Research Paper
Energy Economy
Seyyed Mohammad Ghaem Zabihi; Fatemeh Akbari; Narges Salehnia
Abstract
The relationship between economic, financial, political risks and per capita carbon emission (CO2) is considered as one of the major global challenges. The effect of these three factors on carbon emissions is very important. Therefore, the current research seeks to investigate the role of economic, financial, ...
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The relationship between economic, financial, political risks and per capita carbon emission (CO2) is considered as one of the major global challenges. The effect of these three factors on carbon emissions is very important. Therefore, the current research seeks to investigate the role of economic, financial, and political risk in reducing per capita carbon emissions (CO2) by using the very new and fresh approach of quantile-on-quantile regression (QQR) modeling in the annual period from 1990 to 2018. The statistical relationship between the variables mentioned in Eviews12 and Matlab2022 software platform has been investigated for Iran. The results show that the economic risk variable in all quantiles (0.5 to 0.95) had a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95), and this positive relationship was relatively stronger in the quantiles (0.3 to 0.95), of the economic risk variable. the financial risk variable in all quantiles (0.5 to 0.95) had a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95), and this positive relationship was relatively weak in all quantiles (0.5 to 0.95) of the financial risk variable, as well as Politics risk has a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95) and this positive relationship is relatively weak in all quantiles (0.5 to 0.95) of the Politics risk variable. Thus, the need to pay attention to Iran's economic, financial, and political stability to improve the environment's quality and reduce carbon emission (CO2) is very important.1.IntroductionThe most significant global threat in the 21st century is climate change and global warming, primarily driven by carbon dioxide (CO2) emissions (Akadiri et al., 2021; Oladipopo et al., 2021). The rapid development of modern industrial societies worldwide in recent years has led to a gradual rise in the consumption of fossil fuels, including coal, oil, and natural gas. The increased consumption has resulted in the substantial emission of CO2 (Danish et al., 2019; Dong et al., 2018; Zhao et al., 2021). In recent years, there has been an enhanced awareness among governments and international organizations worldwide regarding the impact of climate change on the economy, society, and the environment (Gambier et al., 2022). The heightened awareness has prompted the adoption of environmental protection policies (Roncroni et al., 2021). However, the implementation of these policies requires significant expenditures. Consequently, the role of financial stability in addressing the risks associated with climate change and reducing greenhouse gas (GHG) emissions has gained increasing importance (Sun et al., 2022). Research indicates that a stable financial environment is conducive to stimulating production and investment, albeit with a potential increase in energy consumption and CO2 emissions (Solimana et al., 2017).Global warming and CO2 emissions are closely intertwined with economic and political risks (Adoms et al., 2018). Global uncertainties have increased the volatility of economic and political policies on a global scale. Any form of uncertainty, be it social, political, economic, or war-related, invariably impacts economic activities (Blatman & Miguel, 2010; Guidolin & La Ferrara, 2010). Economic (in)stability plays a crucial role in shaping the environment in which companies operate, influencing the decision-making processes of economic entities. Similarly, political instability can significantly impact investors’ decision-making. Moreover, political risk is on the rise in nearly all countries, exerting pressure on military budgets at the expense of construction budgets. This situation leads to a reduction in overall production within the country, and the decreased production results in a further decline in energy consumption, and ultimately leading to a decrease in carbon emissions (Ahmad et al., 2022). Employing a novel methodology known as quantile-on-quantile regression (QQR), the present research aimed to explore the impact of economic, financial, and political risks on per capita carbon emissions in Iran during 1990–2018. Regarding the methodology and the specific focus, no similar research has been conducted in Iran. Therefore, the current study stands out for its innovation in terms of subject matter, methodology, and the targeted context, potentially yielding significant findings.2. Materials and MethodsThe QQR approach is a novel method for analyzing bivariate equations. Introduced by Sim and Zhu (2015), it combines ordinary regression and nonparametric estimation, providing more comprehensive insights compared to traditional estimation methods. QQR examines the intricate relationship between the lower and upper quantiles of the data series, which yields a more realistic analytical perspective than conventional methods (Yu et al., 2022). This study used the QQR approach to investigate the relationship between economic, financial, and political risks and per capita carbon emissions. In this line, the econometric model was formulated as in Equation (1): (1) In Equation (1), CO2t denotes per capita carbon emissions in year t. ERt represents economic risk in year t. FRt is financial risk in year t. PRt indicates political risk in year t, and 𝜀𝑡 is a component of the model error.Several methods were used to analyze the data, including the descriptive analysis, assessment of variable reliability, the diagnostic test (esp., the disruption components autocorrelation test), the correlation test, Johansen’s co-accumulation, and finally the quantile-by-quantile model estimation.3. Results and DiscussionUtilizing the innovative econometric approach of quantile-on-quantile regression (QQR), the research explored the statistical relationship between economic, financial, and political risk variables and per capita carbon emissions in Iran during 1990–2018. The findings revealed that the economic risk variable had a positive effect on carbon emissions per capita across all quantiles (0.5 to 0.95), with this positive relationship being relatively stronger in the 0.3–0.95 quantiles of the economic risk variable. Similarly, the financial risk variable had a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95), although this positive relationship is relatively weak across all quantiles of the financial risk variable. Likewise, political risk positively influenced carbon emissions per capita in all quantiles (0.5 to 0.95), with this positive relationship being relatively weak across all quantiles of the political risk variable. The research results align with the findings of Zhang and Chiu (2020), Abbasi and Riaz (2016), Mehmet et al. (2018), and Zaidi et al. (2019).4.ConclusionThe present study aimed to examine the correlation between economic, financial, political risks, and per capita carbon emissions in Iran during 1990–2018. The findings emphasize the significance of maintaining economic, financial, and political stability in Iran as it is crucial for enhancing the quality of the environment and mitigating carbon emissions.
Research Paper
Macroeconomics
Mohammad Hossein Jafari; Amineh Mahmudzadeh; Masoud Nili
Abstract
This paper examines the potential of government fiscal support in mitigating the consequences of shocks, particularly in relation to the infection rate of contagious diseases. The focus is on the emergence of Covid-19 and the various interventions implemented by governments to combat it. The study utilizes ...
