Research Paper
Economic Development
Mahya Allahgholi; Farshad Momeni
Abstract
The Economic Complexity Index (ECI) can be considered as a development indicator, given its superior predictive power in predicting economic growth and income inequality. However, it suffers from shortcomings such as the inability to distinguish differences in the complexity levels of economies. This ...
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The Economic Complexity Index (ECI) can be considered as a development indicator, given its superior predictive power in predicting economic growth and income inequality. However, it suffers from shortcomings such as the inability to distinguish differences in the complexity levels of economies. This highlights the need for a theoretical model to explain the ECI. Within the framework of the social orders approach, the type of social order is identified as a key distinction between developed and developing economies. Using a descriptive–analytical method, this study aimed to identify the influential factors of the ECI. In this respect, three key variables were examined: the state of property rights, the business environment, and the type of people’s access to organizations. The analysis focused on Iran’s ECI over a ten-year period (2010–2020), alongside three other indices, namely International Property Rights, Ease of Doing Business, and Economic Freedom— as an estimates of the chosen variables. According to the results, the International Property Rights Index, compared to the other two indices, has the strongest positive correlation with the ECI.IntroductionAssuming stable conditions, the Economic Complexity Index (ECI) measures a country’s production capacity and its potential future development by analyzing the information about its exports. Countries that produce and export more diverse and unique products tend to have more complex economies, leading to higher economic growth prospects and reduced inequality. Such countries manage to produce better and smarter products by sharing knowledge among citizens and reintegrateing knowledge within institutions, organizations, and groups. However, the ECI does not explain why some economies become more complex over time while others do not. Understanding these dynamics requires more in-depth studies of various economic conditions and factors influencing the ECI. Using the social order approach as an analytical framework, the present research aimed to explore why Iran’s economy has failed to improve in the ECI rankings over recent years. A key determinant of economic complexity is productive knowledge. This kind of knowledge manifests in the variety of existing companies, the range of jobs required to sustain them, and the level of interaction between companies within the society. Therefore, understanding differences in the levels of economic complexity and their future trajectories depends on how effectively these economies expand the total pool of knowledge, as in increasing the diversity of knowledge available to people and enterprises and fostering conditions that enable its integration through organizations, which serve as human interaction networks.In their analysis of social orders vis-à-vis violence control, North et al. (2009) categorized countries into two types: limited access (natural states) and open access. The key distinguishing factor of an open access order is the presence of competition at all economic and political levels. Based on this approach, three levels of explanation can be proposed to account for differences in economic complexity and the processes these countries undergo. The first level concerns the type of people’s access to organizations. In an open access order, this access provides opportunities for economic entrepreneurs to engage in creative destruction. Within this structure, entrepreneurs can advance toward more complex products by modifying existing production processes or developing new products through investment in human capital and the accumulation of physical capital by shifting the boundaries of existing knowledge. The second level focuses on the business environment, which influences the motivations of individuals and companies. The business environment within a society is considered as a key factor shaping individual and corporate choices to diversify knowledge and learning. Finally, the third level addresses transaction costs associated with integrating knowledge within organizations. Given that transaction costs and property rights are measured by similar criteria, the third key factor influencing economic complexity is the state of property rights.Materials and MethodsThe theoretical–conceptual model illustrating the relationship between three factors influencing economic complexity is shown in Figure 1. To assess the type of people’s access to organizations, the business environment, and property rights, the following indices were used: the Economic Freedom Index from the Heritage Foundation, the Ease of Doing Business Index from the World Bank, and the International Property Rights Index. The research adopted a descriptive–analytical method and conducted data mining using Power BI and Excel. It is important to note that some data on sub-indices for Iran are incomplete or inaccurately estimated. Consequently, using them in a model to study Iran’s economy may lead to errors. Therefore, this study is limited to analyzing existing trends.Figure 1. The theoretical–conceptual model of the relationship between property rights, business environment and people’s access to organizations with economic complexity Source: Research findingsResults and DiscussionAccording to the Atlas of Economic Complexity, in 2020, Iran ranked 85th out of 133 countries, with an economic complexity index of -0.39. Over the past decade, the index has shown only a slight improvement, and Iran’s ranking has risen by 11 points. Given the significant share of fuels in Iran’s exports, an analysis of this sector alongside the economic complexity index trend revealed that a decline in the fuel sector’s share generally coincided with an improvement in complexity index. However, the lack of improvement in Iran’s ECI—despite a decrease in the fuel sector’s share in 2012, 2015, and 2019—can be attributed to the addition of more low-complexity goods to the export basket during those years. Conversely, the increase in the fuel sector’s share in 2017, alongside no change in the complexity index compared to 2016, resulted from the inclusion of products with positive complexity in the export basket that year. According to the findings, the improvement in Iran’s ECI resulted from the improvements in the sub-indices of political stability, registering property, and patent protection (in the International Property Rights Index), protecting minority investors, dealing with construction permits, and getting electricity (in the Ease of Doing Business Index), as well as governance integrity and trade freedom (in the Economic Freedom Index). Additionally, the study found that the ECI has a positive and moderate correlation with the International Property Rights Index (0.49), the Ease of Doing Business Index (0.55), and the Economic Freedom Index (0.41). The correlation coefficients of these indices with Iran’s ECI were found to be 0.52, 0.21, and 0.48, respectively.ConclusionThis study used of the social order approach as a theoretical framework, which significantly contributed to a better understanding of the factors influencing the ECI. The analysis also demonstrated that the International Property Rights Index provided a better explanation of the trend in Iran’s ECI compared to the other two indices.
