فصلنامه پژوهشهای اقتصادی (رشد و توسعه پایدار)
سال بیست و چهارم شماره 2 (تابستان 1403)

Despite the increasing debate around the role of alternative renewable sources of energy such as solar and nuclear power, oil still has a central role for a vast portion of the world’s countries. Therefore, oil price is one of the key prices in the international economy, and its effects and mechanisms on macroeconomic variables has been an important topic of economic research. In oil-exporting countries, oil price fluctuations have implications for all macroeconomic and prudential policies but due to the government ownership of natural resources, fiscal policy is especially important and can be a main mechanism for transferring these fluctuations to the economy. In this regard, this study aims to analyze the complex relationships and dynamic co-movements between international oil price movements and macroeconomic variables, emphasizing the role of fiscal policy in a time-frequency approach in the years 1978-2020. For this purpose, we implement two novel wavelet analysis techniques, namely, multiple wavelet coherence (MWC) and partial wavelet coherence (PWC), which are used to explore the real relationship between variables. The use of the wavelet tool is superior to traditional tools because it allows the analyst to determine how the series interact at different frequencies and how they evolve over time. To the best of our knowledge, the current is the first paper to implement the wavelet framework to analyze the effects of oil price dynamics on macroeconomic variables in Iran. Therefore, this study makes a modest contribution to the empirical literature by unveiling the main transmission mechanism of oil prices at different time horizons.

The econometrics techniques that have been previously used are focused on time domain analysis. This analysis may return incomplete and ambiguous information on the relationship between economic variables. Therefore, this study is focused on time and frequency domain analysis using the wavelet transformation approach that has been left out for the relationship dynamics among these variables.The origin of wavelets can be traced back to Fourier analysis, which is the foundation of modern time-frequency analysis. Fourier transform, examine the periodicity of phenomena by assuming that they are stationary in time. But most economic and financial time series exhibit quite complicated patterns over time. The wavelet transform approach was introduced to overcome the limitations of the Fourier transform. In fact, if the frequency components are not stationary traditional spectral tools may miss such frequency components. The wavelet analyses do not follow the initial checks to observe if the series have unit root or not. The superior feature of the wavelet analysis is related to its flexibility in monitoring several non-stationary signals.Wavelet Analysis is a method that allows simultaneous decomposition of original time series according to both time and frequency domains. This is very important for economics and finance, as many of the variables in this field can operate and interact differently on dissimilar time scales. So, in this paper, we used two innovative wavelet approaches to study and compare the interdependence between oil prices, non-oil GDP, public expenditure, and trade balance. This approach implements the estimation of the spectral features of time series as a function of time, displaying how the various periodic components of time series vary through time. To check the relevance of the coherence of multiple independents on a dependent one, we use multiple wavelet coherence (MWC), a similar method to the multiple correlations. The partial correlation is one of the tools that can be used in a simple correlation concept. In the wavelet, the researchers can attain this using partial wavelet coherence (PWC). This approach is able to identify the partial wavelet coherence between the two-time series y and x1 after eliminating the influence of the third time series x2. Hence, we use partial wavelet coherence to identify the wavelet coherence between oil prices and government expenditure when canceling out the effect of non-oil GDP and trade balance.

The results of the wavelet analysis show that there is a strong coherence between oil prices and the macroeconomic variables at different frequencies. multiple wavelet coherence, shows a high coherency between the four variables in the short-run (1-4 years) and in the long-run horizons (8-16 years). In fact, multiple wavelet coherence between variables shows that there is always a relationship between variables over time and different scales with different coefficients.Partial wavelet coherence between oil and non-oil GDP has been significant by removing the effects of government expenditure in the short term during the years 1988 to 1992 and also  2000 to 2012. In the scale of 6 to 8 years from 2010, the partial coherence shows an approximate value of 0.6, which is maintained at this frequency until the end of the period. This issue shows the greater correlation between oil price fluctuations and non-oil GDP by removing the effects of fiscal policy fluctuations in these years. Also, by removing theeffects of the trade balance, there is a partial wavelet coherence between the pairs of oil price and non-oil GDP from 1996 to 2012 in the short-term time horizon.The partial wavelet coherence between oil price and trade balance by removing the effect of fiscal policy and also by removing the effect of non-oil GDP indicates a limited relationship between the pair of oil price and trade balance by removing the effects of other two variables during the study period. In both cases, the relationship between the two variables is limited to the early years of the study period, and there is no independent relationship in other areas.The results of the partial wavelet coherence between oil price and government expenditure showed that by removing the effect of non-oil GDP, the highest correlation of the variable occurred in the short-term and medium-term region. In the short-term time horizon, during the years 1979 to 1992, a strong wavelet coherence can be seen between the oil prices and government expenditure, which was repeated during the years 2010 to 2011. Also, by keeping the variable effects of the trade balance constant until the end of the 80s, there is a co-movement between oil price and government expenditure independent of the effects of the trade balance. This net correlation between the two variables well indicates the role of fiscal policy in the transmission of oil price fluctuations in multiple time scales.

