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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
One of the most important issues in Iran's economy is related to managing the exchange rate, inflation and budget deficit. During tightening of the sanctions, the oil revenues are limited which potentially leads to an increase in the budget deficit as well as a decrease in the currency supply which accelerates the exchange rate. On the other hand, with the increase in the budget deficit, the probability of borrowing from the banking system and also the issuance of bonds increases, which in turn rise the monetary base and liquidity. In addition, inflationary expectations also increase, which can be effective in improving assets prices. With an increase in inflation, based on the inflation-currency spiral, there is a possibility of a grow in exchange rate in order to maintain the competitiveness of domestic production. This can accelerate the price of imported commodities and cause domestic inflation again. With the increase in inflation and households spending, nominal wages will have a higher growth compared to normal conditions in order to maintain minimum purchasing power, which can again face the government with limited resources and more borrowing to meet current expenses. From the monetarists’ point of view and the classical economics, in general, the main stimulator in increasing inflation is the growth of money and liquidity. However, from the post-Keynesian economists’ point of view, inflation increases the demand of money and subsequently liquidity. On the other hand, with an increase in the exchange rate, the government's expenses usually increase more than its income, which can lead to an increase in the government's budget deficit. Also, considering the existence of a monopoly in currency supply by the central bank, the hypothesis of using currency exchange revenues (the difference between free and budget-approved currency) will be applicable and this issue can raise the impact of the budget deficit on the exchange rate. Therefore, there has always been a serious challenge among economists as well as macroeconomic decision-makers about the connectedness between macroeconomic variables. What is the main driver of the network between macro variables? Is there a different way of communication in different thresholds of their growth rate? These cases show that it is very important to examine the time-varying interrelationships between these macroeconomic variables.
Accordingly, there is a complex connection between exchange rate, inflation, budget deficit and liquidity, which can be varied in different years. Therefore, in this research, using the TVP-TVAR technique, the time-varying connectedness across exchange rate, inflation, budget deficit and liquidity is examined during March, 2006 to August, 2023.
Methodology
In the current research, the relationship between exchange rate fluctuations, inflation, government budget deficit and liquidity based on monthly data using the TVP-TVAR technique is investigated. It should be noted that all the required information is extracted from the economic indicators of the central bank, and the government's budget deficit data from 2017 onward are extracted from Iran's Program and Budget Organization.
Findings
The results show that exchange rate and liquidity are, respectively, the largest net transmitter of volatilities in the network. Moreover, inflation rate and government budget deficit, respectively, are the largest net receivers of shocks from network. On average, the TCI is 23%, and more than 70% of this interrelationship between variables is explained by other factors such as political ones. Moreover, if the variables underestimated grow up to 36% annually (3% monthly), the connection between them will be cut off. In the conditions of decreasing the growth rate of variables up to -3% per month, the exchange rate has played a dominant role and its volatilities are transferred more strongly to inflation rate and less strongly to the budget deficit and liquidity.
If the growth rate of the variables is up to 24% annually (threshold of +2% monthly growth rate), the exchange rate volatilities are transferred to inflation and no interconnectedness between other variables is observed.
Discussion and Conclusion
Our results show that, on average, the total connectedness index from 2012 to 2016 has been upward, which is caused by the tightening of sanctions and the increase in inflationary expectations, psychological factors and emotions. Moreover, the connectedness between them is increased in 2018 and 2019, which is related to the intensification of sanctions and the reduction of currency supply and the increase in inflation and budget deficit and subsequently the increase in the issuance of debt securities in the capital market in order to manage the budget deficit and as a result increase liquidity. The results show that exchange rate is a main net transmitter of volatilities in most years and the inflation rate is a main net receiver of volatilities in many years. From 2016 onwards, the budget deficit is the net receiver of shocks from network in most periods, except for one period in 2019. It is interesting to note that in 2019, with the increase in the budget deficit and the issuance of debt securities, the budget deficit is transmitter, liquidity is receiver and inflation is more receiver variable than liquidity in the network. Totally, the results show that exchange rate is the major net transmitter of shocks to other macro variables.
Moreover, based on the results of the sensitivity analysis and thresholds effect, if the growth rate of variables is up to 24% annually (threshold of +2% monthly growth rate), the exchange rate fluctuations will be transferred to inflation and no connection between other components is observed. This shows that the macroeconomic management of the economy is very sensitive to the growth rate of the thresholds of the macroeconomic components, and before the political economy and also the factors of expectations and emotions dominated the economy, the macroeconomic management, especially the exchange rate, is required. Otherwise, it is impossible to manage the investigated variables with monetary and fiscal policies. Therefore, the managed floating exchange rate should be taken into consideration and if the goal is to manage the network using macroeconomic theories, the variables should not be allowed to increase by more than 24% annual growth. Other factors such as the political economy, and especially inflationary expectations will get the dominant role in the economy
One of the most important issues in Iran's economy is related to managing the exchange rate, inflation and budget deficit. During tightening of the sanctions, the oil revenues are limited which potentially leads to an increase in the budget deficit as well as a decrease in the currency supply which accelerates the exchange rate. On the other hand, with the increase in the budget deficit, the probability of borrowing from the banking system and also the issuance of bonds increases, which in turn rise the monetary base and liquidity. In addition, inflationary expectations also increase, which can be effective in improving assets prices. With an increase in inflation, based on the inflation-currency spiral, there is a possibility of a grow in exchange rate in order to maintain the competitiveness of domestic production. This can accelerate the price of imported commodities and cause domestic inflation again. With the increase in inflation and households spending, nominal wages will have a higher growth compared to normal conditions in order to maintain minimum purchasing power, which can again face the government with limited resources and more borrowing to meet current expenses. From the monetarists’ point of view and the classical economics, in general, the main stimulator in increasing inflation is the growth of money and liquidity. However, from the post-Keynesian economists’ point of view, inflation increases the demand of money and subsequently liquidity. On the other hand, with an increase in the exchange rate, the government's expenses usually increase more than its income, which can lead to an increase in the government's budget deficit. Also, considering the existence of a monopoly in currency supply by the central bank, the hypothesis of using currency exchange revenues (the difference between free and budget-approved currency) will be applicable and this issue can raise the impact of the budget deficit on the exchange rate. Therefore, there has always been a serious challenge among economists as well as macroeconomic decision-makers about the connectedness between macroeconomic variables. What is the main driver of the network between macro variables? Is there a different way of communication in different thresholds of their growth rate? These cases show that it is very important to examine the time-varying interrelationships between these macroeconomic variables.
Accordingly, there is a complex connection between exchange rate, inflation, budget deficit and liquidity, which can be varied in different years. Therefore, in this research, using the TVP-TVAR technique, the time-varying connectedness across exchange rate, inflation, budget deficit and liquidity is examined during March, 2006 to August, 2023.
Methodology
In the current research, the relationship between exchange rate fluctuations, inflation, government budget deficit and liquidity based on monthly data using the TVP-TVAR technique is investigated. It should be noted that all the required information is extracted from the economic indicators of the central bank, and the government's budget deficit data from 2017 onward are extracted from Iran's Program and Budget Organization.
Findings
The results show that exchange rate and liquidity are, respectively, the largest net transmitter of volatilities in the network. Moreover, inflation rate and government budget deficit, respectively, are the largest net receivers of shocks from network. On average, the TCI is 23%, and more than 70% of this interrelationship between variables is explained by other factors such as political ones. Moreover, if the variables underestimated grow up to 36% annually (3% monthly), the connection between them will be cut off. In the conditions of decreasing the growth rate of variables up to -3% per month, the exchange rate has played a dominant role and its volatilities are transferred more strongly to inflation rate and less strongly to the budget deficit and liquidity.
If the growth rate of the variables is up to 24% annually (threshold of +2% monthly growth rate), the exchange rate volatilities are transferred to inflation and no interconnectedness between other variables is observed.
Discussion and Conclusion
Our results show that, on average, the total connectedness index from 2012 to 2016 has been upward, which is caused by the tightening of sanctions and the increase in inflationary expectations, psychological factors and emotions. Moreover, the connectedness between them is increased in 2018 and 2019, which is related to the intensification of sanctions and the reduction of currency supply and the increase in inflation and budget deficit and subsequently the increase in the issuance of debt securities in the capital market in order to manage the budget deficit and as a result increase liquidity. The results show that exchange rate is a main net transmitter of volatilities in most years and the inflation rate is a main net receiver of volatilities in many years. From 2016 onwards, the budget deficit is the net receiver of shocks from network in most periods, except for one period in 2019. It is interesting to note that in 2019, with the increase in the budget deficit and the issuance of debt securities, the budget deficit is transmitter, liquidity is receiver and inflation is more receiver variable than liquidity in the network. Totally, the results show that exchange rate is the major net transmitter of shocks to other macro variables.
Moreover, based on the results of the sensitivity analysis and thresholds effect, if the growth rate of variables is up to 24% annually (threshold of +2% monthly growth rate), the exchange rate fluctuations will be transferred to inflation and no connection between other components is observed. This shows that the macroeconomic management of the economy is very sensitive to the growth rate of the thresholds of the macroeconomic components, and before the political economy and also the factors of expectations and emotions dominated the economy, the macroeconomic management, especially the exchange rate, is required. Otherwise, it is impossible to manage the investigated variables with monetary and fiscal policies. Therefore, the managed floating exchange rate should be taken into consideration and if the goal is to manage the network using macroeconomic theories, the variables should not be allowed to increase by more than 24% annual growth. Other factors such as the political economy, and especially inflationary expectations will get the dominant role in the economy
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Today, the environment is considered as one of the most important pillars of sustainable development, and the development of other economic and social sectors depends on its sustainability and proper functioning. Environmental pollution has become one of the main challenges of countries. Environmental health is currently one of the most critical concerns of people and officials round the world. Almost all managers and decision makers believe that this national wealth should be protected not only for the current generation but also for future generations, since the pollutants caused by industries are highly costly and detrimental to health.
Active industries are one of the main sources of environmental pollution. One of the necessary conditions for economic progress and the introduction of extensive structural changes in economic and technological fields is industrialization and industrial development. In the production process, using production inputs whose main source is the environment, in addition to desirable outputs such as consumer goods, undesirable outputs such as environmental pollutants are also produced. If the number of outputs is not controlled and disproportionate, the losses from undesirable outputs will be greater than the benefits of desirable products in such a way that damages to the environment would be irreparable and sustainable development less likely to be achieved.
One of the most important concerns related to industrialization is the effects and environmental consequences of industrial activities. Therefore, achieving the necessary solutions to control such consequences is vitally important. Minerals are essential for human survival, but their extraction and processing are not environmentally friendly practices which contribute to problems such as soil erosion, air and water pollution. On the other hand, mineral sector is one of the largest energy consumers which has active contribution to air pollution and global warming. The main purpose of this study is to investigate the economic effects of Gol Gohar mine in Sirjan. For this purpose, it is intended to determine the type and amount of pollutants released from this complex, and also to determine the amount of the green tax of the complex as a solution to reduce pollution and examine the social welfare resulting from reducing pollution.
