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Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction:
A growing body of research highlights the bidirectional relationship between conflict and economic performance. Findings indicate that economic decline—particularly severe recessions that reduce income levels, exacerbate inequalities, and intensify widespread economic distress—can fuel social unrest and internal conflicts. Periods characterized by a high risk of government collapse are associated with significantly lower rates of economic growth compared to more politically stable periods. Although such violent events may not occur frequently, they are prevalent worldwide and have affected numerous countries.
The Middle East, in particular, has long been afflicted by internal unrest, persistent conflicts, and intra- and intergovernmental tensions—all of which adversely influence national economies. Political economy literature underscores a complex interplay between political forces and economic direction, suggesting that political instability can disrupt economic continuity and hinder economic growth—a central indicator of national economic performance.
Accordingly, the primary objective of this study is to model the effects of political instability and conflict on economic growth in a sample of developing and developed countries, namely Iran, Iraq, Saudi Arabia, Russia, the United States, India, China, and Canada.
Methodology:
This study adopts a descriptive-analytical approach with practical applications, relying on secondary data collected through documentary research. The analytical method employed is the Bayesian Markov Switching Panel Regression, which effectively captures symmetric and asymmetric effects across different economic regimes.
The selected countries—spanning both developed and developing contexts—include Iran, Iraq, and Saudi Arabia, which have historically faced political tension and oil revenue fluctuations, as well as Russia, Canada, the United States, India, and China. The inclusion of India and China reflects their status as major global energy consumers. These countries were chosen based on their exposure to international tensions and their substantial influence on the global energy landscape.
The study period covers 1990 to 2020. The Markov switching panel framework enables the model to differentiate the impact of explanatory variables across distinct economic regimes. For instance, political stability may influence economic growth differently during recessionary periods compared to times of economic expansion. The variables analyzed include conflict intensity, political instability, oil income, population growth, foreign direct investment, life expectancy, government expenditure, budget deficits, trade openness, and the governance quality index.
Results and Discussion:
The analysis reveals that conflict and economic instability exert statistically significant effects on economic growth across both recession and growth regimes. In the recession regime, the coefficients for conflict and instability are 0.17% and 0.12%, respectively, while in the growth regime, they are slightly lower at 0.16% and 0.11%. Although both variables remain significant in both regimes, their influence is more pronounced during recessions, implying that political instability and conflict are more detrimental to growth when the economy is already underperforming.
These findings are consistent with prior research by Ashenfelter and Troeger (2006), Gaybulov and Sandler (2019), and Bart et al. (2021). Additionally, variables such as oil income, population growth, foreign direct investment, life expectancy, government expenditure, trade openness, and governance quality all exhibit positive and statistically significant effects on economic growth in both regimes.
The dominant economic regime identified in the study is the growth regime. Notably, with the exception of Iraq, Iran, and Saudi Arabia, the other countries analyzed have been experiencing economic growth in recent years. This observation underscores the correlation between political stability and sustained economic performance.
Conclusion:
The findings of this research emphasize the critical role of political stability in fostering a robust and resilient economic environment. A stable political climate is not only essential for social cohesion but also serves as a prerequisite for sustained economic growth and development. Policymakers are thus encouraged to invest in institutional reforms, infrastructure development, and inclusive governance frameworks that enhance citizens’ participation in decision-making processes. These measures can significantly contribute to both political stability and long-term economic prosperity in the countries under study.
A growing body of research highlights the bidirectional relationship between conflict and economic performance. Findings indicate that economic decline—particularly severe recessions that reduce income levels, exacerbate inequalities, and intensify widespread economic distress—can fuel social unrest and internal conflicts. Periods characterized by a high risk of government collapse are associated with significantly lower rates of economic growth compared to more politically stable periods. Although such violent events may not occur frequently, they are prevalent worldwide and have affected numerous countries.
The Middle East, in particular, has long been afflicted by internal unrest, persistent conflicts, and intra- and intergovernmental tensions—all of which adversely influence national economies. Political economy literature underscores a complex interplay between political forces and economic direction, suggesting that political instability can disrupt economic continuity and hinder economic growth—a central indicator of national economic performance.
Accordingly, the primary objective of this study is to model the effects of political instability and conflict on economic growth in a sample of developing and developed countries, namely Iran, Iraq, Saudi Arabia, Russia, the United States, India, China, and Canada.
Methodology:
This study adopts a descriptive-analytical approach with practical applications, relying on secondary data collected through documentary research. The analytical method employed is the Bayesian Markov Switching Panel Regression, which effectively captures symmetric and asymmetric effects across different economic regimes.
The selected countries—spanning both developed and developing contexts—include Iran, Iraq, and Saudi Arabia, which have historically faced political tension and oil revenue fluctuations, as well as Russia, Canada, the United States, India, and China. The inclusion of India and China reflects their status as major global energy consumers. These countries were chosen based on their exposure to international tensions and their substantial influence on the global energy landscape.
The study period covers 1990 to 2020. The Markov switching panel framework enables the model to differentiate the impact of explanatory variables across distinct economic regimes. For instance, political stability may influence economic growth differently during recessionary periods compared to times of economic expansion. The variables analyzed include conflict intensity, political instability, oil income, population growth, foreign direct investment, life expectancy, government expenditure, budget deficits, trade openness, and the governance quality index.
Results and Discussion:
The analysis reveals that conflict and economic instability exert statistically significant effects on economic growth across both recession and growth regimes. In the recession regime, the coefficients for conflict and instability are 0.17% and 0.12%, respectively, while in the growth regime, they are slightly lower at 0.16% and 0.11%. Although both variables remain significant in both regimes, their influence is more pronounced during recessions, implying that political instability and conflict are more detrimental to growth when the economy is already underperforming.
These findings are consistent with prior research by Ashenfelter and Troeger (2006), Gaybulov and Sandler (2019), and Bart et al. (2021). Additionally, variables such as oil income, population growth, foreign direct investment, life expectancy, government expenditure, trade openness, and governance quality all exhibit positive and statistically significant effects on economic growth in both regimes.
The dominant economic regime identified in the study is the growth regime. Notably, with the exception of Iraq, Iran, and Saudi Arabia, the other countries analyzed have been experiencing economic growth in recent years. This observation underscores the correlation between political stability and sustained economic performance.
Conclusion:
The findings of this research emphasize the critical role of political stability in fostering a robust and resilient economic environment. A stable political climate is not only essential for social cohesion but also serves as a prerequisite for sustained economic growth and development. Policymakers are thus encouraged to invest in institutional reforms, infrastructure development, and inclusive governance frameworks that enhance citizens’ participation in decision-making processes. These measures can significantly contribute to both political stability and long-term economic prosperity in the countries under study.
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Achieving sustained and long-term economic growth necessitates the optimal allocation and utilization of resources at the national level. This goal relies heavily on the existence of efficient financial markets, particularly well-functioning and extensive capital markets. Numerous macroeconomic variables can influence the level of risk associated with shareholder rights, corporate cash flows, and adjusted discount rates. Additionally, changes in economic conditions can alter both the quantity and nature of investment opportunities.
However, establishing a fixed and consistent relationship between macroeconomic variables and stock price indices remains challenging. The complex and dynamic nature of financial markets makes it difficult to identify a method that accurately reflects economic conditions and captures the most critical influencing variables. Therefore, this study employs machine learning models to identify the key macroeconomic factors affecting stock price indices.
Methodology
Feature selection is one of the most common and crucial techniques in data preprocessing and serves as an essential component of machine learning. This study employs feature selection models to identify the most relevant predictors of the stock price index. The models utilized include the random forest method and regularized linear regression. To examine the nature of the relationships between variables, the jointness method was applied. Additionally, the mutual information analysis was conducted to assess the influence of key variables over different decades, enabling a deeper understanding of how the impact of macroeconomic factors on stock prices has evolved over time.
Findings
The study analyzed the impact of selected macroeconomic variables on stock price indices, focusing on the Tehran Stock Exchange. The findings from the Random Forest (RF) and Regularized Linear Regression (RLR) models indicate that exchange rates, financial development, inflation, economic growth, trade openness, and global uncertainty significantly influence Iran’s stock price index. The results demonstrate that global uncertainty, interest rates, and trade openness exert negative effects on stock prices, whereas the other variables positively influence stock prices.
The jointness method was employed to analyze the relationships between these variables, further confirming their significance. Moreover, the Mutual Information method was used to examine how the influence of these key variables varied across different decades.
Discussion and Conclusion
Among the variables examined, exchange rates, financial development, inflation, economic growth, trade openness, and global uncertainty emerged as the most significant factors influencing Iran’s stock price index. This finding is not surprising, given Iran’s historical experience with significant exchange rate fluctuations and persistent inflationary pressures. Global uncertainty has consistently influenced domestic markets in Iran due to political and economic instability. Previous research has highlighted the complex relationship between exchange rate fluctuations and stock price indices (Ratanapakorn & Sharma, 2007). Scholars have argued that the relationship between stock prices and exchange rates can significantly affect monetary and fiscal policy, as a recessionary stock market can reduce overall demand and impact broader economic performance.
Extensive research has also investigated the relationship between inflation and stock prices, identifying inflation as a significant factor affecting stock indices
(Boudoukh & Richardson, 1993; Fama & Schwert, 1977; Jaffe & Mandelker, 1976) . While some studies have reported a positive correlation between inflation and stock prices, others have found a negative relationship.
Moreover, trade openness has been recognized as a key factor influencing stock market fluctuations. Open economies are more vulnerable to external shocks due to increased global risk-sharing among markets. Although some studies have not found conclusive evidence of a direct effect between trade openness and stock prices, trade openness remains one of the influential factors (Nickmansh, 2016).
Stock prices reflect the present value of future cash flows, which are subject to two main effects: cash flow changes driven by increased production and interest rates, which serve as a discount factor. Stock prices tend to decline when expected cash flows decrease or interest rates rise. The level of actual economic activity directly influences cash flows, as higher economic activity generally leads to increased cash flow. Among the various indicators used to predict commodity markets, real Gross Domestic Product (GDP) is considered the most comprehensive measure of economic activity (Yuhasin, 2011; Christopher et al., 2006).
mouseout="msoCommentHide('_com_1')" onmouseover="msoCommentShow('_anchor_1','_com_1')">Finally, global uncertainty plays a significant role in shaping the internal economic environment of countries, making it an important global macroeconomic variable that influences the performance of publicly traded companies on the stock exchange.
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Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Many theories and models of economic growth have identified capital as one of the most important drivers and determinants of economic growth and development. For years, it was believed that abundant natural resources, as part of a country’s capital, constituted a divine blessing, as they could be converted into other forms of capital and contribute to overall economic development. Consequently, countries rich in natural resources were expected to perform better economically than those without such resources. However, over time, particularly after World War II, empirical evidence revealed that most resource-rich countries performed poorly compared to resource-poor countries.
some empirical studies have highlighted a positive relationship between natural resource abundance and economic growth. Stijns (2001), using an alternative variable from Sachs and Warner (1995) to measure resource abundance, found no evidence of the detrimental effect of natural resources on economic growth. Lederman and Maloney (2003) also reported a positive relationship between resource abundance (measured by net resource exports per worker) and economic growth.