The most important effective factor in increasing oil price fluctuations is the unforeseen and increasing risks related to oil and its related industries. Since the world has seen rapid and successive developments in recent years (including the spread of disease, war, etc.), severe fluctuations have been observed in the global oil markets during these years. Therefore, in a fluctuating environment, oil prices have forced governments and policymakers to formulate policies to deal with the uncertainty of oil prices. To implement such policies, it will be useful to examine the relationship between oil price dynamics and its transmission mechanisms in the economy. In this regard, the present article analyzes the relationship between oil price dynamics and macroeconomic variables, emphasizing the role of fiscal policy in Iran through time-frequency analysis and the new approach of multiple and partial wavelet coherence.The results of multiple wavelet coherence show the co-movement between oil price and other variables of the model in different time scales. In such a way that this co-movement shows the greatest intensity in short and long-time horizons. Also, the partial wavelet correlation results between the variables of oil price and non-oil GDP as well as government expenditures showed that by removing the effects of other variables, the co-movement between the pair of variables can still be observed in all time horizons. While regarding the trade balance, this net relationship with oil price was not observed.In general, based on the partial wavelet coherence results, it can be shown that fiscal policy and economic growth are the main channels of oil price fluctuations transmission in this period, which are in line with the studies of Hossein et al. (2008) and El Anshasi (2008) who showed that Fiscal policies are the main propagation mechanism that transmits the oil price shocks to the economy.Therefore, the reduction of oil price correlation by removing the effects of fiscal policy and business cycles shows the importance of the channel of fiscal policy and GDP in the transmission of oil price fluctuations. Therefore, it is recommended that the policymakers who adjust various economic stabilization schemes for greater stability, while paying attention to the main channels of oil financial resources flowing into the economy, should consider different frequency bands as well.

Economic stability via Information and Communication Technology (ICT) has sparked interesting discussions among scholars. ICT plays a crucial role in realizing sustainable development objectives. Globally, the prospective advantages of ICT are widely acknowledged. Some research has solely emphasized ICT's role in mitigating air pollution, but the ecological implications of ICT have largely been overlooked. This article is pioneering in domestic studies of ICT's influence on ecological footprint. In addition, the present research uniquely computes the ICT index through the principal component method, distinguishing it from other ICT studies conducted within Iran. In recent times, the ecological footprint has been embraced as a broader gauge for assessing environmental damage. One reason for this choice is that other environmental harm indicators, such as air and water pollution, deforestation, and others, only represent a part of the total environmental degradation. However, the ecological footprint index incorporates diverse elements like agricultural lands, pastures, fishing areas, forests, carbon footprint, and constructed lands, hence offering a more holistic measure. Concerning the topic in question, it is evident from national studies that there has been little research on identifying the factors contributing to the ecological footprint.

In this research, we investigate the impact of the information and communication technology (ICT) index on selected oil-exporting countries' ecological footprint from 2006 to 2020. To do this, we use the generalized moments method. We extracted the model of this research from the studies of Higon et al. (2017) and Caglar et al. (2021) for carbon dioxide emissions. The variables of our study include the ecological footprint (as the dependent variable), the information and communication technology index (an explanatory variable calculated using the principal component analysis (PCA) method), and control variables such as GDP per capita, exports of goods and services, financial development, and economic Complexity Index which is chosen on the review of other studies. The data used for this study are taken from databases such as the World Bank and the Global Resource Footprint Network and the Atlas of Economic Complexity.
Discussion and

Given the challenges posed by global warming to current and future generations, this study aims to explore the impact of Information and Communication Technology (ICT) on the ecological footprint in chosen oil-exporting nations. This study studied the inverse U relationship of the information and communication technology index with the emission of ecological footprints from 2006 to 2020. The ecological footprint is an index of the amount of environmental pollution and a more comprehensive index than CO2. A data description was undertaken before estimating the model. The research model, built on theoretical underpinnings and past studies, was structured, and estimated by the Generalized Moments Method.The findings showed a non-linear connection between ICT and the ecological footprint in oil-exporting countries. ICT augments the ecological footprint per capita before a certain threshold, but it begins to diminish after that.The positive and significant coefficient of GDP per capita indicates the increase in ecological footprint per capita for the increase of GDP per capita. This result indicates that economic activities such as industrialization and development cause the exploitation of natural resources, which causes more pollution.Financial development has had a positive and significant effect on the ecological footprint. To prevent the destructive effect of financial development on the environment, governments in selected oil-exporting countries should develop financial markets in such a way that financial resources are available for investing in projects that help introduce clean energy technologies. The economic complexity index has had a negative and significant effect on the per capita ecological footprint. In fact, the expansion of economic complexity in the studied countries will lead to the reduction of the ecological footprint. According to the obtained result, the economic complexity index can be considered as one of the ecological footprint control factors; Therefore, the production of more complex goods that contain higher technology can lead to a reduction in energy consumption and ecological footprint; Therefore, governments can provide tax exemptions and subsidies for those companies that use new technology and clean energy, and also support knowledge-based products.The influence of goods and services exports on the ecological footprint has been negative and substantial. The significance of the quality and diversity of exported goods regarding environmental destruction has not yet been thoroughly considered. Therefore, the focus should be on enhancing the quality of export goods via cleaner production methods. Overall energy consumption should also be reduced in all countries, with policymakers prioritizing the use of renewable energy resources and promoting the reduction of fossil-fuel energy export products.The influence of urban population growth on the ecological footprint has been positive and substantial. Essentially, uncontrolled population growth, especially in developing countries, creates grave issues including scarcity of food, poor air and water quality, environmental contamination, degradation of the ecological structure, waste disposal problems, and high energy usage.

The emission of greenhouse gases caused by fossil fuels and other human activities is a serious threat to many countries, which is more prevalent due to its nature and is noticeable in most regions of the world. In the last three decades, with the increase of greenhouse gases in the  atmosphere, the air temperature is increasing, and it is expected that with the continuation of this trend, unfavorable changes will be made in the environment. According to the report of the Intergovernmental Panel on Climate Change (IPCC), in 2014, 76% of all greenhouse gases are composed of carbon dioxide. Therefore, it can be said that carbon dioxide emissions plays an important role in protecting the environment and sustainable development (Omari, 2013). Many studies have identified the factors affecting carbon dioxide emissions and its relationship with other economic, social and environmental factors in order to achieve sustainable development, among which, energy consumption and economic growth can be mentioned. Energy as a driving force plays an effective role in most production and service activities. On the other hand, energy consumption leads to air pollution due to carbon dioxide emissions. The purpose of this study is to examine economy, energy and environment nexus in Asian countries. It is noteworthy that Asian countries such as China, India, Japan, Iran, Saudi Arabia, and South Korea rank less than 10th in terms of carbon dioxide emissions among the countries of the world. The significance of these effects has an important message for environmental policymakers in solving environmental issues and climate change.