Methodology
In this study, the economic effects of environmental pollutants of Gol Gohar Iron Mine in Sirjan (Southeastern Iran), is investigated using the input distance function model from 2001 to 2022. Through calculating the shadow price of pollutants, a criterion for determining the green tax is determined, and then the amount of social cost resulting from the emission of pollutants is calculated.
The shadow price of the undesirable output is the cost that the producer must bear if they plan to reduce the production of the undesirable output. In fact, it can be interpreted as the marginal cost of reducing pollution for each producer. Therefore, the shadow price of the desirable output is considered positive and equal to the market price of that output, but the shadow price of the undesirable output must be estimated to be less than zero.
Findings
The products of Gol Gohar Iron Ore Complex in Sirjan, include granulated iron ore, iron ore concentrate and pelletized in the production process. The most greenhouse gases and air pollutants are related to carbon dioxide (CO2), sulfur oxides (Sox), nitrogen oxides (Nox) and particulate matter (SPM). According to the obtained results, the average shadow price for air pollutants in Gol Gohar complex for CO2, Sox Nox, and SPM was calculated as 11.15, 3,074.5, 5,529.62, and 1,875.62 rials per kilogram respectively. Moreover, the average total social costs resulting from the production of Gol Gohar Sirjan Complex was calculated as 92,710 billion Rials according to the amount of pollution produced over the period.
Discussion and Conclusion
The estimation of environmental costs is actually an introduction to providing solutions for internalizing and reducing environmental costs, using the input distance function model and the shadow price of environmental pollutants in the industrial and mineral complex of Gol Gohar, Sirjan. The title of the largest producer of iron ore in the country was calculated, and the social cost resulting from the emission of pollutants was also evaluated. Finally, in this study, solutions and mechanisms for reducing environmental costs have been proposed.
Considering that the ability to absorb pollutants by the environment is limited, the shadow price of pollutants, which represents their real social cost, should be taken into consideration. The damages should also be determined based on the shadow price of the pollutants. In other words, the amounts of pollutant emission should be calculated and while taking into account the allowed limit of pollutant emission and shadow prices, based on a legal plan, the environmental costs should be reimbursed. Taking such measures would surely require more studies and capable executive management system
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
This paper examines the distribution of income in Iran from 2006 to 2016 and evaluates the validity of one of the latest economic theories concerning income distribution, namely, the Econophysics Two-Class Theory of Income Distribution (EPTC).
According to this model, income distribution generally comprises two classes. The lower class of this distribution, typically representing 97 to 99% of th society, follows the exponential (thermal) Boltzmann-Gibbs distribution, primarily driven by labor income. This distribution remains stable over time and undergoes minimal fundamental changes. Conversely, the income distribution of the upper class, constituting approximately 1 to 3% of society, follows the Pareto distribution, recognized as a superthermal distribution in econophysics. Notably, this distribution exhibits high variability over time, closely mirroring fluctuations in the stock market.
For this study, a review of the theoretical literature on the statistical distribution of income is conducted, tracing its evolution from Pareto's initial attempts to the formulation of the two-class distribution of income. In the methodology section, emphasis is placed on delineating the characteristics of two Probability Density Functions (PDFs) and Complementary Cumulative Distribution Functions (CCDFs) associated with exponential and Pareto distributions. The methodology elaborates on the approach to detecting income distribution patterns within the framework of the aforementioned theory. Subsequently, in the data and findings section, an examination of the income data spanning the specified time period in Iran is undertaken. The section meticulously explores the compatibility of these data with the EPCT, offering detailed discussions on the observed patterns and their alignment with the theoretical framework. Finally, the implications of the EPCT are elucidated, and the paper's conclusions are presented in the concluding remarks section.
Methodology
In complex systems concluding big data or complex models, alternative approaches beyond conventional statistical tests may be employed to estimate distributions. Visual inspection and descriptive analysis, facilitated by histograms and distribution charts, serve as effective tools for approximating distributions without relying on statistical tests. The selection of distributions is informed by theoretical considerations that align with the underlying characteristics of the system. These alternative methods offer practicality and informativeness, particularly in scenarios where traditional statistical assumptions may not hold or when dealing with extensive and unconventional data. The present article adopts this methodological approach to analyze income distribution in Iran.
The initial step involves drawing the histogram and probability density function (PDF). The shape of the histogram guides the identification of distribution. Given the potential complexity arising from large datasets, and the ambiguity that may arise from visual inspection of merely the PDF, a Complementary Cumulative Distribution Function (CCDF) plot serves as a valuable aid. Subsequently, following the first step and the selection of candidate theoretical distributions, the CCDFs are plotted to ascertain the optimal fit with the experimental data distribution. Consequently, the combined use of PDF and CCDF serves as indispensable tools for delineating annual income distribution patterns.
The resemblance between the graphs of the PDF for both exponential and Pareto distributions on a linear-linear scale poses challenges in distinguishing between these distributions. Similarly, the CCDF curve lacks clarity on a linear-linear scale due to this similarity. However, employing a logarithmic-linear scale to plot the survival function related to the data of the lower part of society proves beneficial, as it reveals a smooth line representative of the exponential Boltzmann-Gibbs law. Similarly, plotting the survival function for the upper part of the society on a logarithmic-logarithmic scale serves to elucidate the Pareto power law. Consequently, plotting the survival function for the entire dataset on a logarithmic-logarithmic scale, as per the hypothesis of the EPTC, should unveil two distinct segments: exponential and Pareto.
Findings
The data utilized in this study were derived from the raw tables pertaining to the household expenditure-income (budget) plan, annually published by the Statistical Center of Iran. Specifically focusing on data sourced from the urban population, which constituted approximately three-quarters of the total population during the study period. Data preparation commenced with the meticulous removal of zero and negative values, followed by deflation adjustments based on the consumer price index. Subsequently, data normalization was conducted utilizing the slope of the line of the CCDF for the lower part of the dataset, plotted on a logarithmic-linear scale for each year. This normalization process was initiated based on the initial estimate of the border income, set at the 99.7th percentile. Finally, an appropriate binning strategy was selected, with a uniform value of 0.4 (∆r≈0.4T) applied to all data subsequent to the initial 0.2 portion.
Plotting the PDF of the income pertaining to the lower class of the society across three scales—linear-linear, logarithmic-linear, and logarithmic-logarithmic—alongside the fitting line of the exponential distribution function for the year 2016 revealed a notable alignment, indicative of a robust fit with the theoretical exponential distribution.
Alternatively, the survival function chart was employed to analyze the income distribution among the upper class of society. Presenting this data graphically across three scales—linear-linear, logarithmic-linear, and logarithmic-logarithmic—for the entirety of 2016 underscored two key findings. Firstly, the tail-end distribution of income follows the Pareto distribution. Secondly, and of paramount significance, these graphical representations unequivocally affirmed the appropriateness of dividing the dataset into two distinct segments.
Plotting the PDF for the 11-year period revealed that the data pertaining to the lower part of the society, representing 99.7% of the total population, converged onto a singular curve following normalization across the entire duration under study. Subsequently, depicting the survival functions for the aforementioned 11-year time frame in a unified graph, utilizing both logarithmic-linear and logarithmic-logarithmic scales, served as a more definitive validation of the two-class theory of income distribution.
Discussion and Conclusion
The analysis of income data in Iran from 2006 to 2016 reveals a distinct two-class structure in the country's income distribution.
Firstly, the lower class, encompassing approximately 97 to 99.7% of the population, follows the exponential Boltzmann-Gibbs distribution, primarily driven by labor income. This statistical distribution reflects a cumulative process characterized by a constant rate of decrease, as indicated by the exponential distribution's parameter. The consistency observed in the exponential fit graphs of the survival function and data histogram across different years suggests the stability of income distribution within the lower class over time. This stability parallels thermal equilibrium in physics, suggesting that the majority of the population is in a stable equilibrium. Notably, the high-resolution histogram of the PDF reveals a sharp and narrow peak at low incomes, attributed to governmental policies such as the imposition of minimum wage regulations.
Conversely, the upper class, constituting approximately 0.3 to 3% of the population, follows a Pareto distribution, predominantly influenced by capital income. However, unlike the lower class, the distribution of income within this part does not align along a single line in the power law segment. This part undergoes discernible fluctuations from year to year, indicating instability within this economic sector. These fluctuations are attributed to the variability of capital income
This paper examines the distribution of income in Iran from 2006 to 2016 and evaluates the validity of one of the latest economic theories concerning income distribution, namely, the Econophysics Two-Class Theory of Income Distribution (EPTC).
According to this model, income distribution generally comprises two classes. The lower class of this distribution, typically representing 97 to 99% of th society, follows the exponential (thermal) Boltzmann-Gibbs distribution, primarily driven by labor income. This distribution remains stable over time and undergoes minimal fundamental changes. Conversely, the income distribution of the upper class, constituting approximately 1 to 3% of society, follows the Pareto distribution, recognized as a superthermal distribution in econophysics. Notably, this distribution exhibits high variability over time, closely mirroring fluctuations in the stock market.
For this study, a review of the theoretical literature on the statistical distribution of income is conducted, tracing its evolution from Pareto's initial attempts to the formulation of the two-class distribution of income. In the methodology section, emphasis is placed on delineating the characteristics of two Probability Density Functions (PDFs) and Complementary Cumulative Distribution Functions (CCDFs) associated with exponential and Pareto distributions. The methodology elaborates on the approach to detecting income distribution patterns within the framework of the aforementioned theory. Subsequently, in the data and findings section, an examination of the income data spanning the specified time period in Iran is undertaken. The section meticulously explores the compatibility of these data with the EPCT, offering detailed discussions on the observed patterns and their alignment with the theoretical framework. Finally, the implications of the EPCT are elucidated, and the paper's conclusions are presented in the concluding remarks section.
Methodology
In complex systems concluding big data or complex models, alternative approaches beyond conventional statistical tests may be employed to estimate distributions. Visual inspection and descriptive analysis, facilitated by histograms and distribution charts, serve as effective tools for approximating distributions without relying on statistical tests. The selection of distributions is informed by theoretical considerations that align with the underlying characteristics of the system. These alternative methods offer practicality and informativeness, particularly in scenarios where traditional statistical assumptions may not hold or when dealing with extensive and unconventional data. The present article adopts this methodological approach to analyze income distribution in Iran.
