جستجوی مقالات مرتبط با کلیدواژه "i" در نشریات گروه "اقتصاد"

Today, the issue of employment and unemployment is highly problematic and crisis-prone economically, socially, and politically. The lack of adequate employment in a society can challenge social trust in the government and, consequently, its political legitimacy. Terrorism has a negative impact on the economic performance of countries, but the impact varies from country to country. The negative impact of terrorism is very low in high-income countries, but very high in low-income countries. The reason is that rich countries can use their resources to fight terrorism, but low-income countries are poor and do not have institutions that can respond to the negative impact of terrorism.The Middle East is a region of great ethnic and religious diversity. The high share of energy supply, the concentration of regional and extra-regional powers in recent years accounts for a high share of terrorist attacks in the Middle East, which, in addition to human losses, have large and negative impacts on investment, tourism, and economic growth in the Middle Eastern countries. On the other hand, factors such as political risk, socio-political unrest, high levels of corruption, political instability, political uncertainty, oil price, and lack of transparency have led to an increase in terrorist attacks and the effects of sustained terrorist attacks in these countries.

Spatial econometrics adds spatial effects to the power of periodic or mixed (panel) regression models. Thus, in spatial econometrics, sample information has a spatial component. When the data have a spatial component, two issues can be discussed, including spatial dependence and spatial heterogeneity.In spatial econometric models, it is necessary to choose the numerical value of spatial directions in order to model spatial responses. For this purpose, we have two sources of assumptions. The first is concerned with the position in the coordinate plane, which is expressed by latitude and longitude and such that the distance of each point in the place or the distance of each observation located at each point can be calculated relative to fixed or central points or observations. The second assumption is that the source of spatial information is neighborhood and proximity, which expresses the relative location in space of a seen peripheral unit compared to other such scales. The degree of proximity and contiguity will be available based on the data obtained from the model of the community under study. According to these data, it can be determined which areas are adjacent to each other. In this case, the scales (areas) that have a neighborhood relationship have a higher degree of connectedness than the units that do not have neighbors. The adjacency matrix contains the elements 0 and 1, where the components on the main diameter are equal to zero and the components outside the main diameter are equal to one if two countries are adjacent to each other, and zero otherwise. Therefore, the objective of the current study is to examine the impact of shocks caused by terrorist activities on employment in the selected countries of the Middle East region during the period 2000-2021 using the spatial econometric model.

The results of the research showed that terrorist activities have a negative and significant impact on the level of employment in the affected countries and lead to a decrease in the level of employment and economic activities there in both direct and indirect ways. The result shows that one of the goals of terrorism is to divert the business environment from normal to critical conditions. Based on other research results, the variable of GDP per capita, inflow of foreign direct investment and trade have positive and significant effect on the level of employment in the selected countries of the Middle East, while the variable of inflation rate has negative effect on the employment there.

Conclusions and policy implications:

 According to the results of the research, in order to control the effectiveness of terrorist activities in the selected countries of the Middle East, the institutional quality, political and economic stability, and the structure of democracy should be enhanced. Therefore, if these conditions are accompanied by the instability of internal politics and low institutional quality, it creates a double effect for increasing terrorist activities. Of course, political instability and the existence of corruption and lack of transparency can contribute to the financing of terrorism too. Therefore, it is recommended that the laws and policies of governments in these countries be more stable and transparent in order to counter the creation of terrorist groups. Moreover, since terrorist shocks impose many economic costs on these countries, the cost of securing the region can be considered as an investment in improving the productive environment. Therefore, increasing the budget to deal with terrorist attacks and their complications, as well as funding regional cooperation to deal with terrorism, can bring significant economic benefits to these countries. Another important policy that is recommended is to provide more guarantees in these countries to protect the interests of investors against terrorist attacks. To this end, government guarantees and legal and economic protections for foreign investments to ensure the stability of their operations can reduce investment uncertainty to some extent. In this regard, even the insurance system of the countries can be activated and support foreign investors against terrorist risks. These joint activities can create common interests in unity and cooperation to fight terrorism. Finally, planning to keep economic and investment areas away from terrorist targets and ensuring the security of foreign investment areas and targets can also reduce foreign investment uncertainty to some extent.

This research paper aims to examine how changes in Iran's industrial exports, specifically the Untapped Export Potential (UEP), affect the employment level. We analyzed the data from 120 industrial activities in Iran spanning the years 2002 to 2020 and then categorized them according to the four-digit ISIC Rev. and four codes. Previous studies mentioned in the existing literature have primarily focused on the impact of the realized exports on employment. However, this article adopts a different approach by introducing the concept of Untapped Export Potential and exploring its influence on the employment-trade relationship. The UEP concept refers to the potential volume of exports between Iran and its trading partners.

To account for the autoregressive nature of employment over consecutive years, we employ a dynamic labor demand regression model. In this model, the main control determinants of the outcome of interest are production, wages, research and development (R&D) costs, imports and, particularly, the Untapped Export Potential (UEP). We anticipate that employment will be positively influenced by the lagged employment level, value added of activities, R&D costs, and imports. On the other hand, we expect employment to be negatively affected by wages and the UEP.

