rate and its fluctuations will reduce the efficiency of banks. <br />Considering the interbank variables, the ratio of investment to total assets has a positive relationship with efficiency. The reason for the positive relationship is that banks, by increasing the debt ratio, accumulate more deposits, can increase their efficiency by increasing facilities or service and production activities. In relation to the variables of the ratio of deposits to total assets and the ratio of debt to total assets, both models are considered to have a negative and significant effect on efficiency. In the case of the variables above, it can be said that increase of these variables increases the ability of the bank when it is subject to bankruptcy or when it has to pay non-financial debt. The size of the bank also has a positive and significant relationship with the efficiency of banks. <br />According to the proposal results, in order to increase the efficiency of banks, it is suggested as follows: <br />-Independence of Central Bank: The results of the research show that the uncertainty of inflation and exchange rate caused the ineffectiveness of Iranian banks; therefore, the central bank's independence to control more liquidity and reduce inflation will make banks more efficient. Also, by controlling the shocks affecting the real sector of the economy (import, exchange rate, ...) and in terms of appropriate economic policies, it can create a margin for economic enterprises, so that its impact by Some policies, such as uncertainty in the exchange rate and inflation, etc., do not lead to bankruptcy. <br />-Privatization and Specialization of Banks: The results of the research show that the efficiency of private and private banks is more than government banks. Therefore, it can be said that one of the factors influencing the efficiency of Iranian banks is their type of ownership (public, private). The implementation of general privatization policies, including Article 44 of the Constitution, in the banking system of the country, can be effective in making Iranian banks more efficient. The review of research literature also shows that the efficiency of private banks is higher than that of government banks. <br />-Banking firms: Due to the large amount of fixed assets in Iranian banks, the issue of banking is raised. The assessment of the banks financial statements indicates that their shareholder combination has many complexities and ambiguities. This complexity has even led banks to devote part of their resources to buying shares in other banks to reduce the risk of their investment. Although not legally prohibited it does in practice, make other firms and economic operators more vulnerable to open-minded facilities and healthy competition. Accordingly, Central bank should increase its oversight and stop banks from taking over. <br />- Ranking System of Iranian Banks: Although ranking has ambiguities and problems, it serves a large service to the financial markets. The ranking of banks and financial institutions is also important, with the difference that the ranking of banks and financial institutions is much more complicated than non-financial institutions and institutions in the manufacturing and service sector. It is recommended that the central bank announce bank ratings based on health of the bank and then link the legal reserve rate to the banks' ratings. As dysfunctional banks should pay more money to the central bank and should be integrated into efficient banks.In economy based on the market, the banking system has a very high responsibility, and one of the most important economic components of the country is the banking system. Banks can provide good conditions for investment and increase employment and national production by providing financial capital for the sectors of the economy. Banks can create new job opportunities and better income distribution at the community level. In addition, banks are owners of personal and public property and domestic and foreign exchanges by keeping cash and facilitating its transfer. Also banks as agents of monetary policy, play an important role in economic stability. Considering the role and importance of the banking system in the economy, the fundamental question arises whether the banking system can meet the needs of monetary and banking affairs in the economy or not. In general, factors such as differences in equipping and allocating resources optimally, different constraints in laws and regulations (the ceiling of short-term and long-term facilities, etc.) and the cost of contracting between banks and the people (payroll) causes differences in efficiency and efficiency of various banks. Hence, evaluation of banks and financial institutions efficiency is of particular importance; Because, with the help of process of evaluating efficiency, we will be able to obtain useful and beneficial information on how to effectively manage affairs in order to achieve the goals set. <br />Iran economy in current situation, passes one of the most sensitive and breathtaking times of its life. In such a situation, there is a double burden and heavy duty on financial system, especially the banking industry of the country and making this industry more effective will help the country's economy. This research is an approach that can be used to evaluate Iran's banks dynamically on the basis of several real variables, considering the economic and environmental