The initial step involves drawing the histogram and probability density function (PDF). The shape of the histogram guides the identification of distribution. Given the potential complexity arising from large datasets, and the ambiguity that may arise from visual inspection of merely the PDF, a Complementary Cumulative Distribution Function (CCDF) plot serves as a valuable aid. Subsequently, following the first step and the selection of candidate theoretical distributions, the CCDFs are plotted to ascertain the optimal fit with the experimental data distribution. Consequently, the combined use of PDF and CCDF serves as indispensable tools for delineating annual income distribution patterns.
The resemblance between the graphs of the PDF for both exponential and Pareto distributions on a linear-linear scale poses challenges in distinguishing between these distributions. Similarly, the CCDF curve lacks clarity on a linear-linear scale due to this similarity. However, employing a logarithmic-linear scale to plot the survival function related to the data of the lower part of society proves beneficial, as it reveals a smooth line representative of the exponential Boltzmann-Gibbs law. Similarly, plotting the survival function for the upper part of the society on a logarithmic-logarithmic scale serves to elucidate the Pareto power law. Consequently, plotting the survival function for the entire dataset on a logarithmic-logarithmic scale, as per the hypothesis of the EPTC, should unveil two distinct segments: exponential and Pareto.
Findings
The data utilized in this study were derived from the raw tables pertaining to the household expenditure-income (budget) plan, annually published by the Statistical Center of Iran. Specifically focusing on data sourced from the urban population, which constituted approximately three-quarters of the total population during the study period. Data preparation commenced with the meticulous removal of zero and negative values, followed by deflation adjustments based on the consumer price index. Subsequently, data normalization was conducted utilizing the slope of the line of the CCDF for the lower part of the dataset, plotted on a logarithmic-linear scale for each year. This normalization process was initiated based on the initial estimate of the border income, set at the 99.7th percentile. Finally, an appropriate binning strategy was selected, with a uniform value of 0.4 (∆r≈0.4T) applied to all data subsequent to the initial 0.2 portion.
Plotting the PDF of the income pertaining to the lower class of the society across three scales—linear-linear, logarithmic-linear, and logarithmic-logarithmic—alongside the fitting line of the exponential distribution function for the year 2016 revealed a notable alignment, indicative of a robust fit with the theoretical exponential distribution.
Alternatively, the survival function chart was employed to analyze the income distribution among the upper class of society. Presenting this data graphically across three scales—linear-linear, logarithmic-linear, and logarithmic-logarithmic—for the entirety of 2016 underscored two key findings. Firstly, the tail-end distribution of income follows the Pareto distribution. Secondly, and of paramount significance, these graphical representations unequivocally affirmed the appropriateness of dividing the dataset into two distinct segments.
Plotting the PDF for the 11-year period revealed that the data pertaining to the lower part of the society, representing 99.7% of the total population, converged onto a singular curve following normalization across the entire duration under study. Subsequently, depicting the survival functions for the aforementioned 11-year time frame in a unified graph, utilizing both logarithmic-linear and logarithmic-logarithmic scales, served as a more definitive validation of the two-class theory of income distribution.
Discussion and Conclusion
The analysis of income data in Iran from 2006 to 2016 reveals a distinct two-class structure in the country's income distribution.
Firstly, the lower class, encompassing approximately 97 to 99.7% of the population, follows the exponential Boltzmann-Gibbs distribution, primarily driven by labor income. This statistical distribution reflects a cumulative process characterized by a constant rate of decrease, as indicated by the exponential distribution's parameter. The consistency observed in the exponential fit graphs of the survival function and data histogram across different years suggests the stability of income distribution within the lower class over time. This stability parallels thermal equilibrium in physics, suggesting that the majority of the population is in a stable equilibrium. Notably, the high-resolution histogram of the PDF reveals a sharp and narrow peak at low incomes, attributed to governmental policies such as the imposition of minimum wage regulations.
Conversely, the upper class, constituting approximately 0.3 to 3% of the population, follows a Pareto distribution, predominantly influenced by capital income. However, unlike the lower class, the distribution of income within this part does not align along a single line in the power law segment. This part undergoes discernible fluctuations from year to year, indicating instability within this economic sector. These fluctuations are attributed to the variability of capital income
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
The financial sector has seen considerable growth in many post World War II western economies. The consequences of the Great Financial Crisis of 2007-2009 displayed how large the reach of the industry is, and how actions taken by a few important role players, can harm the general public. It is due to the consequences of the Great Financial Crisis that the notion of reforming the banking sector came about. The call for reform occurred in the 1940s as well, after the Great Crash. It was here that Full Reserve Banking (FRB), the broad term for the proposed banking reform and the subject of this dissertation, originated.
The Great Crash ended a period of expansion and growth in the USA in the 1920s where credit was easily available, and the money supply grew. The subsequent Great Depression was an economic event of unprecedented dimensions (Temin, 2000). The years 1929-1933 held a stock market crash, a banking crisis, and a collapse of commodity prices. Friedman and Schwartz (1963) contended that the primary propagation mechanism of the Depression was the contraction in the US money supply, together with banking panics. There were three banking crises in that short period, and it was the failure of two large banks, the Bank of United States and Caldwell and Company, that caused most of the problem. These banks had undergone rapid credit expansion in the 1920s and collapsed under the pressure of the recession (Temin, 2000: 307). A response to the recession was to say that the root cause was bad banking practice and that stricter regulations should be imposed to prevent future crises. Regulation was introduced in The Glass-Steagall Act (1933) however, a more severe suggestion was that bank deposits should be fully backed by bank reserves, Full Reserve Banking, an approach proposed in the Chicago Plan.
The Chicago Plan was proposed by Henry Simons, Irving Fisher and others, to prevent another crisis. It proposed requiring banks to hold 100 per cent reserves. This would simultaneously curb the possibility of reckless lending, and eliminate the risk of bank runs, thereby eliminating the possibility of another banking crisis.
Over the past years, the nominal capacity of the supply of bank facilities has increased significantly, and the main increase in bank assets has come from the increase in granting facilities. On the liabilities side of the banks' balance sheets, non-governmental sector deposits (due to paying high interest rates to depositors) during the year 2013 to 2022 has increased by 33.6% on average.
Statistical evidence shows that the real sector of the economy has not benefited much from the expansion of the banking network's balance sheet and the allocation of bank resources has not led to economic growth. On the other hand, it can be seen that the liquidity created by the banking system has not been absorbed by the real sector of the economy and its effects have been manifested in nominal variables in the form of price increases or turbulences in the currency market and other assets. The average growth of real GDP (without oil) during the years 2013 to 2022 was about 1.6 percent.
In general, it can be seen that due to the endogenous nature of money, the central bank has not had a significant success in controlling the growth of monetary aggregates through controlling the growth of the monetary base and its components (statistical evidence in recent decades confirms this); So that the credibility of the central bank's monetary policies has been challenged and the economy has been exposed to continuous threats of inflation and monetary and financial instabilities.
Methodology
This study will employ several techniques for gathering data, including a library type, a documentary branch, and the use of databases, such as those of the Central Bank of the Islamic Republic of Iran and the World Bank. Based on the characteristics of the Iranian economy under fractional & full reserve banking, a random dynamic general equilibrium model was developed for the period 1991-2021. Typical econometric methods are also used to evaluate the hypotheses. This has enabled assessing the effects of the exchange rate shock under two scenarios. It should be noted that the models were estimated in the dynare program space under MATLAB software.
Findings
The exchange rate shock has a negative effect on the consumption of the private sector at real prices, probably due to an increase in import prices. This has led to a decrease in the import of goods. Since imports form a part of the consumption for the private sector, therefore, the consumption by this sector decreases by about 0.5 percent. The Exchange rate shock has had a positive effect on the net foreign exchange reserves of the central bank. The growth rate of the monetary base is also affected by the currency shocks. With the increase in the exchange rate, although the central bank first reacts to the inflationary conditions resulting from the currency shocks through the currency reaction function and reduces the base monetary growth rate, but this situation is not very durable and finally the monetary base growth rate will increase by about 0.4 percent.
If these resources enter the banking system, due to the 100 percent reserve, it has led to the crediting of the banks, and as a result, inflation and final costs have decreased. But in fractional reserve banking, banks create money by attracting deposits, which in turn creates money by them. As a result of this jump, inflation and the final cost will increase.
The exchange rate shock also increases inflation because with the increase in the nominal growth rate of the exchange rate, the marginal cost of each import unit increases and finally the country's inflation increases by 0.7 percent.
Discussion and Conclusion
The purpose of this research is to investigate the effects of exchange rate impulse on the macroeconomic variables of Iran's economy in the conditions of partial and full reserve banking. To achieve this goal, a new Keynesian stochastic dynamic general equilibrium model was designed considering fractional and full reserve banking system (FRB). The realities of the Iranian economy are considered, and then the effects of exchange rate shocks under two types of banking are investigated. After determining the input values of the model and estimating the parameters using the seasonal data of Iran's economy during the period of 1991-2022 using the Bayesian estimation method, the results obtained from the simulation of the model variables indicate the validity of the model in describing the fluctuations of the Iranian economy. The results of the model indicate that, as a result of the exchange rate shock, the growth rate of the monetary base and consequently the amount of money is affected. Under full reserve banking, due to the full reserve of deposits, this has led to a lower increase in inflation and final cost. However, in partial reserve banking, due to the less control of the banking system, despite having two tools to control the growth of the monetary base and the nominal exchange rate, it will create higher fluctuations in the inflation rate and other macroeconomic variables. In other words, the study model has been slightly different from the basic model in the face of the currency impulse, both in terms of the amplitude and the length of the fluctuation
The financial sector has seen considerable growth in many post World War II western economies. The consequences of the Great Financial Crisis of 2007-2009 displayed how large the reach of the industry is, and how actions taken by a few important role players, can harm the general public. It is due to the consequences of the Great Financial Crisis that the notion of reforming the banking sector came about. The call for reform occurred in the 1940s as well, after the Great Crash. It was here that Full Reserve Banking (FRB), the broad term for the proposed banking reform and the subject of this dissertation, originated.
The Great Crash ended a period of expansion and growth in the USA in the 1920s where credit was easily available, and the money supply grew. The subsequent Great Depression was an economic event of unprecedented dimensions (Temin, 2000). The years 1929-1933 held a stock market crash, a banking crisis, and a collapse of commodity prices. Friedman and Schwartz (1963) contended that the primary propagation mechanism of the Depression was the contraction in the US money supply, together with banking panics. There were three banking crises in that short period, and it was the failure of two large banks, the Bank of United States and Caldwell and Company, that caused most of the problem. These banks had undergone rapid credit expansion in the 1920s and collapsed under the pressure of the recession (Temin, 2000: 307). A response to the recession was to say that the root cause was bad banking practice and that stricter regulations should be imposed to prevent future crises. Regulation was introduced in The Glass-Steagall Act (1933) however, a more severe suggestion was that bank deposits should be fully backed by bank reserves, Full Reserve Banking, an approach proposed in the Chicago Plan.