This nexus is estimated in the time period of 2002-2018 in the form of Spatial Panel Simultaneous Equations Model (SPSEM) with the Generalized Spatial Panel Two Stage Least Squares (GS2SLS) method. It is tried to analyze this correlation by considering the influence of these countries. Therefore, Spatial Panel Simultaneous Equations Model is used to investigate the three-way communication of economy, energy and environment. The model of simultaneous spatial panel equations makes it possible to analyze the correlation and relationship of economy, energy and environment by considering the influence of countries on each other and spatial spillover effects. In other words, the spatial correlation of countries in terms of economic growth is considered to be the spatial correlation of carbon dioxide emissions.

The results of this research confirm the spatial correlation of economic growth, energy consumption and environmental quality. In other words, the spatial spillover effects of economic growth, energy consumption, and environmental quality exist significantly, and economic growth, energy consumption, and environmental quality in each country are affected by the economic growth, energy consumption, and environmental quality of another country, respectively. In addition, the quality of the environment spatially has a stronger correlation than the other two series. In other words, selected Asian countries are significantly affected by environmental conditions compared to economic conditions.On the other hand, economic growth and energy consumption, economic growth and environmental quality, and energy consumption and environmental quality have a significant mutual relationship. The two-way relationship between economic growth and environmental quality indicates that while more production brings more pollution, pollution has direct and indirect harmful effects on economic growth through increasing treatment costs and reducing labor productivity. The two-way relationship between economic growth and energy consumption is also confirmed.Political stability significantly leads to an increase in environmental quality. The growth of urbanization leads to a decrease in environmental quality due to unfavorable structure of the cities. The share of industrial exports and imports leads to a significant increase and decrease in energy consumption, respectively.

According to the results obtained, it can be pointed out that energy consumption and economic growth are incompatible with the environment. It is clear that energy consumption with carbon dioxide emissions leads to an increase in environmental pollution. The reduction of carbon dioxide emissions depends on the modification of the energy consumption pattern and the replacement of renewable energies instead of fossil energies simultaneously in the countries. Policymakers should adopt strategies to reduce energy consumption. There should be an increase in investment in energy infrastructure with the approach of preventing energy waste and looking for alternative energy sources such as solar energy and new and renewable energies to reduce carbon dioxide emissions. In order to adopt effective policies on climate change issues, policy makers must consider the spatial spillover effects of countries. These new empirical results will help policymakers in Asian countries to design appropriate environmental and energy policies to meet the goals for economic development and sustainability.

Economic complexity is an index that has been raised in the last decade and indicates the use of technology in the process of producing goods and services of a country, which leads to increased economic growth and prosperity by creating a productive structure in the composition, increased productivity and diversity of manufactured products. Economic complexity is expected to affect energy consumption because the type of products produced is an important determinant of energy consumption. If countries operate in energy-intensive industries such as metals, chemicals, and forest products, energy consumption will be high, and if they specialize in low energy and highly complex products, energy consumption in these countries will decrease. In addition, the level of technological knowledge of countries can significantly affect energy efficiency. Therefore, in this research, the effects of economic complexity and economic growth on renewable, non-renewable and total energy consumption in developing and developed countries in the period of 2000-2020 have been investigated by GMM method.

GMM estimator is a subset of instrumental variable method estimators. In this method, in addition to solving the problem of correlation of the independent variable with disturbance components, the endogeneity of the variables and the heterogeneity of the variance of the model are also solved. It should be noted that this method is applicable when T is smaller than N (number of segments).

The results of this research show that the economic complexity index affects the development of renewable energy in developing and developed countries and also causes a decrease in the use of non-renewable energy and total energy consumption in developed countries and an increase in the use of non-renewable and total energy consumption in developing countries. It is currently being developed. In this research, the opening of trade has had a positive effect on the consumption of renewable energy in both groups of countries, and in developed countries, the opening of trade has reduced the consumption of non-renewable and total energy, and in developing countries, the opposite result has been obtained. In both groups of countries, energy consumption has a positive relationship with income level. Also, the results show that if economic growth is accompanied with higher technology, it can lead to a lower increase in total energy consumption in both groups of countries.

As mentioned in the introduction, economic complexity represents a complex and knowledge-based production structure of a given country that takes a long time to mature. When economic complexity increases, the use of non-renewable energy and environmental degradation increases first in a given country. However, with the increase of environmental preferences in a society, the economic actors change their energy by using non-renewable energy habits. This is completely consistent with the results of the estimation models as explained above. Based on the obtained results, it can be said that economic complexity is a policy factor for the overall transformation of renewable energy and demand for greener energy. The study recommends that complexity and structural change policies should be implemented for cleaner and greener growth and overall promotion of greener energy in developing and developed countries. Due to the movement of developing countries towards the development of technology, the need for energy will increase in the coming years. Hence, there is a need for policymakers to plan to meet energy needs. Considering the existing limitations in the use of fossil energy, which leads to complications such as environmental pollution and resource depletion, necessary investments should be made for the development of clean and renewable energy. In order to reduce energy consumption, policies that increase energy efficiency or prevent any form of  waste should be formulated, especially in economic sectors. In this regard, Can and Guzgur recommend that the level of fossil energy consumption in each industry should be clearly estimated and based on that, governments should establish specific laws for each industry. Through developing policies related to trade facilitation, they should also reduce the cost of importing new technologies, or decrease the cost of discovering new technologies via financing research and development institutions. Governments should promote energy regulations to reduce fossil fuel dependence and energy intensity. Future studies could examine the impact of economic complexity on energy demand in terms of oil-importing versus oil-exporting economies. Examining the effects of economic complexity on different aspects of energy (e.g., electric and nuclear energy) can be an important research question for researchers working on energy strategy.