conditions of Iran, so that the scarce resources of the community would be desirable to be a step towards improving the efficiency of the Iranian banks. <br />In this research, the efficiency of Iranian banks is measured by using data envelopment analysis method and by selecting the input and output variables of the bank. For this purpose, fixed asset variables, operating costs, deposits and equity are used as input variable and net income variables, total income and facilities as output variable. Also, in the next stage, the effect of macroeconomic variables and internal banking variables on efficiency in two models is reviewed. In first model, the interbank variables include the ratio of investment to total assets, the ratio of debt to total assets and bank size, and macroeconomic variables including economic growth, inflation rate, exchange rate and liquidity growth rate. In second model, fluctuations are used instead of exchange rate and inflation variables. <br />Regarding the results of government banks during the years of research has always been inefficient (87%). Among the government banks, Maskan bank was more efficient than other banks, and the bank's efficiency was 97%. Also, private banks during the 10-year period have an efficiency of 94%, whose efficiency is relatively good. Among the private banks, Parsian Bank is the best-performing bank. Finally, three of Maskan, Tejarat and Saderat Banks, which were formerly state-owned banks and now are private banks, have the best efficiency levels, and the average efficiency of these three banks is 98%. Be Among these three banks, Mellat Bank is more efficient than the rest of the banks. <br />In next step, in the form of panel models, the internal and external variables on the banking system efficiency during the period of 1394-1385 are considered in the form of two models. Regarding the results, it can be seen that the economic growth rate in both models has a positive and significant effect on efficiency of banks. Therefore, the conditions of the economic downturn and boom are effective on banks' efficiency, so that increase in the economic growth rate leads to increase the efficiency of the banks and vice versa. Also, the growth rate of liquidity does not have a significant relationship with the efficiency of banks. Inflation and exchange rate variables and their fluctuations have a negative and significant relationship with the efficiency of banks; so that the inflation rate and its fluctuations as well as the exchange rate and its fluctuations will reduce the efficiency of banks. <br />Considering the interbank variables, the ratio of investment to total assets has a positive relationship with efficiency. The reason for the positive relationship is that banks, by increasing the debt ratio, accumulate more deposits, can increase their efficiency by increasing facilities or service and production activities. In relation to the variables of the ratio of deposits to total assets and the ratio of debt to total assets, both models are considered to have a negative and significant effect on efficiency. In the case of the variables above, it can be said that increase of these variables increases the ability of the bank when it is subject to bankruptcy or when it has to pay non-financial debt. The size of the bank also has a positive and significant relationship with the efficiency of banks. <br />According to the proposal results, in order to increase the efficiency of banks, it is suggested as follows: <br />-Independence of Central Bank: The results of the research show that the uncertainty of inflation and exchange rate caused the ineffectiveness of Iranian banks; therefore, the central bank's independence to control more liquidity and reduce inflation will make banks more efficient. Also, by controlling the shocks affecting the real sector of the economy (import, exchange rate, ...) and in terms of appropriate economic policies, it can create a margin for economic enterprises, so that its impact by Some policies, such as uncertainty in the exchange rate and inflation, etc., do not lead to bankruptcy. <br />-Privatization and Specialization of Banks: The results of the research show that the efficiency of private and private banks is more than government banks. Therefore, it can be said that one of the factors influencing the efficiency of Iranian banks is their type of ownership (public, private). The implementation of general privatization policies, including Article 44 of the Constitution, in the banking system of the country, can be effective in making Iranian banks more efficient. The review of research literature also shows that the efficiency of private banks is higher than that of government banks. <br />-Banking firms: Due to the large amount of fixed assets in Iranian banks, the issue of banking is raised. The assessment of the banks financial statements indicates that their shareholder combination has many complexities and ambiguities. This complexity has even led banks to devote part of their resources to buying shares in other banks to reduce the risk of their investment. Although not legally prohibited it does in practice, make other firms and economic operators more vulnerable to open-minded facilities and healthy competition. Accordingly, Central bank should increase its oversight and stop banks from taking over. <br />- Ranking System of Iranian Banks: Although ranking has ambiguities and problems, it serves a large service to the financial markets. The ranking of banks and financial institutions is also important, with the difference that the ranking of banks and financial institutions is much more complicated than non-financial institutions and institutions in the manufacturing and service sector. It is recommended that the central bank announce bank ratings based on health of the bank and then link the legal reserve rate to the banks' ratings. As dysfunctional banks should pay more money to the central bank and should be integrated into efficient banks.https://aes.basu.ac.ir/article_2313_c8f8ddea80a7ca24d28ef61f98e71a4d.