First, we conducted an Im-Pesaran-Shin (IPS) test to check for the presence of unit roots in the panel time series. Once the presence of unit roots was proved, we proceeded to estimate dynamic panel regressions. The estimated parameters exhibited statistical significance, and their signs aligned with the expected theoretical outcomes. The expansion of imports was found to have a positive impact on industrial employment; there was a 0.025% increase observed. Additionally, a one percent increase in value added by industrial activities would lead to a 0.37% increase in employment. Although the effect was smaller, an increase in R&D costs still had a significant impact on industrial employment, with a 0.037% increase observed. On the other hand, an increase in paid wages had a negative effect on labor demand. Specifically, a one-unit increase in wages would result in a 0.15% decrease in employment. Regarding the UEP, the results demonstrated its inverse effects on Iran's industrial employment. On average, each unit increase in the use of UEP corresponded to a 0.004% decrease in industrial employment. However, it is to be noted that the effect on employment was not evenly distributed among industries.

Conclusions and policy implications: 

In light of the declining trend in Iran's exports, it is crucial for policymakers to facilitate international trade, particularly exports. Considering that the use of UEP and an increase in imports have positive effects on the country's industrial employment, expanding foreign trade will not a threat to industrial employment in Iran; rather, it contributes to its improvement. Furthermore, to enhance Iran's position in the global arena, significant political decisions must be made regarding the country's international relations. The calculation of potential export volumes based on the current state of Iran's manufacturing industries does not indicate a favorable situation. Many of these industries heavily rely on oil production and contribute significantly to carbon pollution, which not only worsens the country's climate but also has potential international consequences in the future. Moreover, the continued production of such products with low-quality technology can lead to a decline in the country's overall production capacity. To address these challenges, it is essential for Iran to focus on increasing its industrial production capacity with medium and high technologies. This requires creating conditions to attract foreign direct investment, technology transfer, efficient utilization of skilled labor and new tools, all of which depend on establishing internal peace and security and correcting perceptions of stability among the local population, immigrants, and foreigners.

The main purpose of this study is to identify the various aspects of the Justice and Progress Fund and delineate its directions in the future. In fact, the important goals of this study are to evaluate the strengths and criticisms of the Omid Entrepreneurship Fund, to identify ways getting out of the bottlenecks of the Omid Entrepreneurship Fund, to present competing alternatives to the Justice and Progress Fund, to present the advantages and bottlenecks and limitations of the Justice and Progress Fund, and to identify solutions for the implementation of the Justice and Progress Fund.

The methods used in this research are descriptive, evaluation, content analysis and comparative studies. From the point of view of aim, the research is an applied and developmental type. The tools of this study were a researcher-made questionnaire, interviews and library and documentary studies. The data were collected as a library work by using the theses, scientific and research papers as well as conference articles on the sites of Magiran, academic jihad database and scientific and research magazines of the ministers. Latin references were also used for their scientific and research articles cited on the google scholar site. To collect the research data, after making the necessary arrangements with the managers, experts and elites in the field, the research questions were provided to them in the form of a questionnaire. In total, 55 experts were selected using the snowball sampling method.

The results of the investigations show that the low cost of creating the fund (the structure, facilities and hardware of the Omid Entrepreneurship Fund), the alignment and sometimes overlapping of the goals of the Justice and Progress Fund with the goals of the Omid Entrepreneurship Fund, the possibility of holding provincial officials accountable for the optimal and efficient use of resources allocated, and the possibility of proposing and applying new financing models and leveraging them have been the most important strengths and opportunities of the Justice and Progress Fund. On the other hand, the lack of transparency in grants in paragraph (a) of note (18) have caused weaknesses such as the application of taste, lack of giving and receiving reports, replacing relationships and connections instead of regulations, eliminating economic justification in activities, and creating rent and corruption in the country.

Conclusions and policy implications: 

One of the most important results of the current research was determining the tasks and clarifying the direction of the Justice and Progress Fund in the future. Therefore, in a process of identifying dualities of orientation, an attempt has been made to prepare the basis for developing a conceptual model to form the Justice and Progress Fund.Based on the summation of the opinions of managers and elites in this field, the research groups of the Development and Foresight Research Center, the Management and Planning Organization of the provinces of the country, and the expert opinion of the researcher based on the different parts of the article, and the opinions of all the interviewed people, the two options of continuing the Omid Entrepreneurship Fund and creating the Iran Justice and Progress Fund were taken into consideration. Although the Justice and Progress Fund has been examined in detail as the main idea of this research and placed in the center of attention of the study, the continuation of the Omid Entrepreneurship Fund is proposed as another option based on the opinions of experts in this field. The disadvantages of the Omid Entrepreneurship Fund have been evaluated to be far more than its merits. In general, based on the evaluations of growth and development funds and the views and opinions of experts and managers, it is possible to realize the Justice and Progress Fund. The key orientations in the design of the Justice and Progress Fund include emphasis on providing financial resources for non-governmental and second-optimal projects with specific criteria and conditions, creating financial leverage to use private sector resources, supporting businesses with high added value, supporting bases of investment and provision of facilities, financing small and medium-sized projects, emphasis on the method of providing domestic financial resources, allocation of facilities to projects based on prioritizing economic growth (realization of 8 percent growth) based on the national spatial development document, focusing on limited and specific goals, decentralizing and increasing the authorities of the Provincial Planning and Development Council, strengthening the local government, and the management and planning of the organization as a custodian institution.The coordinates of the Progress and Justice Fund are proposed in this section in the areas of activity limits, realization and monitoring, how to provide financial resources, how to allocate facilities to plans and projects and management structures (powers and infrastructure) among them some are mentioned:Completing and developing the production infrastructure in the provinces, developing transparent and program-oriented mechanisms for the allocation of financial resources of the Fund, emphasizing the provision of financial resources for non-governmental and second-optimum projects with specific criteria and conditions (allocating a maximum of 10 percent of the Fund's resources to governmental projects with high physical progress), creating a separate account in the province to inform the governors of the amount of resources and expenses in the fund, decentralization and increasing the authorities of the Provincial Planning and Development Council (allocating the fund resources to the provinces), further opening the path to the formation of the local government and the proposed trustee body for the Justice and Progress Fund, planning and budget organization of the country and at the provincial level, and altering the management and planning organization of the provinces into a provincial planning and development council (because the chairmanship of the council is the governor’s task; while enjoying the cooperation and support of the governors, the development priorities of the province will also be taken into account).