The Chicago Plan was proposed by Henry Simons, Irving Fisher and others, to prevent another crisis. It proposed requiring banks to hold 100 per cent reserves. This would simultaneously curb the possibility of reckless lending, and eliminate the risk of bank runs, thereby eliminating the possibility of another banking crisis.
Over the past years, the nominal capacity of the supply of bank facilities has increased significantly, and the main increase in bank assets has come from the increase in granting facilities. On the liabilities side of the banks' balance sheets, non-governmental sector deposits (due to paying high interest rates to depositors) during the year 2013 to 2022 has increased by 33.6% on average.
Statistical evidence shows that the real sector of the economy has not benefited much from the expansion of the banking network's balance sheet and the allocation of bank resources has not led to economic growth. On the other hand, it can be seen that the liquidity created by the banking system has not been absorbed by the real sector of the economy and its effects have been manifested in nominal variables in the form of price increases or turbulences in the currency market and other assets. The average growth of real GDP (without oil) during the years 2013 to 2022 was about 1.6 percent.
In general, it can be seen that due to the endogenous nature of money, the central bank has not had a significant success in controlling the growth of monetary aggregates through controlling the growth of the monetary base and its components (statistical evidence in recent decades confirms this); So that the credibility of the central bank's monetary policies has been challenged and the economy has been exposed to continuous threats of inflation and monetary and financial instabilities.
Methodology
This study will employ several techniques for gathering data, including a library type, a documentary branch, and the use of databases, such as those of the Central Bank of the Islamic Republic of Iran and the World Bank. Based on the characteristics of the Iranian economy under fractional & full reserve banking, a random dynamic general equilibrium model was developed for the period 1991-2021. Typical econometric methods are also used to evaluate the hypotheses. This has enabled assessing the effects of the exchange rate shock under two scenarios. It should be noted that the models were estimated in the dynare program space under MATLAB software.
Findings
The exchange rate shock has a negative effect on the consumption of the private sector at real prices, probably due to an increase in import prices. This has led to a decrease in the import of goods. Since imports form a part of the consumption for the private sector, therefore, the consumption by this sector decreases by about 0.5 percent. The Exchange rate shock has had a positive effect on the net foreign exchange reserves of the central bank. The growth rate of the monetary base is also affected by the currency shocks. With the increase in the exchange rate, although the central bank first reacts to the inflationary conditions resulting from the currency shocks through the currency reaction function and reduces the base monetary growth rate, but this situation is not very durable and finally the monetary base growth rate will increase by about 0.4 percent.
If these resources enter the banking system, due to the 100 percent reserve, it has led to the crediting of the banks, and as a result, inflation and final costs have decreased. But in fractional reserve banking, banks create money by attracting deposits, which in turn creates money by them. As a result of this jump, inflation and the final cost will increase.
The exchange rate shock also increases inflation because with the increase in the nominal growth rate of the exchange rate, the marginal cost of each import unit increases and finally the country's inflation increases by 0.7 percent.
Discussion and Conclusion
The purpose of this research is to investigate the effects of exchange rate impulse on the macroeconomic variables of Iran's economy in the conditions of partial and full reserve banking. To achieve this goal, a new Keynesian stochastic dynamic general equilibrium model was designed considering fractional and full reserve banking system (FRB). The realities of the Iranian economy are considered, and then the effects of exchange rate shocks under two types of banking are investigated. After determining the input values of the model and estimating the parameters using the seasonal data of Iran's economy during the period of 1991-2022 using the Bayesian estimation method, the results obtained from the simulation of the model variables indicate the validity of the model in describing the fluctuations of the Iranian economy. The results of the model indicate that, as a result of the exchange rate shock, the growth rate of the monetary base and consequently the amount of money is affected. Under full reserve banking, due to the full reserve of deposits, this has led to a lower increase in inflation and final cost. However, in partial reserve banking, due to the less control of the banking system, despite having two tools to control the growth of the monetary base and the nominal exchange rate, it will create higher fluctuations in the inflation rate and other macroeconomic variables. In other words, the study model has been slightly different from the basic model in the face of the currency impulse, both in terms of the amplitude and the length of the fluctuation
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Measurement and examination of unobservable variables directly such as inflation expectations or potential output, is really challenging. Inflation expectations have been considered a key variable in many macroeconomic models, particularly in the realm of monetary economics. Macroeconomic models assume that economic agents make consumption, savings, and labor market decisions based on their perception of future inflation levels, and these decisions play a great role in realizing economic variables, including inflation. The role of inflation expectations differs from other inflation-generating factors. While factors such as money supply, budget deficit, exchange rate, and to some extent, economic sanctions can be considered as policy tools. Inflation expectations normally result from the interaction of other factors and may potentially predict future inflation. For example, an increase in the budget deficit, if not addressed independently by the Central Bank, can lead to an increase in money supply, inflation, and intensification of inflation expectations. Thus, inflation expectations can be considered as a variable that evolves within society and changes due to other inflation-generating factors. However, once formed, these expectations themselves become significant factors in inflation and other economic variables. Unlike many countries, in Iran, despite the importance of inflation due to decades of double-digit inflation, no action has been taken to produce and provide survey data related to this variable. However, according to existing literature, comparing the results of alternative methods incorporating inflation expectations with survey data can provide valuable insights. In practice, incorporating inflation expectations can improve the performance of inflation prediction models.
Methodology
Empirical research indicates that methods that consider inflation expectations along with its fluctuations and dynamics outperform models that do not consider these dynamics. Therefore, paying proper attention to how inflation expectations form and fluctuate, as well as avoiding simple methods, is necessary in calculating inflation expectations. In this research, an attempt was made to calculate and present data related to this variable in the framework of rational expectations for the period of 1996 to 2021 using the random forest regression method, considering the strengths and weaknesses of each method of mapping inflation expectations. Subsequently, after learning the random forest-based model, by conducting an in-sample prediction, the data were extracted and the features related to rational expectations regarding these data were examined.
Findings
The coefficient of determination value for the test data was found to be 80%, indicating that, on average, 80% of inflation variations are correctly predicted by economic factors using the model inputs or features. Based on this and by examining the features related to estimation residuals, it was determined that economic factors in predicting inflation do not exhibit systematic errors and, with a sufficiently large time interval and having an adequate information set, can have a proper understanding of inflation behavior. Moreover, the results of comparing inflation expectations based on random forest regression-based predictions show superiority of this approach compared to competing methods such as the Hodrick-Prescott filter. After that, the importance of each of the factors in the basket of information related to inflation expectations was ranked. It should be noted that the selection of features for predicting inflation expectations was not based on the direct attention of households and economic factors to these features. Rather, economic factors and households may find the effect of these features in other evidence. For example, the effect of an increase in the exchange rate on the prices of goods that are somehow related to this variable may be apparent to households, and fundamentally, the prevalent interpretation of rational expectations in the literature of this field is based on this approach. The results of this ranking indicate that among the entire information set, factors such as inflation breaks, exchange rates, and economic sanctions had the highest importance in shaping inflation expectations.
Discussion and Conclusion
It is worth mentioning that inflation breaks have been identified as the most important factor among the entire information set as a manifestation of the adaptive section of inflation expectations. However, this does not mean that expectations are entirely adaptive. Based on the research findings, it is clear that if economic factors rely solely on the adaptive section to predict inflation, zero estimation error, unpredictability of errors, and consequently the formation of rational expectations will not be achieved. Using a combination of three approaches: gradient boosting algorithm, random forest algorithm, and linear regression, a voting regression was also performed, showing a 3% improvement in determination coefficient compared to random forest (83%). Moreover, other results, such as the order and intensity of feature importance, and predicted inflation values, are similar to the random forest method with slight variations which means, estimating rational expectations is reliable
Measurement and examination of unobservable variables directly such as inflation expectations or potential output, is really challenging. Inflation expectations have been considered a key variable in many macroeconomic models, particularly in the realm of monetary economics. Macroeconomic models assume that economic agents make consumption, savings, and labor market decisions based on their perception of future inflation levels, and these decisions play a great role in realizing economic variables, including inflation. The role of inflation expectations differs from other inflation-generating factors. While factors such as money supply, budget deficit, exchange rate, and to some extent, economic sanctions can be considered as policy tools. Inflation expectations normally result from the interaction of other factors and may potentially predict future inflation. For example, an increase in the budget deficit, if not addressed independently by the Central Bank, can lead to an increase in money supply, inflation, and intensification of inflation expectations. Thus, inflation expectations can be considered as a variable that evolves within society and changes due to other inflation-generating factors. However, once formed, these expectations themselves become significant factors in inflation and other economic variables. Unlike many countries, in Iran, despite the importance of inflation due to decades of double-digit inflation, no action has been taken to produce and provide survey data related to this variable. However, according to existing literature, comparing the results of alternative methods incorporating inflation expectations with survey data can provide valuable insights. In practice, incorporating inflation expectations can improve the performance of inflation prediction models.
Methodology
Empirical research indicates that methods that consider inflation expectations along with its fluctuations and dynamics outperform models that do not consider these dynamics. Therefore, paying proper attention to how inflation expectations form and fluctuate, as well as avoiding simple methods, is necessary in calculating inflation expectations. In this research, an attempt was made to calculate and present data related to this variable in the framework of rational expectations for the period of 1996 to 2021 using the random forest regression method, considering the strengths and weaknesses of each method of mapping inflation expectations. Subsequently, after learning the random forest-based model, by conducting an in-sample prediction, the data were extracted and the features related to rational expectations regarding these data were examined.
Findings
The coefficient of determination value for the test data was found to be 80%, indicating that, on average, 80% of inflation variations are correctly predicted by economic factors using the model inputs or features. Based on this and by examining the features related to estimation residuals, it was determined that economic factors in predicting inflation do not exhibit systematic errors and, with a sufficiently large time interval and having an adequate information set, can have a proper understanding of inflation behavior. Moreover, the results of comparing inflation expectations based on random forest regression-based predictions show superiority of this approach compared to competing methods such as the Hodrick-Prescott filter. After that, the importance of each of the factors in the basket of information related to inflation expectations was ranked. It should be noted that the selection of features for predicting inflation expectations was not based on the direct attention of households and economic factors to these features. Rather, economic factors and households may find the effect of these features in other evidence. For example, the effect of an increase in the exchange rate on the prices of goods that are somehow related to this variable may be apparent to households, and fundamentally, the prevalent interpretation of rational expectations in the literature of this field is based on this approach. The results of this ranking indicate that among the entire information set, factors such as inflation breaks, exchange rates, and economic sanctions had the highest importance in shaping inflation expectations.