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This paper examines the potential of government fiscal support in mitigating the consequences of shocks, particularly in relation to the infection rate of contagious diseases. The focus is on the emergence of Covid-19 and the various interventions implemented by governments to combat it. The study utilizes a cross-country analysis, using a dataset that includes government fiscal measures, infection rates, and selected institutional and economic metrics from different countries. To isolate the effects of vaccinations, the analysis is specifically focused on the year 2020. The findings indicate that a one percentage point increase in the ratio of direct government spending to GDP corresponds to an approximate 0.08 percentage point reduction in the confirmed infection rate. Given the average infection rate of 1.6 percent in 2020, this translates to a significant 5 percent decrease in infection rates. Additionally, the study reveals that the effectiveness of fiscal support measures is influenced by the institutional quality of the countries. Higher institutional quality is associated with greater effectiveness of fiscal support measures in reducing the infection rate. Furthermore, the study highlights that the impact of government spending on reducing the infection rate is enhanced when accompanied by the implementation of governmental rules.1.IntroductionWith the outbreak of the COVID–19 pandemic, countries faced with a widespread shock that precipitated health–economic crises. In an effort to curb the spread of the disease, governments implemented quarantine measures while concurrently endeavoring to aid vulnerable households and businesses, aligning citizens with restrictive policies. It is essential to note that government responses extended beyond financial support; a comprehensive set of policies was enacted to address the outbreak and its ramifications. In this respect, the present research aimed to study the impact of fiscal measures on the prevalence of COVID–19.The study investigated the hypothesis that government financial support may contribute to diminishing the prevalence of COVID–19. Should this hypothesis prove valid by drawing from the lessons learned during the COVID–19-induced shock, we can advocate for a more widespread application of fiscal policy tools in similar circumstances. This recommendation may extend beyond the conventional goal of stabilizing the macroeconomy, encompassing a proactive approach towards reducing infection rates. Yet a significant portion of the existing literature refers to the limited role of fiscal policies in stabilizing economy.The significance of this study lies in its quantitative assessment of the impact of monetary and fiscal policies, along with the identification of institutional factors that influence the scale and composition of supportive policies. This information can help policymakers to make necessary institutional changes, enabling a more adept response to potential future shocks.In line with the hypothesis testing, the research also investigated the impact of certain fiscal support measures adopted by governments on reducing the infection rate of COVID–19.2.Materials and MethodsDue to the absence of the necessary database for testing the research hypothesis, the researchers constructed a suitable database by amalgamating and refining data sourced from various databases, including research centers specializing in infectious diseases, the World Bank, and other international statistical institutions. The study used a cross-country panel analysis to measure the impact of fiscal policies while controlling for influential variables.3.Results and DiscussionThe study showed that the overall direct government expenditures aimed at combating the outbreak of COVID–19 had a significantly negative relationship with the confirmed infection rate. This finding demonstrates a satisfactory level of stability in relation to changes in the control variables.Furthermore, the study employed the rule of law index to measure the impact of institutional quality on the effectiveness of expenditures. The index did not show a direct correlation with the infection rate. However, the significance of the coefficient associated with the product of the rule of law and government direct expenditures suggests that enhancing institutional quality can increase the effectiveness of expenditures in reducing the infection rate.The analysis of the expenditures indirectly linked to health revealed that a one-percentage-point increase in the ratio of such expenditures to GDP led to a 0.13 percentage-point decrease in the confirmed infection rate. Given the average 1.6% infection rate in 2020, this translates to a 5% decrease in the infection rate.The research results indicate that support provided through grants to small businesses, aids to tenants, income support for households, and expenditures resulting from reductions in various tax bases or similar measures proved successful in aligning businesses and households with quarantine policies. Moreover, these measures demonstrated a relatively acceptable ability to reduce the infection rate. Considering the average ratio of 3.4% of these expenditures to GDP of countries, it can be asserted that with an approximately 30% increase in support (equivalent to a one-percentage-point increase in this ratio), the average infection rate has decreased by 5%, hence a decrease in the mortality rate.4.ConclusionThe research results indicate that fiscal policies, beyond their role in stabilizing the macroeconomy, remain a potent tool in the hands of policymakers. Appropriately employed, these policies have the potential to mitigate the adverse effects of severe shocks, such as the outbreak of a disease. Specifically, the research highlights the effectiveness of fiscal support policies adopted during the COVID–19 outbreak in aligning households and businesses with imposed restrictions. There was evident reduction in the infection rate, even when controlling for other influential variables. Furthermore, the study underscored the impact of institutional quality, measured by the rule of law index, on the effectiveness of government fiscal support. It suggests that fiscal support measures carried out within a robust institutional framework demonstrate greater effectiveness. Conversely, in contexts characterized by weak institutions, the effectiveness of fiscal support is diminished.
Research Paper
international trading
Seyedeh Marveh Nasersadrabadi; Farhad Ghaffari; Teymour Mohammadi; Abbas Memarnejad
Abstract
The negative consequences of financial crises require the attention of economic policymakers and decision making centers.Therefore, considering the importance of the subject, the present study has investigated the effects of global financial crises on the trade patterns of Iran and its partners during ...