Economic development is closely related to the consumption of natural resources. In particular, fossil energy is a vital resource for urbanization and industrialization and the main driving force for the development of a modern society. What is the way to decouple economy from energy or at least coordinate between the two? Globalization is like a modern evolution. By removing barriers among countries and expanding business and investment and sharing technology, it causes economic growth and accelerates it. In this regard, the aim of this research is to test the hypothesis that globalization (economic, social and political) improves the decoupling of economic growth from energy consumption and pollution.

The main goal of this research is to investigate the effect of economic, social and political globalization on the decoupling of economic growth from energy consumption and carbon dioxide emissions in 94 selected countries based on the maximum available data during the period of 2000-2021. For this purpose, based on the literature in the field, a model was chosen and specified in the form of Equations 1 to 6. In these equations, the production coefficients (α1 and β1) are estimates of Tapio's decoupling index. The independent variables in the equations include economic globalization (models 1 and 2), social globalization (models 3 and 4), political globalization (models 5 and 6), economic structure (industry share), energy price, technology, and urbanization. They were entered multiplicatively with production in Equations 1 to 6 in order to estimate their effects on decoupling and identify the change of the decoupling interval:In these equations,  is energy consumption,  is carbon dioxide emission,   is economic growth,  is economic structure,  is economic globalization index,   is social globalization index,  is political globalization index,  is energy price,  is urbanization share, and  is technology. They are all for country i in year t. These equations were calculated using the generalized method of moments (GMM). In addition to its dynamics and ability to solve the problems of autocorrelation and heteroscedasticity, this method poses no problem of endogeneity of explanatory variables.

According to the results, the decoupling of economic growth from energy consumption was placed in the first region of the Tapio coordinates, and its value shows a weak decoupling. Specifically, the  coefficients of the selected models (1, 3 and 5) were found to be 0.339911, 0.309906 and 0.312093, respectively. The positive effect of economic growth on energy consumption was expected because every economic activity and its development requires energy. The coefficients of the multiplicative variables of economic, social and political dimensions of globalization were -0.06765, -0.003012 and -0.00991939, respectively. Based on these results, if economic growth is accompanied by the three dimensions of globalization, the decoupling of economic growth from energy consumption will be improved. The findings showed that, with these variables, there was no significant change in the decoupling interval. Also, according to the results, despite the worsening effect of economic structure (industry share) on the decoupling of economic growth from energy consumption, the decoupling interval would not change substantially. Of course, this finding was expected because the increase in the share of industry increases both economic growth and energy consumption. The results of this research further showed that the price of energy has worsened the decoupling. On the other hand, technology has expectedly improved the decoupling. Finally, it was shown that urbanization improves the decoupling. Despite these results, the individual and simultaneous effects of these variables do not change the decoupling interval.Regarding the decoupling of economic growth from pollution (carbon dioxide), the  coefficients of the selected models (2, 4 and 6) were 0.318560, 0.109514 and 0.213233, respectively, which indicate a weak decoupling. The coefficients of the multiplicative variables of economic globalization and political globalization were -0.046588 and -0.022803, respectively. Based on these estimates, economic and political dimensions of globalization have improved the decoupling of economic growth from pollution. On the other hand, the coefficient of the multiplicative variable of social globalization was 0.003401, based on which, social globalization has led to the worsening of the decoupling of economic growth from pollution. Also, the coefficients of the multiplicative variable of economic structure (industry share) in the selected models showed that industrialization has worsened the decoupling. Moreover, the multiplicative variable coefficients of energy price in the selected models showed that the increase in the energy price has worsened the decoupling. The coefficients of the multiplicative variable of technology in the selected models, however, suggested that technology improves the decoupling of economic growth from pollution. Finally, according to the results, urbanization has worsened the decoupling of economic growth from pollution. In general, the individual and simultaneous effects of the variables have not caused a serious change in the final interval of decoupling of economic growth from pollution. Wald test was used to check whether the coefficients of the globalization dimensions were significantly different. The results of this test indicated a statistical difference between the coefficients of globalization dimensions and the effect of globalization dimensions on decoupling.