Research Paper
Political economy
Hossein Tavakolian; Reza Taleblou; Shaghayegh Abbasali
Abstract
Despite efforts to improve the governmental budget system in Iran’s current economic situation, no significant progress has been made. The relationship between the key stakeholders—namely the government, parliament, regulatory bodies, and the general public as the ultimate beneficiaries of ...
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Despite efforts to improve the governmental budget system in Iran’s current economic situation, no significant progress has been made. The relationship between the key stakeholders—namely the government, parliament, regulatory bodies, and the general public as the ultimate beneficiaries of the budget—is not properly regulated. As a result, there is decreased transparency and accountability among various government officials. Using the Generalized Method of Moments (GMM) for the period 1993–2018, the current study aimed to examine the government off-budget operations and their impact on inflation, with a focus on fiscal dominance via the banking system. The findings suggested that increased fiscal dominance via the banking system’s debt channel had a positive effect on inflation, thereby confirming the presence of fiscal dominance. The results also highlighted a negative relationship between political stability and corruption, on the one hand, and inflation, on the other. These variables remained at low levels throughout the analyzed period, indicating a need for greater attention from the government and political factions across the country.IntroductionThe inflation rate in Iran is a major concern that contributes to rising levels of poverty, inequality, economic instability, and reduced private sector investment. This persists despite the fact that many other countries have successfully managed similar issues. Unfortunately, monetary authorities in Iran have struggled to tackle this pressing problem. The Iranian economy has long struggled with the persistent issue of high inflation, which is largely attributed to fiscal dominance over the Central Bank. Moroever, addressing the budget deficit is partially achieved through off-budget operations facilitated by the banking system. Covering the period 1993–2018, this study examined the government off-budget operations and their impact on inflation, with a focus on fiscal dominance within the banking system. It actually dealt with the Central Bank’s inability to control inflation, highlighting fiscal dominance as a key factor. Fiscal dominance arises when fiscal policymakers are not required to balance expenditures with tax revenues, thereby compelling monetary policymakers to address government budget deficits.Materials and MethodsApplying the Generalized Method of Moments (GMM) to the period 1993–2018 in Iran, the present research examined the government off-budget operations and their impact on inflation, with a focus on fiscal dominance in the banking system. The study used the data from the Central Bank, international transparency websites, and Palta to analyze the quantitative effects of government borrowing from the banking system on inflation.Results and DiscussionThe findings revealed a direct relationship between inflation and government financing through the banking system, emphasizing its analytical significance for Iran’s economy. Due to legal restrictions on borrowing directly from the Central Bank, the government turns to state-owned commercial banks for financing, resulting in a form of active fiscal dominance. This approach increases government debt to the banking system, thus affecting various economic sectors and contributing to instability and chronic inflation. The financial practice, known as off-budgeting, is marked by a lack of transparency and inefficiency in government expenditures, further exposing fiscal dominance via the banking system. Additionally, variables of political stability and corruption control play a role, highlighting the need for government attention across different sectors and political factions in the country.Table 1. GMM Estimation of the Mode )Inflation as the Dependent Variable(VariablesGMMCoef.Std. Err.z|P>|zPolitical StabilityPOLITY-0.0002726.14E-5-4.4336930.0000 Corruption controlGC-0.0003782.10E-5-18.005330.0000Exchange rate growthGER0.0706140.0232223.0407630.0034Interest rateIR(-1)-0.0459500.008164-5.6285540.0000Money base growth rateGM2(-1)0.3848440.03588510.724310.0000Government debt growth rate to private banksGGBPB(-1)0.0076620.00038220.061880.0000Government debt growth rate to state-owned banksGGBGB0.0287050.00196614.599790.0000Government debt growth rate to privatized banksGGBSPB(-2)0.0064590.00011058.499070.0000Growth rate of government oil revenuesOILP-0.1363780.018692-7.2962170.0000R2= 0.614559 J-statistic=17.89458 Prob (J-statistic)=0.985014Instrument rank=43This study explored the off-budget operations of the Iranian government and their impact on inflation, with a particular focus on fiscal dominance through the banking system. Despite efforts to reform the government budget system, the lack of proper regulation in the interactions between budget stakeholders—including the government, parliament, regulatory bodies, and the general public—has led to reduced transparency and accountability among government officials. The research showed that increasing fiscal dominance through the banking system’s debt channel had a positive effect on inflation, thereby confirming the presence of fiscal dominance. Furthermore, the study underscored the negative correlation between political stability and corruption control, on the one hand, and inflation, on the other. This highlights the need for government attention across various sectors and political factions within the country. In sum, the Iranian economy faces a persistent challenge of high inflation, primarily driven by fiscal dominance over the Central Bank. The study highlighted the government’s dependence on off-budget operations, especially through the banking system, to address budget deficits. This financial strategy is marked by a lack of transparency and efficiency, resulting in economic instability and chronic inflation. The findings emphasized the urgent need for the government to review and reform regulations, reduce structural budget deficits, and improve transparency to effectively control inflation. The low levels of political stability and corruption control throughout the analyzed period underscored the current government’s challenges, stressing the need for comprehensive reforms and action across all sectors and political factions in the country.