The tourism industry is one of the main factors of economic growth and improvement of social welfare in many developed and developing countries, stimulation of foreign investment, foreign exchange income, development of infrastructure, creation of job opportunities, and social interaction among tourists. Tourism promotes globalization and international cooperation between countries and increases awareness of environmental protection. The performance of tourism depends on the level of development of the industry. Countries with developed tourism, experience a much  greater economic growth compared to the countries with less developed tourism industry. Several factors affect the development of tourism industry. For example, tourism needs an advanced transportation network and other facilities to facilitate the movement of tourists from their own countries to the host countries and also within the host country. Therefore, the physical infrastructure is considered an important determining factor in the arrival of tourists. The current research examines the factors influencing the development of the tourism industry from other perspectives. The existing literature shows that tourism is vulnerable and prone to political risks related to poor  governance strategies, crime, conflicts, political instability, corruption, and terrorism. High levels of political risk make countries inaccessible to international tourists, making visiting those countries seem highly risky and expensive at the same time. In addition to the mentioned items, other factors such as inflation rate, exchange rate fluctuations and real exchange rate affect the tourism industry. In case of currency devaluation in the host country, the visiting rate will increase  as tourism products and services in the country become relatively cheap for tourists from countries with strong currencies. Another parameter in this regard is inflation category where there is a close correlation between the purchasing power of consumers, and rampant inflation rate. When the purchasing power of tourists decreases, they lose interest to travel to such destinations where life and travel expenditures are rather expensive and hardly affordable. However, when inflation decreases, more tourists visit the host country, where the cost of living and transportation is far cheaper.The research findings have shown that tourism has played a vital role in many low-income countries like Iran. Over the  recent years, the country has experienced severe fluctuations in the exchange rate and inflation rate. The accurate validation of exchange rate policies, inflation rate, and political risk is not only useful in the academic field but also for policymakers in practice to support the activity. The current research is innovative in terms of the subject and the econometric methods used. The research hypotheses are as follows: 1- There is a negative and significant relationship between political risk and tourism development. 2- There is a positive and significant relationship between the exchange rate and tourism development. 3- There is a negative and significant relationship between the inflation rate and tourism development.

This research analyzes the effects of political risk, exchange rate, and inflation rate on the development of tourism in the case of Iran in the period of 2000-2021 and uses the non-linear econometric approach (NARDL) to estimate the short-term and long-term coefficients. To perform statistical and econometric analysis, Eviews 13 software was used. The QARDL method is also used to check the robustness of the results.

Short-term and long-term evaluations of NARDL model shows that the effect of a positive exchange rate shock on tourism development is positive and significant both in the short and long term, while the effect of a negative exchange rate shock on tourism development in both the short and long term is negative and significant. In the case of political risk and inflation rate, the results are the opposite of the exchange rate, so the impact of the positive shock of political risk and inflation rate on the development of tourism is negative and significant both in the short and long term, while the effect of the negative shock of political risk and inflation rate on it is positive and significant. The results of long-term and short-term estimates are consistent and differ only in the size of influence in terms of coefficients, and they confirm the hypothesis of the present study. The biggest impact in the short term, with a reduction factor of 0.611, is related to the inflation rate. In the long term, the greatest impact with a reduction factor of 0.790 units is related to the positive shock of political risk.

The tourism economics literature has conclusively proved that tourism entering a country leads to economic development in the destination country. According to the reports of the World Tourism Organization (2018), tourism is the third largest industry in terms of global export earnings. Based on the results of this research, the following recommendations are suggested: Institutional reforms can help strengthen the economy of countries with low-quality institutions, and policymakers should consider the conditions of the tourism industry when setting country stabilization strategies. The increase in the inflation rate in recent years destroys the advantage of being cheap to travel to Iran due to the increase in the exchange rate, so policymakers should consider controlling the inflation rate.

The Covid-19 pandemic, as one of the recent world crises, has brought costs to the economies, which has drawn the attention of researchers and politicians to the concept of economic vulnerability in the form of a warning index to evaluate this external shock. The main aim of this study is to investigate the impact of the COVID-19 pandemic on economic vulnerability in high, medium, and low-income levels countries. This study was conducted for 150 countries using the Panel Smooth Transition Regression (PSTR) approach over 2020-2021. In this regard, the Briguglio method was used to calculate the Economic Vulnerability Index. The results of this research indicate that the COVID-19 pandemic has had a positive and significant effect on the economic vulnerability of countries. The linear test results confirm the non-linear relationship between the variables. Moreover, by considering a transfer function with a threshold parameter (the level of COVID-19 morbidity and mortality), a two-regime model is presented to specify the non-linear relationship between the pattern variables for three groups of high, medium, and low-income countries. The slope parameter (transfer rate) for these three groups of countries is 5.9876, 6.1569, and 3.9987, respectively. The model estimation results show that in both linear and non-linear regimes, COVID-19 has a positive impact on the economic vulnerability of countries with high, medium, and low incomes, meaning that an increase in the COVID-19 pandemic has led to a decrease in the economic vulnerability of these groups of countries.