Discussion and Conclusion
It is worth mentioning that inflation breaks have been identified as the most important factor among the entire information set as a manifestation of the adaptive section of inflation expectations. However, this does not mean that expectations are entirely adaptive. Based on the research findings, it is clear that if economic factors rely solely on the adaptive section to predict inflation, zero estimation error, unpredictability of errors, and consequently the formation of rational expectations will not be achieved. Using a combination of three approaches: gradient boosting algorithm, random forest algorithm, and linear regression, a voting regression was also performed, showing a 3% improvement in determination coefficient compared to random forest (83%). Moreover, other results, such as the order and intensity of feature importance, and predicted inflation values, are similar to the random forest method with slight variations which means, estimating rational expectations is reliable
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
Economic globalization has many economic benefits, but it has also been accompanied by environmental challenges that have increased concern about the impact of these trends on the environment. Environmental welfare plays a key role in the organization of societies and drawing attention to environmental issues as one of the main dimensions of sustainability. This is also true for the development structures and decisions related to the environment. The purpose of the present study is to investigate the impact of economic globalization on environmental well-being in developed and developing countries during the years 2000 to 2020 using soft panel regression. The results show the existence of a non-linear relationship between the research variables. For developed and developing countries, a transfer function and two threshold limits, representing a two-regime model, were also chosen as the optimal model. The slope factor for developed and developing countries was equal to 1.28 and 159.78 respectively. The results of the model estimation indicate that in developed countries, the variable of economic globalization has a negative effect on environmental welfare in the first extreme regime and a positive and significant effect in the second extreme regime. In developing countries, the variable of economic globalization has also a negative and significant effect on environmental well-being in both regimes. On the other hand, in developed countries, for the first limit regime, economic globalization may lead to an increase in unsustainable use of resources and environmental pollution. But in the second extreme regime, it can promote the improvement of international cooperation in the field of environmental protection and the development of clean and green technologies. In developing countries, increased economic globalization may lead to increased industrial pressures and inappropriate use of natural resources, which causes damages to the environment and rampant pollution. Due to technical, financial, and regulatory constraints, these countries may not be able to take advantage of the benefits of globalization in a positive way for the environment and thus have a negative impact on environmental well-being. According to the research results, with the development of technology and industrial control, along with sustainable policies, it is possible to ensure the improvement of environmental well-being and strengthen the positive effect of economic globalization on environmental well-being.
Methodology
This study examines the impact of globalization on environmental well-being in developed and developing countries (133 countries) for the period 2000-2020 using the panel smooth transition regression (PSTR) model. Statistical tables, global databases, data from the Swiss Economic Institute KOF, and the Social Science Institute (SSI) - TH Köln website were used to collect statistics and quantitative information. The environmental welfare variable in this research as a dependent variable is the geometric mean of seven indicators of biodiversity, renewable water resources, energy consumption, energy efficiency, energy reserves, greenhouse gases and renewable energy. Economic globalization is considered as a transition variable, and to better explain the issues of GDP per capita growth (percentage per annum), general government final consumption expenditure (percentage of GDP), foreign direct investment, net inflows (percentage of GDP) and population growth (percentage per annum) were selected as influential factors. PSTR as a statistical model is usually used to analyze non-linear relationships between economic variables, especially to investigate non-linear patterns or changes in the behavior of variables over time. This flexible model can depict complex relationships between different variables and is known as a popular choice in various fields such as economics, finance and social science. The model is an extension of the smooth transition regression (STR) that allows the determination of the transition function between two different regimes. With PSTR, the transfer function is extended for panel data, which allows the analysis of nonlinear relationships between variables in multiple units, such as countries or firms, over time. PSTR is a powerful tool for analyzing the impact of various economic factors on different regions or countries and can be used to examine the impact of a specific economic policy or event on different regions. PSTR can also be used for different types of data such as cross-sectional, time series and panel data, which makes it a versatile tool for analyzing various economic phenomena.
Findings
The research shows the estimated results of the model upon which the slope parameter, which expresses the speed of adjustment from one regime to another, is equal to 1.28 and 159.78 for developed and developing countries, respectively, i.e, the transition from linear regime to non-linear regime in developed countries is done at a much lower speed than in developing countries. The estimation of the model shows the nonlinear relationship in two threshold points for developed countries c_1=79.5617 and c_2=85.0326 and c = (79.56+85.03)/2 = 82.29 also for developing countries c_1= 50.6518 and c_2 = 62.4416 and c = (50.65+62.44) /2 = 56.54 and the transfer function is in two regimes. If the economic globalization exceeds 82.29 in developed countries and 56.54 in developing countries, the behavior of the variables will be according to the second regime, and if it is less than the above threshold, they will be in the first regime.
In developed countries, the coefficients are such that the variable of economic globalization has a negative and significant effect on environmental welfare in the first limit regime and a positive and significant effect in the second limit regime. GDP per capita growth has a positive and non-significant effect on environmental well-being in the first limit regime and a significant negative effect in the second limit regime. Government size and population growth have also a positive effect in the first limit regime and a negative and significant effect in the second limit regime. Foreign direct investment in both regimes has a negative and insignificant effect on environmental well-being.
In developing countries, the coefficients are such that the variable of economic globalization, the growth of GDP per capita in both marginal regimes has a negative and significant effect, as well as the size of the government and population growth in both marginal regimes have a negative and insignificant effect on the dependent variable (welfare). Foreign direct investment has also a positive and insignificant effect in the first limit regime and a negative and significant effect in the second limit regime on environmental well-being.
Discussion and Conclusion
The results of the research show that the impact of various factors on environmental well-being in developed and developing countries is different from each other. These differences may be due to different economic, social, and cultural conditions in these countries.
In developed countries in the first limit regime, economic globalization leads to an increase in economic pressures and international competition, which can cause more use of natural resources, increase the production of pollutants, and decrease the quality of the environment. Moreover, in the second extreme regime, the Economic globalization variable has a positive and significant effect on environmental well-being. This may be due to increased access to advanced technologies, higher environmental standards, and increased international cooperation in environmental protection.
In developing countries, economic globalization variables have a negative effect on environmental well-being in both regimes. In other words, the increase of these variables in both limit regimes leads to a decrease in the quality of the environment and environmental well-being. In other words, economic globalization leads to an increase in the per capita production and consumption of energy and natural resources, which can lead to air and water pollution, a decrease in biodiversity, and a reduction in air and water quality.
In general, it can be concluded that in developed countries, increasing economic growth, government size, and population growth lead to improved environmental conditions, but in developing countries, these factors usually cause a decrease in environmental quality and environmental well-being. For the optimal management of environmental welfare in any country, it is necessary to pay attention to the economic, social and cultural conditions of that country. It is also vitally important to formulate appropriate policies and strategies to deal with environmental challenges
Economic globalization has many economic benefits, but it has also been accompanied by environmental challenges that have increased concern about the impact of these trends on the environment. Environmental welfare plays a key role in the organization of societies and drawing attention to environmental issues as one of the main dimensions of sustainability. This is also true for the development structures and decisions related to the environment. The purpose of the present study is to investigate the impact of economic globalization on environmental well-being in developed and developing countries during the years 2000 to 2020 using soft panel regression. The results show the existence of a non-linear relationship between the research variables. For developed and developing countries, a transfer function and two threshold limits, representing a two-regime model, were also chosen as the optimal model. The slope factor for developed and developing countries was equal to 1.28 and 159.78 respectively. The results of the model estimation indicate that in developed countries, the variable of economic globalization has a negative effect on environmental welfare in the first extreme regime and a positive and significant effect in the second extreme regime. In developing countries, the variable of economic globalization has also a negative and significant effect on environmental well-being in both regimes. On the other hand, in developed countries, for the first limit regime, economic globalization may lead to an increase in unsustainable use of resources and environmental pollution. But in the second extreme regime, it can promote the improvement of international cooperation in the field of environmental protection and the development of clean and green technologies. In developing countries, increased economic globalization may lead to increased industrial pressures and inappropriate use of natural resources, which causes damages to the environment and rampant pollution. Due to technical, financial, and regulatory constraints, these countries may not be able to take advantage of the benefits of globalization in a positive way for the environment and thus have a negative impact on environmental well-being. According to the research results, with the development of technology and industrial control, along with sustainable policies, it is possible to ensure the improvement of environmental well-being and strengthen the positive effect of economic globalization on environmental well-being.
Methodology
This study examines the impact of globalization on environmental well-being in developed and developing countries (133 countries) for the period 2000-2020 using the panel smooth transition regression (PSTR) model. Statistical tables, global databases, data from the Swiss Economic Institute KOF, and the Social Science Institute (SSI) - TH Köln website were used to collect statistics and quantitative information. The environmental welfare variable in this research as a dependent variable is the geometric mean of seven indicators of biodiversity, renewable water resources, energy consumption, energy efficiency, energy reserves, greenhouse gases and renewable energy. Economic globalization is considered as a transition variable, and to better explain the issues of GDP per capita growth (percentage per annum), general government final consumption expenditure (percentage of GDP), foreign direct investment, net inflows (percentage of GDP) and population growth (percentage per annum) were selected as influential factors. PSTR as a statistical model is usually used to analyze non-linear relationships between economic variables, especially to investigate non-linear patterns or changes in the behavior of variables over time. This flexible model can depict complex relationships between different variables and is known as a popular choice in various fields such as economics, finance and social science. The model is an extension of the smooth transition regression (STR) that allows the determination of the transition function between two different regimes. With PSTR, the transfer function is extended for panel data, which allows the analysis of nonlinear relationships between variables in multiple units, such as countries or firms, over time. PSTR is a powerful tool for analyzing the impact of various economic factors on different regions or countries and can be used to examine the impact of a specific economic policy or event on different regions. PSTR can also be used for different types of data such as cross-sectional, time series and panel data, which makes it a versatile tool for analyzing various economic phenomena.
Findings
The research shows the estimated results of the model upon which the slope parameter, which expresses the speed of adjustment from one regime to another, is equal to 1.28 and 159.78 for developed and developing countries, respectively, i.e, the transition from linear regime to non-linear regime in developed countries is done at a much lower speed than in developing countries. The estimation of the model shows the nonlinear relationship in two threshold points for developed countries c_1=79.5617 and c_2=85.0326 and c = (79.56+85.03)/2 = 82.29 also for developing countries c_1= 50.6518 and c_2 = 62.4416 and c = (50.65+62.44) /2 = 56.54 and the transfer function is in two regimes. If the economic globalization exceeds 82.29 in developed countries and 56.54 in developing countries, the behavior of the variables will be according to the second regime, and if it is less than the above threshold, they will be in the first regime.
In developed countries, the coefficients are such that the variable of economic globalization has a negative and significant effect on environmental welfare in the first limit regime and a positive and significant effect in the second limit regime. GDP per capita growth has a positive and non-significant effect on environmental well-being in the first limit regime and a significant negative effect in the second limit regime. Government size and population growth have also a positive effect in the first limit regime and a negative and significant effect in the second limit regime. Foreign direct investment in both regimes has a negative and insignificant effect on environmental well-being.