Sala-i-Martin and Subramanian (2003) contended that the relationship between natural resource abundance and economic growth loses statistical significance once institutional quality is accounted for. They suggested that the effect of natural resources depends on the type of resource, indicating that fuel and mineral resources negatively affect institutions (and thus economic growth), whereas the relationship between economic growth and other types of resources is not statistically significant. Similarly, Papyrakis and Gerlagh (2004) demonstrated that when variables such as corruption, investment, degree of freedom, terms of trade, and education are controlled and managed, the abundance of natural resources would have a positive effect on economic growth.
Thus, it can be concluded that not all resource-rich countries have experienced poor economic performance or economic decline. In certain cases, the optimal utilization of abundant resources has led to significant economic growth and increased per capita income.
Economic growth remains the primary goal of all economies, as it is directly linked to maximizing societal welfare. Economic growth encompasses increased utilization of inputs, improved productivity of production factors, and enhanced employment opportunities. Natural resources are among the most crucial sources of production in any country. According to growth and development theories, as well as international trade theories, these resources can provide a comparative advantage for an economy. Income generated from natural resource abundance can create national wealth, spur economic progress, increase societal welfare, and reduce poverty. In this regard, mineral resources are considered a key factor in accelerating investment and economic growth.
Methodology
This study examines the economic growth patterns of Iran and a group of mineral-rich countries from 2000 to 2020. A panel data method was employed to estimate and evaluate the results, considering the similarities between the selected countries and Iran in terms of mineral resource abundance.
In the research process, the final variables and the functional form of the model were identified, and data processing, analysis, and model estimation were conducted using Stata software. The data used in the study were collected from official sources, including the Central Bank, the Statistical Center of Iran, and the Ministry of Industry, Mine, and Trade. Additionally, for data on other countries, international sources such as the World Trade Organization (WTO), the World Bank, the Organization for Economic Cooperation and Development (OECD), and the International Monetary Fund’s (IMF) STAN database were utilized.
Findings
The study investigated the direct and indirect effects of natural resource abundance on economic growth through channels such as physical capital accumulation, research and development (R&D) investment in technology, labor, financial development, and economic freedom across three groups of countries. The first group includes countries with both mineral resources and oil, the second group consists of countries with only minerals, and the third group comprises countries with only oil resources. The generalized fixed effects model was selected as the final model for all three groups. According to the results:
The results suggest that the hypothesis of natural resource abundance positively influencing economic growth is supported for the first and second groups of countries. However, this hypothesis is not confirmed for the third group, which includes Iran.
The findings underscore that the impact of natural resources on economic growth is contingent upon various factors, including the type of resource, the quality of institutions, and the effectiveness of economic and governance policies. While some resource-rich countries have successfully translated their natural wealth into economic prosperity, others, including Iran, have faced challenges in maximizing the economic benefits of their natural resources.
Many theories and models of economic growth have identified capital as one of the most important drivers and determinants of economic growth and development. For years, it was believed that abundant natural resources, as part of a country’s capital, constituted a divine blessing, as they could be converted into other forms of capital and contribute to overall economic development. Consequently, countries rich in natural resources were expected to perform better economically than those without such resources. However, over time, particularly after World War II, empirical evidence revealed that most resource-rich countries performed poorly compared to resource-poor countries.
some empirical studies have highlighted a positive relationship between natural resource abundance and economic growth. Stijns (2001), using an alternative variable from Sachs and Warner (1995) to measure resource abundance, found no evidence of the detrimental effect of natural resources on economic growth. Lederman and Maloney (2003) also reported a positive relationship between resource abundance (measured by net resource exports per worker) and economic growth.
Sala-i-Martin and Subramanian (2003) contended that the relationship between natural resource abundance and economic growth loses statistical significance once institutional quality is accounted for. They suggested that the effect of natural resources depends on the type of resource, indicating that fuel and mineral resources negatively affect institutions (and thus economic growth), whereas the relationship between economic growth and other types of resources is not statistically significant. Similarly, Papyrakis and Gerlagh (2004) demonstrated that when variables such as corruption, investment, degree of freedom, terms of trade, and education are controlled and managed, the abundance of natural resources would have a positive effect on economic growth.
Thus, it can be concluded that not all resource-rich countries have experienced poor economic performance or economic decline. In certain cases, the optimal utilization of abundant resources has led to significant economic growth and increased per capita income.
Economic growth remains the primary goal of all economies, as it is directly linked to maximizing societal welfare. Economic growth encompasses increased utilization of inputs, improved productivity of production factors, and enhanced employment opportunities. Natural resources are among the most crucial sources of production in any country. According to growth and development theories, as well as international trade theories, these resources can provide a comparative advantage for an economy. Income generated from natural resource abundance can create national wealth, spur economic progress, increase societal welfare, and reduce poverty. In this regard, mineral resources are considered a key factor in accelerating investment and economic growth.
Methodology
This study examines the economic growth patterns of Iran and a group of mineral-rich countries from 2000 to 2020. A panel data method was employed to estimate and evaluate the results, considering the similarities between the selected countries and Iran in terms of mineral resource abundance.
In the research process, the final variables and the functional form of the model were identified, and data processing, analysis, and model estimation were conducted using Stata software. The data used in the study were collected from official sources, including the Central Bank, the Statistical Center of Iran, and the Ministry of Industry, Mine, and Trade. Additionally, for data on other countries, international sources such as the World Trade Organization (WTO), the World Bank, the Organization for Economic Cooperation and Development (OECD), and the International Monetary Fund’s (IMF) STAN database were utilized.
Findings
The study investigated the direct and indirect effects of natural resource abundance on economic growth through channels such as physical capital accumulation, research and development (R&D) investment in technology, labor, financial development, and economic freedom across three groups of countries. The first group includes countries with both mineral resources and oil, the second group consists of countries with only minerals, and the third group comprises countries with only oil resources. The generalized fixed effects model was selected as the final model for all three groups. According to the results:
- The share of mineral resources in exports was significant and positive for the first and second groups of countries, whereas it was significant and negative for the third group, which includes Iran.
- The share of oil and gas resources in exports was significant and positive for the first group of countries, but it had a significant negative impact for the third group.
- The unemployment rate had a significant negative relationship with per capita income across all groups.
- The total factor productivity index was positive and significant for all groups, positively influencing per capita income.
- Research and development expenditures had a significant positive effect on per capita income across all groups.
- The economic openness index was significant for all groups, positively affecting per capita income.
- The institutional quality index was significant for all groups, positively influencing per capita income.
- The net foreign direct investment variable was significant for the second group but had a negative effect.
The results suggest that the hypothesis of natural resource abundance positively influencing economic growth is supported for the first and second groups of countries. However, this hypothesis is not confirmed for the third group, which includes Iran.
The findings underscore that the impact of natural resources on economic growth is contingent upon various factors, including the type of resource, the quality of institutions, and the effectiveness of economic and governance policies. While some resource-rich countries have successfully translated their natural wealth into economic prosperity, others, including Iran, have faced challenges in maximizing the economic benefits of their natural resources.
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Commuting is a socio-economic phenomenon that arises from spatial imbalances between labor supply and demand across different locations. While some trips are recreational or incidental, a significant proportion occurs due to the inability of individuals to meet essential needs—such as employment—at their place of residence. In this context, commuting serves as a practical response to spatial mismatches. However, constraints in transportation infrastructure and increased demand for urban travel have made trip reduction an effective strategy for improving the performance of urban transportation systems.
Since a considerable share of daily trips is generated by land patterns—particularly workplace locations—modifying commuting patterns by relocating workers closer to their places of employment can significantly reduce trip generation. This study assumes that all workers currently living in Isfahan but employed elsewhere relocate to reside in their respective places of work. As a result, transportation costs associated with commuting to and from Isfahan would be eliminated, thereby creating a negative shock to the city’s final demand.
Conversely, the inflow and outflow of workers and their families would induce changes in local economic dynamics. Specifically, increased demand for housing would raise real estate rental prices, generating a positive shock in final demand. This research explores the economic consequences of such shifts through a regional input-output framework.
Methodology
To estimate interregional economic changes, this study employs a multi-regional input–output (MRIO) model. Given the availability of regional account data in Iran, regional tables were constructed using the Location Quotient (LQ) method. To address the common shortcomings of traditional LQ techniques—namely, the overestimation of regional coefficients and underestimation of imports—the Flag method was adopted. This approach incorporates three economic dimensions and addresses spatial factors, improving the accuracy of regional estimates.
A key challenge in compiling MRIO tables is obtaining reliable interregional trade data to calculate import and export coefficients. To this end, the gravity model—based on Newton’s law of gravitation—was utilized to estimate economic flows. The model correlates the volume of interregional trade with the economic size of the origin and destination and inversely with the distance between them. Thus, this study combines the LQ and gravity methods to model economic interactions among three regions in Iran: (1) Isfahan city, (2) other cities within Isfahan province, and (3) other provinces nationwide. Data sources include the national input-output table (1395) and regional accounts provided by the Statistical Center of Iran.
Results and Discussion
Findings indicate that the reduction in transportation costs within Isfahan city leads to a decline in production across all three regions, with the most pronounced effects observed in the industrial production and wholesale/retail sectors. Conversely, rising real estate rental costs initially stimulate employment growth in the construction, financial, insurance, industrial, and transportation sectors.
The simultaneous impact of reduced commuting costs and increased housing expenses results in a net rise in employment in Isfahan’s construction and real estate sectors. Similar employment gains are observed in the real estate, construction, and financial sectors in other cities within Isfahan province. In other provinces, the positive effects extend to the real estate, construction, financial, insurance, and water and sewage sectors. However, most other economic sectors across all regions experience a decline in employment.
Conclusion
This study underscores the complex economic implications of altering commuting patterns. Future research should explore the broader effects of these shocks on variables such as energy savings, reduced fossil fuel consumption, decreased air pollution and greenhouse gas emissions, fewer traffic accidents, lower healthcare costs, and less congestion—especially during peak commuting hours. Additionally, reduced commuting times can increase employees’ available time, some of which could be allocated to productive activities, warranting supply-side investigations. Furthermore, lower transportation costs may function as increased household income, potentially influencing household consumption patterns—an area that merits further exploration in subsequent studies.
Commuting is a socio-economic phenomenon that arises from spatial imbalances between labor supply and demand across different locations. While some trips are recreational or incidental, a significant proportion occurs due to the inability of individuals to meet essential needs—such as employment—at their place of residence. In this context, commuting serves as a practical response to spatial mismatches. However, constraints in transportation infrastructure and increased demand for urban travel have made trip reduction an effective strategy for improving the performance of urban transportation systems.
Since a considerable share of daily trips is generated by land patterns—particularly workplace locations—modifying commuting patterns by relocating workers closer to their places of employment can significantly reduce trip generation. This study assumes that all workers currently living in Isfahan but employed elsewhere relocate to reside in their respective places of work. As a result, transportation costs associated with commuting to and from Isfahan would be eliminated, thereby creating a negative shock to the city’s final demand.
Conversely, the inflow and outflow of workers and their families would induce changes in local economic dynamics. Specifically, increased demand for housing would raise real estate rental prices, generating a positive shock in final demand. This research explores the economic consequences of such shifts through a regional input-output framework.