Through extensive research and data collection, a sample of 150 countries for the period 2020-2021 has been selected. The primary criterion for selecting countries and the period is the availability of data. The research database includes sources such as the World Bank, the International Monetary Fund, and the United Nations Development Organization. The dependent variables in this study are the Vulnerability Index. The Vulnerability Index is constructed based on the Briguglio method using four components: 1) Trade openness 2) Export concentration 3) Dependency on strategic imports, and 4) Exposure to natural disasters. Other variables included in the model are the number of COVID-19 deaths, per capita gross domestic product (GDP), foreign direct investment, and remittances as a percentage of GDP, which have been collected from the World Bank and other reliable sources. This study used Panel Smooth Transition Regression (PSTR) approach. PSTR is a statistical model that is commonly used to analyze the non-linear relationships between economic variables. This model is particularly useful for investigating the behavior of variables that exhibit non-linear patterns or changes in their behavior over time. PSTR is a flexible model that can be used to capture the complex relationships between different variables, making it a popular choice in various fields, such as economics, finance, and social sciences. The PSTR model is an extension of the Smooth Transition Regression (STR) model, which is a non-linear regression model that allows for the specification of the transition function between two different regimes. In the PSTR model, the transition function is extended to include panel data, which allows for the analysis of the non-linear relationships between variables across multiple units, such as countries or firms, over time. PSTR is a powerful tool for analyzing the impact of various economic factors on different regions or countries. For example, it can be used to investigate whether the impact of a particular economic policy or event is uniform across different countries or regions, or whether it varies depending on the level of economic development or other relevant factors. Additionally, PSTR can be applied to different types of data, including cross-sectional, time series, and panel data, making it a versatile tool for analyzing a wide range of economic phenomena.

the vulnerability model indicates that the slope parameter, which represents the speed of transition from one regime to another, is equal to 1191.414, and the regime change location is 435.6, with the logarithm of its anti-value being 2213094. Therefore, as long as the COVID-19 pandemic (mortality) value is less than the anti-logarithm values, the variables will behave according to the first regime. If the value of the COVID-19 pandemic exceeds the anti-logarithm values, the variables will follow the second regime. Based on the results of the two regimes, it is evident that the COVID-19 pandemic variable has had a positive and significant impact, both linear and nonlinear on countries. This means that the increase in the COVID-19 pandemic has led to an increase in the economic vulnerability of countries. In other studies, such as Brzyska & Szamrej (2021), Marti (2021), and Puertas, it has been demonstrated that the COVID-19 pandemic has had a positive and significant effect on the vulnerability of countries in the European Union, which mostly includes high-income countries.

This paper examines the impact of the COVID-19 pandemic on economic vulnerability in 150 countries during 2020-2021. The results obtained from the Panel Smooth Transition Regression (PSTR) model confirm a nonlinear relationship between the variables and the presence of two threshold regimes with a threshold for economic vulnerability and model. It also indicates that the COVID-19 pandemic has a positive effect on vulnerability. This means that an increase in the COVID-19 pandemic has led to an increase in vulnerability and a decrease in economic resilience in these countries.

The question of why different countries vary in terms of  per capita welfare, has always been a fundamental issue in economics. It is generally agreed within the economic literature that the disparity in per capita income among nations cannot be primarily attributed to differences in the production inputs. Instead, it seems that the main discrepancy lies in the total factor productivity (TFP) of each country.Another crucial question is what factors contribute to the differences in TFP. The related literature generally falls into two categories. The first suggests the problem is the lack of advanced technology usage. According to these theories, various factors, including detrimental government businesses in developing countries fail to utilize the recent technologies, which lead to lower TFP.The second, more recent line of thought emphasizes the heterogeneity and disparity of firms within each country rather than considering a representative firm for each nation. The "misallocation" literature builds on the idea that differences in TFP among countries not only depend on individual firms productivity but also on how production inputs are distributed among these firms.This research seeks to answer what factors contribute to the misallocation of physical capital in the Iranian economy. Such an unnderstanding is crucial to address misallocation and move towards optimal allocation, thereby enhancing productivity and welfare. However, it is first necessary to identify what factors cause misallocation as each one demands a distinct solution. Some of these factors are inherently optimal, such as misallocation caused by the firms technology heterogeneity or the presence of physical capital adjustment costs, which do not require any corrective action. In contrast, others result from disruptive government policies and a hostile economic environment that stifle the economy.

In order to address this research question, we utilize a general equilibrium model based on the work of David and Venkateswaran (2019), featuring heterogeneous firms. We estimate the parameters using panel data from industrial workshops from 2003 to 2013 (the most recent data available) and employ the Generalized Method of Moments (GMM). The estimation of structural parameters allows us to disentangle the influences of five misallocation-causing factors. These are: 1. Investment adjustment costs, 2. Information frictions, 3. Distortions, 4. Heterogeneity in firms mark-ups, and 5. Heterogeneity in firms technology. It should be noted that in this model, information frictions refer only to firm's uncertainty about its own future productivity, not macro-level uncertainty.One notable feature of this research is its consideration of several misallocation factors within a single model. As will be discussed further, concentrating on one misallocation factor without considering others could significantly bias estimates of that factor role. By incorporating multiple factors into a unified framework, we can obtain unbiased estimates of each factor. Choosing appropriate moments to match the data and the model is a major challenge in this process. David and Venkateswaran (2019) demonstrate that by selecting five specific moments, the model parameters can be estimated uniquely and without bias.Another strength of this study lies in its use of statistical data from the annual survey of industrial plants. Given the significant differences between smaller businesses and large corporations, along with the former considerable role in employment in Iran,studies based solely on large corporations cannot paint an accurate picture of the Iranian economy.

Our findings indicate that capital adjustment costs, heterogeneity in the production function, and heterogeneity in firms mark-ups are the three primary causes of misallocation in the Iranian economy, accounting for over 80% of the variance in average capital production. Furthermore, the results demonstrate that misallocation has grown during the study period, with a particularly notable increase after 2007 due to the growing impact of disturbances.