Conclusions and policy implications:

 The results of the present research show that the economic, social and political dimensions of globalization improve the decoupling of economic growth from energy consumption. According to the results, economic globalization has a greater impact than the other two components of globalization. This can be due to the greater importance of economic fundamentals than social and political variables. Moreover, the economic and political dimensions of globalization improve the decoupling of economic growth from carbon dioxide emissions, but social globalization worsens this decoupling. It is shown that, compared to political globalization, economic globalization has a greater impact on the decoupling of economic growth and pollution, which is due to the dominance of the economic process in globalization. As another finding, technology improves the decoupling of economic growth from energy consumption and pollution. Also, urbanization improves the decoupling of economic growth from energy consumption, but it worsens the decoupling of economic growth from pollution. The main recommendation of the current research is that countries should pay attention to both internal border issues and international interactions in order to control energy consumption, preserve the environment, and reduce pollution.

In recent years, most of the currency fluctuations in Iran have occurred after the spread of influential domestic and foreign political-economic news. Despite this significant effect, the impact of the news content is not considered, and its results are used in the form of numerical data in modeling. However, numerical indirect results are available after the impact of the news on the market and are practically useless in forecasting. Therefore, there needs to be a model that includes the news directly in forecasting the exchange rate. Another important phenomenon about the exchange rates and news is the framing effect. This effect is a linguistic-cognitive phenomenon resulting in people with two different attitudes, positive and negative, making different choices regarding the same item or reality. While this action is done explicitly or implicitly in policymakers' announcements, criticisms, and statements, there needs to be a systematic way to analyze and predict its effect on the exchange rate. This paper presents a comprehensive model based on machine learning to predict exchange rate moves by receiving all kinds of textual, numerical, and categorical data. Also, using this model, it is possible to analyze the effect of framing on exchange rate changes systematically.

A processing method has been designed to predict the exchange rate from receiving data to producing the final model. In this method, various types of input data, including time series data, batch data, and political-economic news, are received and preprocessed from essential and relevant sources. In numerical data preprocessing, noises and outliers are removed, and values are normalized. Text data are also the texts normalized and converted into tokens. In the next step, custom embedding is provided based on Pars-BERT embedding and the collected news data. Using this embedding, the textual data are converted into numerical vectors, and all the data are given to the model. Here are three supervised models. The first and third models use textual and numerical data to predict the exchange rate, and the second model only uses numerical data. The core of all the models is a Bi-GRU, a deep neural network. Dropout and batch normalization and regularization techniques have been used in these models to avoid overfit and bias.

The tests performed to evaluate the models' efficiency are divided into two parts. The first part evaluates all the three developed models and their results. The second part is dedicated to assessing and analyzing the issue of news framing and the model's performance in this field. Based on the accuracy metric, the first to third models perform properly at the rate of 96.5, 87.2 and 97.07, respectively. The difference between the results of the second model and the first and third ones shows the effect of news on increasing the model's accuracy. In the second part, a dataset of the news affecting the exchange rate with different framings is prepared to evaluate the effect of framing. This set includes 74 main pieces of news and 435 secondary ones. In approximately 32% of the cases, where the news was announced to reduce the rate, the behavior of the market matched the purpose of the report. Meanwhile, in 68% of the other cases, the news that was expected to have a downward effect on the exchange rate did not match the market behavior. This can indicate their incorrect framing. On the other hand, the news that is expected to have a negative (increasing) effect on the exchange rate has worked about 78% of the time and has led to an increase in the rate. This observation also confirms the imbalance effect of positive and negative news on the currency market. Interestingly, the first and third models correctly identified 98.8% and 99.05% of the samples, including negative and positive news of internal and external origins, respectively. This indicates that the models resist different framings of a news story and have made correct predictions.

Conclusions and policy implications:

 In this article, using machine learning models, an operational method was presented to investigate the effect of framing on the exchange rate in Iran. The proposed architecture includes the five stages of preprocessing textual, numerical and categorical data, data vector representation, feature engineering, model training, and final evaluation and analysis. In the stage of textual data representation, customized embedding is used for economic data. In this regard, there are three models presented, including a model based on numerical data and two models based on numerical and textual data with a data fusion structure. All the three models are recurrent neural networks trained with deep learning techniques. The results of the experiments show that the models with combined data perform very well in forecasting the exchange rate. In addition to predicting the exchange rate, the produced models have been used to analyze the effect of the framing of the published news on the currency market. The research results show that negative news still has a far more significant impact on the currency market, and no positive framing can reduce its impact. Also, the mainly domestic report published to reduce the exchange rate has been successful in about 32% of the cases. Another significant achievement is the excellent performance of the presented models in framed samples. In other words, these models have correctly predicted market behavior by considering different aspects of a news story, news history, market history, and information, regardless of the type of the news framing.

