economic growth

Renewable energy plays a pivotal role in achieving sustainable development by addressing environmental challenges and fostering economic growth. This study examines the asymmetric effects of renewable energy consumption and capital stock on Iran’s economic growth over 2001–2021 using the nonlinear autoregressive distributed lag (NARDL) model. The findings indicate that negative shocks in renewable energy exert stronger and more significant effects on economic growth than positive shocks. Although the Wald test provides limited evidence of asymmetry, the results confirm the long-term stabilizing role of renewable energy, with both positive and negative shocks influencing growth equally in magnitude. These insights underscore the need for balanced and supportive clean energy policies to mitigate adverse shocks and sustain economic growth.

This research investigates how government price subsidies for renewable energy affect Iran’s economic growth and environmental sustainability using a Dynamic Stochastic General Equilibrium (DSGE) model. The study incorporates technological shocks in energy sectors and total productivity to assess their impact on key variables, including output, consumption, investment, emissions, and energy composition. Findings reveal that subsidies lower clean energy costs, boosting short-term consumption and investment while shifting resources toward greener technologies. The policy increases household disposable income, curbs fossil fuel use, and reduces greenhouse gas emissions. Additionally, renewable energy shocks demonstrate more stable and positive effects on both economic and environmental outcomes compared to fossil fuel shocks. By incentivizing clean energy adoption, subsidies gradually enhance environmental quality and support the transition to a low-carbon economy. The study concludes that price subsidies are an effective policy tool for balancing economic and ecological goals in fossil fuel-reliant nations like Iran, promoting sustainable development without compromising growth.

Energy diversification, as a strategic necessity in fossil fuel-dependent economies like Iran, plays an important role in reducing structural dependency and achieving sustainable economic growth. This study discusses the causal relationship between the energy diversification index and economic growth in Iran over the period 1970–2023. A novel nonlinear approach based on deep neural networks is employed within a sliding window framework. The performance of three machine learning models (MLP, LSTM, and GRU) is assessed in predicting each variable (economic growth and energy diversification), first using only its own lags, and then by adding the lags of the other variable. The difference in prediction accuracy is quantified using a causality intensity index (Ft) and its significance is tested via the Wilcoxon test. Results reveal that including energy diversification as an input significantly improves the prediction of economic growth—particularly in LSTM and GRU models, where MSE dropped by approximately 46% and 39%, respectively, and Cohen’s d values reached 3.90 and 4.15, indicating very large effects. In contrast, adding economic growth to models predicting energy diversification did not improve, and sometimes worsened, model performance—evidenced by a negative Cohen’s d in the LSTM model. Dynamic analysis also showed that the strength of causality from economic growth to energy diversification varied over time, intensifying during periods of structural or policy change. Overall, the nonlinear neural network approach proved superior to traditional linear methods in capturing complex and time-varying relationships between energy diversification and economic growth. Advanced models like LSTM and GRU yielded stronger and more accurate causal insights. The findings support the "growth hypothesis," indicating that energy diversification can act as a driver of economic growth in Iran. Accordingly, policymakers are advised to foster institutional support and invest in renewable energy to promote sustainable development.

Global policy uncertainty refers to situations in which economic decision-making faces significant challenges and the future of the economy becomes unpredictable due to various factors, such as global changes and policy fluctuations. This study examines the impact of institutional quality and global policy uncertainty on Iran’s economic growth from 1984 to 2021 using the discrete threshold regression model. The results show that Iran falls into two distinct institutional regimes, with a threshold level of 4.66. In the first regime, where institutional quality is below the threshold, an increase in global policy uncertainty has a negative and significant effect on economic growth. Conversely, in the second regime, where institutional quality exceeds the threshold, the negative effects of global policy uncertainty are moderated, and economic growth improves. The findings also indicate that the effects of globalization and physical investment on economic growth depend on the level of institutional quality. Under weak institutional conditions, globalization and physical investment positively influence economic growth, while institutional quality exerts a negative effect. However, once institutional quality surpasses the threshold, its positive effects on growth strengthen, and the impacts of globalization and physical investment improve. These findings underscore the importance of institutional reforms and improving institutional quality to reduce economic vulnerability and optimize the benefits derived from global opportunities. 