Research Paper
Banking
Mohammad Ali Dehghan Dehnavi; Meysam Amiri; Amin Khorshidsavar
Abstract
Banks play a crucial role in maintaining financial stability within an economy. Their importance arises from the various functions they perform, which contribute to the overall stability and growth of the financial system. Additionally, the significance of banks for real economic growth lies in their ...
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Banks play a crucial role in maintaining financial stability within an economy. Their importance arises from the various functions they perform, which contribute to the overall stability and growth of the financial system. Additionally, the significance of banks for real economic growth lies in their role as financial intermediaries that facilitate allocating capital efficiently, supporting businesses and individuals, and contributing to the economy’s overall stability and development. Relying on data from 16 banks in Iran between 2011 and 2023, this research aimed to investigate the factors influencing bank risk-taking, with a focus on monetary policy, regulations, and macroeconomic variables. The analysis used two models and the Generalized Method of Moments (GMM) as the estimation method. The results of the research show that there is an inverse relationship between monetary policy and risk-taking. The results indicated an inverse relationship between monetary policy and risk-taking. Moreover, while the capital adequacy ratio (a regulatory factor) and GDP growth rate positively influence risk-taking, there is an inverse relationship between the inflation rate and risk-taking.IntroductionBorio and Zhu (2012) introduced a new transmission mechanism of monetary policy known as the risk-taking channel. Building on the seminal work of Borio and Zhu (2012), numerous theoretical and empirical studies have validated and expanded this channel in various countries, including China (Li & Tian, 2020; Tan & Li, 2016). In general, the risk-taking channel is underpinned by three key mechanisms: search-for-yield; valuation, income, and cash flow expansion; and central bank communication, announcements, and feedback (Altunbas et al., 2012; Borio & Zhu, 2012; De Nicolò et al., 2010). Since interest rates influence banks’ risk-taking behavior through agency problems (Altunbas et al., 2012), other bank characteristics must also influence bank risk-taking via the same channel (Bonfim & Soares, 2018). Altunbas et al. (2011) note that banks with less capital, more assets, and a greater reliance on short-term market funding are exposed to higher risk. In addition, most studies have explore this theme from the perspective of internal bank characteristics, such as capital, liquidity, leverage, and the proportion of traditional business (Altunbas et al., 2012; Bonfim & Soares, 2018).Traditional moral hazard theory suggests that under-capitalized banks face significant agency problems and are more likely to take excessive risks (Jiang et al., 2020). Shim (2013) demonstrates that a capital buffer (i.e., capital above the required minimum) helps limit moral hazard and absorbs adverse economic shocks. During the early stages of a financial crisis, banks with higher Tier I capital and more liquid assets tend to perform better (Beltratti and Stulz, 2009; Demirgüç-Kunt et al., 2013). Some empirical studies of the U.S. banking system suggest that capital is an effective risk indicator, showing a significant negative correlation with bank risk-taking (Hogan, 2015). Overall, it is widely accepted among scholars that holding more capital reduces bank risk-taking.The findings of Gizki et al. (2001) support the relationship between financial institutions and the real economy. First, the effect of real credit growth on banks’ credit risk and profitability aligns with the view that challenges in monitoring bank performance can lead to weakened credit standards during periods of rapid aggregate credit expansion.Second, the observed relationship between property prices and bank risk supports the proposition that difficulties in monitoring borrowers’ viability—coupled with the effect of collateral values on signaling borrower creditworthiness—play a crucial role in determining credit supply. Third, the results are consistent with theoretical analyses suggesting that cyclical changes in agents’ preferences for leverage significantly influence bank risk and profitability.Materials and MethodsA key assumption of regression analysis is that the right-hand side variables are not correlated with the disturbance term. If this assumption is violated, both ordinary least squares (OLS) and weighted least squares (WLS) estimations become biased and inconsistent. There are several situations in which some of the right-hand side variables may be correlated with the disturbance term. Classic examples of such cases include: 1) There are endogenously determined variables on the right-hand side of the equation, and 2) these right-hand side variables are measured with error. For simplicity, we refer to variables that are correlated with the residuals as endogenous, and those that are not correlated with the residuals as exogenous or predetermined. The standard approach when right-hand side variables are correlated with the residuals is to estimate the equation using instrumental variables regression. The concept behind instrumental variables is to identify a set of variables, called instruments, that are both correlated with the explanatory variables in the equation and uncorrelated with the disturbances. These instruments are then used to remove the correlation between the right-hand side variables and the disturbances. There are several approaches to using instruments to eliminate the effect of variable and residual correlation. The current study proposed instrumental variable estimators that employ the Generalized Method of Moments (GMM).Results and DiscussionThe GMM was used to estimate the model. The results are shown in Table 1.Table 1. Model estimation resultsModel 2Model 1SymbolVariable-0/07***RWAlag dependent variable0/57***-NPLlag dependent variable-0/33***-1/48***OvernightInterbank interest rate0/16***0/48***CARCapital adequacy ratio0/07**0/25***GDP_GrowthGross domestic product growth-0/02**-0/35***inflationInflation rate-0/13***-0/03SizeSize0/000**0/00*LeverageLeverage0.990/99SarganSargan-0/13**-1/99**AR(1)First-Order Autoregressive-0/06-1/67AR(2)Second-Order Autoregressive* The coefficient is significant at 10% level.** The coefficient is significant at 5% level.*** The coefficient is significant at 1% level.Source: Research calculations.ConclusionThe role of the financial system in the economy, along with its development and health, forms the foundation for strengthening and driving economic growth. Monitoring and reforming this system contribute to stability by addressing needs and reinforcing the real sector of the economy. The research findings indicated an inverse relationship between monetary policy and risk-taking. While the capital adequacy ratio (a regulatory factor) and GDP growth rate have a positive effect on risk-taking, there is an inverse relationship between the inflation rate and risk-taking.