One of the consequences of financial intermediation activities in banks is credit risk, which is the oldest, largest and, at the same time, the most important banking risk. As the society is growing and developing, the amount of facilities and liquidity circulation in it increases, and the importance of credit health becomes more necessary. Therefore, evaluating and managing credit risk is a vital thing for banks and is also an important solution for implementing banking policies and business strategies. In addition, the existence of an evolving credit risk management framework indicates the financial prosperity of the banking system in general. It is also an important indicator for the stability and financial stability of each bank in particular.

In this research, the modeling and identification of causal relationships between the main factors of credit risk is done in order to predict the default repayment of customers by referring to credit experts and using the DEMATEL method. Structuring complex factors in the form of cause and effect groups is an important function of DEMATEL method in problem solving processes. Therefore, 22 variables describing credit risk are divided into the two categories of cause and effect.

 The results show that the job status of the applicant has the most influence in the model. The variables of applicant's annual income, workplace ownership and marital status respectively have the next degrees of influence. The number of collaterals has the least influence in the model. The monthly repayment burden has the highest level of influence compared to other variables. Also, the variables of annual income and job status have the most interactions with the other studied variables.

Conclusions and policy implications:

 Demographic and socio-economic indicators, along with financial and credit indicators, should be given more attention in credit bureau models, and different and comprehensive systems to measure credit should be linked to each other more quickly if they are to be used by banks and credit bureau institutions. Therefore, our findings allow providing an effective decision support system for banks in order to detect and reduce the rate of bad borrowers, thus reducing credit risks.

The flow of investment in various industries is affected by macro-economic and political conditions. By analyzing these factors and the internal factors of the stock market, investors buy and sell stocks. In such conditions, fluctuations are transferred to other markets. Following the increased volatility in an industry, it becomes difficult for investors to understand and analyze that industry. It increases the expectation of speculation, and, in such a situation, trust in a particular industry is lost. In the best case, capital outflow from the stock market does not occur. The continuous growing trend of extreme fluctuations in financial assets attracts the attention of many financial investors, policy makers and academic researchers. The transfer of returns and volatility between financial markets has been significantly strengthened and complicated due to globalization, technological development and financialization of commodity markets. It is widely accepted that the integration and financialization of the global market not only leads to increased liquidity and ease of trading in financial markets but also strengthens speculation and thus increases market volatility, which may act as a channel for temporal changes. There have been overflows of asymmetric fluctuations in financial markets as well as other markets over time. The small and large fluctuations there create different performance in the markets. The financialization of global commodities significantly contributes to stronger net contagion effects among financial markets and highlights the central role of financial markets in the transmission of volatility. Based on this, the occurrence of fluctuations in a financial industry can be transferred to other financial industries. Of course, the causality and intensity of transfer and receipt of fluctuations can be different over time and in different decimals of financial market returns, which is very important in the risk management of the investment portfolio. A review of the literature and the studies conducted in Iran's economy and financial markets show that, with the economic shocks brought to the country especially the recent sanctions, the capital market can play an important role to attract astray liquidity and finance the government. This can be done through the sale of shares of state-owned companies as well as the issuance of debt securities, which can show the importance of investigating the time-frequency relationship between the fluctuations of different stock market industries. Based on this, the current research deals with transmission, receipt, and the causal relationship between the fluctuations of the banking, insurance and investment industries during different years and in short-term, medium-term and long-term periods. This can be very important for investors as well as policy makers.

The purpose of this study is to investigate the frequency-time dynamic relationship using the vector autoregression model with time-varying coefficients for insurance, bank, and investment companies in Iran's economy. In order to analyze the results, the TVP-VAR-BK pattern method was used in the period of 2011-2023 based on the frequency of the daily data. The variables used in this study included the index of banking, insurance and investment companies present in the stock exchange.

Based on the results, the banking and insurance industries were the receivers of returns, and the investment companies industry were the transmitters of returns in the short-term period. This shows that investment companies have been the main operators of the country's stock market financial network in the short term and have followed the movement and changes in the efficiency of the banking and insurance industries. Also, the average amount of communication between these industries was 17.53%. Investment companies were net contributors, and banks were net contributors. In the long-term period, insurance companies have received returns from other industries exactly equal to the returns that they have transferred. Accordingly, they have had a neutral role in the investigated network in this period of time. An important point is that investment companies have been net contributors, and the banking industry has been a net contributor in all the three time periods. This shows that any shock to investment companies' efficiency can cause a change in the efficiency of the banking and insurance industries. In general, the banking and insurance industries have been more passive in this network. This issue has been the case more in the short than the long term. Also, as the time period has passed on, the degree of connection between these three important industries of the capital market has decreased.

Conclusions and policy implications: 

The results of the research network analysis showed that, in general, the yield spillover from investment companies was transferred with a high intensity to the insurance companies and with a lower intensity to the banking industry. Also, the yield spillover was weakly transferred from the industry. The insurance has been transferred to the bank. In the short-term period, the yield overflow has been strongly transferred from investment to the bank and less intensively from investment to the insurance industry. In the medium-term period, there has been an overflow of returns from investments to insurance and weakly from banks to investments. Also, during this period, there has been a transfer of profits from the insurance industry to the bank. In the long-term period, the return has been strongly transferred from investment to insurance and more strongly from insurance to bank. According to the results of the research, the following suggestions can be considered by investors and policy makers. In the short-term period, if the investment companies that determine the network grow, the efficiency may be transferred to the banking and insurance industries, which is important to be considered by investors

It is commonly known that the exchange rate is one of the most important macroeconomic variables whose changes strongly influence a country's balance of payments and international competitiveness. Exchange rate fluctuations affect the production costs of companies whose products have an import component. The manufacturing sector is also affected by the foreign exchange market and its fluctuations, as it interacts with the global economy through the export of products and the import of means of production. Economic policy makers should know how changes in the nominal exchange rate will affect prices at the sectoral level. This paper aims to examine the impact of the exchange rate on manufacturing prices both at the general level and at the sub-sector level.