Methodology
To estimate interregional economic changes, this study employs a multi-regional input–output (MRIO) model. Given the availability of regional account data in Iran, regional tables were constructed using the Location Quotient (LQ) method. To address the common shortcomings of traditional LQ techniques—namely, the overestimation of regional coefficients and underestimation of imports—the Flag method was adopted. This approach incorporates three economic dimensions and addresses spatial factors, improving the accuracy of regional estimates.
A key challenge in compiling MRIO tables is obtaining reliable interregional trade data to calculate import and export coefficients. To this end, the gravity model—based on Newton’s law of gravitation—was utilized to estimate economic flows. The model correlates the volume of interregional trade with the economic size of the origin and destination and inversely with the distance between them. Thus, this study combines the LQ and gravity methods to model economic interactions among three regions in Iran: (1) Isfahan city, (2) other cities within Isfahan province, and (3) other provinces nationwide. Data sources include the national input-output table (1395) and regional accounts provided by the Statistical Center of Iran.
Results and Discussion
Findings indicate that the reduction in transportation costs within Isfahan city leads to a decline in production across all three regions, with the most pronounced effects observed in the industrial production and wholesale/retail sectors. Conversely, rising real estate rental costs initially stimulate employment growth in the construction, financial, insurance, industrial, and transportation sectors.
The simultaneous impact of reduced commuting costs and increased housing expenses results in a net rise in employment in Isfahan’s construction and real estate sectors. Similar employment gains are observed in the real estate, construction, and financial sectors in other cities within Isfahan province. In other provinces, the positive effects extend to the real estate, construction, financial, insurance, and water and sewage sectors. However, most other economic sectors across all regions experience a decline in employment.
Conclusion
This study underscores the complex economic implications of altering commuting patterns. Future research should explore the broader effects of these shocks on variables such as energy savings, reduced fossil fuel consumption, decreased air pollution and greenhouse gas emissions, fewer traffic accidents, lower healthcare costs, and less congestion—especially during peak commuting hours. Additionally, reduced commuting times can increase employees’ available time, some of which could be allocated to productive activities, warranting supply-side investigations. Furthermore, lower transportation costs may function as increased household income, potentially influencing household consumption patterns—an area that merits further exploration in subsequent studies.
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Economic and social instability, insecurity, and poor governance significantly increase transaction costs and investment risks while reducing incentives for productive economic activities. Institutional conditions and the political environment are fundamental factors influencing economic growth, as they affect the motivations of economic agents and thereby influence investment decisions, production organization, and overall economic performance. Macroeconomic instability, as an undesirable phenomenon, imposes both economic and social costs on society. Its persistence disturbs the national economic structure and diminishes household welfare by undermining financial security and increasing economic uncertainty.
Furthermore, effective economic policy-making and national development planning require a comprehensive understanding of the economy’s formal and informal sectors. The informal or underground economy includes activities outside the scope of official oversight, such as unregistered income, tax evasion, and operations beyond legal, social, and economic regulations. These activities are typically excluded from official GDP calculations but represent a significant share of economic production.
Modern definitions of economic growth encompass not only increases in GDP but also broader improvements in societal economic well-being. Notably, economic production occurs in both formal and informal sectors; thus, a thorough analysis of both is essential for developing effective and inclusive growth strategies. This study aims to evaluate the influence of political and economic risk, instability, and governance quality on both sectors of Iran’s economy over the period 1370–1401 (1991–2022). To achieve this, relevant indices were constructed to measure risk and instability in economic, financial, and social domains, as well as Iran’s governance performance, with the goal of identifying key determinants of formal sector strengthening and informal sector reduction.
Methodology
This research employs an endogenous growth model to investigate the factors influencing economic growth in Iran. Data on the underground economy are drawn from estimates produced using the Multiple Indicators and Multiple Causes (MIMIC) model. The methodological framework combines econometric techniques, notably Principal Component Analysis (PCA) and the Autoregressive Distributed Lag (ARDL) model.
PCA is applied to construct composite indices where multiple explanatory variables are involved, particularly in capturing instability and governance indicators. ARDL is used to examine relationships among variables, given the mixed order of integration in the time series data. This dual approach enables the study to assess the impact of governance, risk, and economic instability on both the formal and informal economic sectors.
Results and Discussion
The results show that within the economic growth function, property rights and political management exert a positive influence, while economic instability and international sanctions negatively affect Iran’s economic growth. Specifically, an increase of one unit in the political management index results in a 3.0033% increase in economic growth, whereas a one-unit rise in the economic instability index leads to a 0.1935% decline in growth.
In analyzing the informal (underground) economy, the study finds that increased risk and instability, unemployment, government size, tax revenues, and sanctions all contribute to the expansion of the informal sector. Conversely, improvements in political management reduce informal economic activities. Notably, the risk and instability index shows a high impact, with a coefficient of 3.99, signifying its strong correlation with the growth of Iran’s underground economy.
Conclusion
Improved political management enhances formal economic activity while suppressing informal sector expansion. Specifically, advancements in governance indicators—such as political participation, accountability, and rule of law—help reduce the size of the underground economy and promote formal sector growth. On the other hand, economic and social instabilities, including financial market volatility, inflation, speculation, and societal insecurity, incentivize informal economic behavior, thereby undermining the formal structure of the economy.
To address these challenges, the study recommends implementing comprehensive governance and economic reforms. On the governance side, strategies should include corruption control, enhanced oversight, legal enforcement, public trust-building, and increased legitimacy of political institutions. On the economic front, stabilizing inflation, exchange rates, and market speculation—as well as improving social cohesion through targeted policies—can mitigate the growth of informal economic activities. A balanced, multi-pronged approach will foster sustainable economic development and enhance the resilience of Iran’s formal economy.
Economic and social instability, insecurity, and poor governance significantly increase transaction costs and investment risks while reducing incentives for productive economic activities. Institutional conditions and the political environment are fundamental factors influencing economic growth, as they affect the motivations of economic agents and thereby influence investment decisions, production organization, and overall economic performance. Macroeconomic instability, as an undesirable phenomenon, imposes both economic and social costs on society. Its persistence disturbs the national economic structure and diminishes household welfare by undermining financial security and increasing economic uncertainty.
Furthermore, effective economic policy-making and national development planning require a comprehensive understanding of the economy’s formal and informal sectors. The informal or underground economy includes activities outside the scope of official oversight, such as unregistered income, tax evasion, and operations beyond legal, social, and economic regulations. These activities are typically excluded from official GDP calculations but represent a significant share of economic production.
Modern definitions of economic growth encompass not only increases in GDP but also broader improvements in societal economic well-being. Notably, economic production occurs in both formal and informal sectors; thus, a thorough analysis of both is essential for developing effective and inclusive growth strategies. This study aims to evaluate the influence of political and economic risk, instability, and governance quality on both sectors of Iran’s economy over the period 1370–1401 (1991–2022). To achieve this, relevant indices were constructed to measure risk and instability in economic, financial, and social domains, as well as Iran’s governance performance, with the goal of identifying key determinants of formal sector strengthening and informal sector reduction.
Methodology
This research employs an endogenous growth model to investigate the factors influencing economic growth in Iran. Data on the underground economy are drawn from estimates produced using the Multiple Indicators and Multiple Causes (MIMIC) model. The methodological framework combines econometric techniques, notably Principal Component Analysis (PCA) and the Autoregressive Distributed Lag (ARDL) model.
PCA is applied to construct composite indices where multiple explanatory variables are involved, particularly in capturing instability and governance indicators. ARDL is used to examine relationships among variables, given the mixed order of integration in the time series data. This dual approach enables the study to assess the impact of governance, risk, and economic instability on both the formal and informal economic sectors.
Results and Discussion
The results show that within the economic growth function, property rights and political management exert a positive influence, while economic instability and international sanctions negatively affect Iran’s economic growth. Specifically, an increase of one unit in the political management index results in a 3.0033% increase in economic growth, whereas a one-unit rise in the economic instability index leads to a 0.1935% decline in growth.
In analyzing the informal (underground) economy, the study finds that increased risk and instability, unemployment, government size, tax revenues, and sanctions all contribute to the expansion of the informal sector. Conversely, improvements in political management reduce informal economic activities. Notably, the risk and instability index shows a high impact, with a coefficient of 3.99, signifying its strong correlation with the growth of Iran’s underground economy.
Conclusion
Improved political management enhances formal economic activity while suppressing informal sector expansion. Specifically, advancements in governance indicators—such as political participation, accountability, and rule of law—help reduce the size of the underground economy and promote formal sector growth. On the other hand, economic and social instabilities, including financial market volatility, inflation, speculation, and societal insecurity, incentivize informal economic behavior, thereby undermining the formal structure of the economy.
To address these challenges, the study recommends implementing comprehensive governance and economic reforms. On the governance side, strategies should include corruption control, enhanced oversight, legal enforcement, public trust-building, and increased legitimacy of political institutions. On the economic front, stabilizing inflation, exchange rates, and market speculation—as well as improving social cohesion through targeted policies—can mitigate the growth of informal economic activities. A balanced, multi-pronged approach will foster sustainable economic development and enhance the resilience of Iran’s formal economy.
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Over the past few decades, the housing market has experienced recurrent boom-and-bust cycles and considerable price volatility. A significant portion of this volatility can be attributed to speculative activities. Speculators often purchase properties with the expectation of future price increases, which contributes to the formation of housing price bubble. These bubbles not only destabilize the economy but also lead to serious social consequences. As such, policymakers have consistently focused on identifying the determinants of speculative behavior and housing market bubbles. One of the government’s regulatory instruments in this domain is the transfer tax, intended to influence trader behavior and mitigate housing price bubbles. This study investigates the effect of transfer tax policies on the housing price bubble in Shiraz city.
Methodology
This research employs an Agent-Based Model (ABM) to simulate the dynamic processes of the housing market and analyze the contributing factors to price bubble formation. The model incorporates four key agents active in the housing market: sellers, buyers (including both personal-consumption and speculative buyers), developers, and real estate agencies. Data and statistics up to the beginning of 1401 (2022) were incorporated into the model to forecast housing prices in Shiraz through 1409 (2030).
Three scenarios were tested by varying the proportion of speculative buyers—30%, 50%, and 70%—and applying different transfer tax rates of 1% and 5%. The simulation explores how these variables influence the magnitude and growth of the housing price bubble under different market conditions.
Results and Discussion
The findings reveal that, regardless of the proportion of speculative buyers, the implementation of transfer taxes can reduce the housing price bubble in Shiraz. However, the extent of this effect varies with market conditions. These results align with prior studies, such as Chen (2017) and Izadkhasthi et al. (2018), which found that transfer taxes can mitigate housing price volatility.
Proponents of transfer taxes argue that speculative activities drive housing price bubbles and that such taxes increase transaction costs, thereby reducing speculative trading and contributing to market stability. For instance, with a 70% speculative buyer share and a 5% tax rate, the housing price bubble decreased by approximately 25% between 1401 and 1409. In contrast, a 1% tax rate under the same market conditions led to a 22% reduction in the bubble. However, when only 30% of buyers were speculative, the tax had a comparatively more minor effect, indicating that the efficacy of the tax diminishes when fewer speculators are present.