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The negative consequences of financial crises require the attention of economic policymakers and decision making centers.Therefore, considering the importance of the subject, the present study has investigated the effects of global financial crises on the trade patterns of Iran and its partners during the years 1995-2018.The variables have been estimated in the framework of the gravity model using the pseudo poisson maximum likelihood method.The findings show that the Asian financial crisis (1997) has an effective role in reducing the volume of trade but this result is the opposite in relation to the American financial crisis (2007); Because instead of a threat, it has become an opportunity for the movement of business flows. In this situation, it seems that the difference in the intensity and type of impact of financial crises on trade patterns can be affected by the nature of the crisis or the region where the crisis started.1.IntroductionCountries consistently grapple with economic and financial turmoil, which at specific junctures, can escalate into full-fledged financial crises (Moshiri & Nadali, 2013). These crises manifest as conditions where a significant number of financial institutions suddenly experience a substantial decline in the nominal value of their financial assets (Bonis et al., 1998). Given the historical recurrence of financial crises worldwide, it becomes imperative for economic policymakers and decision making centers to address and mitigate their adverse effects. This necessity stems from the detrimental impact that financial crises have on the real sector of economy (Kord-Zangeneh et al., 2019). Compounding the issue is the transnational nature of these crises, as they may transfer from one country to another. The transmission of financial crises occurs through various channels, including trade flows, foreign direct investment, commercial loans and financial aid (Massa & Velde, 2008). Among these channels, trade relations emerge as crucial communication pathways on a global scale, playing a pivotal role in influencing the performance of diverse economic sectors affected by financial crises.Hence, understanding international trade patterns holds greater significance than any other phenomenon in the economy, particularly in times of crisis. Furthermore, drawing insights from the experiences of other nations aids in understanding how trade flows unfold in countries that have recently weathered financial crises (Santana-Gallego & Perez-Rodriguez, 2018). Financial crises impact on the trade in two ways. First, they exert a negative influence on trade by disrupting the trade balance. Then crises transfer from one affected country to another through interconnected trade links. Consequently, the extent to which different countries engage with the global economy dictates the degree to which they are affected by the repercussions of a financial crisis.As countries are interlinked through trade flows, in the event of a shock impacting one economy, it has the potential to extend to the entire network, indirectly influencing trade relations between countries. This connection is particularly crucial because a financial crisis can transfer to other economic sectors through fluctuations in exchange rate variables, exports, imports and changes in international commodity prices (Brave & Butters, 2011).2.Materials and MethodsThe gravity model, proposed by Tinbergen (1966) to explain bilateral trade flows, is distinctive for its emphasis on reflecting international relations. In the field of international trade studies, traditional challenges arise in estimating the gravity model. Specifically, when employing the ordinary least squares method for estimating the gravity model, there is a tendency to exclude zero statistical observations. This limitation stems from the conventional method’s inability to compute a logarithm for the trade variable when trade between countries is not realized in certain years. Consequently, the omission of statistical observations in such instances renders it impossible to generate a zero logarithm. Moreover, when the model is estimated using the non-linear least squares method, there is a potential issue with the heterogeneity of variance, which can compromise the accuracy of interpretations based on the coefficients. Recognizing this challenge, Santos-Silva and Tenreyro (2006) introduced the Poisson pseudo maximum likelihood method to address the estimation of such models. A noteworthy aspect of this method is the non-elimination of zero statistical observations, ensuring unbiased and reliable estimation of variable coefficients. This is achieved by assigning equal weight to all statistical observations. Therefore, the method not only increases the number of statistical observations but also enhances the efficiency of the estimator.The main estimation approach revolves around the Poisson pseudo maximum likelihood method, as explained by theoretical foundations and existing literature that detail the connection between financial crises and international trade. This approach draws inspiration from the works of Santos-Silva and Tenreyro (2006) as well as of Santana-Gallego and Perez-Rodriguez (2018). In this respect, the research model was delineated following the model proposed by Glick and Rose (2016), as shown in Equation (1). (1) The present study aimed to investigate trade patterns by examining the volume of bilateral trade (total export and import) between Iran and its twenty trading partners. The analysis used annual data spanning from 1995 to 2018. The study developed the proposed model, leveraging the flexibility inherent in the gravity model as well as incorporating variables such as the logarithm of the Linder economic similarity index, the logarithm of Iran’s and its trading partners’ populations, the logarithm of the nominal exchange rate, the logarithm of geographical distance and financial crises. This comprehensive formulation is defined as the generalized gravity model expressed in Equation (2). (2) 3.Results and DiscussionDiagnostic tests are imperative before model estimation. Initially, the Chow test was employed to determine the suitable regression method. Subsequently, the Hausman test was used to decide between the methods of fixed effects or random effects.Table 1. Diagnostic testsResultProbabilityStatisticsTestNull Hypothesis Rejected0.00023.04ChowNull Hypothesis Rejected0.000437.51HausmanSource: Research findings The results outlined in Table 1 demonstrate the rejection of the null hypothesis in both the Chow and Hausman tests. To control the multilateral resistance to trade, the study estimated the coefficients of the variables by considering the country’s annual fixed effects. The process was conducted within the framework of the gravity model, employing the Poisson pseudo maximum likelihood method (see below).Table 2. Model estimation resultsProbabilityStandard deviationCoefficientsVariables***0.0000.000-0.179CRIijt 1997***0.0000.0000.135CRIijt 2007***0.0000.184-32.358LnLINijt***0.0005.9153.005LnPOPit***0.0000.1000.111LnPOPjt***0.0000.0000.017LnERijt***0.0000.000-0.400LnDISijt***0.0000.113-52.430CNumber of obs = 240R-Squared = 0.81Pseudo Log Likelihood = -171.405*** Indicates the significance of the coefficients at the level of 1 percent.Source: Research findingsThe results provided in Table 2 reveal that global financial crises exerted a significant impact on the trade volume, yet the nature of their influence on trade patterns varies. Specifically, the findings indicated that the Asian financial crisis of 1997 played a substantial role in reducing the trade volume, while the outcome was opposite in the case of the American financial crisis of 2007.The negative coefficient in the logarithm of the Linder economic similarity index indicates that the volume of trade increases as the per capita income difference decreases. Consequently, the countries with similar tastes or demand structures become the optimal markets for a country’s export goods.Conversely, the positive coefficient in the logarithm of the population of Iran and its trading partners signifies that an increase in population correlated with a rise in the trade volume. This association can be attributed to the utilization of a larger labor force, inherent in higher population figures, which positively affects the production of goods. The outcome is manifested in an increase in the trade volume.The positive coefficient in the logarithm of the nominal exchange rate indicates an increase in trade volume corresponding to an increase in this variable. This pattern emerges because foreign goods become more expensive compared to domestic ones. Consequently, both domestic and foreign consumers are inclined to substitute Iranian goods with foreign alternatives. Conversely, the negative coefficient in the logarithm of geographical distance reveals that this variable exerted a negative impact on the trade volume. In other words, the greater the distance between countries, the higher the transportation costs. As a result, distant markets become less attractive for establishing trade relations.4.ConclusionThe present study examined the effects of global financial crises on the trade patterns of Iran in relation its key trading partners. In this respect, the research used annual data from the studied countries during 1995–2018, then the coefficients of the variables were estimated within the framework of the gravity model as well as the Poisson pseudo maximum likelihood method.According to the findings, the examined countries experience the repercussions of financial crises, yet the magnitude and nature of their impact differ based on the specific characteristics of each crisis. In this context, the Asian financial crisis of 1997 played a significant role in reducing the trade volume in the countries under consideration, while the outcome was opposite in the case of the American financial crisis of 2007. Moreover, the positive coefficients of the variables specifically the logarithm of the Linder economic similarity index, the logarithm of the nominal exchange rate and the logarithm of the population of Iran and its trading partners underscore their favorable impact on the trade volume aligned with the increased trade flows in the countries. Given the negative coefficient in the logarithm of geographical distance, it is anticipated that trade with countries farther away from Iran will be comparatively lower. In fact, the majority of Iran’s trade relations are established with neighboring countries. In light of these findings, it is recommended to implement trade policies that support export-oriented domestic production in the country. This approach, in addition to generating foreign currency income, can serve as a mitigating factor against the adverse effects of financial crises.