The methodology used in this study is based on Bayesian vector auto-regression models. The data are seasonal and cover the period from spring 1996 to summer 2023. In this study, the BVAR model was used for 22 manufacturing subsectors at the two-digit ISIC code level and for the entire manufacturing sector, resulting in a total of 23 models. Before estimations, the data were tested to determine whether they had a unit root or not. In addition, before estimating the VAR model, the appropriate number of axis intercepts should be determined for the model. The likelihood ratio test was used for this purpose. First, the LR test was used to sequentially test the null hypothesis that the interval between the equations was not significant and the counter-hypothesis that at least one of the coefficients was non-zero. After selecting the variables and estimating the models, the pass-through of the exchange rates to the different prices was examined by analyzing the shock-response functions. Given the different effects of the exchange rate pass-through on the chain of price indices, the question arises as how the increase in the exchange rate affects the individual price indices as a whole and what percentage share this increase has in the individual price indices. They reflect the exchange rate pass-through. To answer this question, a method introduced by Farooqi (2006) was used. After estimating the BVAR model, the shock response functions were defined cumulatively. Then, the size of the cumulative effect caused by the exchange rate change on each price index was compared with the size of the cumulative effect of the currency shock on the exchange rate.

The results show that the price indices for imported and exported goods at the level of total manufacturing do not react in the same way as an exchange rate shock. The price indices of imported goods (76.5 percent) and exported goods (32.3 percent) show the highest increase in the first year compared to the other price indices. The effect of the exchange rate shock on the price index of manufactured goods in the first months is very similar to the effect of the price of imported goods. It reaches its peak in the first year, but, after that, the effect of the exchange rate on the prices of manufactured goods gradually disappears by the end of the second year. This suggests that the impact of the exchange rate on domestic manufacturing prices is incomplete. The results also show that the "manufacture of coke, refined petroleum products and nuclear fuel", "manufacture of medical, precision and optical instruments and watches", and "manufacture of radio, television and communication equipment" industries react most strongly to the shock. By contrast, the sectors showing the least reaction to the exchange rate shock are "manufacture of textiles", "tanning and dressing of leather, luggage, handbags, saddlery, harness and footwear", "manufacture of wearing apparel, dressing and dyeing of fur", and "manufacture of tobacco products”.

Conclusions and policy implications: 

According to the results of the study, it is recommended that the policy of stabilizing the foreign exchange market be continued. This is because the increase in the exchange rate leads to an increase in production costs and affects various industries in the country. Finally, it increases the price of products. Therefore, the continued stabilization of the foreign exchange market will reduce the inflation rate.

Most studies conducted in Iran have used indicators such as air pollution emissions (CO2 emissions) and ecological footprint (EF) to assess environmental sustainability. However, these two indicators have limitations and do not show the different dimensions of the environment. They only focus on the consumption and demand for resources and neglect the supply of resources. Recently, a new indicator called ‘carrying capacity’ has been used in environmental economics studies to assess environmental sustainability. This indicator shows how much load the environment can bear to meet human needs and economic activities. Therefore, in this study, the impact of human capital and green energy on carrying capacity for sustainable development was investigated during the period 1990-2021.

In previous studies conducted in Iran to investigate the impact of human capital and green energy, indicators such as CO2 and EF were used. Due to the shortcomings of these indicators, this research makes use of the load capacity index. Unlike the CO2 index which only examines the emissions caused by fossil fuels, as well as the ecological footprint index which only shows the demand side of resources and ignores the supply side, this index represents the supply side in addition to the demand side. In other words, the load capacity factor, compared to the biological capacity and the ecological footprint, shows an ecological threshold, so it makes a comprehensive research on environmental sustainability assessment possible. So far, the impact of human capital and green energy on the load capacity index has not been checked in Iran.In this study, the documentary method was used to collect the data. The research was conducted based on the annual data from the period of 1990 to 2021 in the Iranian economy. The EViews software was used to estimate the model. It should be noted that the data on the variables related to the research were extracted from the World Bank and the global footprint Network. The model was estimated by the GMM time series econometric method. This is because the model in this study is dynamic and meets the conditions of the generalized moment. In fact, GMM serves for time series models that are linear and satisfy the conditions of generalized moments and secondary properties. Using this method to estimate models can have many advantages.