Conclusion
The results suggest that increasing the transfer tax rate does not necessarily reduce the housing price bubble. In scenarios with 30%, 40%, and 50% speculative buyer presence, higher average tax rates did not result in a significant reduction in the housing bubble and, in some cases, slightly intensified it. This supports earlier warnings in financial economics literature—such as those by Schwert and Seguin (1993) – that excessive transaction taxes may deter informed traders, who play a vital role in maintaining market efficiency and price stability. Similarly, Friedman (1953) emphasized the stabilizing role of rational traders in financial markets.
According to the simulation results, Article 59 of Iran’s Direct Taxes Law, which stipulates a 5% transfer tax, may help reduce housing bubbles in Shiraz and potentially nationwide. However, the optimal tax rate should be adaptive and context-specific, considering the varying proportions of speculative and non-speculative market participants. Therefore, the government is advised to collect comprehensive data on the structure of the housing market, assess the share of speculative transactions, and adjust tax rates accordingly.
Moreover, since the transfer tax only applies to documented transactions, many informal or contract-based transactions—particularly those occurring prior to property completion—escape taxation. In such cases, builders may sell properties through promissory notes or undocumented agreements, which are difficult to track and tax. As a result, it is recommended that the government strengthen monitoring mechanisms for such transactions. This includes identifying and intercepting units exchanged informally or without official documentation to ensure both effective taxation and bubble control.
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction:
Vulnerable employment, a segment of the informal economy, includes home-based businesses that emerge due to a lack of opportunities for formal employment. These businesses often operate without essential benefits such as medical insurance, social security, bonuses, and pensions, which exposes workers to economic instability. Consequently, many individuals engaged in vulnerable employment seek loans and financial assistance to expand their business activities and transition to the formal sector. Banks, as the primary providers of such loans, request collateral from borrowers – typically in the form of property documents – to ensure repayment and mitigate financial risk. Strengthening legal rights related to loan collateral enhances banks’ confidence in issuing loans, thereby increasing access to credit for vulnerable workers.
Due to the oil-dependent nature of OPEC economies and their reliance on oil revenues, many of these countries often lack robust production infrastructures capable of generating sufficient formal employment opportunities. This study aims to analyze the effect of strengthening loan-related legal rights on vulnerable employment in OPEC member countries, including Iran, Iraq, Algeria, Angola, Congo, Gabon, Kuwait, Saudi Arabia, the United Arab Emirates, Venezuela, Guinea, Libya, and Nigeria, during the period from 2013 to 2021.
Methodology:
Following the approach of Herkenhoff et al. (2021), this study employs a model in which the independent variables include the strength of legal rights related to loans, oil revenues, secondary school enrollment rates, and the urbanization ratio. Given the study’s objective of analyzing the threshold effects of legal loan rights on vulnerable employment, the Panel Smooth Transition Regression (PSTRmouseout="msoCommentHide('_com_1')" onmouseover="msoCommentShow('_anchor_1','_com_1')">[A1] ) method is used to estimate the model.
Results and Discussion:
The analysis identifies a 6.22% threshold in the legal rights index, distinguishing two distinct regimes. In the first regime, the strength of legal loan rights does not significantly impact vulnerable employment. However, in the second regime, a higher index value reduces vulnerable employment, suggesting that more substantial legal loan rights facilitate the transition of workers from the vulnerable to the formal sector. Additionally, oil revenues and secondary school enrollment rates exhibit a negative effect on vulnerable employment, while the urbanization ratio has a positive effect.
Conclusion:
The findings of this study indicate that strengthening legal loan rights has contributed to a reduction in vulnerable employment, which is a subset of informal employment. This shift has contributed to growth in formal sector employment. Banking regulations and enhanced requirements for obtaining collateral have increased banks’ confidence in lending, as they are better able to mitigate the risk of non-repayment. However, this system primarily benefits individuals who can pledge valid collateral, such as real estate and housing documents. Given the high value of such collateralized assets, borrowers are more likely to invest their loans in business development, transitioning their employment from the informal to the formal sector. In addition to securing stable employment, they also gain access to social benefits such as insurance and social security. This financial stability enables them to make timely loan repayments, preventing defaults and preserving their financial credibility.
Based on these findings, it is recommended that governments and banking authorities in the investigated countries implement strict laws and regulations to guarantee loan security and identify factors contributing to bank insolvency. Such measures would help prevent financial resource mismanagement in the banking sector and reduce the probability of bank failures. Strengthening financial regulations and risk management strategies would facilitate lending, ultimately promoting employment growth in the formal sector and reducing the prevalence of vulnerable employment.
Furthermore, the study reveals that oil revenues negatively impact vulnerable employment, which may be attributed to increased government spending on productive investments and formal job creation. This suggests that redirecting oil revenues toward investment, production, and employment generation—rather than short-term expenditures—can facilitate the transition of workers from the informal to the formal sector. Thus, policymakers are encouraged to prioritize long-term economic strategies that allocate oil revenues to sectors that foster sustainable employment opportunities.
The findings also highlight the positive effect of education on labor force transition. Higher levels of education and training result in a more skilled workforce, increasing their acceptance and employability in formal job markets. Therefore, governments should allocate additional resources to public education, provide free schooling, and expand access to higher education for economically disadvantaged groups. Promoting scientific education and fostering a culture that values learning can further enhance workforce skills and economic mobility.
Finally, the study finds that urbanization has had a positive effect on vulnerable employment, indicating that increasing urbanization has not been accompanied by industrial advancements or skill development, thereby failing to support the expansion of the formal sector. Instead, urbanization in the studied countries has often been driven by unfavorable business environments, weak regulatory frameworks, and a lack of political transparency, contributing to the growth of the informal economy. To address these challenges, policymakers should focus on improving governance, strengthening legal and economic structures, and fostering a business-friendly environment that supports formal employment
Vulnerable employment, a segment of the informal economy, includes home-based businesses that emerge due to a lack of opportunities for formal employment. These businesses often operate without essential benefits such as medical insurance, social security, bonuses, and pensions, which exposes workers to economic instability. Consequently, many individuals engaged in vulnerable employment seek loans and financial assistance to expand their business activities and transition to the formal sector. Banks, as the primary providers of such loans, request collateral from borrowers – typically in the form of property documents – to ensure repayment and mitigate financial risk. Strengthening legal rights related to loan collateral enhances banks’ confidence in issuing loans, thereby increasing access to credit for vulnerable workers.
Due to the oil-dependent nature of OPEC economies and their reliance on oil revenues, many of these countries often lack robust production infrastructures capable of generating sufficient formal employment opportunities. This study aims to analyze the effect of strengthening loan-related legal rights on vulnerable employment in OPEC member countries, including Iran, Iraq, Algeria, Angola, Congo, Gabon, Kuwait, Saudi Arabia, the United Arab Emirates, Venezuela, Guinea, Libya, and Nigeria, during the period from 2013 to 2021.
Methodology:
Following the approach of Herkenhoff et al. (2021), this study employs a model in which the independent variables include the strength of legal rights related to loans, oil revenues, secondary school enrollment rates, and the urbanization ratio. Given the study’s objective of analyzing the threshold effects of legal loan rights on vulnerable employment, the Panel Smooth Transition Regression (PSTRmouseout="msoCommentHide('_com_1')" on
Results and Discussion:
The analysis identifies a 6.22% threshold in the legal rights index, distinguishing two distinct regimes. In the first regime, the strength of legal loan rights does not significantly impact vulnerable employment. However, in the second regime, a higher index value reduces vulnerable employment, suggesting that more substantial legal loan rights facilitate the transition of workers from the vulnerable to the formal sector. Additionally, oil revenues and secondary school enrollment rates exhibit a negative effect on vulnerable employment, while the urbanization ratio has a positive effect.
Conclusion:
The findings of this study indicate that strengthening legal loan rights has contributed to a reduction in vulnerable employment, which is a subset of informal employment. This shift has contributed to growth in formal sector employment. Banking regulations and enhanced requirements for obtaining collateral have increased banks’ confidence in lending, as they are better able to mitigate the risk of non-repayment. However, this system primarily benefits individuals who can pledge valid collateral, such as real estate and housing documents. Given the high value of such collateralized assets, borrowers are more likely to invest their loans in business development, transitioning their employment from the informal to the formal sector. In addition to securing stable employment, they also gain access to social benefits such as insurance and social security. This financial stability enables them to make timely loan repayments, preventing defaults and preserving their financial credibility.
Based on these findings, it is recommended that governments and banking authorities in the investigated countries implement strict laws and regulations to guarantee loan security and identify factors contributing to bank insolvency. Such measures would help prevent financial resource mismanagement in the banking sector and reduce the probability of bank failures. Strengthening financial regulations and risk management strategies would facilitate lending, ultimately promoting employment growth in the formal sector and reducing the prevalence of vulnerable employment.
Furthermore, the study reveals that oil revenues negatively impact vulnerable employment, which may be attributed to increased government spending on productive investments and formal job creation. This suggests that redirecting oil revenues toward investment, production, and employment generation—rather than short-term expenditures—can facilitate the transition of workers from the informal to the formal sector. Thus, policymakers are encouraged to prioritize long-term economic strategies that allocate oil revenues to sectors that foster sustainable employment opportunities.
The findings also highlight the positive effect of education on labor force transition. Higher levels of education and training result in a more skilled workforce, increasing their acceptance and employability in formal job markets. Therefore, governments should allocate additional resources to public education, provide free schooling, and expand access to higher education for economically disadvantaged groups. Promoting scientific education and fostering a culture that values learning can further enhance workforce skills and economic mobility.
Finally, the study finds that urbanization has had a positive effect on vulnerable employment, indicating that increasing urbanization has not been accompanied by industrial advancements or skill development, thereby failing to support the expansion of the formal sector. Instead, urbanization in the studied countries has often been driven by unfavorable business environments, weak regulatory frameworks, and a lack of political transparency, contributing to the growth of the informal economy. To address these challenges, policymakers should focus on improving governance, strengthening legal and economic structures, and fostering a business-friendly environment that supports formal employment
mouseout="msoCommentHide('_com_1')" onmouseover="msoCommentShow('_anchor_1','_com_1')" style="text-align: justify;">
mouseout="msoCommentHide('_com_1')" onmouseover="msoCommentShow('_anchor_1','_com_1')" style="text-align: justify;">
mouseout="msoCommentHide('_com_1')" onmouseover="msoCommentShow('_anchor_1','_com_1')" style="text-align: justify;"> [A1]The written abbreviation is for “the Panel Smooth Transition Regression”
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Asset bubbles arise when the prices of assets – such as real estate or stocks –significantly exceed their intrinsic value due to excessive speculation and investor euphoria. These bubbles are typically characterized by rapid price escalations that become disconnected from fundamental economic indicators, driven more by market psychology than by real economic value. Although asset bubbles may generate short-term economic benefits, they pose serious risks to financial stability, as their eventual collapse often results in sharp market corrections, financial crises, and broader economic downturns.