The volume of the external money supply is determined by the policymaker, but the amount of money and liquidity will be influenced by the individual's decision to combine their portfolios and the behavior of banks (through lending channels and balance sheets) in the internal money supply. From this perspective, the initial change in external currency (monetary base) causes changes in the supply and demand of all types of assets (such as external and internal money) and their rate of return, and the behavior of individuals and banks determines the optimal composition of the portfolio of assets of individuals and banks and the new and balanced composition of liquidity volume. . Due to differences in the structure of the economy in different countries, the external currency itself can be created from different origins, the exogenous increase of each component of the central bank's asset column (monetary base) causes a change in the relative supply of that asset and its rate of return. Liquidity changes have different sources and are due to changes in the supply of different assets that make up different components of liquidity resources and since the components of liquidity resources are not of the same kind and originate from different processes can have different effects on the performance of macroeconomic variables. The purpose of this article is to analyze and investigate the mechanism of the effect of the components of liquidity resources on the macroeconomic variables of Iran. Changes in liquidity have different sources and are caused by changes in the supply of different assets that form different components of liquidity sources and can have different effects on the performance of macroeconomic variables. For this purpose, a macroeconomic model by including the components of liquidity resources including net foreign assets of the central bank, net foreign assets of banks and non-bank credit institutions, net debt of the public sector to the central bank, net debt of the public sector to banks and non-bank credit institutions and Non-governmental sector debt is designed to show the relationships of economic variables in the framework of a dynamic stochastic general equilibrium model provides.

The model presented in this research is a small open economy consisting of six sectors of households, firms, foreign sectors, banks and credit institutions, government and central bank within the framework of dynamic stochastic general equilibrium model of new Keynesians with respect to nominal and real frictions. By optimizing the objective functions of each of the above brokers, the result of the obtained economic relations is a system of nonlinear differential equations under rational expectations that are currently not empirically solvable, especially in larger patterns. But we can use approximation technique to calculate the model solution in the approximate range functionally. In this research, the set of equations is linear logarithmic using the Ahlik method (1999). In the next step, the input values of the pattern and calibration of parameters and variables have been done using the Iranian economy data during the period 2000-2020. Then, using the Dynar software, the system of equations based on the Bunchard-Kahn method is solved. The results of the statistical tests and moments indicate that the proposed model is suitable for simulating Iran's economy.

In order to evaluate the different effects of liquidity resources on economic variables, the reaction of these variables to liquidity component shocks based on instantaneous reaction functions has been investigated. The findings of the research show that the net assets of the banking system through balance of payments and net debt to the banking system through the channel of the state financial balance, if the source of liquidity is created, increases the variables of production, consumption and investment and causes mild growth or decrease of inflation and exchange rate variables. However, if the source of the liquidity creation of non-governmental debts is from the channel of facilitation, it has a decreasing effect on the variables of production, consumption and investment, and only increases inflation and exchange rate. The two sources of the net assets of the banking system and the net of government liabilities to the banking system, contrary to the source of non-governmental sector debt due to the creation of added value in the economy, have more productive effects and investment and less inflationary effects, hence, macroeconomic stability will bring.

The reaction of macroeconomic variables for the same liquidity growth based on instantaneous reaction functions shows that different components of liquidity sources have different effects on macroeconomic variables.  These results carry the policy message that, in addition to liquidity management, attention to the developments in liquidity resources components is also important in the field of monetary policy. Considering that liquidity has increased by about 5% in all five components of liquidity components, the effects and implications of the five components of liquidity creation sources can be examined. Comparative results indicate that for the specific growth of liquidity, the increases caused by the net assets of the banking system and the net of public sector liabilities to the banking system have more productive and investment effects and less inflationary effects, hence macroeconomic stability. Therefore, it is recommended that the monetary transition policy as much as possible prevent the increase in non-governmental sector debt which leads to increased liquidity.

In Islamic financial markets, Sukuk is the most important Islamic financial securities. Sukuk is designed in a way that is compatible with Islamic laws. These characteristics of sukuk have made it an attractive source of capital for issuers outside the Islamic world who seek to access the liquidity provided by Islamic investors. The sukuk instrument is a partial ownership in an asset (lease sukuk) or property interests (benefit sukuk) or participation in a business or project (participation sukuk). Sukuk diversifies investors' portfolios and offers opportunities to invest in new assets, and on the other hand, issuers can benefit from additional liquidity resulting from growing demand among a large number of investors. Institutions and individuals benefit from Sharia compliant investment tools. The main goal of this research is the comparative analysis of the impact of sukuk market development on bank profitability in Islamic and conventional banks.

 In order to achieve the above goal, the information of 15 countries in the Persian Gulf was analyzed in the period from 2014 to 2021. To test the hypotheses of the research, the multivariate regression method was used using the combined data method.

The results of the research showed that the development of the sukuk market increases the profitability of Islamic banks. Also, the results showed that the development of sukuk market has no effect on the profitability of conventional banks. In addition, the results showed that the Covid-19 crisis does not moderate the impact of the development of the sukuk market on the profitability of Islamic and conventional banks.

In general, it can be said that with the development of the sukuk market, the private sector is increasingly interested in this market, and it is expected that this market will take a large part of the society's liquidity. Financial institutions will also be interested in this market. The sukuk instrument is a partial ownership in an asset (lease sukuk) or property interests (benefit sukuk) or participation in a business or project (participation sukuk). Sukuk diversifies investors' portfolios and offers opportunities to invest in new assets, on the other hand, issuers can benefit from the increased liquidity resulting from the growing demand among a large number of institutional investors. and individuals to benefit from Sharia compliant investment instruments. Therefore, many companies are currently waiting to enter this market due to the greater diversity of the sukuk market compared to traditional bank loans. Sukuk is a financial instrument that allows market participants to obtain a large amount of money and capital from investors, which is possible through the development of a diverse structure of sukuk. Also, according to the presented results, the development of the sukuk market has not played an effective role in the profitability of banks against economic shocks and crises. Therefore, due to the limitations of the research regarding the limited time period and the difficulty of accessing the information related to the variables, one should act cautiously in generalizing the results to the periods before and after the scope of this research.