Research Paper
Employment
Leyla Jabari; Ali Asghar Salem
Abstract
Climate change, caused by the increase in the emission of carbon dioxide and other greenhouse gases, is one of the critical issues that mankind has faced and has created significant risks for both humans and the environment. In recent decades, many researchers have studied the factors that cause and ...
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Climate change, caused by the increase in the emission of carbon dioxide and other greenhouse gases, is one of the critical issues that mankind has faced and has created significant risks for both humans and the environment. In recent decades, many researchers have studied the factors that cause and affect carbon dioxide and their control. Among the factors affecting the emission of carbon dioxide, we can mention the structural labor change, which can play an important role in increasing the emission of carbon dioxide through the increase of industrial activities and economic growth. Therefore, in the present study, the effect of structural labor change on carbon dioxide emissions in Iran’s provinces was investigated using the Quantile regression with non-additive fixed effects presented by Powell (2016). The results show that increasing labor transfer from the agricultural sector to other economic sectors, including services and industry, increases carbon dioxide emissions. Additionally, indirectly, the structural labor change index has a positive and significant effect on carbon dioxide emissions in Iran’s provinces. The study also confirmed an inverse N relationship between carbon dioxide emissions and economic growth. The coefficients obtained for income inequality are negative and significant, while those for per capita energy consumption, industrialization, and urbanization are positive and significant. IntroductionSince the early 1990s, the emission of carbon dioxide and other greenhouse gases has increased in most countries, aligning with economic growth. This has given rise to numerous challenges for humanity, inflicting detrimental effects on ecosystems across various parts of the world. The increase in carbon dioxide emissions over the past two decades has prompted researchers to delve into the factors influencing such emissions and their control. One significant factor influencing carbon dioxide emissions is the transfer of labor from the agricultural sector to other sectors. This transition is recognized as a hallmark of economic development, commonly referred to as a structural labor change in the field of development economics. Though most economic theories view the labor transfer as an indicator of socio-economic progress, this phenomenon also has disadvantages that can result in abnormal consequences affecting culture, the environment, society, and economy. Shao et al. (2021) and Yang et al. (2021) highlight it as a pivotal factor influencing carbon dioxide emissions and environmental degradation. Understanding the impact of this phenomenon on carbon dioxide emissions is crucial for formulating policies aimed at regulating the emitted carbon dioxide levels. In Iran, the transfer of labor from the agricultural sector to other economic sectors has risen, driven by diverse motives and concurrent with the expansion of urbanization and industrialization. This shift may entail numerous environmental challenges. Long-term statistics reveal that since 1956, the agricultural sector has lost its superiority, while the industrial and service sectors have experienced an increase in the number of workers. The disparity between the industry and services sectors compared to agriculture has widened (Mohinizadeh et al., 2019). However, in Iran, the impact of structural labor change on carbon dioxide emissions has not received significant scholarly attention. In this respect, the present research aimed to explore the nonlinear effects of structural labor change across 31 provinces in Iran during 2010–2020. The study first calculated the carbon dioxide emissions in each province. Subsequently, the analysis focused on the impact of structural labor change, particularly the transfer of labor from the agricultural sector to other economic sectors, on carbon dioxide emissions in the provinces. Materials and MethodsThe study adopted the experimental model proposed by Liu et al. (2019) and Yang et al. (2021), utilizing the subform presented in Equation (1). (1) Equation (1) defines the following variables: lnCO_2 represents the logarithm of carbon dioxide emissions per capita; lnGDP signifies the logarithm of real GDP; ln2GDP denotes the square of the logarithm of real GDP; ln3GDP represents the cube of the logarithm of real GDP; lnRatioagr indicates the logarithm of structural labor change; lnGini is the logarithm of income inequality; lnUrb denotes the logarithm of urbanization; lnIndst is the logarithm of industrialization; and lnEC stands for the logarithm of energy consumption. Furthermore, lnRatioagr×GDP represents the logarithm of the interaction term between structural labor change and real GDP. This variable was incorporated into the model due to the indirect impact of structural labor change on carbon dioxide emissions. In addition to the variable of structural labor change, the study examined the effect of other explanatory variables on carbon dioxide emissions. These variables are summarized in Table 1. Table 1. Introduction of explanatory variablesSourceDescriptionVariableStatistical Center of IranThe ratio represents the percentage of the working labor force in the agricultural sector compared to the total working population. A higher percentage indicates less change in the employment structure, while a lower percentage signifies more pronounced structural changes in the labor force.Structural labor changeEnergy balanceTotal energy consumption per capita, encompassing natural gas, kerosene, fuel oil, and gasoline (thousand liters).Energy consumptionStatistical Center of IranThe ratio of the added value of the industrial sector to the GDP (million rials)IndustrializationMinistry of Economic Affairs and FinanceReal GDP (million rials).Economic growthStatistical Center of IranGini coefficient of total consumption expenditure of urban and rural households in each province, weighted by population (percentage)Income inequalityStatistical Center of IranThe ratio of the urban population in each province to the total population of the province (percentage)Urbanization Results and DiscussionFocusing on the transfer of labor from rural and agricultural areas to urban and industrial or service centers, the present study investigated the impact of this labor transfer on carbon dioxide emissions across 31 provinces in Iran during 2010–2020. First, the carbon dioxide emissions for each province were calculated. Then, the study introduced a model based on quantile regression with nonadditive fixed effects at varying quantile levels. The primary rationale behind employing this regression technique was to offer a detailed and comprehensive analysis of the model’s response variable. This approach allows for intervention not only at the center of gravity of data but also at all levels of the distribution particularly the extremes avoiding the issues associated with assumptions such as ordinary regression, heterogeneity of variance, and the potential impact of outlier data on coefficient estimations. Consequently, the panel quantiles were used to estimate the regression model, and the results are presented in Tables 2 and 3.Table Table 2. Estimation results ation resultsvariables / (τ)5040302010 -48.59***-30.69***29.14***-24.32***-24.46*** 3.13***1.94***1.84***1.52***1.56*** -0.067***-0.041***-0.039***-0.032***-0.033*** -0.622***-0.592***-0.508***-0.758***-0.525*** -0.161-0.068***-0.120***-0.117***-0.202*** 0.0520.722***0.996***1.089***0.918*** 0.143***0.123***0.096***0.103***0.076*** 0.614***0.684***0.646***0.662***0.719*** 0.038***0.046***0.044***0.059***0.042***Source: Research resultsTble Table 3. Estimation results ation resultsvariables / (τ)90807060 -154.60***-73.48***-41.63***-46.65***ln2GDP9.99***4.73***2.96***30.9*** -0.214***-0.101***-0.058***-0.068***Ratioagri-1.99**-0.221***0.0070.612 -0.144-0.257***-0.017-0.046***lnUrb0.340***0.0360.184***0.396*** 0.106***0.130***0.135***0.128*** 0.645***0.724***0.671***0.586*** 0.126**0.015**0.0007-0.039Note: ***, ** and * represent the significance level of 1, 5 and 10%, respectively.Source: Research resultsIncreasing the proportion of the working population in the agricultural sector relative to other sectors or minimizing changes in the labor structure, except between the 60th and 70th percentiles, leads to a reduction in carbon dioxide emissions. As a result, the structural labor change exerts a direct and significant impact on the levels of carbon dioxide emissions across Iran’s provinces. As changes in the labor structure intensify, the agricultural sector might resort to machinery to compensate for the workforce reduction, maintaining production and moving towards capitalization that, in turn, amplify energy consumption and carbon dioxide emissions. Furthermore, the transition from rural areas and agricultural hubs to urban and industrial centers can increase income, thereby contributing to an increase in carbon dioxide emissions. The study also examined the indirect impact of structural labor change on carbon dioxide emissions through the economic growth channel. According to the estimation results, the coefficient for the interaction term of structural labor change and economic growth is positive and statistically significant in all quantiles, except the 60th and 70th percentiles. As noted by Yang et al. (2021), the increased transfer of labor from the agricultural sector to other sectors, particularly industry, during the course of economic development can indirectly boost economic growth and carbon dioxide emissions. The labor transfer increases as the scale and GDP rise, and there is an expansion in fossil fuel consumption accompanying economic growth, leading to a subsequent increase in carbon dioxide emissions in the provinces of Iran. The study validated two direct and indirect effects of structural labor change on carbon dioxide emissions in Iran’s provinces. In both scenarios, structural labor change contributed to an increase in carbon dioxide emissions. The first effect stems from the increasing use of machinery to compensate for the labor force depleted from the agricultural sector, leading to increased energy consumption and subsequent carbon dioxide emissions. The second effect can be explained with an eye to the increased economic growth and GDP resulting from the structural labor change, as discussed in the Lewis model. ConclusionThe study examined both the direct and indirect effects of structural labor change, in conjunction with other socio-economic variables, using a nonlinear method. The data was gathered from 31 provinces of Iran spanning from 2010–2020, and the study used a quantile regression with nonadditive fixed effects. The variable denoting labor transfer from the agricultural sector to other sectors was used as the ratio of the working population in the agricultural sector to the total working population, serving as the index for structural labor change. The findings revealed that structural labor change has a direct effect on carbon dioxide emissions. Furthermore, concerning indirect effects, it can be affirmed that the index has a positive and significant effect on the dependent variable through the indirect channel of economic growth. Considering the positive effect of labor transfer and its negative impact on carbon dioxide emissions and environmental degradation, it is recommended to adopt measures to control and regulate the labor transfer. Specifically, strategies should be devised to increase the income of workers in the agricultural sector, aiming to establish an equitable wage balance relative to other sectors. Moreover, provincial authorities should prioritize initiatives that increase the real added value in agriculture, with a focus on expanding industries associated with agricultural production, such as transformative and complementary sectors.
Research Paper
Behavioral economics
Mohammad Amin Zandi
Abstract
The precise measurement of individual time preferences in assessing the economic plans that individuals are involved in, in the estimation of social time preferences, in the assessment of environmental and health plans is very crucial. The purpose of this research is to estimate and also describe the ...
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The precise measurement of individual time preferences in assessing the economic plans that individuals are involved in, in the estimation of social time preferences, in the assessment of environmental and health plans is very crucial. The purpose of this research is to estimate and also describe the method of estimating individual intertemporal preferences. The sample is 70 students of Allameh Tabataba'i (A.S) and Payam Noor Universities. For this purpose, the experimental method, which allows controlling the confounding variables, is used. In order to estimate the discount function among various functions, the hyperbolic function had a better fit on the data. In this type of function, the discount does not take place at a fixed rate, but with the extension of the selection period, the discount decreases. The fitting of data using the hyperbolic function showed that this kind of discounting is consistent with past research. The average individual discount rate obtained was 0.0615 with a standard deviation of 0.796.1.IntroductionDecisions with varying consequences across different time periods are referred to as intertemporal choices. The scope of these decision types is extensive in human life, encompassing economic considerations like saving for retirement, investing in stocks, choosing between mortgage and renting, buying insurance, planning for student loan repayments, initiating a business, budgeting, planning for financial issues, buying energy-efficient equipment, purchasing a car, planning for estate, and deciding on the retirement withdrawal strategy. Moreover, decisions extend to non-economic realms, including investing in education, practicing delayed gratification in daily life, making choices regarding health and wellness, selecting a career path, deciding on healthcare options, engaging in environmental protection, and establishing education budgets for children. In essence, a myriad of intertemporal decisions shape the course of an individual’s life.In Samuelson’s framework for intertemporal choices, the total utility is determined as the weighted sum of utility across each time period. (1) The weight in each period is determined by the discount function. (2) represents the total utility from the perspective of the current period (i.e., t). T is the final period of life. signifies the instantaneous utility in the period t+k. is the discount function. k denotes the time delay from the present moment, and ρ is the instantaneous discount rate reflecting time preferences. The discount function, as incorporated in this model, takes the form of an exponential function. When computing the growth rate of the discount function, we have: (3) The growth rate of the discount function is independent of the delay in receiving goods (or rewards) postponed from the present time (i.e., k). This implies that altering the delay period for receiving delayed goods does not lead to a change in a person’s intertemporal preferences. For instance, if an individual favors receiving one apple today over receiving two apples tomorrow, this preference should extend to preferring one apple in one year over receiving two apples in one year and one day. This is the example introduced by Strotz (1955) to illustrate temporal consistency.Experimental research based on the discounted utility model has highlighted its limitations. First, extensive studies indicate that the discount rate tends to decrease as the delay in receiving the reward increases (Chapman, 1996; Heller & Pender, 1996; Redelmeier, 