The results show that the estimated model is in good conditions in terms of statistical indicators. The null hypothesis of the Sargan test cannot be rejected; therefore, it can be said that the instrumental variables used in the estimated model are valid. Indeed, the null hypothesis of the Sargan test implies the validity of the instruments. In view of the linearity of the model, all the estimated coefficients indicate elasticity. The coefficient of the carrying capacity of the previous period had a positive and significant impact on the coefficient of carrying capacity of the present period. In other words, with a 1% increase in carrying capacity in period t-1, the carrying capacity in period t increases by 0.64%. This result is in line with research theories and is in good agreement with the results of previous studies. With an increase of 1% in human capital, the carrying capacity increases by 0.28%. Considering the p-value corresponding to the T statistic of the human capital coefficient, human capital has a positive and significant impact on the environmental carrying capacity in Iran. The consumption of green energy has a positive and significant impact on the carrying capacity. It is such that an increase of 1% in the consumption of green energy increases the carrying capacity by 0.13%. Renewable energies have the least impact on the environment and the ecosystem. Therefore, the use of these energies reduces the ecological footprint. The economic growth variable has a negative and significant impact on the carrying capacity. In other words, with an increase of 1% in economic growth, the carrying capacity decreases by 0.72%. As the economic growth increases, pressure on the environment increases too, leading to a decrease in ecological sustainability. The trade variable has a negative impact on the carrying capacity. So, with an increase of 1% in trade, the carrying capacity decreases by 0.24%. Theoretically, the impact of trade openness on the environment can have positive or negative effects.

Conclusions and policy implications: 

The results of the study showed that the effect of green energy on the carrying capacity coefficient is positive. In general, environmental changes and the reduction of its sustainability due to the increase in greenhouse gas emissions are important factors in the occurrence of climate change, environmental hazards, natural disasters, and serious damage to the environment. The expanded use of renewable energy as a clean energy source can play an important role in reducing environmental pollution and the emission of pollutants. Using green energy can reduce dependence on fossil fuels and lower the greenhouse gas emissions from consumption and production sectors. Green energy is reliable and, if developed correctly, can lead to environmental sustainability and reduce environmental destruction. Based on the results, human capital has a positive and significant impact on the carrying capacity coefficient. Training of human capital is one of the ways to achieve sustainable development and improve the environment. On the other hand, the use of human capital increases awareness, research, development, and green technologies. Incentives for people, the market, and businesses so as to protect the natural environment and promote green consumption and production affect environmental quality and lead to a sustainable environment. Therefore, by improving human capital through the three components of health, skill and education, it is possible to help improve and sustain the environment. Based on the results of the study, economic growth has a negative impact on carrying capacity, which is consistent with other previous studies.

The viability and growth of any business depend on securing the required capital and financial resources. Decisions regarding the optimal capital structure are crucial, not only for survival, growth, and efficiency but also for navigating the competitive environment. Managers, in the realm of company financing, aim to identify the optimal blend of financial resources, known as the capital structure, involving a combination of debt and equity. So, this research aims to determine the threshold level of optimal capital structure in two scenarios, namely debt and equity, and assess their impacts on the financial performance of pharmaceutical and chemical companies listed on the Iranian stock exchange. Previous research on financial management decisions regarding the determination of the optimal capital structure and its impact on company financial performance has presented diverse perspectives and theories, categorizable into distinct groups. Among them, both agency theory and trade-off theory can substantiate the existence of positive or negative associations between capital structure and the financial performance of companies and industries. Within the first category, studies rooted in agency theory elucidate a direct positive relationship between capital structure and company financial performance. They posit that managers lack the motivation to maximize efforts in securing shareholder interests; instead, they pursue personal interests or the policies aligned with their own benefits. Consequently, managerial decisions to finance the capital structure through debt serve as a control mechanism to curb opportunistic behavior and safeguard shareholder interests, leading to a reduction in the company's free cash flows and the compultion of managers to avoid negative investments for the benefit of shareholders. Conversely, studies in the second category focus on interpreting the negative and indirect relationship between capital structure and company performance and contend that, based on the trade-off theory, the potential for incurring bankruptcy costs does exist. As financing with debt entails tax advantages and costs associated with debt financing (bankruptcy costs and financial distress costs), an increase in debt leads to a decrease in net income and an increase in total costs. Excessive debt may also push the company into a financial crisis or bankruptcy, resulting in a decline in the company's value and performance. In the light of previous studies and theories, it is evident that determining the threshold level of capital structure and its impact on financial performance is a challenging issue that has garnered the attention of numerous researchers. Using the data for 31 companies in the pharmaceutical and chemical sector from 2011 to 2022, a non-linear threshold regression approach was applied to address the core questions ‘What is the relationship between optimal capital structure and the financial performance of these companies, considering the trade-off theory and the agency cost theory?’ and ‘Is this relationship linear or non-linear?’

This research sought to determine the optimal threshold level of capital structure in two scenarios, modeling its relationship with the company's financial performance. We identify this threshold in scenarios of financial leverage and equity. Building upon prior theories and foundations, we elucidate the threshold levels of financial leverage and asset return in the first scenario and equity and asset return in the second scenario using the Panel Smooth Transition Regression (PSTR) model. The PSTR model, a non-linear threshold regression model in econometrics, accommodates varying coefficients over time and regimes, addressing the challenge of heterogeneous estimated parameters. Following the recommendations of Gonzalez et al. (2017) and Collitaz & Hurlin (2006), we model the potential non-linear relationship between variables using a transition function and a threshold variable. The functional form of the PSTR model, incorporating two extreme regimes and a continuous transition function, is employed.