In response to global commitments to address climate change, economic policies must align with the goal of reducing carbon dioxide (CO₂) emissions and achieving the targets set by the Intergovernmental Panel on Climate Change (IPCC). Information and Communication Technology (ICT) is increasingly recognized as a strategic tool influencing both economic productivity and environmental sustainability. Assessing the appropriate level of economic support for ICT in alignment with IPCC goals is, therefore, a critical step toward managing this technology for sustainable development. This study aims to estimate the ICT subsidy levels required to achieve IPCC environmental targets using the Regional Integrated Climate–Economy (RICE) model. It also investigates the impact of ICT on economic growth and CO₂ emissions. The model is calibrated for Iran and Middle Eastern countries using structural parameters developed by Nordhaus in 2013, and simulations are conducted under four scenarios: (1) a baseline scenario, (2) baseline with ICT, (3) a 2°C temperature constraint scenario (aligned with IPCC goals), and (4) a 2°C scenario with ICT integration. Results show that ICT subsidies are lowest in the baseline scenario and increase significantly under the 2°C constraint scenario. ICT implementation leads to higher economic growth, lower CO₂ concentrations, reduced global temperature, and decreased carbon pricing compared to non-ICT scenarios. These findings highlight the need for targeted ICT support through subsidies for low-carbon digital projects, improved infrastructure in underserved regions, and tax incentives for technology-based businesses—advancing CO₂ reduction, IPCC compliance, and economic development simultaneously.

The purpose of this research is to examine how government spending shocks affect Iran's economic growth.To achieve this goal, multivariate modeling of Ram's (1986) growth patterns and Hansen's threshold regression method have been used. The threshold criteria used in this study for government size are: the ratio of government current expenditures to GDP (GS1) and the ratio of government capital asset acquisition costs to GDP (GS2). The findings of this study confirm the positive effect of government spending shocks on capital asset acquisition costs and their lack of confirmation in the area of current spending on Iran's economic growth. Also, the results indicate that the Bars (Army) curve has not been realized in the Iranian economy during the period 1961 to 2023; meaning that in none of the small, medium, and large government sizes, the relationship between government size and economic growth is negative, and we do not witness an inverted U curve in this relationship. However, this effect is faced with a decrease in efficiency and effectiveness in large government sizes. The positive side effects of government spending in the areas of current spending and capital asset acquisition on non-government sector production are another finding of this study, which is faced with a decrease in elasticity in the current spending sector from small to large government and an increase in elasticity in the capital asset acquisition sector.

This study introduces a novel framework for nowcasting Iran's quarterly GDP growth by leveraging sentiment analysis of economic news. A web crawler was employed to collect economic news articles from March 2005 to December 2023. The sentiment orientation (negative, neutral, positive) and intensity of each article were quantified using the Persian adaptation of the SentiStrength lexicon. Quarterly aggregation of these scores formed a composite news sentiment index. Statistical analysis reveals a significant correlation between this index and quarterly economic growth, offering valuable insights into Iran's short-term economic fluctuations. Furthermore, incorporating the sentiment index into a forecasting model leads to superior performance metrics compared to a benchmark univariate time-series model. By capturing shifts in economic sentiment in real-time, this approach provides a more timely and comprehensive understanding of Iran's economic trajectory, even prior to the release of official GDP statistics.

Economic growth stands as one of the most pivotal macroeconomic objectives for Iran. Consequently, identifying and examining the factors influencing the country's economic growth is of paramount importance. This study employs a Mixed Data Sampling (MIDAS) approach to investigate the impact of exports on economic growth during the period 2004-2023 (1383-1402 in the Persian calendar). The findings reveal that the effect of exports on economic growth is contingent upon the prevailing monetary policy stance and the rate of exchange rate growth. Specifically, the results demonstrate that when low export levels coincide with a contractionary monetary policy and high exchange rate growth, economic growth exhibits a strong positive response to the export situation. Conversely, under an expansionary monetary policy, the response of economic growth to exports is conditional on exchange rate movements. If the exchange rate remains stable amid a high number of exchange rate interventions, economic growth shows little responsiveness to exports. This research underscores that the impact of exports on economic growth is not direct, but rather mediated by the interplay between monetary policy and exchange rate dynamics.