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253072620180723The effect of property rights and political risk to attract foreign direct investment using with PVAR approachThe effect of property rights and political risk to attract foreign direct investment using with PVAR approach115144231710.22084/aes.2017.14465.2519FAYousefMohammadzadeh0000-0002-4364-5832KhalilJahangiriDepartment of Economics, Urmia University0000-0001-9755-1981ArashRefah-kahrizM.A. in Economics, Urmia UniversityElnazValizadehM.A. in Economics, Urmia UniversityJournal Article20170830<strong>Goal</strong> <br />The attract of foreign direct investment for the economies of different countries, especially in the developing countries, is necessary due to the lack of sufficient resources to achieve development aims, as well as the possibility of technology transfer and competitive production. According to the advantages of foreign capital inflows in terms increase of productivity, simultaneous entry of technology and competitive production, this kind of investment has always been the attention of economic researchers and planners. But the level of entry of this capital can be to a large extent, associate with the legal structure and political risks of societies. Political forces in a given society may sabotage for the profitability or endeavors of multinational corporations to achieve their other goals that will have a negative impact on them. Also, if a country seeks to attract foreign investment, it must protect property rights. Of course, the protection of property rights requires cost, but the important point is that the burden of supporting property rights for countries with strong institutional institutions is far less that of countries with lower institutional quality. <br /><strong>Method</strong> <br /> Hence, the present study, considering the global changes and current situation in the economies of developing countries, examines the effect of protecting property rights and political risks on the attraction of foreign direct investment in the 52 selected developing countries during the 1990-2015 period by applying the Panel VAR approach. In in this study, than Internal conflict Index was used as a representative of the country's political risk index that provided by the International Country Risk Guide Methodology. Also, than index provided by the Fraser Institute was used for property rights.To use this method first, we investigates stationary data with Im et al (2003) test. After ensuring that the variables are stationary, using the lowest amount of information criterias, the optimal lag was selected. Then the stability test was performed. After testing the stability of the model, in order to investigate the effect of the shock of a variable on the other variable in model, the analysis of impulse response functions and the error variance decompositions of variables are investigated. <br /><strong>Results</strong> <br />The results of the study demonstrate that, due to a positive shock in the political situation in developing countries, the attraction of foreign direct investment is strongly reacts positively. The positive reaction of the FDI to political risk shocks from the second to the seventh year has slowed down with a gentle slope and it’s indicative of that the consequences of the existence of safe conditions in host countries could for a long time, lead to an increase in foreign investment in these countries. As well as, due to a positive shock in the laws of the developing countries, the attraction of foreign direct investment is increase so that, the positive reaction of the FDI to the positive shock of property rights indicates that the existence of laws based on increasing ownership of property in host countries is up to five period that it could be lead to an increase in foreign investment in those countries. In addition, the findings indicate that in developing countries, among selected variables, the impact of political risk shocks and GDP growth is greater than the impact of other variables such as property rights shocks and the degree of openness trade on attracting foreign investment. <br /><strong>Conclusion</strong> <br />Therefore, in order to increase their share of the international flow of foreign direct investment, officials in developing countries need to high attention to the improvement and stability of their political conditions. Also, considering the effects of the degree of trade openness on FDI, paying attention to commercial freedom and facilitating the flow of imports and exports can be able regarded as a positive sign for foreign investors.