In developing countries, the coefficients are such that the variable of economic globalization, the growth of GDP per capita in both marginal regimes has a negative and significant effect, as well as the size of the government and population growth in both marginal regimes have a negative and insignificant effect on the dependent variable (welfare). Foreign direct investment has also a positive and insignificant effect in the first limit regime and a negative and significant effect in the second limit regime on environmental well-being.
Discussion and Conclusion
The results of the research show that the impact of various factors on environmental well-being in developed and developing countries is different from each other. These differences may be due to different economic, social, and cultural conditions in these countries.
In developed countries in the first limit regime, economic globalization leads to an increase in economic pressures and international competition, which can cause more use of natural resources, increase the production of pollutants, and decrease the quality of the environment. Moreover, in the second extreme regime, the Economic globalization variable has a positive and significant effect on environmental well-being. This may be due to increased access to advanced technologies, higher environmental standards, and increased international cooperation in environmental protection.
In developing countries, economic globalization variables have a negative effect on environmental well-being in both regimes. In other words, the increase of these variables in both limit regimes leads to a decrease in the quality of the environment and environmental well-being. In other words, economic globalization leads to an increase in the per capita production and consumption of energy and natural resources, which can lead to air and water pollution, a decrease in biodiversity, and a reduction in air and water quality.
In general, it can be concluded that in developed countries, increasing economic growth, government size, and population growth lead to improved environmental conditions, but in developing countries, these factors usually cause a decrease in environmental quality and environmental well-being. For the optimal management of environmental welfare in any country, it is necessary to pay attention to the economic, social and cultural conditions of that country. It is also vitally important to formulate appropriate policies and strategies to deal with environmental challenges
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Inequality is a multidimensional phenomenon that affects various aspects of households' lives. The economic well-being of individuals depends not only on their income but also on other factors such as access to healthcare, education, transportation, etc. Therefore, one-dimensional methods (income-focused) are insufficient for measuring inequality. The multidimensional approach to inequality considers different aspects of individual welfare, unlike the one-dimensional approach. The concentration of population and activities in some provinces of Iran, along with macroeconomic indicators (inflation and unemployment), exacerbates inequality. These inequalities affect various dimensions of people's lives and endanger their economic welfare. The primary aim of this study is to examine the effects of inflation and unemployment on multidimensional inequality in the provinces of Iran and their reciprocal effects on each other, using a multidimensional Gini coefficient estimated from the household budget microdata of the Statistical Center of Iran for the years 2000-2021.
Methodology
In this study, the multidimensional Gini coefficient by Kumar Banerjee (2010) has been estimated for 9 dimensions of welfare. Then, the effects of inflation and unemployment, along with variables such as per capita real government expenditure and per capita real financial facilities as indicators of financial development, will be analyzed using a spatial econometric model. The mathematical form of the multidimensional Gini coefficient (MGI) is as follows:
Here, the mathematical formula would be inserted) In this equation: represents the non-increasing rank of the unit under study in the individual's overall welfare vector, and represents the sample size. The range of this index fluctuates between zero (completely equal distribution) and one (completely unequal distribution). For measuring multidimensional inequality in this study, the multidimensional Gini coefficient by Kumar Banerjee (2010) has been used which is based on the microdata from the household expenditure (income) survey of the Statistical Center of Iran and involves data mining processes such as aggregating groups of beverages and tobacco, ready meals with food expenditure groups, and communications with transportation, and extracting data related to each household code in each province using R Studio 2020 software. The model is based on the spatial econometric method with spatial panel data, defined using a proximity method in which provinces sharing a border have an element of one and otherwise zero. The adjacency matrix (spatial weight) is normalized, where neighboring provinces carry the most weight, and distant provinces carry the least.
Findings
The results of estimating the multidimensional Gini coefficient for the provinces during 2000-2021 show that most provinces have experienced a high rate of inequality. Provinces such as Bushehr, Khuzestan, Kermanshah, Kurdistan, Markazi, Qazvin, Qom, Semnan, Sistan and Baluchestan, West Azerbaijan, Zanjan, and Yazd are in an unfavorable condition compared to the country, and most of these provinces are border regions. Over these 22 years, Sistan and Baluchestan with 77.66% have the highest rate of multidimensional inequality, while Isfahan with 60.85% has the lowest among the provinces. Additionally, the findings indicate that inflation, unemployment, per capita real government expenditure, and per capita real disbursed financial facilities have a significant positive effect on multidimensional inequality in the provinces of Iran. The proximity of provinces has also worsened the inequality conditions in the neighboring provinces.
Discussion and Conclusion
Four variables including unemployment, inflation, per capita real government expenditure, and per capita real disbursed financial facilities have a significant positive effect on the multidimensional Gini coefficient, worsening income distribution. The most significant impact is seen with per capita real government expenditure, which is not allocated effectively to enhance welfare and improve economic conditions, thus not improving income distribution and reducing inequality. The effects of the other variables are in the following order: per capita real disbursed financial facilities, unemployment, and inflation. It is recommended to consider all welfare dimensions in the household consumption basket, create equal conditions for access to bank facilities, allocate a specific quota of facilities to lessdeveloped provinces, allocate government expenditures to expand public services and infrastructure in deprived provinces, consider the interactive effects between provinces in policymaking, and implement effective policies to improve welfare conditions and balanced income distribution across all provinces
Inequality is a multidimensional phenomenon that affects various aspects of households' lives. The economic well-being of individuals depends not only on their income but also on other factors such as access to healthcare, education, transportation, etc. Therefore, one-dimensional methods (income-focused) are insufficient for measuring inequality. The multidimensional approach to inequality considers different aspects of individual welfare, unlike the one-dimensional approach. The concentration of population and activities in some provinces of Iran, along with macroeconomic indicators (inflation and unemployment), exacerbates inequality. These inequalities affect various dimensions of people's lives and endanger their economic welfare. The primary aim of this study is to examine the effects of inflation and unemployment on multidimensional inequality in the provinces of Iran and their reciprocal effects on each other, using a multidimensional Gini coefficient estimated from the household budget microdata of the Statistical Center of Iran for the years 2000-2021.
Methodology
In this study, the multidimensional Gini coefficient by Kumar Banerjee (2010) has been estimated for 9 dimensions of welfare. Then, the effects of inflation and unemployment, along with variables such as per capita real government expenditure and per capita real financial facilities as indicators of financial development, will be analyzed using a spatial econometric model. The mathematical form of the multidimensional Gini coefficient (MGI) is as follows:
Here, the mathematical formula would be inserted) In this equation: represents the non-increasing rank of the unit under study in the individual's overall welfare vector, and represents the sample size. The range of this index fluctuates between zero (completely equal distribution) and one (completely unequal distribution). For measuring multidimensional inequality in this study, the multidimensional Gini coefficient by Kumar Banerjee (2010) has been used which is based on the microdata from the household expenditure (income) survey of the Statistical Center of Iran and involves data mining processes such as aggregating groups of beverages and tobacco, ready meals with food expenditure groups, and communications with transportation, and extracting data related to each household code in each province using R Studio 2020 software. The model is based on the spatial econometric method with spatial panel data, defined using a proximity method in which provinces sharing a border have an element of one and otherwise zero. The adjacency matrix (spatial weight) is normalized, where neighboring provinces carry the most weight, and distant provinces carry the least.
Findings
The results of estimating the multidimensional Gini coefficient for the provinces during 2000-2021 show that most provinces have experienced a high rate of inequality. Provinces such as Bushehr, Khuzestan, Kermanshah, Kurdistan, Markazi, Qazvin, Qom, Semnan, Sistan and Baluchestan, West Azerbaijan, Zanjan, and Yazd are in an unfavorable condition compared to the country, and most of these provinces are border regions. Over these 22 years, Sistan and Baluchestan with 77.66% have the highest rate of multidimensional inequality, while Isfahan with 60.85% has the lowest among the provinces. Additionally, the findings indicate that inflation, unemployment, per capita real government expenditure, and per capita real disbursed financial facilities have a significant positive effect on multidimensional inequality in the provinces of Iran. The proximity of provinces has also worsened the inequality conditions in the neighboring provinces.
Discussion and Conclusion
Four variables including unemployment, inflation, per capita real government expenditure, and per capita real disbursed financial facilities have a significant positive effect on the multidimensional Gini coefficient, worsening income distribution. The most significant impact is seen with per capita real government expenditure, which is not allocated effectively to enhance welfare and improve economic conditions, thus not improving income distribution and reducing inequality. The effects of the other variables are in the following order: per capita real disbursed financial facilities, unemployment, and inflation. It is recommended to consider all welfare dimensions in the household consumption basket, create equal conditions for access to bank facilities, allocate a specific quota of facilities to lessdeveloped provinces, allocate government expenditures to expand public services and infrastructure in deprived provinces, consider the interactive effects between provinces in policymaking, and implement effective policies to improve welfare conditions and balanced income distribution across all provinces
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
In most world economies, governments have been proposed as a complementary institution and are bound to interfere in the economy. The degree of government involvement in any economy depends on the political and economic system in that country. One of the government's intervention tools is subsidy payments considered as financial aids aiming at transferring government resources to buyers and sellers. Therefore, one of the most well-known ways of transferring income to vulnerable groups is subsidy payment. This tool has a long history in different economies. In general, subsidies can be divided into four categories: a) based on the government's goals, which include economic subsidies, development subsidies, social subsidies, political subsidies, and cultural subsidies; b) Based on the stages of the goods, which include consumption subsidies, production subsidies, distribution subsidies, service subsidies, export subsidies, and import reduction subsidies and currency savings; c) Based on the classification of the subsidy itself, which includes direct subsidy and indirect subsidy; and d) based on the reflection of its costs, which includes hidden subsidies and open subsidies. Also, regarding the methods of applying subsidies, it should be noted that subsidies in the consumption sector are mainly paid in cash, goods, general prices and coupons. On the other hand, the payment of subsidy will disrupt the price system and lead to deviation in production and investment.
Since governments dependent heavily on oil revenues usually seek to pay subsidies in general, they normally encounter many problems including waste of resources, increase in consumption, smuggling, lack of efficient allocation of resources and reduction of efficiency in the economy. This happens because the price of subsidized goods is not realistic. For this purpose, Iran and other developing economies are seeking to apply the policy of targeting subsidies. One of the results of policies targeting subsidies is the realization of prices, which will improve the performance of producers and choose an optimal production process.
Since targeting subsidies has significant effects on the relative advantage of manufactured goods and subsequent sustainable growth and development, therefore, it is crucially important and essential to investigate the effects of this policy on the business model.
A review of the experimental studies conducted inside the country indicates that the effect of reducing (eliminating) the subsidy of basic goods (Sections 25-22 in the software package of the Global Trade Analysis Project) on the trade pattern has not been investigated so far.