Monetary policy, primarily executed by central banks, plays a critical role in influencing macroeconomic conditions through liquidity management, credit accessibility, and interest rate adjustments. On the one hand, expansionary monetary policies—characterized by low interest rates and increased liquidity—can stimulate speculative investment and contribute to the formation of asset bubbles. On the other hand, central banks can use contractionary policies—such as raising interest rates or reducing liquidity—to dampen excessive market exuberance and promote financial stability.
The complex relationship between asset bubbles and monetary policy underscores a significant challenge for economists and policymakers, who must balance the goals of economic growth and financial stability. A nuanced understanding of this relationship is crucial for designing effective regulatory frameworks and policy interventions capable of mitigating harmful boom-and-bust cycles and fostering sustainable economic development.
Methodology
This study examines stock market bubbles and the influence of monetary policy in five D-8 countries, Iran, Turkey, Indonesia, Malaysia, and Egypt, over the period 2009–2023. Two key analytical approaches are employed:
Log-Periodic Power Law Singularity with Confidence Interval (LPPLS-CI) for detecting stock price bubbles, and
Asset bubbles arise when the prices of assets – such as real estate or stocks –significantly exceed their intrinsic value due to excessive speculation and investor euphoria. These bubbles are typically characterized by rapid price escalations that become disconnected from fundamental economic indicators, driven more by market psychology than by real economic value. Although asset bubbles may generate short-term economic benefits, they pose serious risks to financial stability, as their eventual collapse often results in sharp market corrections, financial crises, and broader economic downturns.
Monetary policy, primarily executed by central banks, plays a critical role in influencing macroeconomic conditions through liquidity management, credit accessibility, and interest rate adjustments. On the one hand, expansionary monetary policies—characterized by low interest rates and increased liquidity—can stimulate speculative investment and contribute to the formation of asset bubbles. On the other hand, central banks can use contractionary policies—such as raising interest rates or reducing liquidity—to dampen excessive market exuberance and promote financial stability.
The complex relationship between asset bubbles and monetary policy underscores a significant challenge for economists and policymakers, who must balance the goals of economic growth and financial stability. A nuanced understanding of this relationship is crucial for designing effective regulatory frameworks and policy interventions capable of mitigating harmful boom-and-bust cycles and fostering sustainable economic development.
Methodology
This study examines stock market bubbles and the influence of monetary policy in five D-8 countries, Iran, Turkey, Indonesia, Malaysia, and Egypt, over the period 2009–2023. Two key analytical approaches are employed:
Log-Periodic Power Law Singularity with Confidence Interval (LPPLS-CI) for detecting stock price bubbles, and
- Panel Vector Autoregression (P-VAR) for assessing the dynamic impact of monetary policy variables.
The LPPLS-CI model enhances traditional LPPLS techniques by incorporating confidence intervals, thus improving the accuracy and robustness of bubble detection. This model identifies unsustainable asset price growth and log-periodic oscillations—signals typically preceding bubble collapses. Its predictive capacity offers early warning signals that are valuable for financial market monitoring.
To evaluate the effects of monetary policy on these bubbles, the study employs the P-VAR model. This econometric framework captures interdependencies between multiple time-series variables—including stock prices, interest rates, inflation, and liquidity—by analyzing their lagged interactions. This comprehensive approach facilitates a dynamic understanding of how monetary policy decisions shape speculative trends and bubble formation. The effectiveness of this analysis depends on key methodological considerations, including appropriate model specification, lag length selection, and rigorous validation techniques.
Results and Discussion
The LPPLS-CI analysis confirms the presence of stock price bubbles across various time scales (short-, medium-, and long-term) in the selected countries throughout the 2009–2023 period. These bubbles were characterized by rapid price increases fueled by speculative behavior and optimistic market sentiment, ultimately followed by sharp corrections.
The P-VAR results demonstrated that high inflation, increased liquidity, and low interest rates were key contributors to bubble formation. These conditions encouraged capital inflows into financial markets, driving up stock prices beyond sustainable levels. However, as monetary policy conditions tightened or external economic shocks emerged, these bubbles burst, resulting in significant financial losses and increased market volatility.
The findings underscore the dual nature of monetary policy: while accommodative policies can promote growth and investment, they also risk inflating asset bubbles. The study emphasizes the need for balanced and proactive policy responses to prevent systemic instability. Regulatory oversight, timely monetary adjustments, and enhanced early warning mechanisms are crucial in minimizing the risks associated with speculative excesses.
Conclusion
Monetary policy in the examined D-8 countries significantly influences the formation and trajectory of stock market bubbles. Expansionary policies may exacerbate bubbles, leading to financial shocks, economic contractions, and capital flight when the bubbles burst. The study underscores the imperative for central banks in emerging markets to carefully manage accurate interest rates, control inflation, and stabilize liquidity to safeguard financial markets.
Key components of monetary policy affecting asset bubbles include:
To evaluate the effects of monetary policy on these bubbles, the study employs the P-VAR model. This econometric framework captures interdependencies between multiple time-series variables—including stock prices, interest rates, inflation, and liquidity—by analyzing their lagged interactions. This comprehensive approach facilitates a dynamic understanding of how monetary policy decisions shape speculative trends and bubble formation. The effectiveness of this analysis depends on key methodological considerations, including appropriate model specification, lag length selection, and rigorous validation techniques.
Results and Discussion
The LPPLS-CI analysis confirms the presence of stock price bubbles across various time scales (short-, medium-, and long-term) in the selected countries throughout the 2009–2023 period. These bubbles were characterized by rapid price increases fueled by speculative behavior and optimistic market sentiment, ultimately followed by sharp corrections.
The P-VAR results demonstrated that high inflation, increased liquidity, and low interest rates were key contributors to bubble formation. These conditions encouraged capital inflows into financial markets, driving up stock prices beyond sustainable levels. However, as monetary policy conditions tightened or external economic shocks emerged, these bubbles burst, resulting in significant financial losses and increased market volatility.
The findings underscore the dual nature of monetary policy: while accommodative policies can promote growth and investment, they also risk inflating asset bubbles. The study emphasizes the need for balanced and proactive policy responses to prevent systemic instability. Regulatory oversight, timely monetary adjustments, and enhanced early warning mechanisms are crucial in minimizing the risks associated with speculative excesses.
Conclusion
Monetary policy in the examined D-8 countries significantly influences the formation and trajectory of stock market bubbles. Expansionary policies may exacerbate bubbles, leading to financial shocks, economic contractions, and capital flight when the bubbles burst. The study underscores the imperative for central banks in emerging markets to carefully manage accurate interest rates, control inflation, and stabilize liquidity to safeguard financial markets.
Key components of monetary policy affecting asset bubbles include:
- Interest Rates: Low rates can stimulate borrowing and speculation, while higher rates can curb overheating but may suppress growth.
- Quantitative Easing (QE): Although QE enhances liquidity and asset values, prolonged implementation can fuel speculative bubbles.
To prevent crises, Policy recommendations include:
- Regulatory Oversight: Strengthen financial regulations to enhance transparency and mitigate systemic risks.
- Macroprudential Tools: Implement counter-cyclical capital buffers and risk-weighted asset requirements.
- Monetary Policy Adjustments: Implement forward guidance and timely rate changes to manage expectations.
- Early Warning Systems: Monitor key financial indicators to detect signs of market overheating.
- Investment Diversification: Encourage asset diversification to reduce systemic exposure.
Implementing these strategies can help minimize the occurrence and adverse consequences of asset bubbles, contributing to more resilient financial systems and sustainable economic growth in the D-8 member countries.
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
By integrating insights from psychology—especially cognitive psychology—into economic theory, behavioral economics provides a more realistic understanding of human behavior and economic decision-making (Thaler, 2017). A key subset of this field is behavioral finance, which posits that investment decisions are not always based on rational optimization. Instead, behavioral factors often lead to perceptual distortions, biased judgments, and irrational interpretations. These tendencies stem from various behavioral biases—collectively referred to as irrational behaviors—which commonly arise due to investors’ limited capacity to process information and the impact of emotional factors on their decision-making (Abildgren et al., 2018; Di Stefani, 2021; He & Xia, 2020; Glavatsky et al., 2021; Lan, 2014; Mayer & Siani, 2009; Tan, 2022; Yang et al., 2020).
One notable cognitive bias is herding behavior, which refers to individuals mimicking the actions of the majority. This phenomenon is particularly notorious in markets such as housing, coins, and currency, where it is widely regarded by experts as a primary driver of severe and irrational price fluctuations (Rook, 2006).
Methodology
This research employs spatial econometric techniques to analyze the effects of dependency culture on herding behavior in the housing market across 31 Iranian provinces from 1390 to 1400 (2011–2021) on a seasonal basis. Spatial econometrics extends traditional panel data models by incorporating geographical dimensions, which enables the analysis of spatial interdependence and regional heterogeneity. In the presence of spatial components, two primary issues must be addressed: spatial dependence, which refers to correlation among geographically proximate units, and spatial heterogeneity, which refers to structural differences across regions.
Before estimating the spatial panel models, tests for spatial autocorrelation were conducted to determine the necessity of incorporating spatial effects into the analysis. Specifically, Moran’s I, Geary’s C, and Getis-Ord J statistics were used to assess the presence of spatial autocorrelation among the error terms. A significant spatial dependence justifies the application of spatial econometric models. To define spatial relationships, two forms of spatial weighting structures were considered: coordinate-based distances derived from latitude and longitude, and neighborhood-based contiguity matrices that capture the relative location of each province in relation to others. Based on the detection of significant spatial autocorrelation, the Spatial Autoregressive (SAR) model was selected to capture the dynamic spatial interactions within the housing market across Iranian provinces.
Findings
The results of the spatial econometric analysis confirm that exchange rate fluctuations have a positive and statistically significant impact on the housing market across both the target provinces and their neighboring regions. This finding supports the hypothesis that dependency culture, shaped by sensitivity to macroeconomic signals such as exchange rate movements, plays a key role in fostering herd behavior within Iran’s housing sector during the study period. The presence of spatial spillovers indicates that changes in one province can influence housing activity in surrounding areas, reinforcing regional contagion effects.
In addition to the exchange rate, the variables of inflation rate, population density index, and the logarithm of stock exchange transaction volume were also found to have positive and significant effects on housing market dynamics. These factors appear to stimulate speculative behavior and intensify market activity. Conversely, the logarithm of the distance from Tehran province exhibited a negative and significant effect on housing market outcomes.
Discussion and Conclusion
In Iran, there are no legal limitations on the frequency of property transactions, which allows a residential unit or parcel of land to be repeatedly traded within a year. This lack of regulation encourages speculative and herding behavior. To mitigate this, the study recommends implementing transaction limits and a more effective taxation system, similar to those used in developed countries. For example, imposing higher taxes on multiple home ownership and on vacant housing units can discourage speculation.
Despite the high number of vacant units, a significant proportion of Iranian households remain without access to adequate housing and face declining welfare due to soaring rents. Targeted housing assistance—including free land allocation—could help meet the actual demand and reduce speculative demand, thereby limiting herd behavior.
Furthermore, price booms typically originate in metropolitan and affluent regions, suggesting that a more balanced spatial development strategy could help diffuse housing market pressures. Introducing region-specific construction and transaction regulations, especially in high-risk speculative areas, could further manage housing price volatility.