<strong>Goal</strong> <br />The attract of foreign direct investment for the economies of different countries, especially in the developing countries, is necessary due to the lack of sufficient resources to achieve development aims, as well as the possibility of technology transfer and competitive production. According to the advantages of foreign capital inflows in terms increase of productivity, simultaneous entry of technology and competitive production, this kind of investment has always been the attention of economic researchers and planners. But the level of entry of this capital can be to a large extent, associate with the legal structure and political risks of societies. Political forces in a given society may sabotage for the profitability or endeavors of multinational corporations to achieve their other goals that will have a negative impact on them. Also, if a country seeks to attract foreign investment, it must protect property rights. Of course, the protection of property rights requires cost, but the important point is that the burden of supporting property rights for countries with strong institutional institutions is far less that of countries with lower institutional quality. <br /><strong>Method</strong> <br /> Hence, the present study, considering the global changes and current situation in the economies of developing countries, examines the effect of protecting property rights and political risks on the attraction of foreign direct investment in the 52 selected developing countries during the 1990-2015 period by applying the Panel VAR approach. In in this study, than Internal conflict Index was used as a representative of the country's political risk index that provided by the International Country Risk Guide Methodology. Also, than index provided by the Fraser Institute was used for property rights.To use this method first, we investigates stationary data with Im et al (2003) test. After ensuring that the variables are stationary, using the lowest amount of information criterias, the optimal lag was selected. Then the stability test was performed. After testing the stability of the model, in order to investigate the effect of the shock of a variable on the other variable in model, the analysis of impulse response functions and the error variance decompositions of variables are investigated. <br /><strong>Results</strong> <br />The results of the study demonstrate that, due to a positive shock in the political situation in developing countries, the attraction of foreign direct investment is strongly reacts positively. The positive reaction of the FDI to political risk shocks from the second to the seventh year has slowed down with a gentle slope and it’s indicative of that the consequences of the existence of safe conditions in host countries could for a long time, lead to an increase in foreign investment in these countries. As well as, due to a positive shock in the laws of the developing countries, the attraction of foreign direct investment is increase so that, the positive reaction of the FDI to the positive shock of property rights indicates that the existence of laws based on increasing ownership of property in host countries is up to five period that it could be lead to an increase in foreign investment in those countries. In addition, the findings indicate that in developing countries, among selected variables, the impact of political risk shocks and GDP growth is greater than the impact of other variables such as property rights shocks and the degree of openness trade on attracting foreign investment. <br /><strong>Conclusion</strong> <br />Therefore, in order to increase their share of the international flow of foreign direct investment, officials in developing countries need to high attention to the improvement and stability of their political conditions. Also, considering the effects of the degree of trade openness on FDI, paying attention to commercial freedom and facilitating the flow of imports and exports can be able regarded as a positive sign for foreign investors.https://aes.basu.ac.ir/article_2317_faaa5b4c9e43ee120d05ce6ba6b77c14.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253072620180723Introduction and Calculation of the physical production function for the Iran economyIntroduction and Calculation of the physical production function for the Iran economy145166231210.22084/aes.2018.14258.2504FAMohammad SharifKarimiAssistant Professor of Economics, Razi UniversityMaryamHeidarianPh.D. student of Public Economics, Razi UniversityJournal Article20170731<strong>Purpose</strong><strong>:</strong> The production function is the technical relation of converting inputs into products. The production function is a completely physical concept and simply shows the relationship between output and production inputs. Regarding the selection of key production factors, in the initial research, has been highlighted work role and physical capital, along with other factors, but the use of energy has not been sufficiently addressed as a major input of production. Considering that energy use is necessary at all stages of production and there is no production possibility without energy consumption, in the last few decades research has been carried out to investigate the role of energy in production functions. Some have used physical laws to justify the use of energy in economics and production. In the present study, the production function called "physical production function" is introduced by the definition of work in physics, which suggests that capital interactions with labor and energy are separated. Thompson (2016) states that the