Research Paper
Financial Economics
Iman Dadashi; Vahid Omidi
Abstract
Considering the impact of global variables on stock market industries, the present study relied on the Quantile-on-Quantile Connectedness (QQC) and Structural Vector Autoregression (SVAR) to examine the impact of geopolitical risk fluctuations on the volatility of the petroleum products, chemical products, ...
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Considering the impact of global variables on stock market industries, the present study relied on the Quantile-on-Quantile Connectedness (QQC) and Structural Vector Autoregression (SVAR) to examine the impact of geopolitical risk fluctuations on the volatility of the petroleum products, chemical products, metal ores, and basic metals sectors in the Tehran Stock Exchange. The analysis focused on the period from January 1, 2020, to December 24, 2024. The results from the QQC model revealed that fluctuations in geopolitical risk exhibited the strongest correlation with the volatility of the petroleum products industry index at extreme deciles, indicating a significant impact. In other industries, the highest susceptibility to geopolitical risk fluctuations had occurred when their volatility was in the 9th and 10th deciles. In addition, the SVAR model results indicated that the immediate response of industry index volatility to geopolitical risk shocks was positive across all cases. Over 360 periods, this response converged to a positive value, reflecting the persistence of the shock. The cumulative response analysis further demonstrated an exponential increase in all industries, suggesting a rising trend in the effect of geopolitical risk over time. Specifically, after 360 periods, the volatility of the petroleum products industry index increased by 0.34, chemical products by 0.06, metal ores by 0.03, and basic metals by 0.06.IntroductionRecently, the Tehran Stock Exchange (TSE) has been grappling with various risk factors, including the government budget, uncertainties in domestic and foreign policies, the Al-Aqsa Storm and Promise Fulfilled operations, interest rates, the exchange rate, and inflation. Notably, the TSE has not consistently mirrored the behavior of global markets across different periods. For instance, at the height of the COVID–19 pandemic, when most global stock markets experienced significant downturns, the TSE reached historic record highs. Conversely, at times when global markets were on the rise and commodity prices increased, the TSE entered a decline. This divergence was primarily due to internal risks unique to the TSE, which prevented the domestic market from benefiting from global market growth. The present study aimed to examine the impact of geopolitical risk fluctuations on the price index volatility of selected industries listed in the TSE. The industries were selected based on their specific characteristics and their sensitivity to geopolitical risks.Materials and MethodsThe study employed the Quantile-on-Quantile Connectedness (QQC) model to examine the relationship between the overall stock index and Islamic Treasury Bonds (Sukuk). To this end, the QVAR(P) model, which enables the estimation of relationships across different quantiles, is utilized as follows: (1)In this equation, and represent the vector of endogenous variables with a dimension of . The vector τ denotes the quantiles within the range [0,1], while P indicates the lag order of the QVAR model. Additionally, μ(τ) is the vector of conditional means, is the coefficient matrix, and is the vector of error terms. Subsequently, the Generalized Forecast Error Variance Decomposition (GFEVD) for an F-step-ahead forecasting, which represents the impact of a shock in series j on series i, is expressed as follows: (2)In this equation, denotes the variance-covariance matrix of the error terms. The vector is the standard basis vector or unit vector of dimension , with its the i-th element equal to one and all other elements set to zero.In this case, the rows of do not sum to one. Therefore, is standardized to obtain the scaled GFEVD: (3)Using this, the overall adjusted connectedness index (quantile-to-quantile) is calculated as follows: (4)In Equation (4), the higher the Total Connectedness Index (TCI), the higher the market risk.The analysis also used the Structural Vector Autoregression (SVAR) model. In the QQC model, the volatility of geopolitical risk was analyzed in relation to each of the other variables in the model, with results extracted accordingly. The SVAR model followed the same principle. Consequently, four models were estimated.The VAR model in this study is represented in its general form as follows: (5)Where is a vector containing the volatility of geopolitical risk and the index of each industry analyzed individually. The matrices to contain the coefficients of the lagged variables, and represents the residuals, which follow a normal distribution with zero mean and covariance . However, the shocks derived from Model (5) are not structural. To address this, the following model is used, allowing constraints to be imposed on matrices A and B: (6)In Equation (10), represents the structural error terms. The relationship between the VAR and SVAR models is expressed as .Results and DiscussionThe results indicated that geopolitical risk had a significant and varying impact on different industries within the TSE. This impact is influenced not only by each industry’s volatility level but also by the distribution of risk quantiles and industry indices. The QQC results revealed that the petroleum products industry was the most sensitive to geopolitical risk, particularly in extreme quantiles, where its connection to geopolitical risk reaches its peak. This finding suggests that during periods of high volatility, risk transmission accelerates. Similarly, in the chemical, metal ore, and basic metals industries, increased volatility heightened their susceptibility to geopolitical risk shocks. Notably, when these industries experience higher volatility quantiles, their connection to geopolitical risk strengthens across all levels. Structural shock analysis using the SVAR model indicated that all industries exhibited a positive immediate response to geopolitical risk volatility shocks. This reaction is strongest in the short term and gradually weakens over time. Among the industries analyzed, the petroleum products sector displayed the highest sensitivity, with an increase of 1 unit, while the impact on the chemical products, metal ore, and basic metals industries was 0.6, 0.3, and 0.5 units, respectively.ConclusionAccording to the findings, the relationship between geopolitical risk and the petroleum products industry is strongest in extreme quantiles. For other industries, the QQC model identifies two key patterns: first, when geopolitical risk volatility is in the 9th and 10th quantiles, it has the greatest impact on these industries; second, when the industries’ own volatility is in the 9th and 10th quantiles, they show the highest susceptibility to geopolitical risk across all quantiles. In addition, the results from the SVAR model indicated that the impact of geopolitical risk shocks on these industries would remain positive even after 360 periods. In other words, geopolitical risk shocks have a lasting effect on the volatility of the industries analyzed in this study.