The impact of fiscal policies on economic growth is a complex and multifaceted issue that requires a thorough analysis and deep understanding of each country's specific economic conditions and dynamics. In the case of Iran, the effectiveness of fiscal policy is significantly influenced by economic shocks, international sanctions, and shifts in fiscal strategies.  This study investigates the relationship between fiscal policies and economic growth in Iran over the period 1997–2022, utilizing a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with quarterly data.  The findings indicate an inverted U-shaped relationship between current government expenditures and economic growth. In other words, a moderate increase in current expenditures can stimulate economic growth, but beyond a certain threshold, further increases may lead to a decline in growth. Additionally, the study reveals that during periods when economic growth responds positively to current expenditures, the impact of capital (development) expenditures on growth reaches its peak. The empirical evidence suggests that the composition of current and capital government expenditures is a key determinant of the path of economic growth.  Moreover, the study shows that tax rates should be dynamically adjusted based on the state of the gross domestic product (GDP). Specifically, during periods of higher economic growth, raising tax rates may have a more positive effect on growth. In contrast, during periods of lower GDP, increasing tax rates could negatively affect economic growth. Other findings emphasize that Iran’s business cycle plays a crucial role in the effectiveness of fiscal policy, particularly regarding tax rates, on economic growth. These findings provide valuable insights for Iranian policymakers, enabling them to design more effective fiscal policies that are responsive to changing economic conditions and business cycle fluctuations.

Since the Islamic Revolution, population growth in the outskirts of large cities has increased significantly. Many studies have examined the relationship between migration and urban growth; however, few have considered the impact of urban crime patterns on the economic performance of surrounding regions. If urban crime affects a broader geographical area, it should not be regarded solely as an “inner‑city” issue, but rather as a regional problem that can influence residents, businesses, and economic activities in neighboring areas.In addition to the direct effects of crime within a specific geographical area, crime can also generate indirect spillover effects on nearby regions. These spillovers may manifest differently depending on the type of crime. The core–periphery model helps explain why some central urban areas experience significant prosperity while others exhibit signs of urban deprivation and poverty.

Given this importance, an important question arises: To what extent can crime affect regional economic growth? What types of effects does it generate, and which categories of crime have the greatest impact on economic growth? Moreover, can crime in one region influence economic growth in surrounding areas?Accordingly, the main objective of this study is to investigate crimes occurring in the core region and their impacts on surrounding areas. For this purpose, Tehran province is considered as the core region, while the other 30 provinces of Iran represent the periphery. The study covers the period from 1998 to 2020.

The estimation results indicate that the growth of gross domestic product (GDP) in Tehran province has a negative and statistically significant effect on the economic growth of other provinces. In addition, the results show that a one‑percent increase in the poverty index of the provinces leads to an increase in economic growth. Similarly, a one‑percent increase in the unemployment rate of the provinces is associated with higher economic growth. The population growth rate also has a positive and significant effect on provincial economic growth.Furthermore, the results indicate that a one‑percent increase in the number of unintentional murders in Tehran province, as well as assaults and injuries, leads to an increase in the economic growth of other provinces. However, changes in assaults and injuries have a negative and significant effect on the economic growth of the provinces.The findings also show that an increase in the number of attempted stabbings and intentional poisonings in Tehran leads to higher economic growth in other provinces. Nevertheless, changes in the logarithm of intentional poisoning have a negative and significant effect on the economic growth of the provinces.The logarithm of thefts from government facilities in Tehran province has a negative effect on the economic growth of the provinces, whereas the differential of the logarithm of thefts from such places has a positive and significant effect. The logarithm of residential burglaries in Tehran province shows a positive and significant effect on the economic growth of the provinces.A one‑percent increase in thefts from shops and from industrial and commercial centers in Tehran province reduces economic growth in other provinces. However, the logarithmic differential of shoplifting in Tehran province has a positive and significant effect on provincial economic growth.The results also indicate that a one‑percent increase in motorcycle and bicycle thefts, as well as cattle theft in Tehran province, increases the economic growth of other provinces. In contrast, the differential of the logarithm of theft of motor boats and marine equipment has a negative and significant effect on provincial economic growth. Moreover, the growth of theft of car accessories or items inside vehicles in Tehran province has a negative and significant impact on the economic growth of other provinces. Similarly, theft of motor boats and marine equipment in Tehran province negatively affects the economic growth of the provinces.