In this regard, the aim of the current research is to investigate the effects of reducing (eliminating) subsidies for basic goods (sections 22-25 in the software package of the Global Trade Analysis Project) on Iran's trade using the GTAP model.
Methodology
The Global Trade Analysis Project model is one of the types of Computable General Equilibrium (CGE) models, the software related to it (GEMPACK, RunGTAP) and the database are provided to the researchers by its designers.
In the current research, the data has been gathered in the form of four sectors (dairy, rice, sugar and other foods) and two regions (Iran and other parts of the world) and the analysis has been done in two scenarios which are designed as follows:
1) 50% reduction in the subsidy paid to the firm's consumption-domestic goods.
2) 100% reduction in the subsidy paid to the firm's consumption-domestic goods.
Findings
The results showed that in both scenarios, the economic welfare of Iran and the rest of the world decreased and increased, respectively, and the intensity of these changes is greater in the second scenario (removal of basic commodity subsidies). The share of resource allocation efficiency and term of trade and savings-investment relationship in reducing economic welfare is higher in the second scenario. The highest decrease in economic welfare in the first and second scenario is related to sector 25 and the lowest decrease in economic well-being in both scenarios is related to sector 22.
Reducing the subsidy paid on the firm's consumption-domestic goods in these sectors will increase the export of these goods. The most positive changes in Iran's trade balance in the first scenario (50% reduction in subsidies) are related to sector 25 and equal to 48.4 million dollars, and the most negative trade balance of Iran in the second scenario (complete elimination of subsidies) is related to sector 25 and equal to 7.96 has been negative. In total, the reduction of subsidies for basic goods simultaneously in all 4 sectors has led to positive changes in Iran's trade balance.
Discussion and Conclusion
According to the economic results of this research, it is recommended to gradually remove the subsidy paid to the firm's consumption domestic goods so that, while having a positive effect on the changes in Iran's trade balance, the economic welfare does not face a large and one-time decline
In most world economies, governments have been proposed as a complementary institution and are bound to interfere in the economy. The degree of government involvement in any economy depends on the political and economic system in that country. One of the government's intervention tools is subsidy payments considered as financial aids aiming at transferring government resources to buyers and sellers. Therefore, one of the most well-known ways of transferring income to vulnerable groups is subsidy payment. This tool has a long history in different economies. In general, subsidies can be divided into four categories: a) based on the government's goals, which include economic subsidies, development subsidies, social subsidies, political subsidies, and cultural subsidies; b) Based on the stages of the goods, which include consumption subsidies, production subsidies, distribution subsidies, service subsidies, export subsidies, and import reduction subsidies and currency savings; c) Based on the classification of the subsidy itself, which includes direct subsidy and indirect subsidy; and d) based on the reflection of its costs, which includes hidden subsidies and open subsidies. Also, regarding the methods of applying subsidies, it should be noted that subsidies in the consumption sector are mainly paid in cash, goods, general prices and coupons. On the other hand, the payment of subsidy will disrupt the price system and lead to deviation in production and investment.
Since governments dependent heavily on oil revenues usually seek to pay subsidies in general, they normally encounter many problems including waste of resources, increase in consumption, smuggling, lack of efficient allocation of resources and reduction of efficiency in the economy. This happens because the price of subsidized goods is not realistic. For this purpose, Iran and other developing economies are seeking to apply the policy of targeting subsidies. One of the results of policies targeting subsidies is the realization of prices, which will improve the performance of producers and choose an optimal production process.
Since targeting subsidies has significant effects on the relative advantage of manufactured goods and subsequent sustainable growth and development, therefore, it is crucially important and essential to investigate the effects of this policy on the business model.
A review of the experimental studies conducted inside the country indicates that the effect of reducing (eliminating) the subsidy of basic goods (Sections 25-22 in the software package of the Global Trade Analysis Project) on the trade pattern has not been investigated so far.
In this regard, the aim of the current research is to investigate the effects of reducing (eliminating) subsidies for basic goods (sections 22-25 in the software package of the Global Trade Analysis Project) on Iran's trade using the GTAP model.
Methodology
The Global Trade Analysis Project model is one of the types of Computable General Equilibrium (CGE) models, the software related to it (GEMPACK, RunGTAP) and the database are provided to the researchers by its designers.
In the current research, the data has been gathered in the form of four sectors (dairy, rice, sugar and other foods) and two regions (Iran and other parts of the world) and the analysis has been done in two scenarios which are designed as follows:
1) 50% reduction in the subsidy paid to the firm's consumption-domestic goods.
2) 100% reduction in the subsidy paid to the firm's consumption-domestic goods.
Findings
The results showed that in both scenarios, the economic welfare of Iran and the rest of the world decreased and increased, respectively, and the intensity of these changes is greater in the second scenario (removal of basic commodity subsidies). The share of resource allocation efficiency and term of trade and savings-investment relationship in reducing economic welfare is higher in the second scenario. The highest decrease in economic welfare in the first and second scenario is related to sector 25 and the lowest decrease in economic well-being in both scenarios is related to sector 22.
Reducing the subsidy paid on the firm's consumption-domestic goods in these sectors will increase the export of these goods. The most positive changes in Iran's trade balance in the first scenario (50% reduction in subsidies) are related to sector 25 and equal to 48.4 million dollars, and the most negative trade balance of Iran in the second scenario (complete elimination of subsidies) is related to sector 25 and equal to 7.96 has been negative. In total, the reduction of subsidies for basic goods simultaneously in all 4 sectors has led to positive changes in Iran's trade balance.
Discussion and Conclusion
According to the economic results of this research, it is recommended to gradually remove the subsidy paid to the firm's consumption domestic goods so that, while having a positive effect on the changes in Iran's trade balance, the economic welfare does not face a large and one-time decline
Volume 0, Issue 0 (12-2024)
Abstract
Aim and Introduction
Ecological footprint accounting is composed of two metrics, the “demand-side” (ecological footprint) and the “supply-side” (biocapacity). While the ecological footprint calculates the demand for natural assets in global hectares, biocapacity symbolizes the supply capacity of nature to meet this demand with the same unit of measurement. Ecological deficit also shows the difference between ecological footprint and biological capacity. Globally, the degree of ecological deficits continued to expand over the last decade due to the increase in EF and reduction in biocapacity, which is caused by the following: increasing consumption of fossil fuel energy, overexploitation of natural resources, unsustainable production methods, and economic activities.
Iran is one of the countries that has a weak environmental performance. According to the Global Footprint Network, Iran's ecological footprint exceeded 333% of its biological capacity in 2022. Iran's ecological deficit, which was - 0.55 global per capita hectares in 1961, has increased by 554% to 2.50 global per capita hectares in 2022, and the destruction and pollution of the environment in Iran have reached unsustainable levels. Therefore, the analysis of the determinants of environmental quality can provide insights into the design of appropriate environmental policies in Iran.
In this regard, the environmental effects of dependence on crude oil have attracted considerable attention. Crude oil is an important and largest source of energy, especially for developing countries such as Iran. It is a fossil-based fuel and a major source of carbon emissions in the world. Hence, many studies have linked oil price shocks to environment quality. In contrast to oil-importing economies, where oil price increases encourage a shift to cheaper and cleaner alternative energy sources, the environmental policy issue in oil-exporting countries is entirely different. Indeed, a fall in oil prices may be associated with a decreased investment in environmentally friendly energy sources. By comparison, an increase in oil prices revealed a reluctance to diversify the economy away from its reliance on non-eco-friendly fossil fuel energy.
Based on the explanations above, the main purpose of this article is to investigate the asymmetric impact of scaled oil price impulses on the environmental Load Capacity Factor (LCF) in Iran using the Non-linear Autoregressive Distributed Lag (MATNARDL) approach. The paper intends to make the following contributions to the literature. Firstly, this article is the first to look into the effect of oil prices on the LCF in Iran by applying asymmetric methodologies. Secondly, it is the first study with a reverse load capacity factor as an environmental sustainability indicator. Thirdly, this paper applied the advanced and newly developed MATNARDL for asymmetric and nonlinear analysis to provide a more robust result that exhibits relevant policy implications. Finally, this innovative study investigated the effects of oil prices on the LCF in Iran between 1961 and 2022 in the framework of the LCC hypothesis.
Methodology
The study compiles annual data for the period 1961-2022 for Iran from three different sources. According to Statista, OP represents average annual OPEC crude oil price (in US dollars per barrel). The data are obtained from the World Bank, GDP per capita, (constant 2015 dollars), Energy Consumption (EC) as kg of oil equivalent per capita, Ecological Footprint (per capita, gha) and LCF (the load capacity factor) are obtained from Global Footprint Network. Because the LCF includes biocapacity in the numerator and EF in the denominator, it allows for simultaneous environmental assessment on the supply and demand sides. A higher LCF indicates a better environment. The current paper's economic functions are illustrated in Equations (1):
2
The main objective of this study is to examine the major, medium and minimal scales of positive and negative changes in oil price on the environmental quality index in Iran. For this purpose, the MATNARDL is used as an estimator to examine the effect of minor to major adverse shocks and minor to major positive surprises in the explanatory variable on the explained variable.
Findings
The bounds cointegration test results confirm a long-term relationship in the asymmetric model. The estimation of the model has been performed by categorizing the positive and negative impulses of the oil price in three small (quantiles less than the τ30 threshold), medium (quantiles between the τ30 and τ70 thresholds), and large (quantiles greater than the τ70 threshold) scales in the form of MATNARDL approach. The results indicate that in the long term, small scale of positive (negative) oil price impulses had a positive (negative) and significant effect on the load capacity factor; while these impulses have a negative effect on the load capacity factor in the long term in both medium and large scales. Based on other results, energy consumption has a negative and significant effect on the load capacity coefficient, and the environmental hypothesis of the load capacity curve (LLC) in Iran is confirmed.
Discussion and Conclusion
Based on the obtained results, it can be said that the effect of oil price on the load capacity factor in Iran is asymmetric. Among positive impulses, only with increase in small scale of oil price, we can see an increase in load capacity factor and environmental sustainability in the country. Moreover, the positive impulses of the oil price on both medium and large scales lead to the increase of environmental instability by prioritizing economic achievements and activities over environmental issues
Ecological footprint accounting is composed of two metrics, the “demand-side” (ecological footprint) and the “supply-side” (biocapacity). While the ecological footprint calculates the demand for natural assets in global hectares, biocapacity symbolizes the supply capacity of nature to meet this demand with the same unit of measurement. Ecological deficit also shows the difference between ecological footprint and biological capacity. Globally, the degree of ecological deficits continued to expand over the last decade due to the increase in EF and reduction in biocapacity, which is caused by the following: increasing consumption of fossil fuel energy, overexploitation of natural resources, unsustainable production methods, and economic activities.