1993; Thaler, 1981). In other words, the growth rate of the discount function should also be contingent on the delay in receiving the goods (or reward). The second observed shortcoming in these investigations is termed inverse utility. This occurs when an individual prefers $1000 today to $1100 tomorrow but favors $1100 one year and one day later over $1000 one year later. Consequently, the behaviors noted in these studies lack time consistency. Additional research has identified instances of reverse preferences in individuals (Elster, 1979; Laibson, 1997; O'Donoghue & Rabin, 1999). The exponential discount function employed in the discounted utility model falls short in explaining such phenomena, as it conducts discounting at a fixed rate, irrespective of whether the delay in receiving the bonus increases or decreases.To address this issue, Mazur (1987) made modifications to the discount function originally proposed by Bam and Rachlin (1969) by incorporating k into the denominator. The adjustment resulted in a discount function that overcame the shortcomings of the exponential function. This hyperbolic function found extensive application in subsequent research and demonstrated a better alignment with the data acquired from experiments. The hyperbolic function is expressed as follows:(4) Here, k represents the discount rate, and D signifies the delay in receiving the reward from the present time. The discount rate in the hyperbolic discount function is given by: (5) In this rate, there is an inherent consideration for the delay in receiving goods (or rewards) from the present time. Consequently, the discount rate will undergo changes corresponding to alterations in this interval. This adjustment serves to rectify the deficiencies noted in this functional form.The findings of the meta-analysis on discount rates, encompassing both experimental and empirical methods, reveal that the variance of discount rates obtained from experimental approaches is lower than that observed in empirical methods. This discrepancy can be attributed to several factors. First, the limited availability of field data for determining time preferences contributes to the higher variance in empirical results. In addition, there is no available field data in which individuals make comparative choices. Third, the complexity arises from the numerous intervening variables influencing real-world data, making it challenging to isolate and analyze specific factors. The estimates obtained from experimental methods demonstrate greater predictability of intertemporal behaviors in the real world.Despite the significant importance of individual time preferences and the consistent data yielded by the experimental method, this approach has been underutilized for measuring individual time preferences in Iran. In this respect, the present research aimed to estimate and describe a methodology for calculating individual intertemporal preferences through the experimental method.2.Materials and MethodsThere are four experimental methods for measuring time preferences. The first method is the choice task, where subjects are prompted to select between a smaller reward in the present or near future and a larger reward in the distant future. Some studies implement this experiment using actual rewards, while others use hypothetical or non-financial rewards, such as a hypothetical job offer. The second method is known as matching tasks, in which subjects are asked to answer a question and fill in the blank. A common structure for this method is exemplified by questions like: 20,000 dollars now or … dollars one year later. Experiments use both real and hypothetical currencies. The third method is termed rating task. Here, subjects are exposed to the rewards provided at specific time intervals. They are tasked with rating the (un)attractiveness of these proposals. The fourth method is called pricing task, where subjects are requested to specify their willingness to pay for a hypothetical reward at a certain time (Feredrick et al., 2002).The present study used the method of choice task, and the task design was based on validated designs (Calluso et al., 2015a, 2015b, 2017, 2020). Each subject was exposed to a series of intertemporal choices, including receiving a fixed amount of money (14500 Tomans) immediately or a variable amount (22000, 36500, 44000, 59000, 66000, 80000, 88000 Tomans) across six time intervals (i.e., 7, 15, 30, 60, 90, and 180 days later). Consequently, the subjects were presented with 42 intertemporal choices, and each question was repeated 10 times. The subjects thus answered a total of 420 questions in a randomly distributed order. To determine the monetary values in intertemporal choices, the study converted the previously-researched valid monetary values into Iranian currency based on the purchasing power parity (PPP) index, utilizing the Central Bank data. The PPP index can be defined as the number of currency units a country needs to purchase the same quantity of goods and services in the domestic market that can be bought with US dollars.3.Results and DiscussionThe hyperbolic function, prevalent in most recent studies and previously discussed, was employed to estimate the discount rate. In this function, as the delay increases, the discount rate concurrently decreases. To obtain this rate for each tested individual, the research relied on conventional process from past research studies (Calluso et al., 2015a, 2015b, 2017, 2020; Iodice et al., 2017; Kable & Glimcher, 2007; Li et al., 2013). Concerning each delay period (7, 15, 30, 60, 90, and 180 days), a ratio of responses was obtained, where subjects expressed a preference for the future over the present, taking into account the delayed reward amounts. Subsequently, the Points of Subjective Equivalence (PSE) was calculated, representing the amount at which subjects chose an equal number of future and present options. To achieve this, the study estimated a logistic function that regressed the preference ratio of future-to-present responses on the reward amounts. Using this function, the research determined the amount equivalent to fifty percent of the frequency of the ratio of future-to-present preferences (i.e., PSE). Then the following formula was used to calculate the subjective value for each delay period: (6) The immediate reward was set at 14,500 Tomans. The subjective value was then normalized to the immediate reward. Subsequently, the discount rate for each subject was determined by fitting a hyperbolic function (Grossbard & Mazur, 1986; Laibson, 1997) to the relationship between the subjective value and the delay time in receiving the delayed reward.(7) Below is the scatter diagram depicting delays by day and the PSE for the aforementioned three subjects. Figure 1.The scatter diagram of delays by day and the PSE Source: Research resultsThe graphs illustrate that individuals with lower discount rates exhibit a lower PSE in delays, whereas those with higher discount rates demonstrate correspondingly higher PSE.Table 1 presents the results of estimating the individual discount rates for the three subjects.Table 1. Discount rate for the three subjectsR SquareSignificanceDiscount rateSubject0.8071significant0.0182patient0.7965significant0.0484average0.8028significant0.1173hastySource: Research results4.ConclusionThe estimation of the individual discount rate derived from this research confirmed the hyperbolic nature of the individual discount function, yielding a rate of 0.0615. In the evaluation of economic plans, the calculation involves determining the benefits and costs associated with the plan. A comparison of the benefits and costs is used to determine whether the plan is economical or not. Yet this proves challenging due to the presence of time preferences and the time value of money, the occurrence of benefits and costs at different points in times, and the varying weight of these factors in economic plans over time. Therefore, it seems less feasible to judge whether the plan is economical or not simply by adding benefits and costs.