Research Paper
Housing Economy
Ali Hasanvand
Abstract
Housing is a critical economic sector, with its growth benefiting households. However, rising prices in recent years have diminished household welfare. Using provincial-level data (2011–2021) and a spatial econometric approach, this study analyzed the factors driving housing price increases. The ...
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Housing is a critical economic sector, with its growth benefiting households. However, rising prices in recent years have diminished household welfare. Using provincial-level data (2011–2021) and a spatial econometric approach, this study analyzed the factors driving housing price increases. The findings revealed that urbanization and economic growth positively influenced housing prices, while financial development had negative spillover effects, underscoring the role of traders in price determination. Although industrialization had no direct effect, its spillover effects were found to be positive, highlighting the service sector’s influence. The study confirmed the housing price divergence and recommended enforcing tax laws on vacant properties and increasing financing in the housing sector to improve Iran’s housing market.IntroductionHousing is considered a unique commodity due to its dual nature as both a consumption good and a capital asset, along with its inelastic supply, essential role as shelter for primary households, and lack of substitutes. In the short term, housing supply remains inelastic to price changes due to the structural characteristics of durable goods. Given the significant capital aspect of housing, it is consistently subject to price fluctuations, much like other capital goods markets. Urbanization plays a dual role in housing demand: it increases consumption demand while simultaneously creating investment opportunities, driving up demand for housing as an asset. Another key factor influencing housing prices is the credit ratio. A higher credit-to-GDP ratio suggests the expansion of rentier markets—such as speculative housing transactions—that lack real added value, ultimately leading to price increases. Therefore, analyzing the factors influencing housing prices is both important and valuable.Materials and MethodsThe present research relied on theoretical and empirical literature (i.e., Cook et al., 2018; Kavlihua and Kameti, 2019; Su et al., 2021) to identify the factors influencing housing prices. Accordingly, Equation (1) was used to analyze these factors.(1)In Equation (1), pric represents the price per square meter of housing, gdp denotes real gross domestic product, indus refers to industrialization (measured as the ratio of industry value added to total value added), and cred is the ratio of bank credit to GDP, serving as an indicator of financial development. Additionally, urban represents the urbanization rate, calculated as the ratio of the urban population to the total population. Research data was collected from the Central Bank of Iran and the Statistical Center of Iran. To apply the spatial econometric approach, the study used the Moran test to examine spatial effects of the research variables.Results and DiscussionWhen spillover effects were not considered, financial development in each province had a positive and significant effect on the housing price. This is primarily because the production sector lacks more profitable investment opportunities than housing, leading to a substantial portion of credit flowing into the housing market and driving up prices. A high credit-to-GDP ratio indicates a surplus of financial resources, which, in turn, accelerates the growth of rentier activities. As a result, part of the credit expansion’s effect on housing demand stems from speculative investment, while another part is driven by increased access to credit for lower-decile households and consumer demand.The second factor influencing the housing price is GDP, which has a positive and significant effect. The rise in GDP can be analyzed from two perspectives: the macroeconomic level and the household (microeconomic) level. At the household level, an increase in GDP leads to higher per capita income, boosting purchasing power. Consequently, demand for housing—considered a normal good—rises, which drives up prices. This hypothesis is supported by two key observations: first, a relatively high proportion of households do not own homes, and second, there is a significant diversity in housing options. At the macro level, part of the increased economic growth can be attributed to the expansion of the housing sector, which led to a greater supply of housing. However, since micro-level effects outweigh macro-level effects in this case, housing prices still rose. Given that economic growth in the 1990s was below one percent, the impact of this factor was not particularly significant.The spatial camera approach was employed to examine the spatial effects of factors on the housing price. The results indicated that increased urbanization in neighboring provinces does not significantly impact the housing price in a specific province. This suggests that the opportunities and benefits of urbanization are not transferred between provinces. However, other research variables showed a significant spatial effect on the housing price. According to the estimates, a rise in the credit ratio in neighboring provinces reduces speculative demand for housing in a specific province, leading to a decline in the housing price. An increase in the credit ratio indicates greater prosperity in non-value-added speculative activities. The movement of these sources to certain provinces results in declining housing prices in others. Moreover, as GDP rises in neighboring provinces, employment opportunities become more attractive, making those provinces more desirable places to live. This then leads to a drop in the housing price.ConclusionThe rise in the housing price has long been one of the biggest challenges in ensuring affordable housing for households. As housing prices increase and a larger portion of household income is spent on essential goods, overall household welfare declines significantly. In contemporary Iran, the existing legal texts emphasize financing housing for households and enforcing the Production Leap Law. Therefore, analyzing the factors influencing housing prices is crucial for shaping an effective policy roadmap. The results indicated that rising urbanization, the credit-to-GDP ration, and GDP itself have a positive and significant effect on the housing price. Furthermore, convergence analysis suggests that housing prices in Iranian cities are diverging. The negative spatial correlation of housing prices between cities reflects the dominance of housing as a capital asset rather than a consumer good. A key factor behind traders’ strong influence in the housing market is their greater access to financial credits compared to consumer buyers. However, the positive effects of urbanization have not been widely transferable across many cities due to its benefits. Furthermore, the high share of the service sector in GDP and the positive spillover effects of industrialization across provinces have led to an inverse relationship in housing prices—rising prices in neighboring provinces have contributed to price declines in specific provinces. To improve housing conditions, the most critical policy measures include enhancing financial access for low-income households and diversifying housing options to increase their chances of securing affordable housing.AcknowledgmentsThe author sincerely thanks all colleagues and individuals who contributed to the completion of this article.