The research findings demonstrate a non-linear threshold relationship between optimal capital structure and asset return in both scenarios. In the first scenario, an increase in financial leverage results in a decrease in asset return within the optimal capital structure. Conversely, in the second scenario, an elevated equity-to-asset ratio significantly enhances asset return. The impact of asset return through debt is more pronounced than through equity, suggesting a preference for debt financing over equity in financial investment decisions within pharmaceutical and chemical companies.The results regarding large shareholders' share indicate that these companies have successfully limited market dominance, fostered non-competitive behaviors and increased asset returns beyond the threshold level of optimal capital structure. Before reaching the threshold level in the first scenario, variables such as large shareholders' stakes, company size, and intensity of entry barriers enhance the asset return. Despite debt funding during this stage, increased structural variables, representing market dominance and favorable conditions, contribute to the increase of the asset return. Even after reaching the threshold, these variables continue to positively influence financial performance. The impact of financial leverage exhibits a non-linear relationship and suggests a decrease in the asset return beyond the threshold (0.44). Regarding the equity-to-asset ratio, both before and after the threshold level of 0.17, its increase strengthens the asset return.

Conclusion and policy implication: 

According to the results of the research, certain suggestions can be made for future research. Financial planners and stakeholders are advised to resolve their conflict of interests by determining an appropriate concentration level for shareholders to enhance the company's asset return. Managers, in financial decision-making for investment, are also recommended to have a higher percentage of financing from debt channels compared to equity.

Women, as half of the population, play a significant role in the development and growth of any country. The industrial sector has consistently drawn the attention of policymakers, with one of its key concerns being women's employment and the impact of related variables on their participation in this sector. In Iran, the labor market holds particular importance due to various economic and social factors. Over recent years, the supply of labor has consistently exceeded demand, a trend that is expected to intensify in the future, necessitating the evaluation and analysis of influencing factors. Moreover, the labor market is one of the four critical markets in the economy, given its profound influence on and interaction with other economic markets. The balance or imbalance of labor supply and demand serves as a crucial indicator of macroeconomic performance in any country. Given the importance of women's employment in Iran's industrial workshops, this article assesses the factors affecting women's participation in 24 industrial activities, categorized according to the two-digit codes of the International Standard Industrial Classification (ISIC), fourth edition. The analysis focuses on industrial enterprises employing 10 or more people during the period from 2004 to 2020. This research derives the labor demand function by minimizing the firm's cost function, in accordance with Shephard's Lemma. The findings reveal that value-added and employment levels from the previous period have a greater impact on women's employment compared to other influencing variables.

Understanding the distribution of poverty within society—whether chronic or transient—is crucial for designing and implementing effective poverty reduction policies. This paper uses seven panels of data spanning different periods from the 1990s to the 2020s to estimate chronic poverty trends in Iran, applying two methods the components method and the equally distributed equivalent (EDE) poverty gap method. The components method reveals a lower proportion of chronic poverty compared to the EDE approach, which estimates this figure at an average of 88%. Over time, chronic poverty trends have paralleled broader macroeconomic conditions, declining from the 1990s through the early 2010s, stabilizing briefly, and then increasing again by the late 2010s. The EDE poverty gap method also distinguishes chronic poverty into two parts: the average poverty gap and an inequality component. The findings suggest that changes in chronic poverty are primarily driven by fluctuations in the average poverty gap, with inter-individual inequality playing a less prominent role, despite consistently comprising a larger share of chronic poverty.

Floods are among the natural disasters that have occurred in recent years for various reasons, especially due to severe climate changes with different intensities and fluctuations around the world. They have caused changes in the housing market. Iran is also one of the countries that has always been exposed to various natural disasters due to its geographical location and can face challenges in the housing market as a result of these disasters. One of the most severe of these disasters was the 2019 flood in Golestan province, which caused significant economic damage to 14 cities and 437 villages in Golestan province. However, its economic impact, especially on the housing market, has been less studied due to reasons such as lack of data. Thus, the present study aims to explore the effect of the Golestan flood on probability of homeownership between 2018 and 2019. Therefore, initially, 381 household from Golestan and 418 household from Kurdistan and Kermanshah provinces were selected. Then, by defining a Dummy variable for flood, the effect of floods on probability of homeownership was estimated using Panel Probit method. The main results of this study indicate that the flood significantly reduces probability of homeownership by 0.028 units.

This article utilizes the continuous wavelet transform and its associated tools to provide novel evidence regarding fundamental questions about the Phillips curve in the Iranian economy: slope stability, the role of expectations, and the long-run trade-off. The results indicate that the output gap's impact coefficient on inflation is not constant. Specifically, a positive effect was observed in the short run (2008-2010), while a negative effect was observed in the medium run (2000-2004) and the long run (1995-2004). Inflation expectations positively and significantly influence inflation in both the short and medium runs (2004-2013), but this effect becomes insignificant in the long run. Consequently, the slope of the Phillips curve is deemed unstable: it is negative only in the long run (1995-2004). Therefore, expectations cannot be considered a determining factor for inflation in the long run.