The results suggest that a decrease in life security in Tehran province leads to the spillover of investment and production toward other provinces. Consequently, production activities and economic growth increase in those regions.The analysis of different theft indicators reveals that various types of theft have different impacts on the economic growth of other provinces. Indicators of ordinary theft that create demand for goods and services—such as theft from shops, commercial centers, and government facilities—have a negative impact on the economic growth of other provinces. This is because Tehran, as the capital city, plays a crucial role in generating demand for goods produced in other provinces. Therefore, any reduction in security in Tehran’s commercial centers can create ripple effects throughout the national economy.Moreover, due to the concentration of government offices in Tehran and their nationwide functions, the effects of crimes against government facilities extend beyond Tehran itself. Reduced security in these institutions may weaken the allocation of investment resources and hinder the strengthening of economic infrastructure across the country.In contrast, certain types of ordinary theft—such as residential burglary, motorcycle theft, and cattle theft—are more localized in nature. An increase in these crimes in a specific region may encourage the relocation of investment to other provinces, thereby increasing their economic growth.Finally, since crime indicators are highly diverse, the results demonstrate that the impact of crime on economic growth depends on the specific type of crime considered. Comprehensive indicators such as theft from government institutions and commercial centers in Tehran have negative effects on the national economy. In contrast, more localized and changeable indicators—such as residential theft, which can vary with migration patterns—may generate positive spillover effects on economic growth in other provinces.

During the recent years, stagnant inflation is considered as one of the main challenges in the  economy of Iran , which has caused a drop in total demand in the economy by reducing the real per capita income and purchasing power of households. A decrease in the components of the total demand and its continuation will damage the productive capacity of the economy reduce the economic growth and  gross domestic product. Thus, a solution must be suggested to resolve the stagnation which has been formed on the demand side of the economy. In this context, financial development and  facility granting  can stimulate demand and create the necessary platforms for  reconstructing  production processes. Therefore, this study aims to evaluate the effectiveness of the growth of  economic activities in Iran from the facilities granted to socio-economic institutions (non-financial companies and households separated by urban and rural income groups). Based on the literature, researchers have paid less attention to the matrix of social and financial accounting. In addition, no study has focused on the evaluation of the relationship separately by economic sectors through FSAM although the relationship between facilities and economic growth has been analyzed based on an econometric approach . Therefore, this study seeks to  collect FSAM for Iran's economy using the updated data of 2018 in a large format (dimensions 257x257) and the effect of the facilities granted by banks on the growth of activities checking the economy.

Endogenous and exogenous accounts should be separated for operationalizing the financial social accounting matrix and calculating the increasing coefficients like social accounting matrix. Endogenous accounts consists of  1) accounts of the real side of the economy including the production account, production factors account, and the current account of institutions (households, financial companies, and non-financial companies), 2)accounts of the financial side of the economy including capital account (households, financial institutions, non-financial companies) and financial account (cash and deposits, securities, insurance technical reserve, and other accounts). Further, accounts of the government,  central bank, foreign world, monetary gold and special drawing rights, legal reserves of the central bank, loans and government partnership bonds are  presented in the form of external accounts. It is necessary to use the output data table to compile the parts of the exchanges in the real party of the social financial accounting matrix. These tables should be updated due to the time gap between the statistical base year and year of their publication. Therefore, the updated supply and consumption table of Iran's economy for 2018 was collected  and used to complete the social financial accounting matrix.

The results of this study showed that stimulating the current demand of households with current facilities and the capital demand of households and non-financial companies with capital facilities increases the production of economic activities at an appropriate level. Thus, the increasing coefficient of current facilities of households is more than 2 and the increasing coefficient of capital facilities of households (except for the low-income decile) and non-financial companies is more than one. Given the percentage of additional growth, the results of this study showed that the added value (income of production factors) increases, along with the granting of capital facilities to institutions, especially non-financial companies, by applying a shock to banking facilities. The growth of added value provides 0.16%.