output elasticity of energy is twice that of labor. Energy is underpaid relative to productivity suggesting monopsony power in the energy market. In contrast, overpaid labor faces elastic own substitution. Capital is characterized by inelastic substitution. Changes in the price of energy induce substitution toward labor but little substitution toward capital. <br /><strong>Method:</strong> Considering that the production function is an important economic tool in economic analysis, in this study, the production function is introduced and estimated, which is influenced by the separate interactions of capital with labor and energy. Also, in this study, along with the estimation of the physical production function for the Iran economy during the period of 1974-2016, have been used other of two production functions; Cobb Douglas and Translog production function is used to compare and determine the more reliable econometric results. <br /><strong>Findings</strong><strong>:</strong> In this study, in order to select the appropriate production function for the Iran economy, we will estimate the Cobb-Douglas, Translog and physical production functions, then the significance of the coefficients as well as the conformity and compatibility of parameters and elasticities are considered by economic theories, according to Thompson (2016), and finally, by using of the correct diagnosis tests of the model, the proper production function is selected.It should be noted that different states are considered for estimation production functions in order to obtain the best possible results, including the method of ordinary least squares and error correction method, with considering energy input in production functions. <br />The results of the model diagnostic and the accuracy tests as well as the variables coefficients in the estimation of functions indicate that the physical production function has a more suitable pattern than other two functions for the Iran economy. Among the production inputs, physical capital (0.85%) and labor force (1.95%) have the greatest impact on production. The effect of labor force increase on production (except to Translog) have a positive and significant and in the physical production function of this coefficient is 1.95%. Energy in physical production function estimation have a share of 0.90%. <br /><strong>Conclusions</strong><strong>:</strong> Separate capital-labor interactions with labor and energy have led to more reliable econometric characteristics as well as more sensible economic outcomes. Among production inputs, physical capital have the greatest impact on production, which indicates the importance of investments in the country's economy, which should be optimally and with planning in the infrastructure of the country. Because it has the ability to move the manufacturing sector in an optimal combination of labor and technology, and, with the trade boom improving living standards and economic growth. Meanwhile, Iran has suffered from lack of investment and production from the beginning of the planning process and the development path. The results of the estimation of the physical production function emphasize capital and its interaction with other inputs.<strong>Purpose</strong><strong>:</strong> The production function is the technical relation of converting inputs into products. The production function is a completely physical concept and simply shows the relationship between output and production inputs. Regarding the selection of key production factors, in the initial research, has been highlighted work role and physical capital, along with other factors, but the use of energy has not been sufficiently addressed as a major input of production. Considering that energy use is necessary at all stages of production and there is no production possibility without energy consumption, in the last few decades research has been carried out to investigate the role of energy in production functions. Some have used physical laws to justify the use of energy in economics and production. In the present study, the production function called "physical production function" is introduced by the definition of work in physics, which suggests that capital interactions with labor and energy are separated. Thompson (2016) states that the output elasticity of energy is twice that of labor. Energy is underpaid relative to productivity suggesting monopsony power in the energy market. In contrast, overpaid labor faces elastic own substitution. Capital is characterized by inelastic substitution. Changes in the price of energy induce substitution toward labor but little substitution toward capital. <br /><strong>Method:</strong> Considering that the production function is an important economic tool in economic analysis, in this study, the production function is introduced and estimated, which is influenced by the separate interactions of capital with labor and energy. Also, in this study, along with the estimation of the physical production function for the Iran economy during the period of 1974-2016, have been used other of two production functions; Cobb Douglas and Translog production function is used to compare and determine the more reliable econometric results. <br /><strong>Findings</strong><strong>:</strong> In this study, in order to select the appropriate production function for the Iran economy, we will estimate the Cobb-Douglas, Translog and physical production