In recent years, policy makers have increasingly recognized the significance of vulnerability to climate change. This urgent situation necessitates the implementation of immediate, extensive, and comprehensive measures. Extensive scientific consensus has demonstrated that human activities have contributed to significant climate warming trends. However, despite this evidence, there are individuals who remain skeptical and deny the existence of climate change. Consequently, addressing this skepticism and effectively tackling the climate crisis require fundamental changes in behavior and attitudes across various levels and domains of human life. Therefore, the primary objective of this article is to examine the behavioral factors involved in climate policy making, with a particular emphasis on the role of cognitive biases.

This research employed a semi-experimental method, drawing on the principles of behavioral economics. The study utilized a design that included both an experimental group and a control group, with pre-test and post-test assessments. The experimental group was exposed to different information frames, which were developed based on the principles of behavioral economics, while the control group did not receive any framing intervention.
Data for this study was collected through fieldwork and a questionnaire. The statistical population consisted of individuals who had access to WhatsApp, Telegram, and Instagram platforms during the experiment, which took place in the spring of 2023. The target sample size for this research was determined to be 600 participants, divided into six groups of 100 individuals each. The sample size was determined using Cochran's formula for limited populations. Additionally, a random sampling method was employed in this research.

This article aims to establish a connection between climate policy and behavioral sciences by introducing the practical and cost-effective approach of nudge theory. Drawing on insights from behavioral economics, specifically through behavioral interventions that leverage biases such as loss aversion, hyperbolic discounting, and the framing effect, this study investigates how these interventions can encourage individuals to make choices that align with addressing climate change and environmental concerns. Moreover, existing research has demonstrated that integrating the framing effect with other cognitive biases can be an effective and low-cost policy tool for studying environmental behavior.Using a semi-experimental methodology, this article examines the impact of information formatting, including profit and loss framing, hyperbolic discounting bias, as well as extensive and continuous information presentation, on individuals' general attitudes and understanding of climate change. The hypotheses of this research were derived from the literature of behavioral economics, cognitive science, and previous studies in the field of environmental issues. The findings of this research indicate that individuals exhibit a greater sensitivity to loss framing, supporting hypothesis H1. In other words, people are more responsive to potential losses than gains when making decisions. Additionally, the results demonstrate that individuals display a significantly higher willingness to participate when presented with present-loss and present-profit framing compared to future-loss and future-profit framing, aligning with hypothesis H2. This preference for the present over the future, known as present bias and hyperbolic discounting bias, has been extensively examined and validated in behavioral economics literature. Furthermore, the findings show that framing losses and emphasizing the present context contribute to a heightened perception of risk, consequently increasing individuals' willingness to take preventive measures under a loss framework.Additionally, the provision of information in a broad and continuous manner also yielded a significant effect in influencing individuals' behavior, corroborating hypothesis H3.

This study offers novel insights for policymaking and governance regarding public participation in mitigating the impacts of climate change. The findings indicate that the utilization of loss-present framing and continuous framing proves more effective in increasing the willingness of the general public to engage in climate change reduction efforts. Based on the research conducted in this article, climate change mitigation policies can be effectively promoted in public settings through the implementation of nudges that employ loss-present framing when delivering information.Furthermore, the current reliance on economic incentives in most policies to encourage public participation is a noteworthy issue. However, this study proposes the use of non-economic incentives and demonstrates the positive impact of nudges on individuals' willingness to engage in projects aimed at reducing the effects of climate change

The occurrence of various shocks affects economic variables and change their course over time. Knowing the effecst of such shocks on economic variables is necessary for proper policy making in the economy. Therefore, many researches are conducted in this field in the world. Policy-making without recognizing these effects can result into tremendous challenges. One of the most effective shocks in the Iranian economy is the sanctions, especially the nuclear ones, which have had extensive effects on the behavior of brokers and consequently on the country's economic variables.Economic sanctions cause a change in the exchange rate by creating a chaotic atmosphere and confusion in the economy, followed by a change in the supply and demand of housing. Therefore, the purpose of this research is to investigate the effect of economic sanctions on the supply and demand of housing through the exchange rate channel. For this purpose, provincial seasonal data for the period of 2011-2021 have been used and Seemingly Unrelated Regression (SUR) model has been used to analyze the data.

Different methods can be used to estimate the model of the equations of this study, such as single equation methods or methods of solving simultaneous equations, whose estimates are different. The most common methods of solving simultaneous equations are the two-stage and three-stage least squares regression methods, as well as Seemingly Unrelated Regression, which is used when there is a relationship between the error part of the equations or there is a simultaneous correlation. The method discussed in this research is Seemingly Unrelated Regression (SUR) model or Seemingly Unrelated Regression Equations (SURE), which was proposed in 1962 in econometrics.

The findings show that provincial gross domestic product, housing prices, and inflation have a positive effect on demand. The variables of stock market index, exchange rate and sanctions have had a negative effect on housing demand. On the supply side, housing price variables, the number of building permits issued, and inflation have a positive effect on the housing supply, while the exchange rate, sanctions, and the price of construction materials have a negative effect on the housing supply. Imports and embargoes have increased the exchange rate and exports have decreased the exchange rate. On this basis, the embargo has both a direct effect and an indirect effect through the exchange rate on the reduction of housing supply and demand.