Research Paper
Monetary economy
Aso Esmailpour; Jafar Haghighat; Zahra Karimi Tekanlou
Abstract
Recent studies highlight an increasing focus on models that incorporate a wide range of economic data, which is made possible by enhancing traditional vector autoregression (VAR) models with one or more factors. The present study aimed to apply the self-explanatory model of the generalized factor vector ...
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Recent studies highlight an increasing focus on models that incorporate a wide range of economic data, which is made possible by enhancing traditional vector autoregression (VAR) models with one or more factors. The present study aimed to apply the self-explanatory model of the generalized factor vector autoregressive (FAVAR) to investigate macroeconomic and housing market shocks from 1991 to 2022, on a relatively small annual scale. It examined the effects of production shocks, inflation, exchange rates, oil revenues, and the money supply. Housing price levels were estimated using four indices: housing price, fuel and lighting, real estate, rent, and business activity. Additionally, the rental housing index in Tehran and the price index of construction services were used to estimate investment levels in the housing sector. The analysis also included data on new housing investment in major cities, total investment in new houses in Tehran, the number of permits issued by municipalities in urban areas, and the number of permits issued in Tehran. The results revealed that macroeconomic shocks (inflation, production, exchange rates, money supply, and oil revenues) create a wave-like effect in the housing sector, with this effect lasting approximately 6 to 8 years. Inflation, money supply, and exchange rates, compared to GDP and oil revenues, have a greater impact on the housing market. Given the varying effects of these macroeconomic shocks, the central bank and monetary authorities should consider the responses of all sectors to develop more accurate housing plans during monetary policy formulation.IntroductionPlaying a key role in intensifying economic booms and busts, the housing market is a crucial component of the Iranian economy, which can be influenced by macroeconomic shocks. A significant portion of housing demand stems from its function as an asset. When macroeconomic shocks occur, they impact the opportunity cost of holding durable goods (e.g., housing) by increasing inflation, money supply, exchange rates, and oil revenues. These shocks influence both the demand for housing as an asset and the demand for housing services, altering the relative returns on housing investments. As a result, individuals adjust their asset portfolios, including housing, in response to these economic shifts. Consequently, housing demand as an asset fluctuates accordingly. The current study highlighted the conflict between two economic objectives: fostering production and investment growth versus managing inflation, macroeconomic shocks, and their adverse social and distributional effects. The expansion of the housing market can mitigate macroeconomic shocks by improving housing availability for households, contributing to sectoral and national economic growth. However, it may also drive up housing prices. Using the self-explanatory model of the generalized factor vector autoregressive (FAVAR), this empirical research aimed to examine the impact of macroeconomic shocks on housing prices in Iran’s economy.Materials and MethodsThe present study used time series data on macroeconomic variables and bank stability from 1991 to 2022. The data selection followed the general classification outlined in Bernanke et al. (2005), which includes production, inflation, money supply, oil revenues, exchange rates, and the housing market. Since the estimation of factors using the FAVAR requires stationarity, tests such as the generalized Dickey-Fuller unit root test were conducted on the variables. The modeling of FAVAR was based on Bernanke et al. (2005), while the model estimation followed the expectation-maximization algorithm as proposed by Dempster et al. (1977) and Shumway and Stoffer (1982). All variables were obtained from the time series databases of the Central Bank and the Ministry of Housing and Urban Development. After estimating the FAVAR model using EViews and SPSS software, the study presented the instantaneous response analysis of the model variables concerning the key variables over ten periods. The variables were transformed into logarithmic form, and their growth rates were calculated.Results and DiscussionThe response of housing prices and investment in the housing sector to a one-standard-deviation shock was immediate and significant, indicating that the economy quickly adjusts to the shock’s influence in the initial years. As shown in Figure (1), shocks related to production, money supply, exchange rates, oil revenues, and inflation created a wave-like effect in the housing sector, with a duration of approximately 15 years (15 periods), reflecting a period fluctuation. A one-standard-deviation shock in the exchange rate initially caused both housing investment and prices to rise. However, the impact gradually diminished, with investment converging to zero after 9 periods and housing prices after 8 periods—which aligns with expectations. Similarly, a one-standard-deviation shock in money supply initially increased investment in the housing sector while causing housing prices to decline. After 4 periods, both investment and prices converged towards zero, as anticipated. In the case of inflation, a one-standard-deviation shock initially reduced investment in the housing sector, followed by an increase. At the same