functions, then the significance of the coefficients as well as the conformity and compatibility of parameters and elasticities are considered by economic theories, according to Thompson (2016), and finally, by using of the correct diagnosis tests of the model, the proper production function is selected.It should be noted that different states are considered for estimation production functions in order to obtain the best possible results, including the method of ordinary least squares and error correction method, with considering energy input in production functions. <br />The results of the model diagnostic and the accuracy tests as well as the variables coefficients in the estimation of functions indicate that the physical production function has a more suitable pattern than other two functions for the Iran economy. Among the production inputs, physical capital (0.85%) and labor force (1.95%) have the greatest impact on production. The effect of labor force increase on production (except to Translog) have a positive and significant and in the physical production function of this coefficient is 1.95%. Energy in physical production function estimation have a share of 0.90%. <br /><strong>Conclusions</strong><strong>:</strong> Separate capital-labor interactions with labor and energy have led to more reliable econometric characteristics as well as more sensible economic outcomes. Among production inputs, physical capital have the greatest impact on production, which indicates the importance of investments in the country's economy, which should be optimally and with planning in the infrastructure of the country. Because it has the ability to move the manufacturing sector in an optimal combination of labor and technology, and, with the trade boom improving living standards and economic growth. Meanwhile, Iran has suffered from lack of investment and production from the beginning of the planning process and the development path. The results of the estimation of the physical production function emphasize capital and its interaction with other inputs.https://aes.basu.ac.ir/article_2312_c2fcaf04f3cd1951e835ff581d2b8f89.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253072620180723Identification of Superior Data and their Impact on the Statistical Reliability of the Regional Input-Output Table Using the New Mixed CHARM-RAS MethodIdentification of Superior Data and their Impact on the Statistical Reliability of the Regional Input-Output Table Using the New Mixed CHARM-RAS Method167193231410.22084/aes.2018.15050.2564FAMaryamKarimi Sakrabadallameh tabataba'i universityParisaMohajeriAli AsgharBanoueiallameh tabataba'i university0009-0002-1591-2531Journal Article20171115Regional input-output tables (RIOTs) are considered strong tools for planning and policy making in the regional level, so the calculation of regional Input-Output tables and regional Input-Output Coefficients (RIOCs) have attracted much attention of most of input-output analysts. Compiling of RIOTs by using the statistical methods is costly and time consuming. So, since the 1950s regional analysts have introduced non-survey based methods like Location Quotients (LQ, such as , , , , , , and ), Commodity Balances (CB) and Cross Hauling Adjusted Regionalization Method (CHARM) for estimating Regional Input-Output Coefficients (RIOCs) and RIOTs. These alternative methods motivated lively debates regarding the reliabilities and accuracies of regional output multipliers. <br />For eliminating the shortcomings of non-statistical methods, researchers have introduced hybrid methods or simultaneous approaches “from up to down and down to up” and they have been leaded to use statistical methods (which are collected from real statistics, interview with researchers and also resources) for improving the accuracy of tables. The accuracy of hybrid methods is higher than non-survey methods and some economist such as Richardson (1986) and Lahr (1992) concluded that the mechanical non-survey methods are unsatisfactory; the short cuts are ingenious, but probably unacceptable, and therefore, the forward of RIOTs lies with mixed survey/non-survey and other hybrid methods. <br />The departure point of hybrid methods in calculation of RIOTs is using one non-survey method. Since collecting superior data is expensive and time consuming, the question that crops up for analysts is to identify the cells in which the collection of statistics on those data bases plays a greater role in improving the statistical accuracy of estimating. So, identifying cells related to superior data have been noticed by researchers and some criteria have been introduced like important coefficients and key sectors. The first one is applied for identifying individual cells and the other is applied for identifying an entire rows and columns. <br />The main objective of this paper is to demonstrate that the use of superior data in the hybrid methods of estimating regional input-output tables (RIOTs) can improve the statistical validity of tables as well as coefficients. Calculation of this paper is based on two kinds of data; one national and regional (Gilan province) symmetric activity by activity input-output tables and second, regional accounts for 1381. <br />In this paper, the most appropriate criterion for identifying superior data in the RIOTs is selected, using the new CHARM-RAS mixed method and the seven criteria namely LARGE1 (the largest cells in the intermediate