The estimation results for the first equation show that the variables are significant. It can also be said that provincial GDP, housing prices and general price level index have a positive effect on housing demand and with the increase of these variables, housing demand increases. According to the findings of the research and the analysis of the available data, the inverse relationship between the stock market index, the sanctions index and the exchange rate with housing demand is confirmed. So, with the growth of the stock market index, sanctions index and exchange rate, the demand for housing decreases.In the preliminary results, the estimate for the second equation of the average effect of labor wages as a part of the production cost was not significant. But in estimates, the effect of the price of construction materials is significant. This variable was removed from the model. It can be said that one of the reasons for the non-significance of the wage variable is its low relative growth compared to the growth of housing prices and the growth of construction materials. On the other hand, the share of wages in housing construction costs is much lower than the costs of materials and other costs. This causes the wage rate in Iran to be less effective in housing supply. It can also be said that an increase in the provincial GDP, housing prices and the number of building permits issued increases housing supply.The results of the third equation show that exports, imports, sanctions index, liquidity volume and provincial GDP explain 99% of exchange rate changes. It is worth mentioning that any increase in exports and sanctions index increases the exchange rate, but with the increase in imports, the exchange rate decreases, which shows the negative relationship between the exchange rate and imports. Since the exchange rate increases under the influence of the sanctions and the exchange rate has a negative effect on the housing demand, it can be said that sanctions have a direct effect on the economic activities of supply and demand due to the disruption of security, certainty and economic stability. Housing has an effect on the supply and demand of housing due to the change in the exchange rate

In recent years, cryptocurrency analysis has become increasingly popular both in academic research and in the financial system as a whole. Cryptocurrencies are a globally spreading phenomenon that is frequently and also prominently addressed by media, venture capitalists, financial institutions, and governments alike (Glasser et al., 2014). Knowing the relationship between cryptocurrency market, stock market or commodity market will be very useful for managing investors’ portfolios and how much of their investment will be allocated to cryptocurrencies for their assets to be secure.The possible interdependence of stock markets and cryptocurrencies is a crucial concern in the financial market literature due to its paramount importance for investors and portfolio managers. Although cryptocurrencies are a recent phenomenon, with the first cryptocurrency, Bitcoin, appearing in January 2016, they quickly become a worldwide phenomenon that is broadly discussed in the finance literature (Glaser et al., 2014).Many scholars in recent times explored the correlation between cryptocurrencies and stock market. Several studies (see Conrad et al., 2018; Jiang et al., 2021; Tiwari et al., 2019; Corbet et al., 2018; Salisu et al., 2019) considered advanced economies to explore the impact of cryptocurrency and stock market, provided important insightful stories. Few others (see Lahiani and Jlassi, 2021; Dasman, 2021; Vardar and Aydogan et al., 2019; Sami and Abdallah, 2020) explored the association in emerging economies. Additionally, the association between cryptocurrency and stock market also received considerable attention among the scholars in the time of COVID-19 (see Mariana et al., 2021; Grobys, 2021; Nguyen, 2021; Kumah, 2021). The issue discussed in these studies is mainly based on the fact that cryptocurrencies are gradually establishing themselves as a new class of assets with unique characteristics, although skepticism and lack of understanding of their nature still exist. These new financial assets (tokens) can offer new opportunities for portfolio diversification and risk hedging. The common consensus regarding weak correlations between cryptocurrencies and stock markets has recently been challenged by their synchronous downturn during the COVID-19 pandemic.Any financial instrument such as stocks and cryptocurrencies traded in the markets may be subject to price fluctuations based on several factors. Factors such as positive and negative news, financial status of stock companies traded in stock markets, political events, global changes and environmental conditions including market risks. With the globalization of the use of cryptocurrencies, the popularity and use of cryptocurrencies has been steadily increasing in Iran over the past few years. In this new situation, investors are looking to reduce their investment risk and achieve optimal portfolio diversification with the participation of new financial assets. Considering the direct and indirect influence of Iran's economy on global financial markets and the expansion of activities related to cryptocurrencies in the context of international sanctions, the question is raised whether there is a relationship between stock returns and cryptocurrencies returns? This study combines the Variational Mode Decomposition (VMD) method and symmetric and asymmetric copula functions to examine the dependence structure between cryptocurrency and stock markets under different investment horizons. 

The fundamental aim of this study is to investigate the structural dependence between the cryptocurrency market and the Iran Stock Market Index using daily data (common trading days) during the period from 8 August 2015 to 21 February 2023. This study combines VMD method and various symmetric and asymmetric copula functions to examine the short-term and long-term dependence structure between the cryptocurrency markets and the Iranian stock market under different investment horizons. In this study, the Normal copula, the student-t copula and the Archimedean copula family functions such as the Frank copula, the Gumbel copula and the Clayton copula have been used. Also, the dependence structure between markets is investigated with one subroutines of the Vine copula functions, namely C-Vine.

The results show that there is no structural dependence between the return Bitcoin and Iran stock market using the Archimedean copula function, either in the short term or in the long term. In other words, the changes domain in return of Bitcoin during the low and high ranges on the return of the mentioned index are insignificant. The results indicate that the cryptocurrencies researced are strongly correlated. However, the associations between cryptocurrencies and conventional financial assets are negligible. These results are consistent with the findings of Gil-Alana et al. (2020); Tiwari et al. (2019) and Corbet et al. (2018) which reveal that there is no correlation between the cryptocurrency and stock markets.

The results indicate that the cryptocurrency market is separated from the main class of financial and economic assets and hence offers various benefits to investors. Structural dependence using Vine copula functions is better than Archimedean copula in identifying the structural dependence between cryptocurrency and the Iranian Stock Market Index during the period under study.  Based on the research findings, the Clayton copula has been chosen as the suitable model to explain the correlation between the return of Bitcoin and the stock market index on the condition of the growth price Ethereum. This point indicates asymmetric effects the dependence on the negative tail is more than the positive one. The findings in this paper indicate the significant role of cryptocurrencies in investor portfolios since they serve as a diversification option for investors, confirming that cryptocurrency is a new investment asset class.