Finally, encouraging investment in parallel financial markets and increasing stability and public trust in those markets could redirect speculative behavior away from real estate. Creating viable alternative investment opportunities would absorb excess liquidity and help stabilize the housing sector.
By integrating insights from psychology—especially cognitive psychology—into economic theory, behavioral economics provides a more realistic understanding of human behavior and economic decision-making (Thaler, 2017). A key subset of this field is behavioral finance, which posits that investment decisions are not always based on rational optimization. Instead, behavioral factors often lead to perceptual distortions, biased judgments, and irrational interpretations. These tendencies stem from various behavioral biases—collectively referred to as irrational behaviors—which commonly arise due to investors’ limited capacity to process information and the impact of emotional factors on their decision-making (Abildgren et al., 2018; Di Stefani, 2021; He & Xia, 2020; Glavatsky et al., 2021; Lan, 2014; Mayer & Siani, 2009; Tan, 2022; Yang et al., 2020).
One notable cognitive bias is herding behavior, which refers to individuals mimicking the actions of the majority. This phenomenon is particularly notorious in markets such as housing, coins, and currency, where it is widely regarded by experts as a primary driver of severe and irrational price fluctuations (Rook, 2006).
Methodology
This research employs spatial econometric techniques to analyze the effects of dependency culture on herding behavior in the housing market across 31 Iranian provinces from 1390 to 1400 (2011–2021) on a seasonal basis. Spatial econometrics extends traditional panel data models by incorporating geographical dimensions, which enables the analysis of spatial interdependence and regional heterogeneity. In the presence of spatial components, two primary issues must be addressed: spatial dependence, which refers to correlation among geographically proximate units, and spatial heterogeneity, which refers to structural differences across regions.
Before estimating the spatial panel models, tests for spatial autocorrelation were conducted to determine the necessity of incorporating spatial effects into the analysis. Specifically, Moran’s I, Geary’s C, and Getis-Ord J statistics were used to assess the presence of spatial autocorrelation among the error terms. A significant spatial dependence justifies the application of spatial econometric models. To define spatial relationships, two forms of spatial weighting structures were considered: coordinate-based distances derived from latitude and longitude, and neighborhood-based contiguity matrices that capture the relative location of each province in relation to others. Based on the detection of significant spatial autocorrelation, the Spatial Autoregressive (SAR) model was selected to capture the dynamic spatial interactions within the housing market across Iranian provinces.
Findings
The results of the spatial econometric analysis confirm that exchange rate fluctuations have a positive and statistically significant impact on the housing market across both the target provinces and their neighboring regions. This finding supports the hypothesis that dependency culture, shaped by sensitivity to macroeconomic signals such as exchange rate movements, plays a key role in fostering herd behavior within Iran’s housing sector during the study period. The presence of spatial spillovers indicates that changes in one province can influence housing activity in surrounding areas, reinforcing regional contagion effects.
In addition to the exchange rate, the variables of inflation rate, population density index, and the logarithm of stock exchange transaction volume were also found to have positive and significant effects on housing market dynamics. These factors appear to stimulate speculative behavior and intensify market activity. Conversely, the logarithm of the distance from Tehran province exhibited a negative and significant effect on housing market outcomes.
Discussion and Conclusion
In Iran, there are no legal limitations on the frequency of property transactions, which allows a residential unit or parcel of land to be repeatedly traded within a year. This lack of regulation encourages speculative and herding behavior. To mitigate this, the study recommends implementing transaction limits and a more effective taxation system, similar to those used in developed countries. For example, imposing higher taxes on multiple home ownership and on vacant housing units can discourage speculation.
Despite the high number of vacant units, a significant proportion of Iranian households remain without access to adequate housing and face declining welfare due to soaring rents. Targeted housing assistance—including free land allocation—could help meet the actual demand and reduce speculative demand, thereby limiting herd behavior.
Furthermore, price booms typically originate in metropolitan and affluent regions, suggesting that a more balanced spatial development strategy could help diffuse housing market pressures. Introducing region-specific construction and transaction regulations, especially in high-risk speculative areas, could further manage housing price volatility.
Finally, encouraging investment in parallel financial markets and increasing stability and public trust in those markets could redirect speculative behavior away from real estate. Creating viable alternative investment opportunities would absorb excess liquidity and help stabilize the housing sector.
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
One of the most critical criteria for assessing the development of the digital economy is financial deepening. Most analysts agree that financial deepening can accelerate economic growth. Economic policy orientations are increasingly aligned with the objectives of financial deepening, underscoring its growing significance. The digital economy is characterized by its large scale, rapid development, and strong connectivity—factors that demand more accurate and efficient financial support.
Financial deepening refers to the multi-dimensional strategy for establishing a market-oriented, multi-level financial system to foster economic growth. This approach includes the expansion and development of financial markets, the creation of innovative financial products and services, the reform of financial institutions, and the strengthening of financial supervision. The digital economy today extends beyond the realms of electronics, communication, and information industries, and has deeply integrated with the traditional economy. It no longer represents merely a part of the economy but increasingly defines it as a whole. Given its potential for growth and high profitability, the digital economy and its associated industries require substantial financial support.
Financial deepening enhances the stock of liquid assets, expands financing channels, and directs capital flows toward competitive, high-yield digital sectors—thereby promoting their rapid development. Financial institutions are increasingly supporting emerging industries through targeted credit policies and are actively facilitating the digital transformation of traditional sectors. Furthermore, regional financial deepening improves the accessibility of financial intermediaries—such as banks and venture capital firms—to real-time corporate information. This reduces financing constraints, broadens financing channels, lowers capital costs, and provides firms with more diverse financial options.
For digital economy enterprises, financial deepening improves resource allocation efficiency through the integration of advanced technologies such as big data. It enables the effective investment of funds in critical areas of the digital economy, thereby fostering its development. As the digital economy expands, it becomes essential to manage the associated financial risks. Financial deepening addresses these concerns by reinforcing the financial market infrastructure, enhancing the regulatory environment, and adopting comprehensive risk management strategies to ensure the sector's sustainable and healthy growth.
Moreover, financial deepening spurs innovation in digital payment systems, digital currencies, financial technologies, and other related fields, resulting in more efficient and accessible financial services that underpin digital economy growth. Overall, financial deepening plays an important role in reducing financial constraints, enhancing resource allocation, managing financial risks, and providing vital financial services to support the robust and sustainable development of the digital economy.
Methodology
This study employs a random dynamic panel model using the Spatial Generalized Method of Moments—Dynamic Panel Data (SGMM-DPD-SDM) framework with two-stage Arellano-Bond estimation and random dynamic coefficients. To evaluate the effects of financial deepening, economic openness, government size, and economic growth on digital economy development in MENA countries, spatial econometric techniques are applied. In this model, the development of the digital economy is the key dependent variable.
The selection of explanatory variables—namely financial deepening, economic openness, government size, and economic growth—is grounded in theoretical foundations. The inclusion of the lagged dependent variable in the model introduces autocorrelation between the explanatory variables and the error term, violating one of the classical assumptions of panel models. Consequently, the use of ordinary least squares methods in fixed and random effects models would yield biased and inconsistent estimates. Therefore, dynamic panel data techniques are employed to ensure robustness.
Findings
The results indicate that financial deepening significantly enhances digital economy development. Additionally, its spatial effects reveal that financial deepening in one country positively influences neighboring countries. According to the spatial lag estimation, the digital development of a given country is affected by the weighted average of digital development levels in neighboring countries, with an estimated effect of 0.82.
The estimated coefficients for economic growth, government size, and economic openness are all positive and statistically significant, confirming their direct contribution to digital development. Moreover, these variables exhibit spatial spillover effects, further validating the presence of regional interdependencies. All spatial proximity-related variables are statistically significant, underscoring the importance of spatial and regional dynamics in understanding the influence of these factors on digital development.
Discussion and Conclusion
To promote financial deepening and foster the growth of the digital economy, the following recommendations are proposed based on the research findings:
Gradual reforms in the financial system should be pursued, with an emphasis on improving the efficiency and quality of financial services through the establishment of a sound regulatory framework. Strengthening the capacity of financial services for the real economy and encouraging traditional financial institutions to enhance their service offerings will ensure robust financial support. Simplifying approval processes for digital economy enterprises and lowering funding thresholds are also crucial for enabling the rapid development of this dynamic sector.
Encouraging innovation in financial products and services tailored to the needs of the digital economy—such as those based on e-commerce platforms and blockchain technologies—will drive further progress. Additionally, promoting direct financing through the gradual liberalization of capital markets and increasing their share in the financial system can significantly stimulate economic growth.
Welcoming foreign investment and facilitating the entry of international financial institutions will bring in much-needed capital and advanced financial technologies. These measures will not only enrich the financial ecosystem but also accelerate the digital transformation process across the MENA region
One of the most critical criteria for assessing the development of the digital economy is financial deepening. Most analysts agree that financial deepening can accelerate economic growth. Economic policy orientations are increasingly aligned with the objectives of financial deepening, underscoring its growing significance. The digital economy is characterized by its large scale, rapid development, and strong connectivity—factors that demand more accurate and efficient financial support.
Financial deepening refers to the multi-dimensional strategy for establishing a market-oriented, multi-level financial system to foster economic growth. This approach includes the expansion and development of financial markets, the creation of innovative financial products and services, the reform of financial institutions, and the strengthening of financial supervision. The digital economy today extends beyond the realms of electronics, communication, and information industries, and has deeply integrated with the traditional economy. It no longer represents merely a part of the economy but increasingly defines it as a whole. Given its potential for growth and high profitability, the digital economy and its associated industries require substantial financial support.
Financial deepening enhances the stock of liquid assets, expands financing channels, and directs capital flows toward competitive, high-yield digital sectors—thereby promoting their rapid development. Financial institutions are increasingly supporting emerging industries through targeted credit policies and are actively facilitating the digital transformation of traditional sectors. Furthermore, regional financial deepening improves the accessibility of financial intermediaries—such as banks and venture capital firms—to real-time corporate information. This reduces financing constraints, broadens financing channels, lowers capital costs, and provides firms with more diverse financial options.
For digital economy enterprises, financial deepening improves resource allocation efficiency through the integration of advanced technologies such as big data. It enables the effective investment of funds in critical areas of the digital economy, thereby fostering its development. As the digital economy expands, it becomes essential to manage the associated financial risks. Financial deepening addresses these concerns by reinforcing the financial market infrastructure, enhancing the regulatory environment, and adopting comprehensive risk management strategies to ensure the sector's sustainable and healthy growth.
Moreover, financial deepening spurs innovation in digital payment systems, digital currencies, financial technologies, and other related fields, resulting in more efficient and accessible financial services that underpin digital economy growth. Overall, financial deepening plays an important role in reducing financial constraints, enhancing resource allocation, managing financial risks, and providing vital financial services to support the robust and sustainable development of the digital economy.
Methodology
This study employs a random dynamic panel model using the Spatial Generalized Method of Moments—Dynamic Panel Data (SGMM-DPD-SDM) framework with two-stage Arellano-Bond estimation and random dynamic coefficients. To evaluate the effects of financial deepening, economic openness, government size, and economic growth on digital economy development in MENA countries, spatial econometric techniques are applied. In this model, the development of the digital economy is the key dependent variable.