Iran is one of the countries that has a weak environmental performance. According to the Global Footprint Network, Iran's ecological footprint exceeded 333% of its biological capacity in 2022. Iran's ecological deficit, which was - 0.55 global per capita hectares in 1961, has increased by 554% to 2.50 global per capita hectares in 2022, and the destruction and pollution of the environment in Iran have reached unsustainable levels. Therefore, the analysis of the determinants of environmental quality can provide insights into the design of appropriate environmental policies in Iran.
In this regard, the environmental effects of dependence on crude oil have attracted considerable attention. Crude oil is an important and largest source of energy, especially for developing countries such as Iran. It is a fossil-based fuel and a major source of carbon emissions in the world. Hence, many studies have linked oil price shocks to environment quality. In contrast to oil-importing economies, where oil price increases encourage a shift to cheaper and cleaner alternative energy sources, the environmental policy issue in oil-exporting countries is entirely different. Indeed, a fall in oil prices may be associated with a decreased investment in environmentally friendly energy sources. By comparison, an increase in oil prices revealed a reluctance to diversify the economy away from its reliance on non-eco-friendly fossil fuel energy.
Based on the explanations above, the main purpose of this article is to investigate the asymmetric impact of scaled oil price impulses on the environmental Load Capacity Factor (LCF) in Iran using the Non-linear Autoregressive Distributed Lag (MATNARDL) approach. The paper intends to make the following contributions to the literature. Firstly, this article is the first to look into the effect of oil prices on the LCF in Iran by applying asymmetric methodologies. Secondly, it is the first study with a reverse load capacity factor as an environmental sustainability indicator. Thirdly, this paper applied the advanced and newly developed MATNARDL for asymmetric and nonlinear analysis to provide a more robust result that exhibits relevant policy implications. Finally, this innovative study investigated the effects of oil prices on the LCF in Iran between 1961 and 2022 in the framework of the LCC hypothesis.
Methodology
The study compiles annual data for the period 1961-2022 for Iran from three different sources. According to Statista, OP represents average annual OPEC crude oil price (in US dollars per barrel). The data are obtained from the World Bank, GDP per capita, (constant 2015 dollars), Energy Consumption (EC) as kg of oil equivalent per capita, Ecological Footprint (per capita, gha) and LCF (the load capacity factor) are obtained from Global Footprint Network. Because the LCF includes biocapacity in the numerator and EF in the denominator, it allows for simultaneous environmental assessment on the supply and demand sides. A higher LCF indicates a better environment. The current paper's economic functions are illustrated in Equations (1):
The main objective of this study is to examine the major, medium and minimal scales of positive and negative changes in oil price on the environmental quality index in Iran. For this purpose, the MATNARDL is used as an estimator to examine the effect of minor to major adverse shocks and minor to major positive surprises in the explanatory variable on the explained variable.
Findings
The bounds cointegration test results confirm a long-term relationship in the asymmetric model. The estimation of the model has been performed by categorizing the positive and negative impulses of the oil price in three small (quantiles less than the τ30 threshold), medium (quantiles between the τ30 and τ70 thresholds), and large (quantiles greater than the τ70 threshold) scales in the form of MATNARDL approach. The results indicate that in the long term, small scale of positive (negative) oil price impulses had a positive (negative) and significant effect on the load capacity factor; while these impulses have a negative effect on the load capacity factor in the long term in both medium and large scales. Based on other results, energy consumption has a negative and significant effect on the load capacity coefficient, and the environmental hypothesis of the load capacity curve (LLC) in Iran is confirmed.
Discussion and Conclusion
Based on the obtained results, it can be said that the effect of oil price on the load capacity factor in Iran is asymmetric. Among positive impulses, only with increase in small scale of oil price, we can see an increase in load capacity factor and environmental sustainability in the country. Moreover, the positive impulses of the oil price on both medium and large scales lead to the increase of environmental instability by prioritizing economic achievements and activities over environmental issues
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
After the global economic recession in 2008-2009, the discussion about countercyclical and procyclical fiscal policies and their effects on the economy began. Countercyclical fiscal policy is applied to reduce economic fluctuations by adjusting government spending and taxes against the business cycle. The aim of this policy is to stabilize the economy and flatten its fluctuations. On the contrary, procyclical fiscal policy strengthens economic fluctuations in the direction of business cycles. On the other hand, fiscal sustainability refers to the government's ability to maintain expenditures, income and public debt at a certain level in the long term without jeopardizing economic stability or facing a fiscal crisis. A sustainable fiscal policy ensures that the government's debt in the long run is at a level proportional to the size of the economy. The main questions of this research are as follows:
After the global economic recession in 2008-2009, the discussion about countercyclical and procyclical fiscal policies and their effects on the economy began. Countercyclical fiscal policy is applied to reduce economic fluctuations by adjusting government spending and taxes against the business cycle. The aim of this policy is to stabilize the economy and flatten its fluctuations. On the contrary, procyclical fiscal policy strengthens economic fluctuations in the direction of business cycles. On the other hand, fiscal sustainability refers to the government's ability to maintain expenditures, income and public debt at a certain level in the long term without jeopardizing economic stability or facing a fiscal crisis. A sustainable fiscal policy ensures that the government's debt in the long run is at a level proportional to the size of the economy. The main questions of this research are as follows:
- Is Iran's fiscal policy countercyclical or procyclical?
- Is Iran's fiscal policy sustainable?
- What is the effect of cyclical fiscal policy and fiscal sustainability on the Iranian economic growth?
- How is the mutual relationship between fiscal sustainability and cyclical fiscal policy in Iran?
Methodology
The evaluation of fiscal policy cyclicality and fiscal sustainability and their determinants have been previously researched. However, the effect of cyclical fiscal policy and fiscal sustainability on economic growth and their mutual relationship has not been covered. This research, has utilized Iran's 1970-2021 annual data and a state-space model with time-varying parameters and an autoregressive distributed lags model as well as Kalman filter method. Moreover, to evaluate Iran's cyclical fiscal policy and fiscal sustainability, the effect of cyclical fiscal policy and fiscal sustainability on economic growth have been investigated. The research also deals with the mutual effect between cyclical fiscal policy and fiscal sustainability in Iran.
Findings
In this research, in order to evaluate the cyclical behavior of Iran's fiscal policy and obtaining the index, a state-space model with time-varying parameters, is estimated in which the real GDP logarithm coefficient varies over time. Then, in order to assess Iran's fiscal sustainability and obtaining the index, a state-space model with time-varying parameters is estimated. Finally, an autoregressive distributed lags model is utilized to estimate the effect of cyclical fiscal policy index and fiscal sustainability index on economic growth, as well as estimating the mutual effect between cyclical fiscal policy index and fiscal sustainability index.
Discussion and Conclusion
The findings of this research show: First, Iran's cyclical fiscal policy index estimated in all years is positive and has not recorded a negative number in any year, which means that the fiscal policy implemented in Iran during the period 1970-2021, was procyclical. In other words, the fiscal policy implemented in Iran has increased the range of fluctuations of cycles and for this reason, it has made the Iranian economy vulnerable to the economic shocks. Second, the estimated Iran's fiscal sustainability index is negative in most years so that the average fiscal sustainability index in the entire period is -0.068. This indicates the unsustainability of Iran's fiscal policy in the period 1970-2021. The trend of the smoothed changes of the time-varying parameter related to the fiscal sustainability index is also downward, which means that Iran's fiscal sustainability has been weakening over time and has moved in the direction of unsustainability. Third, Iran's cyclical fiscal policy index has had a negative effect on economic growth. In other words, procyclical behavior of Iran's fiscal policy has slowed down the economic growth rate. Fourth, Iran's fiscal sustainability index has a negative and significant effect on economic growth. Based on the estimated fiscal sustainability index, unsustainability is evident within Iran's fiscal policy. Therefore, unsustainability of Iran's fiscal policy has weakened economic growth. Fifth, Iran's fiscal unsustainability has increased the procyclical behavior of fiscal policy and as a result, exacerbated the fluctuations of economic cycles. Sixth, the increasing Iran's cyclical fiscal policy index reduces the reaction of the primary balance to the government debt. In other words, the increase in the procyclical behavior of the fiscal policy weakens Iran's fiscal sustainability
The evaluation of fiscal policy cyclicality and fiscal sustainability and their determinants have been previously researched. However, the effect of cyclical fiscal policy and fiscal sustainability on economic growth and their mutual relationship has not been covered. This research, has utilized Iran's 1970-2021 annual data and a state-space model with time-varying parameters and an autoregressive distributed lags model as well as Kalman filter method. Moreover, to evaluate Iran's cyclical fiscal policy and fiscal sustainability, the effect of cyclical fiscal policy and fiscal sustainability on economic growth have been investigated. The research also deals with the mutual effect between cyclical fiscal policy and fiscal sustainability in Iran.
Findings
In this research, in order to evaluate the cyclical behavior of Iran's fiscal policy and obtaining the index, a state-space model with time-varying parameters, is estimated in which the real GDP logarithm coefficient varies over time. Then, in order to assess Iran's fiscal sustainability and obtaining the index, a state-space model with time-varying parameters is estimated. Finally, an autoregressive distributed lags model is utilized to estimate the effect of cyclical fiscal policy index and fiscal sustainability index on economic growth, as well as estimating the mutual effect between cyclical fiscal policy index and fiscal sustainability index.
Discussion and Conclusion
The findings of this research show: First, Iran's cyclical fiscal policy index estimated in all years is positive and has not recorded a negative number in any year, which means that the fiscal policy implemented in Iran during the period 1970-2021, was procyclical. In other words, the fiscal policy implemented in Iran has increased the range of fluctuations of cycles and for this reason, it has made the Iranian economy vulnerable to the economic shocks. Second, the estimated Iran's fiscal sustainability index is negative in most years so that the average fiscal sustainability index in the entire period is -0.068. This indicates the unsustainability of Iran's fiscal policy in the period 1970-2021. The trend of the smoothed changes of the time-varying parameter related to the fiscal sustainability index is also downward, which means that Iran's fiscal sustainability has been weakening over time and has moved in the direction of unsustainability. Third, Iran's cyclical fiscal policy index has had a negative effect on economic growth. In other words, procyclical behavior of Iran's fiscal policy has slowed down the economic growth rate. Fourth, Iran's fiscal sustainability index has a negative and significant effect on economic growth. Based on the estimated fiscal sustainability index, unsustainability is evident within Iran's fiscal policy. Therefore, unsustainability of Iran's fiscal policy has weakened economic growth. Fifth, Iran's fiscal unsustainability has increased the procyclical behavior of fiscal policy and as a result, exacerbated the fluctuations of economic cycles. Sixth, the increasing Iran's cyclical fiscal policy index reduces the reaction of the primary balance to the government debt. In other words, the increase in the procyclical behavior of the fiscal policy weakens Iran's fiscal sustainability
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.