time, housing prices rose with the inflation shock but gradually declined, with both investment and prices converging towards zero after 8 periods. A shock to GDP led to an initial increase in housing investment and a decline in housing prices. Over time, these effects diminished, with investment and prices converging towards zero after 4 periods and 3 periods, respectively. A one-standard-deviation shock in oil prices resulted in an increase in both housing investment and prices. However, these effects waned, with investment converging to zero after 4 periods and housing prices after 3 periods. Overall, the impact of these shocks diminished over time and eventually converged to zero, as it was anticipated. Figure 1. Macroeconomic shocks ConclusionAccording on the results of the FAVAR model, macroeconomic shocks have both direct and indirect effects on the housing sector and other markets. Macroeconomic shocks were found to increase demand in the housing sector, as housing is an asset that can absorb shocks over the long term and helps preserve the value of money to some extent. The housing channel also exhibited a consistent degree of price stickiness in response to these shocks. The analysis of macroeconomic shocks on housing prices and investment suggested that increasing demand for smaller housing units would drive future demand toward compact-sized homes. This shift is largely due to the declining purchasing power of the middle class, which has been severely impacted by recent shocks of inflation, monetary supply, exchange rates, and oil revenues. As a result, lower-income groups are left with little financial strength to afford housing. The growing gap between housing costs and household income further contributes to this trend. Moreover, the increasing share of housing costs in household expenditures—observed even during periods without economic sanctions—underscores this financial strain.
Research Paper
Energy Economy
Narges Salehnia; Najmeh Souri Naseri; Vahid Rezaei
Abstract
Environmental pollution is one of the most pressing challenges of our time, manifesting in various forms that pose significant risks to both human health and the planet. In this context, the Internet, and institutional economics indexes like government services, and the democratic process— as three ...
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Environmental pollution is one of the most pressing challenges of our time, manifesting in various forms that pose significant risks to both human health and the planet. In this context, the Internet, and institutional economics indexes like government services, and the democratic process— as three key pillars of modern society—play a crucial role in addressing pollution and conserving the environment. Each of these factors, both directly and indirectly, influences the level of pollutant emissions and environmental degradation, making it essential to understand their effects in order to adopt effective solutions. The advent of the Internet has transformed communication, information dissemination, and governance structures across the globe. At the same time, the democratic process is evolving, with a growing emphasis on transparency, citizen participation, and accountability. Moreover, government services delivery, now increasingly facilitated by digital technologies, has undergone significant improvements aimed at enhancing productivity and providing easier access to government services. Despite these improvements, the environmental consequences—particularly in terms of CO2 emissions—have gained attention. Therefore, in this study, using the institutional economics approach, the impact of the Internet, democratic process, and government service provision on CO2 emissions in 63 countries during 2000 to 2020 is examined through the quantile panel method. The results showed that increased internet penetration globally had a positive and significant effect on CO2 emissions at all quantile levels, except for the 0.95 level. The index of government services delivery exhibited a negative relationship with CO2 emissions only at the 0.25 and 0.5 quantile levels. Finally, the democratic process showed no meaningful relationship with CO2 emissions at any quantile level.IntroductionToday’s world faces a multitude of environmental challenges, including climate change, water scarcity, air pollution, and biodiversity loss. Climate change, particularly global warming driven by carbon dioxide emissions from fossil fuel combustion, is widely recognized as one of the most significant threats to sustainable development. It not only causes environmental damage but also imposes substantial economic and health costs. In this context, transitioning to a low-carbon economy is essential, and it is of utmost importance to understand the determinants of carbon emissions. Over the past three decades, the global spread of information and communication technologies (ICT) has accelerated dramatically, becoming a key driver of transformation, growth, and innovation. The Internet, as a flagship outcome of the scientific and technological revolution, has gradually ushered human society into the digital age.Despite these advancements, the role of social structures, governance frameworks, and the delivery of public services remains critical in managing environmental issues. Democratic processes, which emphasize transparency, citizen participation, and accountability can help shape informed environmental policies. Moreover, efficient and digitalized delivery of government services can reduce environmental footprints by decreasing paper consumption and unnecessary travel, whereas inefficient systems may exacerbate pollutant emissions. Consequently, strong political commitments, combined with stringent environmental regulations and improved governmental efficiency, are essential to achieving sustainable development and controlling climate change. In light of these challenges, the present study aimed to examine the simultaneous effects of the growth of the Internet, efficiency of government services delivery, and democratic governance on CO2 emissions in 63 countries during the period 2000–2020. The objective was to provide a comprehensive understanding of the structural differences and heterogeneous effects of macroeconomic and institutional factors on pollutant emissions, while offering policy recommendations for effective environmental regulation.Materials and MethodsTo accurately assess the effects of the variables, this study employed a panel quantile regression model with fixed effects—a sophisticated econometric tool that allows for the analysis of effects of explanatory variables across the entire distribution of the dependent