The selection of explanatory variables—namely financial deepening, economic openness, government size, and economic growth—is grounded in theoretical foundations. The inclusion of the lagged dependent variable in the model introduces autocorrelation between the explanatory variables and the error term, violating one of the classical assumptions of panel models. Consequently, the use of ordinary least squares methods in fixed and random effects models would yield biased and inconsistent estimates. Therefore, dynamic panel data techniques are employed to ensure robustness.
Findings
The results indicate that financial deepening significantly enhances digital economy development. Additionally, its spatial effects reveal that financial deepening in one country positively influences neighboring countries. According to the spatial lag estimation, the digital development of a given country is affected by the weighted average of digital development levels in neighboring countries, with an estimated effect of 0.82.
The estimated coefficients for economic growth, government size, and economic openness are all positive and statistically significant, confirming their direct contribution to digital development. Moreover, these variables exhibit spatial spillover effects, further validating the presence of regional interdependencies. All spatial proximity-related variables are statistically significant, underscoring the importance of spatial and regional dynamics in understanding the influence of these factors on digital development.
Discussion and Conclusion
To promote financial deepening and foster the growth of the digital economy, the following recommendations are proposed based on the research findings:
Gradual reforms in the financial system should be pursued, with an emphasis on improving the efficiency and quality of financial services through the establishment of a sound regulatory framework. Strengthening the capacity of financial services for the real economy and encouraging traditional financial institutions to enhance their service offerings will ensure robust financial support. Simplifying approval processes for digital economy enterprises and lowering funding thresholds are also crucial for enabling the rapid development of this dynamic sector.
Encouraging innovation in financial products and services tailored to the needs of the digital economy—such as those based on e-commerce platforms and blockchain technologies—will drive further progress. Additionally, promoting direct financing through the gradual liberalization of capital markets and increasing their share in the financial system can significantly stimulate economic growth.
Welcoming foreign investment and facilitating the entry of international financial institutions will bring in much-needed capital and advanced financial technologies. These measures will not only enrich the financial ecosystem but also accelerate the digital transformation process across the MENA region
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Financial markets have become one of the most attractive environments for investment in the modern era. Through the efficient allocation of capital, these markets exert a significant influence across various domains, including trade, education, employment, technology, and the broader economy. Financial markets are categorized into specific industries and sectors based on the characteristics of the goods and services produced. These unique features and industry-specific conditions influence productivity, which in turn affects returns.
Industry-level returns reflect a combination of financial and non-financial factors that shape stock market dynamics. Industry data offer critical insights into the sources of a country’s economic growth, particularly from an industrial standpoint. Furthermore, industries often act as a leading force in the stock market, as their performance is closely tied to macroeconomic fundamentals.
There are two primary approaches to investing in stocks and generating returns commensurate with risk: the fundamental approach and the technical approach. The fundamental approach is based on three key levels of analysis. The first is macroeconomic analysis, which considers variables such as gross domestic product, monetary policy, and the broader economic environment, along with their effects on the returns of various industries and sectors. The second is industry analysis, which evaluates the performance of companies within a specific industry based on the unique conditions and characteristics of that industry. The third is company analysis, which focuses on assessing a firm’s current operations and financial status to determine its intrinsic value and future potential. Therefore, industry-level analysis serves as a crucial component within the broader framework of fundamental investment analysis.
At the industry level, macroeconomic variables—especially government monetary policy—play a pivotal role. Monetary policy influences capital markets through four primary transmission channels: the exchange rate, interest rate, credit, and asset prices.
Methodology
To examine the research hypotheses regarding the impact of monetary policy on the returns of small and large industries from April 2010 to March 2024, this study employs the Pooled Mean Group (PMG) estimator on monthly data. A key advantage of this method is its capacity to handle both stationary and non-stationary variables, thereby overcoming issues related to cointegration and the limited power of unit root tests in long-term estimations.
The model used is a Panel Autoregressive Distributed Lag (ARDL) framework, which enables the simultaneous estimation of short-term and long-term coefficients. In this framework, a long-run relationship is assumed betweenY t X t μ i
The error correction model is as follows:
∆ y it = μ i + ∅ i y i , t + β i ' X it + j =1 p - 1 ω ij * ∆ y i , t - j + j =0 q - 1 δ ij *' ∆ X i , t - j +…+ ε it
The final equation is as follows:
∆ y it μ i + ∅ i y i , t - 1 - θ i ' X it + j =1 p - 1 ω ij * ∆ y i , t - j + j =0 q - 1 δ ij *' ∆ X i , t - j +…+ ε it
In this study, the dependent variable is the industry return (IR) for small and large industries, and the key independent variable is Monetary Policy (MP)—measured via the Monetary Conditions Index based on principal component analysis. Additional control variables include Liquidity Volume (LV), Oil Price (OP), and the Consumer Price Index (CPI).
Findings
The results for long-term relationships reveal a positive and significant relationship between monetary policy and the return of small industries on the Tehran Stock Exchange, with an estimated coefficient of approximately 4.1%. However, no significant long-term relationship was found between monetary policy and the return of large industries.
In the short term, the error correction terms are estimated at -0.78 and -0.70 for small and large industries, respectively. This indicates that roughly 78% and 70% of the disequilibrium between the independent and dependent variables is corrected each period, guiding the system toward long-run equilibrium. In the first model (small industries), monetary policy has no immediate impact on returns. Conversely, in the second model (large industries), monetary policy exerts a significant short-term effect at the 5% level.
Conclusion
Government policies exert a profound influence on financial markets, with monetary policy playing a distinct and varying role across industries. Despite its importance, this differentiation has received limited attention in Iran. This study contributes to the literature by analyzing the differential effects of monetary policy on small and large industries, using the PMG model to estimate both short-term and long-term impacts on a monthly basis from April 2010 to March 2024.
The findings reveal that, in the long run, monetary policy exerts a positive and significant impact on the returns of small industries, whereas this effect is absent in large industries. In the short run, with the significance of the error correction term confirming the adjustment toward long-term equilibrium, the dynamics between the independent and dependent variables become balanced over time. Furthermore, the analysis indicates that monetary policy has no significant effect on small industries in the short term but demonstrates a positive and significant impact in the long term. In contrast, for large industries, monetary policy has no discernible effect in the long run but exerts a positive and significant influence in the short term.
These results confirm both the main and sub-hypotheses of the study, which posit that the effects of monetary policy vary between small and large industries and differ across time horizons. Consequently, investors are advised to consider firm size, as reflected in market value, when constructing their portfolios. Specifically, they should align their investment strategies with their time horizons—favoring small industries for long-term investments and large industries for short-term opportunities.
Financial markets have become one of the most attractive environments for investment in the modern era. Through the efficient allocation of capital, these markets exert a significant influence across various domains, including trade, education, employment, technology, and the broader economy. Financial markets are categorized into specific industries and sectors based on the characteristics of the goods and services produced. These unique features and industry-specific conditions influence productivity, which in turn affects returns.
Industry-level returns reflect a combination of financial and non-financial factors that shape stock market dynamics. Industry data offer critical insights into the sources of a country’s economic growth, particularly from an industrial standpoint. Furthermore, industries often act as a leading force in the stock market, as their performance is closely tied to macroeconomic fundamentals.
There are two primary approaches to investing in stocks and generating returns commensurate with risk: the fundamental approach and the technical approach. The fundamental approach is based on three key levels of analysis. The first is macroeconomic analysis, which considers variables such as gross domestic product, monetary policy, and the broader economic environment, along with their effects on the returns of various industries and sectors. The second is industry analysis, which evaluates the performance of companies within a specific industry based on the unique conditions and characteristics of that industry. The third is company analysis, which focuses on assessing a firm’s current operations and financial status to determine its intrinsic value and future potential. Therefore, industry-level analysis serves as a crucial component within the broader framework of fundamental investment analysis.
At the industry level, macroeconomic variables—especially government monetary policy—play a pivotal role. Monetary policy influences capital markets through four primary transmission channels: the exchange rate, interest rate, credit, and asset prices.
Methodology
To examine the research hypotheses regarding the impact of monetary policy on the returns of small and large industries from April 2010 to March 2024, this study employs the Pooled Mean Group (PMG) estimator on monthly data. A key advantage of this method is its capacity to handle both stationary and non-stationary variables, thereby overcoming issues related to cointegration and the limited power of unit root tests in long-term estimations.
The model used is a Panel Autoregressive Distributed Lag (ARDL) framework, which enables the simultaneous estimation of short-term and long-term coefficients. In this framework, a long-run relationship is assumed between
The error correction model is as follows:
The final equation is as follows:
In this study, the dependent variable is the industry return (IR) for small and large industries, and the key independent variable is Monetary Policy (MP)—measured via the Monetary Conditions Index based on principal component analysis. Additional control variables include Liquidity Volume (LV), Oil Price (OP), and the Consumer Price Index (CPI).
Findings
The results for long-term relationships reveal a positive and significant relationship between monetary policy and the return of small industries on the Tehran Stock Exchange, with an estimated coefficient of approximately 4.1%. However, no significant long-term relationship was found between monetary policy and the return of large industries.
In the short term, the error correction terms are estimated at -0.78 and -0.70 for small and large industries, respectively. This indicates that roughly 78% and 70% of the disequilibrium between the independent and dependent variables is corrected each period, guiding the system toward long-run equilibrium. In the first model (small industries), monetary policy has no immediate impact on returns. Conversely, in the second model (large industries), monetary policy exerts a significant short-term effect at the 5% level.
Conclusion
Government policies exert a profound influence on financial markets, with monetary policy playing a distinct and varying role across industries. Despite its importance, this differentiation has received limited attention in Iran. This study contributes to the literature by analyzing the differential effects of monetary policy on small and large industries, using the PMG model to estimate both short-term and long-term impacts on a monthly basis from April 2010 to March 2024.
The findings reveal that, in the long run, monetary policy exerts a positive and significant impact on the returns of small industries, whereas this effect is absent in large industries. In the short run, with the significance of the error correction term confirming the adjustment toward long-term equilibrium, the dynamics between the independent and dependent variables become balanced over time. Furthermore, the analysis indicates that monetary policy has no significant effect on small industries in the short term but demonstrates a positive and significant impact in the long term. In contrast, for large industries, monetary policy has no discernible effect in the long run but exerts a positive and significant influence in the short term.
These results confirm both the main and sub-hypotheses of the study, which posit that the effects of monetary policy vary between small and large industries and differ across time horizons. Consequently, investors are advised to consider firm size, as reflected in market value, when constructing their portfolios. Specifically, they should align their investment strategies with their time horizons—favoring small industries for long-term investments and large industries for short-term opportunities.
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Tourism destinations are neither homogeneous nor universally competitive products, and it is inappropriate to evaluate tourists’ destination choices based on simplified assumptions that disregard perceptual factors. Despite this, most studies on tourism demand rely heavily on quantitative variables, particularly macro-level data, due to the challenges associated with measuring non-quantitative dimensions.