Based on the obtained results, it is possible to provide the basis for stimulating the demand and expect to improve the production of activities and reduce the recession by granting facilities and providing credits to the institutions. In this context, by adopting measures and solutions such as granting purchase facilities to consumers, it is possible to provide the basis for the prosperity of production units. Further, it is possible to help stimulate the supply side by facilitating the receipt of capital facilities for households and non-financial companies. In addition, granting facilities to urban households, as well as households of low income urban and rural groups, and granting facilities to rural households and households of high-income groups can have more productive consequences than other groups. Hence, it is suggested that the groups which can have a greater impact should be targeted in granting facilities to the private sector and households. Further, since the granting of various current or capital facilities can have different effects on the production of economic sectors. In other words, the type of facilities granted should be considered based on the growth of that sector when the growth of the production of a specific sector in the economy is desired.

In recent decades, extreme currency fluctuations have become one of the characteristics of Iran’s economy. In this regard, in addition to the economic factors that justify exchange rate changes (particularly fluctuations in the value of the USD) the government’s policy of undervaluation the national currency has attracted significant attention due to the extent of its predictable effects on the national economy. This policy is particularly emphasized by economic analysts who support development strategies. However, other analysts focus more on the inflationary consequences of implementing such policies. So as to scrutinize the economic effects of implementing this policy, the present study examines impact of national currency undervaluation on the effect of inflation on economic growth in Iran from 1980 to 2023. According to the results, although the direct control of exchange rates cannot be defended, the positive and expansionary effects of inflation on economic growth due to the national currency undervaluation in the free market are demonstrated. Nonetheless, in a country like Iran, where controlling inflation must be a top priority for economic goals and policies, the policy of depreciating the national currency cannot easily be supported. In fact, the successful implementation of advanced economic policies requires a deep examination of the country's infrastructure, influential macroeconomic variables, and level of development. The present study focuses on the level of financial development and physical capital indices as infrastructures and effective variables influencing economic growth.

This study aims to examine the effect of artificial intelligence on countries' economic growth using a panel generalized method of moments (GMM) approach. In this regard, panel data from leading countries in the field of artificial intelligence from 2017 to 2023 have been utilized. The estimation of the dynamic panel model shows that artificial intelligence growth has a positive and significant effect on economic growth. In other words, increased investment in AI activities has led to accelerated GDP growth in the countries under study. On the other hand, the positive interaction between AI growth and labor productivity indicates that the impact of artificial intelligence is strengthened in the presence of higher productivity. Based on the findings, leveraging AI technology can enhance countries' economic growth through improving total factor productivity and other channels of innovation and investment. Additionally, the positive interaction between AI growth and human capital also confirms that artificial intelligence has a greater intensity of impact in the presence of higher human capital. The results of this study confirm the transformative role of artificial intelligence in the economy and demonstrate the necessity of attention to policies for maximum and comprehensive utilization of AI capabilities. Nevertheless, widespread AI adoption without appropriate institutional and policy preconditions may pose challenges. For example, the concentration of AI benefits in large firms or the absence of regulatory frameworks could exacerbate inequality or disrupt fair competition. Despite these concerns, the overall conclusion of the present study emphasizes that artificial intelligence serves as a modern engine of growth and can play a positive role in enhancing and promoting long-run and sustainable economic development, provided sound policies and strategic investments accompany it. Accordingly, to more effectively utilize artificial intelligence capabilities to boost economic growth, economic policymakers are advised to prioritize strategies such as investing in digital infrastructure and artificial intelligence, training in digital skills and developing human capital, facilitating competition and an open ecosystem, and creating appropriate governance and regulatory frameworks in their economic and social planning.