deliveries matrix), LARGE2 (the largest cells in the direct input coefficients) and INVIMP (inverse important coefficients) COLSUM (column-sums of the Leontief inverse Matrix), ROWSUM (row-sums of the Leontief inverse Matrix) and COLHYP (the impact of hypothetically extracting an entire column on the whole economy) and ROWHYP (the impact of hypothetically extracting an entire row on the whole economy). <br />Results of this paper indicate that, first of all, regardless of the criteria used to identifying superior data, the use of these data will improve the accuracy and the statistical validity of the tables. Second, the highest and the lowest improvements in accuracy are related to LARGE1 and ROWSUM, respectively. Third, while criteria in identifying of individual cells have the higher improvement in accuracy than row and column criteria, but with respect to the very high costs of collecting individual cells, there is a trade -off between the statistical credibility and the cost of collecting individual cells. Fourth, column and row criterion in the hypothetical extraction method is more applicable compared to traditional method. Hence, column criteria have less statistical errors than row criteria. Therefore, the most appropriate criterion for identifying superior data is the COLHYP criterion.Regional input-output tables (RIOTs) are considered strong tools for planning and policy making in the regional level, so the calculation of regional Input-Output tables and regional Input-Output Coefficients (RIOCs) have attracted much attention of most of input-output analysts. Compiling of RIOTs by using the statistical methods is costly and time consuming. So, since the 1950s regional analysts have introduced non-survey based methods like Location Quotients (LQ, such as , , , , , , and ), Commodity Balances (CB) and Cross Hauling Adjusted Regionalization Method (CHARM) for estimating Regional Input-Output Coefficients (RIOCs) and RIOTs. These alternative methods motivated lively debates regarding the reliabilities and accuracies of regional output multipliers. <br />For eliminating the shortcomings of non-statistical methods, researchers have introduced hybrid methods or simultaneous approaches “from up to down and down to up” and they have been leaded to use statistical methods (which are collected from real statistics, interview with researchers and also resources) for improving the accuracy of tables. The accuracy of hybrid methods is higher than non-survey methods and some economist such as Richardson (1986) and Lahr (1992) concluded that the mechanical non-survey methods are unsatisfactory; the short cuts are ingenious, but probably unacceptable, and therefore, the forward of RIOTs lies with mixed survey/non-survey and other hybrid methods. <br />The departure point of hybrid methods in calculation of RIOTs is using one non-survey method. Since collecting superior data is expensive and time consuming, the question that crops up for analysts is to identify the cells in which the collection of statistics on those data bases plays a greater role in improving the statistical accuracy of estimating. So, identifying cells related to superior data have been noticed by researchers and some criteria have been introduced like important coefficients and key sectors. The first one is applied for identifying individual cells and the other is applied for identifying an entire rows and columns. <br />The main objective of this paper is to demonstrate that the use of superior data in the hybrid methods of estimating regional input-output tables (RIOTs) can improve the statistical validity of tables as well as coefficients. Calculation of this paper is based on two kinds of data; one national and regional (Gilan province) symmetric activity by activity input-output tables and second, regional accounts for 1381. <br />In this paper, the most appropriate criterion for identifying superior data in the RIOTs is selected, using the new CHARM-RAS mixed method and the seven criteria namely LARGE1 (the largest cells in the intermediate deliveries matrix), LARGE2 (the largest cells in the direct input coefficients) and INVIMP (inverse important coefficients) COLSUM (column-sums of the Leontief inverse Matrix), ROWSUM (row-sums of the Leontief inverse Matrix) and COLHYP (the impact of hypothetically extracting an entire column on the whole economy) and ROWHYP (the impact of hypothetically extracting an entire row on the whole economy). <br />Results of this paper indicate that, first of all, regardless of the criteria used to identifying superior data, the use of these data will improve the accuracy and the statistical validity of the tables. Second, the highest and the lowest improvements in accuracy are related to LARGE1 and ROWSUM, respectively. Third, while criteria in identifying of individual cells have the higher improvement in accuracy than row and column criteria, but with respect to the very high costs of collecting individual cells, there is a trade -off between the statistical credibility and the cost of collecting individual cells. Fourth, column and row criterion in the hypothetical extraction method is more applicable compared to traditional method. Hence, column criteria have less statistical errors than row criteria. Therefore, the most appropriate criterion for identifying superior data is the COLHYP criterion.https://aes.basu.ac.ir/article_2314_facf4923a238b83ec80df8ec46417647.pdf