Research Paper
Monetary economy
Hossein Samsami; Parviz Davoodi; Rana Abbasgholi Nezhad Asbaghi
Abstract
One of the factors that change the results of the expansionary monetary policy through the credit channel on the economy is the financial frictions that affected Iran's economy especially in the 2002’s and 2022’s. These frictions are manifested in variables such as capital adequacy violations, ...
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One of the factors that change the results of the expansionary monetary policy through the credit channel on the economy is the financial frictions that affected Iran's economy especially in the 2002’s and 2022’s. These frictions are manifested in variables such as capital adequacy violations, the ratio of nonperforming loans, the ratio of fixed assets to the total assets of banks, and the government's net debt to banks. In this article, with the help of building a macro structural econometric model in the period of 1968-2022, the effect of expansionary monetary policy on the change of each type of financial friction has been investigated and compared with emphasis on the endogeneity of money on Iran's economy. The obtained results show that due to the endogeneity of money, the influence of the central bank's monetary policy on the real sector of the economy has decreased and most of its effect is manifested in nominal variables such as liquidity, inflation rate and exchange rate. In addition, an increase of one standard deviation in the ratio of nonperforming loans reduces the impact of the expansionary monetary policy on the real sector of the economy more than other mentioned financial frictions. After that, the decrease in capital adequacy, the increase in the government's net debt to banks, and the increase in the ratio of fixed assets to total assets are in the next level of importance of reducing the effectiveness of monetary policy.1.IntroductionIran’s economy heavily relies on banks to finance economic entities, emphasizing the crucial impact of monetary policy through the credit channel. However, the effectiveness of monetary policy on the real sector of economy can be impeded by financial frictions. These frictions intervene in financial transactions and may increase the costs associated with obtaining external financing, such as loans, for investors (Farzinvash et al., 2014). Empirical evidence suggests that, despite high liquidity, Iran’s economy has encountered a credit crunch, especially during the period spanning from 2002 to 2022. This credit crunch can be attributed to violations of prudential ratios, including capital adequacy, nonperforming loan ratio, fixed asset ratios to total bank assets, and the government’s net debt to banks.As a consequence of these frictions, banks face resource shortages and resort to borrowing from the Central Bank through overdrafts. This results in an expansion of the monetary base, subsequently increasing liquidity and leading to a rise in the general price level. Consequently, owing to the endogeneity of money in Iran’s economy, the Central Bank lacks an independent monetary policy instrument to effectively achieve its goals. The impact of liquidity on the real sector of economy is limited, with most impact observed in nominal variables and manifested as price increases.In this respect, the present study aims to examine the impact of financial frictions on the effectiveness of expansionary monetary policy through the credit channel, specifically focusing on the endogeneity of money. Additionally, it tries to compare the respective effects of the frictions on the Iranian economy. The analytical perspective ensures the distinctive and innovative aspect of the study.2.Materials and MethodsConcerning the period from 1968 to 2022, a large-scale macroeconometric model was developed based on aggregate supply–aggregate demand frameworks and national income accounting. The research model encompasses various components, including consumption and investment expenditures, government activities, foreign trade, production, money and credit, general price levels, exchange rates, and the balance of payments. Data for constructing the model was sourced from the Central Bank’s Time Series Data Bank, the Central Bank’s balance sheet, (non-)governmental banks balance sheets, the Statistical Centre of Iran, and the World Bank.The model consisted of 28 behavioral equations, 9 connecting equations, and 91 identities. Auto-Regressive Distributed Lag (ARDL) method was used to estimate the model equations, and all equations were concurrently solved through dynamic simulation. The study relied on the criteria such as Root Mean Square Percentage Error (RMSPE) and the Theil index of inequality (U) to test the model’s performance.3.Results and DiscussionIn order to investigate the influence of individual financial frictions on the impact of expansionary monetary policy on Iran’s economy, the study assumed an annual one standard deviation increase in bank debt to the central bank as a monetary policy instrument in each considered scenario. The scenario development period spans five years, from 2018 to 2022, where the baseline trend represents the state of implementing solely expansionary monetary policy while keeping all types of financial frictions invariable in the current state of Iran’s economy.Moreover, in case of one standard deviation alteration in each financial friction during the implementation of expansionary monetary policy, it can be used to classify capital adequacy, nonperforming loans ratio, the government’s net debt to banks, and fixed asset ratios to total bank assets in the scenarios 1, 2, 3, and 4, respectively (see Table 1). Table 1. The average percentage deviation of the simulated values of the important endogenous variables in the examined scenarios from the base simulated values during the period 2018–2022Scenario 4:Increase in the fixed asset ratios to total bank assetsScenario 3:Increase in the government’s net debt to banksScenario 2:Increase in the nonperforming loan ratio Scenario 1:Reduction in capital adequacy of banksVariables-18.63-23.33-24.9-22.13Depth of bank credits-3.16-3.25-3.22-2.86Production capacity utilization rate-5.14-6.14-6.38-5.96Investment-1.82-2.3-2.4-2.24Employment-1.94-2.26-2.36-2.39Total factor productivity-2.84-3.2-3.3-3.23Gross domestic product-3.1-3.49-3.6-3.52Non-oil gross domestic product12.8913.8413.8414.19Changes in inventories19.3731.4235.4236.24Liquidity3.15.185.935.81Inflation rate7.3312.4414.1114.36Exchange rate* Source: Research resultsTable 1 illustrates that the impact of expansionary monetary policy varies across different scenarios examined. Scenario 2 (i.e., the increased ratio of nonperforming loans) impacts both the real and nominal sectors of economy by causing more significant fluctuations in these variables compared to the baseline simulation. Scenarios 1, 3, and 4 hold subsequent degrees of importance in diminishing the effectiveness of monetary policy.4.ConclusionBased on the findings, it can be concluded that the effectiveness of expansionary monetary policy on the real economy weakens the most when the nonperforming loan ratio increases, compared to three other financial friction indicators. Therefore, to mitigate nonperforming loans in banks, the study suggests that economic policymakers focus on controlling inflation rates, exchange rates, fluctuations in gross domestic product, and fluctuations in investment in the real estate sector. The priority should also be given to monitoring the decline in the quality of bank management due to the increase in the ratio of bank credit balance to total volume deposits after deducting the legal reserves. It is also worth noting that the proper implementation of Islamic contracts by banks can significantly contribute to reducing nonperforming loans.