Sustainable growth and the reduction of environmental pressures have emerged as paramount priorities on the global agenda, creating the need for more comprehensive approaches to assessing environmental quality. Consequently, to alleviate environmental challenges and realize the objectives of sustainable development, a broad and holistic environmental assessment is imperative. This study investigates the dynamic impacts of economic growth, fossil fuels, renewable energy, and urbanization on the sustainable Load Capacity Factor (LCF) in Iran. The analysis utilizes Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) methodologies over the period of 1995–2022.Based on the estimates obtained from cointegration tests, renewable energy consumption enhances the load capacity within the Iranian economy, whereas economic growth leads to the reduction of the load capacity. Furthermore, fossil fuel consumption, urbanization, and natural resource rents negatively impact the load capacity. Specifically, a one percent increase in economic growth reduces carrying capacity by 0.38%, while a one-unit increase in urbanization, fossil fuel consumption, and resource rents decreases carrying capacity by 0.40, 0.12, and 0.09 units, respectively. Conversely, a one-unit increase in renewable energy consumption adds 0.15 units to the carrying capacity.

Shadow banking, as a part of the financial system operating outside the direct supervision of central banks and regulatory institutions, plays a significant role in financing and resource allocation. Given the expansion of shadow banking assets and its impact on macroeconomic variables especially economic growth—this study examines the effect of shadow banking on economic growth in two groups of selected countries (including developed and developing countries) over the period 2002 to 2023. To model the relationships among variables, the dynamic panel method is employed, and the model is estimated using the Generalized Method of Moments (GMM) technique. The estimation results for both groups of countries indicate that the expansion of shadow banking assets leads to an increase in economic growth, and this effect is further amplified through the channel of domestic credit to the private sector.

Numerous factors can influence income distribution, and identifying these factors and examining their impact is of great importance. The objective of the present research is to analyze the impact of economic growth on income distribution in Iran's economy using the Autoregressive Distributed Lag (ARDL) approach over the period 1357-1401 (1978-2022).The results of the study indicate that, using the Nonlinear ARDL (NARDL) approach, in the long run, a negative shock to economic growth has a positive and significant effect on the Gini coefficient, whereas a positive growth shock does not have a significant effect on the Gini coefficient. Furthermore, in the long run, the inflation rate and unemployment rate also have a positive and significant impact on the Gini coefficient; such that an increase in these variables leads to a rise in income inequality. Additionally, in the long run, the variables of per capita bank facilities and per capita government expenditures have a negative effect on the Gini coefficient, meaning that an increase in these variables reduces income inequality. Moreover, in estimating the short-run relationships via NARDL, the results showed that the error correction coefficient adjusts approximately 73% of the disequilibrium in the Gini coefficient in each period for the subsequent period.

Financial development is a key factor in enhancing the economic dynamics of countries, while energy diversification is an important component in ensuring a sustainable energy supply and reducing dependence on fossil fuel resources. This study examines the threshold role of economic growth in the impact of financial development, oil rent, and industrialization on energy diversification in Iran using data from 1980 to 2022 and the Threshold Autoregression (TAR) method. The results indicate that the effects of these variables on energy diversification depend on the economic growth threshold (per capita income of $3,704) in two distinct regimes. In the first regime, where economic growth is below the threshold, oil rent has a positive effect, whereas financial development and industrialization negatively influence energy diversification. In the second regime, where economic growth surpasses the threshold, the negative effect of industrialization diminishes, and financial development has a significant positive impact on energy diversification. These findings suggest that improving economic conditions alongside the simultaneous development of financial systems can help reduce dependence on fossil fuels and enhance the diversification of energy sources in the country.

For resource-dependent economies such as Iran—where oil-revenue volatility and structural fragilities generate recurring macroeconomic instability—fiscal-policy uncertainty can pose a major impediment to stable and sustainable growth. This study examines the time-varying, bidirectional relationship between budget-deficit uncertainty and real economic growth in Iran. Fiscal uncertainty is proxied by the conditional volatility of the operational budget balance, while growth is measured by quarterly real GDP growth. Using quarterly data for 1991–2022, we employ the Continuous Wavelet Transform (CWT) and wavelet coherence to analyze co-movements across the joint time–frequency space, allowing the relationship to vary across business-cycle states and horizons. The results reveal a pronounced asymmetric and business-cycle-dependent pattern. During oil-driven expansions, economic growth tends to increase future fiscal uncertainty, consistent with pro-cyclical fiscal behavior whereby temporary revenue windfalls finance persistent expenditure commitments. In contrast, during recessions and sanction episodes, fiscal uncertainty becomes a leading constraint that discourages investment and deepens downturns. This interaction points to an “instability trap” in which the economy oscillates between unsustainable booms and severe recessions. Importantly, at long horizons no statistically significant relationship is detected, suggesting that cyclical fiscal uncertainty does not determine Iran’s long-run growth path.