In the context of Iran as a cultural-historical tourism destination, travel costs appear to have limited influence on tourists’ decision-making. If such factors were decisive, the devaluation of the Iranian rial would have significantly increased the influx of foreign tourists. Instead, it seems that qualitative factors, especially those linked to tourists’ perceptions and experiences, play a more substantial role in shaping tourism demand.
The novelty of this study lies in its emphasis on perceptual variables in estimating the tourism demand function for the city of Isfahan, a renowned cultural-historical destination.
Methodology
The tourism demand model was estimated using the logit method. The study’s target population consists of cultural-historical tourists, and the sample includes 335 respondents, selected via convenience sampling from locations in Isfahan, cyberspace, and Istanbul.
Results and Discussion
Among conventional quantitative variables commonly used in tourism demand models, only the distance variable proved to be statistically significant. Greater distance between the tourist’s origin and the destination imposes higher time and monetary costs, thereby reducing demand. However, in the case of a unique cultural-historical destination, the exclusivity of the tourism offering may prompt motivated tourists to overcome distance-related obstacles to reach the desired destination.
The estimation results show that the coefficient for the distance variable is –0.049, indicating a negative relationship between distance and the probability of choosing Isfahan as a travel destination. This implies that for each unit increase in distance, the likelihood of travel to Isfahan decreases by approximately 5%. Nevertheless, the relatively small magnitude of the coefficient suggests that, despite the inverse relationship, distance may not constitute a decisive factor in deterring travel to culturally significant destinations.
Furthermore, the coefficient for the safety and security variable was estimated at 0.207 and found to be statistically significant. This finding reveals that a one-unit increase in the perceived level of safety and security at the destination raises the probability of travel to that destination by approximately 21%. This highlights the critical role of perceptual variables—particularly safety and security—in shaping tourism demand.
The results also indicate that the quality of services at the destination, encompassing accommodation and catering services, significantly influences tourism demand. The strong significance of this variable, following the safety and security factor, underscores its substantial role in shaping tourists’ travel decisions.
In contrast, the price variable, although theoretically expected to exhibit a negative relationship with demand—consistent with consumer demand theory—was not found to be statistically significant. This result underscores the dominant role of qualitative variables in influencing tourism demand, suggesting that these factors may outweigh the influence of traditional quantitative indicators such as price.
Conclusion
The findings of this study reveal that the most influential factor affecting tourism demand in Isfahan is the perceived safety and security at the destination. As a key perceptual variable, its impact surpasses that of more traditional economic indicators, emphasizing the importance of fostering a strong sense of security among potential tourists. Accordingly, strategic efforts aimed at enhancing Iran’s international image and strengthening Isfahan’s reputation as a safe destination in key target markets are essential.
The lack of statistical significance for two conventional quantitative variables—price and income—can be interpreted in light of the dominant influence of perceived security. Despite Iran’s position as one of the most affordable tourist destinations globally, concerns regarding safety appear to override cost advantages. Drawing on Lancaster’s theory, which conceptualizes travel as a multidimensional product, this study highlights the pivotal role of qualitative variables such as safety, service quality, and the local community’s attitudes toward tourists. These factors evidently hold greater significance in the decision-making process than price or income, suggesting a paradigm shift in how tourism demand should be modeled and understood.
Tourism destinations are neither homogeneous nor universally competitive products, and it is inappropriate to evaluate tourists’ destination choices based on simplified assumptions that disregard perceptual factors. Despite this, most studies on tourism demand rely heavily on quantitative variables, particularly macro-level data, due to the challenges associated with measuring non-quantitative dimensions.
In the context of Iran as a cultural-historical tourism destination, travel costs appear to have limited influence on tourists’ decision-making. If such factors were decisive, the devaluation of the Iranian rial would have significantly increased the influx of foreign tourists. Instead, it seems that qualitative factors, especially those linked to tourists’ perceptions and experiences, play a more substantial role in shaping tourism demand.
The novelty of this study lies in its emphasis on perceptual variables in estimating the tourism demand function for the city of Isfahan, a renowned cultural-historical destination.
Methodology
The tourism demand model was estimated using the logit method. The study’s target population consists of cultural-historical tourists, and the sample includes 335 respondents, selected via convenience sampling from locations in Isfahan, cyberspace, and Istanbul.
Results and Discussion
Among conventional quantitative variables commonly used in tourism demand models, only the distance variable proved to be statistically significant. Greater distance between the tourist’s origin and the destination imposes higher time and monetary costs, thereby reducing demand. However, in the case of a unique cultural-historical destination, the exclusivity of the tourism offering may prompt motivated tourists to overcome distance-related obstacles to reach the desired destination.
The estimation results show that the coefficient for the distance variable is –0.049, indicating a negative relationship between distance and the probability of choosing Isfahan as a travel destination. This implies that for each unit increase in distance, the likelihood of travel to Isfahan decreases by approximately 5%. Nevertheless, the relatively small magnitude of the coefficient suggests that, despite the inverse relationship, distance may not constitute a decisive factor in deterring travel to culturally significant destinations.
Furthermore, the coefficient for the safety and security variable was estimated at 0.207 and found to be statistically significant. This finding reveals that a one-unit increase in the perceived level of safety and security at the destination raises the probability of travel to that destination by approximately 21%. This highlights the critical role of perceptual variables—particularly safety and security—in shaping tourism demand.
The results also indicate that the quality of services at the destination, encompassing accommodation and catering services, significantly influences tourism demand. The strong significance of this variable, following the safety and security factor, underscores its substantial role in shaping tourists’ travel decisions.
In contrast, the price variable, although theoretically expected to exhibit a negative relationship with demand—consistent with consumer demand theory—was not found to be statistically significant. This result underscores the dominant role of qualitative variables in influencing tourism demand, suggesting that these factors may outweigh the influence of traditional quantitative indicators such as price.
Conclusion
The findings of this study reveal that the most influential factor affecting tourism demand in Isfahan is the perceived safety and security at the destination. As a key perceptual variable, its impact surpasses that of more traditional economic indicators, emphasizing the importance of fostering a strong sense of security among potential tourists. Accordingly, strategic efforts aimed at enhancing Iran’s international image and strengthening Isfahan’s reputation as a safe destination in key target markets are essential.
The lack of statistical significance for two conventional quantitative variables—price and income—can be interpreted in light of the dominant influence of perceived security. Despite Iran’s position as one of the most affordable tourist destinations globally, concerns regarding safety appear to override cost advantages. Drawing on Lancaster’s theory, which conceptualizes travel as a multidimensional product, this study highlights the pivotal role of qualitative variables such as safety, service quality, and the local community’s attitudes toward tourists. These factors evidently hold greater significance in the decision-making process than price or income, suggesting a paradigm shift in how tourism demand should be modeled and understood.
Volume 0, Issue 0 (3-2023)
Abstract
Environmental justice means the pursuit of justice and equal protection, based on the law, in all environmental conditions without discrimination based on race, ethnicity, or the economic and social status of individuals. In the urban planning system, it is very important to identify the factors that play a role in realizing environmental justice; Therefore, the present study was conducted with the aim of analyzing the effect of good urban governance indicators on the state of environmental justice in Mashhad. The research method in this research is descriptive, analytical and based on documentary and library studies. In the field section, a questionnaire with 37 items in eight indicators was designed and after verifying validity and reliability (Cronbach's alpha = 0.95), it was distributed among a statistical sample of 384 citizens of the thirteen districts of Mashhad. Data analysis was done using statistical tests in SPSS software and spatial analysis in GIS.The findings show that the indicators of good urban governance, including participation, legality, accountability, and transparency, are below the standard level (average of 3) in most areas of Mashhad, and this has had a negative impact on environmental justice. Only seven and eleven regions performed well in some indicators. The future path suggests that urban management promotes environmental justice by focusing on increasing public participation and transparency.
Volume 0, Issue 0 (3-2023)
Abstract
The purpose of the research is to analyze and apply the physical resilience of housing in dilapidated urban textures in the neighborhoods of region 7 with 13 neighborhoods with an area of 1970383 square meters, as well as its impact on the sustainability of urban resources. The present research is an applied-developmental type and a descriptive-analytical method and evaluates the physical resilience of housing in the studied area with data from documentary studies available in the Housing and Urban Development Organization, the Center for Statistics and Municipality. Data have been extracted and categorized in terms of building skeleton, building age, and permeability, number of floors, material type, building quality, and particle size distribution in residential fabric. In order to evaluate and discover the pattern of regression tools, spatial autocorrelation method for weighting layers, spatial distribution analysis using Anselin local Morans autocorrelation method in GIS and Geoda software has been used at the levels of neighborhoods in region 7. The results indicate that despite the larger area of the area being in the medium to non-resilient resilience range in terms of separation, the resilience spectrum in neighborhoods is facing different changes and impacts, and its geographical distribution is more evident in the eastern part of the region.
Volume 1, Issue 0 (1-2022)
Abstract
Aims A family history of obesity, physical inactivity, and an unhealthy lifestyle was associated with an increased risk of diabetes in young individuals. Most published studies had focused on single risk factors such as BMI, Physical activity, and dietary lifestyle, while the combined effect and existence of those factors were largely neglected.
Methods This was a cross-sectional study conducted on 666 male students. Based on their family history of diabetes, their anthropometric measurements were also taken, and their BMI was calculated and categorised based on WHO standards.
Findings The results showed a significant correlation between obesity, family history, and diabetes, with a significant correlation between diabetes and obesity and also had a high positive correlation. While remaining correlations were also significant.
Conclusion The study concluded that a high proportion of students are at risk of diabetes and recommended an integrated intervention program to encourage healthy eating habits and physical activity and improve awareness.
Volume 1, Issue 0 (1-2022)
Abstract
Aims: The postpartum period presents significant physical, emotional, and social challenges for mothers. One common issue is postpartum blues, which can negatively impact maternal well-being. This study aims to analyze the effect of yoga exercises on preventing postpartum blues in postpartum mothers using family health media as an intervention.
Instrument & Methods: A quantitative analytical study with a quasi-experimental approach was conducted. The study involved postpartum mothers at the Alisah Clinic, utilizing a total population sampling technique. Data were collected through structured questionnaires and interviews, then analyzed statistically using SEM-PLS to evaluate the relationship between yoga practice frequency, duration, family support, and postpartum blues prevention.
Findings: The results show that yoga exercise significantly reduces postpartum blues symptoms. Higher frequency and longer duration of yoga sessions contribute to better mental and physical well-being. Additionally, the quality of family health media plays a crucial role in enhancing knowledge and promoting behavioral changes supportive of yoga practice. Family support was found to positively influence the consistency of yoga practice.
Conclusion: Yoga exercises, when practiced regularly with adequate duration and supported by quality health media and family involvement, are effective in preventing postpartum blues. These findings highlight the importance of accessible and structured yoga programs as part of postpartum mental health care.
Volume 1, Issue 1 (9-2003)
Abstract
Since most of the stories narrated in Shahname relates to the period after the rise and development of Zoroastrianism, the natural effects of the new religion on these stories, has made some readers imagine that Ferdowsi has had a kind of inclination to this ancient religion of Iran.
This article, by criticizing the mentioned idea, through studying the very text of Shahname, clearly shows that Ferdowsi has not been, but a true moslem.