Research Paper
Financial Economics
Gholamhossein Golarzi; Mahnaz Khorasani
Abstract
The exchange rate, as a fundamental variable, alongside other economic variables, has a significant impact on stock returns. Therefore, this study has investigated the effects of the exchange rate and its fluctuations on the pharmaceutical industry's stock returns through linear and nonlinear models ...
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The exchange rate, as a fundamental variable, alongside other economic variables, has a significant impact on stock returns. Therefore, this study has investigated the effects of the exchange rate and its fluctuations on the pharmaceutical industry's stock returns through linear and nonlinear models during the years 2005 to 2021. In this research, first, the exchange rate fluctuations were modeled using the GARCH model. Then, the symmetrical and asymmetrical effects of the exchange rate and its fluctuations, along with the macroeconomic control variables including the healthcare consumer price index, oil price, and industry-specific control variables including asset return ratio, asset turnover ratio, and debt ratio as well as the COVID-19 dummy variable, were investigated on the return of the pharmaceutical industry stock using both linear ARDL and nonlinear NARDL models. The study shows that in both the short and long term, the impact of the exchange rate on pharmaceutical industry stock returns is greater than the impact of exchange rate fluctuations. Additionally, negative shocks of the exchange rate and its fluctuations have a negative relationship with the pharmaceutical industry's stock returns, while positive shocks of the exchange rate and its fluctuations have a positive effect on the pharmaceutical industry's stock return. The study's findings suggest that the impact of positive and negative shocks of the exchange rate and its fluctuations have asymmetric effects on the return of pharmaceutical industry stock. Results show that control variables and COVID-19 have significant effects on pharmaceutical industry stock returns in linear and nonlinear models.1.IntroductionPharmaceuticals, as a strategically vital industry, can significantly contribute to a country’s economic growth and the enhancement of public health. However, a major challenge faced by this industry in Iran is its heavy dependence on imported raw materials and essential machinery, with nearly 60% of the required raw materials being sourced through imports. The pharmaceutical sector in Iran is particularly vulnerable to exchange rate fluctuations, given its high dependency on foreign currency. Consequently, the exchange rate and its fluctuations emerge as determining factors influencing the profitability and stock returns of companies operating in this sector. Divergent perspectives exist regarding how exchange rate fluctuations impact stock returns, with some studies asserting a positive correlation, others a negative one, and some maintaining a neutral stance. Since there is no consensus on the precise nature of the relationship between exchange rate fluctuations and stock returns, especially within the pharmaceutical sector, the present research tried to investigate and compare the effects of exchange rate and its fluctuations on stock returns in the pharmaceutical industry.2.Materials and MethodsUsing linear and nonlinear autoregressive distributed lag models (i.e., ARDL and NARDL), the study examined both the symmetrical and asymmetrical effects of exchange rate fluctuations on the return of pharmaceutical industry stocks during 2005 to 2021. The research also considered macroeconomic control variables, including healthcare consumer price index, oil price, COVID–19 dummy variable, and the variables specific to the pharmaceutical industry (e.g., asset return ratios, turnover ratios, and debt ratios). First, the generalized autoregressive conditional heteroskedasticity (GARCH) model was employed to model exchange rate fluctuations. The long-term linear equation for the return of pharmaceutical industry stocks can be defined as follows:(1) Also, the long-term nonlinear equation is defined as follows:(2) 3.Results and DiscussionThe research findings reveal that, in the short-term period and based on the linear ARDL model, the exchange rate significantly affects the return of the pharmaceutical industry stocks, with exchange rate fluctuations also causing a significant negative impact on stock returns. Moreover, the analysis of the long-term coefficient estimates from the linear ARDL model suggests a positive correlation between the exchange rate and pharmaceutical industry stock returns. Consequently, the results imply that an increase in the exchange rate can boost the competitive power and stock returns of pharmaceutical companies. However, in the long run, exchange rate fluctuations can have a detrimental effect due to heightened uncertainty in the stock market, dissuading investors from engaging in this industry. Additionally, the study indicates that an increase in oil prices results in a decrease in pharmaceutical industry returns, as investors seek profits in alternative markets. Inflation, too, negatively affects pharmaceutical industry stock returns, as heightened inflation fosters uncertainty, reducing investor inclination toward pharmaceutical stocks. Furthermore, the research findings highlight that various factors such as pharmaceutical industry asset returns, asset turnover, debt levels, and the dummy variable of COVID–19 positively impact pharmaceutical industry returns.The results obtained from the nonlinear NARDL model showed that both short-term and long-term negative shocks in the exchange rate and its fluctuations significantly decrease the stock returns of the pharmaceutical industry. In contrast, positive shocks in the exchange rate and its fluctuations positively affect the stock returns of the pharmaceutical industry. Hence, it can be concluded that the exchange rate and its fluctuations have an asymmetrical effect on pharmaceutical industry stock returns in Iran. Unlike the linear ARDL model, the results of the nonlinear NARDL model indicated that inflation and debt levels do not exert significant impact on pharmaceutical industry stock returns in the long run. Additionally, impact of oil prices on pharmaceutical industry returns is significantly negative in the long run, while pharmaceutical asset returns, asset turnover, and the dummy variable of COVID–19 contribute to an increase in pharmaceutical industry returns in Iran.4.ConclusionConcerning the importance of the pharmaceutical industry and the influence of the exchange rate on the stock returns in the Iranian stock market, the present research used ARDL and NARDL models to examine both the linear and nonlinear effects of exchange rate and its fluctuations on pharmaceutical industry stock returns during 2005–2021 in Iran. The research results indicated that, in both the short and long term, the impact of exchange rate is more significant than the impact of exchange rate fluctuations on the returns of pharmaceutical industry stocks. According to the findings, negative shocks to the exchange rate and its fluctuations can lead to a decrease in the returns of pharmaceutical industry stocks, while positive shocks result in an increase. The results suggest an asymmetrical impact of positive and negative exchange rate shocks and its fluctuations on pharmaceutical industry stock returns. In both linear and nonlinear models, the control variables of the study, along with the COVID–19 as the dummy variable, have significant impact in on pharmaceutical industry stock returns. In sum, the findings indicated a significant relationship between the exchange rate and its fluctuations and pharmaceutical industry returns in Iran. However, the impact of exchange rate and its fluctuations on pharmaceutical industry proves to be heterogeneous. It is thus recommended that investors take note of the differing results of linear and nonlinear models and the asymmetric effects of variables, utilizing modern financial engineering instruments to implement appropriate risk-hedging strategies against exchange rate fluctuations.