variable, in this case, CO2 emissions. Collected from authentic international sources (particularly the World Bank), the data included key economic indicators such as GDP growth, urbanization rates, the proportion of renewable energy consumption, fossil fuel consumption, and the net flow of foreign direct investment. Additionally, the analysis relied on the indices measuring the quality of democratic governance and government services delivery. While the former is based on criteria such as freedom of expression and political stability, the latter encompasses administrative efficiency, regulatory quality, the rule of law, and corruption control. Before model estimation, the dataset underwent rigorous statistical testing, including checks for normality, correlation, and cointegration. Moreover, the variables were transformed into their natural logarithms to ensure homogeneity and consistency. One of the advantages of using the panel quantile regression model is its robustness to outliers and its ability to capture the differential effects of variables at both the lower and upper quantiles of CO2 emissions. In other words, the effects of Internet penetration or the efficiency of government services delivery may differ between countries with relatively low versus high emission levels. Therefore, the chosen model proved to be well-suited to identify structural differences and heterogeneous effects across nations, yielding statistically robust estimates of the key drivers of pollutant emissions.Results and DiscussionThe empirical findings from the panel quantile regression model revealed that an increase in Internet penetration was associated with a statistically significant and positive effect on CO2 emissions across most quantiles. In particular, at the lower quantiles of emissions, the rise in Internet usage—driven by the energy demands of digital infrastructure and data centers—leads to a marked increase in energy consumption and, consequently, in carbon dioxide emissions. This finding aligns closely with previous research, which suggests that while advancements in ICT improve economic efficiency, they also considerably increase energy consumption. In contrast, the index of government services delivery exhibited a negative relationship with CO2 emissions in certain quantiles. This implies that improvements in the efficiency and quality of government services deliver—achieved through institutional reforms and enhanced transparency—can help reduce energy consumption and mitigate pollutant emissions. In effect, policies aimed at streamlining government operations appear to play a vital role in counteracting the adverse environmental effects associated with technological expansion.Moreover, while the democratic governance index tends to reduce emissions, its effect was not statistically significant. This indicates that merely having democratic structures, without comprehensive institutional reforms, may be insufficient to generate meaningful improvements in environmental quality. Furthermore, control variables such as GDP growth and urbanization rates were found to have a positive and significant effect on CO2 emissions across most quantiles. This finding reinforces the idea that economic growth and concentrated urban development, when not managed with environmental considerations, can drive increased energy consumption and higher levels of greenhouse gas emissions. In contrast, the share of renewable energy consumption consistently demonstrated a negative effect on emissions, highlighting the critical importance of transitioning to clean energy sources to mitigate environmental degradation. As anticipated, fossil fuel consumption had a strong positive effect on CO2 emissions across all quantiles.A key finding of the study is the observed heterogeneity in the effects of economic and institutional variables on CO2 emissions. For instance, in countries with relatively low emission levels, an increase in Internet penetration tends to have a more significant negative effect on the environment, whereas in countries with higher emission levels, this effect is diminished. These variations highlight the importance of tailored regional policies, as uniform policy measures may not produce equally effective outcomes across different contexts.ConclusionAccording to the findings, in the digital age, the growing use of the Internet is a significant driver of increased energy consumption and higher CO₂ emissions. Yet, improvements in the quality and efficiency of government services delivery can effectively counterbalance this trend by reducing overall emissions. From a policy perspective, the research suggests that a comprehensive strategy—one that promotes clean technologies, optimizes energy use within the ICT sector, and implements institutional reforms to enhance public service delivery—is essential for achieving substantial reductions in carbon emissions and fostering sustainable development. The study demonstrated that countries experiencing rapid growth in Internet penetration should prioritize investments in renewable energy infrastructure and energy consumption optimization. Additionally, improving government performance through increased transparency, accountability, and stringent environmental regulations can significantly reduce pollutant emissions. In other words, environmental policies should be crafted to support both technological and economic progress while simultaneously mitigating adverse environmental effects. Moreover, the research highlights the importance of considering structural heterogeneity among countries. Uniform policy measures may not be equally effective across different national contexts, making it essential to develop region-specific, tailored strategies that account for each country’s unique economic, institutional, and environmental conditions. Ultimately, the study offers valuable insights for policymakers and economic decision-makers, providing a foundation for the development of long-term strategies aimed at reducing emissions and building a resilient, green economy.In sum, the present study suggests that addressing contemporary environmental challenges requires aligning technological progress with institutional reforms and improved public service efficiency to achieve sustainable development and protect the environment. Policymakers are encouraged to incorporate environmental considerations into their broader development plans and to leverage green technologies as essential tools for reducing pollutant emissions. By doing so, a coherent and integrated approach to economic and environmental policy can be established, balancing the demands of growth with the imperative of environmental protection.