This study aimed to examine the threshold effect of fintech on the relationship between oil rents and economic growth in Iran. To analyze the relationships among variables, the study used a threshold regression approach and seasonal data from 2013 to 2022 in Iran. The results showed that oil rents had a significant negative impact on economic growth both before and after fintech reached its threshold level of 0.146. However, once Fintech surpassed this threshold, the magnitude of the resource curse effect on economic growth decreased. Additionally, the interaction effect between oil rents and fintech had a significantly negative effect on economic growth before fintech reached the threshold. After exceeding the threshold, however, the interaction effect became significantly positive, indicating that higher levels of fintech development mitigate the adverse impact of oil rents on economic growth. The inefficient allocation of oil revenues, accompanied by increased rent-seeking and corruption, constrains economic growth. In contrast, the expansion of fintech through digital technologies enhances access to financial services for firms and entrepreneurs in the non-oil sector. This improved access promotes employment and reduces the economy’s dependence on oil. Therefore, fintech development alleviates the negative effects of oil rents on economic growth. On the basis of the findings, it is recommended that the government promote the development of fintech platforms and blockchain technologies while strengthening oversight of oil revenue allocation within the public budget. In addition, policies should aim to facilitate access to capital for entrepreneurs and small businesses in high-technology sectors. Through optimal resource management and balanced development across production sectors, the negative effects of oil rents on economic growth can be reduced.

 INTRODUCTION

The strategy for successful growth in countries is based on a dual effort: stimulating growth (short-term strategy) and maintaining it (medium to long-term strategy). Stimulating and accelerating economic growth in the short term requires only a few reforms that do not put much pressure on the institutional capacity of the economy. However, in the long term, sustaining economic growth becomes more challenging and requires a smart combination of structural changes and improvements in fundamentals. Iran's historical experience shows that due to the presence of oil and its impact, the economic performance has been somewhat successful in terms of growth in some periods; however, in the medium and long term, it has failed to sustain and stabilize economic growth. Examining the two factors of structural changes and fundamentals in a historical process helps identify their role in shaping the pattern of economic growth. The importance of conducting innovative research lies in determining whether the structural changes in Iran's economy have been a driving force for economic growth. Additionally, what changes have occurred in institutions and human capital alongside the structural changes in the country, and have they contributed to economic growth This research combines the neoclassical and dual economy models to analyze Iran's economic growth under the two factors of institutional capacities and structural changes.

In this study, the Autoregressive Distributed Lag (ARDL) model with distributional lags has been used to depict the pattern of economic growth in Iran during the years 1970-2019. The necessary data has been extracted from the Central Bank of the Islamic Republic of Iran, the World Bank, the Statistical Database of the Federal Reserve System, and the Fraser Institute's Economic Freedom Index. Accordingly, the variables used in the econometric model under investigation are as follows:In the above model, Y represents Gross Domestic Product (GDP), FDI stands for Foreign Direct Investment, HC represents Human Capital, SC represents Structural Changes, and INS stands for Institutional Index. In the empirical model, the logarithmic form of variables has been considered as follows:Here,  represents the error term of the model, which follows classical assumptions.

According to the results of the model estimation, the behavior of FDI in the Iranian economy does not show a significant correlation with economic growth in the short and long term, which may be due to the structure of the Iranian economy, where foreign investments are not directed towards the country's relative advantages. In the long term, human capital and institutional indices have a positive and significant effect on GDP. In the short term, the institutional index has no significant effect on GDP, indicating that short-term economic growth requires limited reforms that do not put much pressure on the economic institutional capacity. Human capital is a complement to physical capital and a significant share of economic growth in countries comes from the development of human capital. The results of the model estimation show that the average growth of years of education and literacy has a positive impact on GDP growth. The coefficient of human capital in the long-term estimate is larger than in the short-term estimate. This is because, in the early stages of economic growth, attention to physical capital is a priority. However, in the long term, attention must be paid to human capital for the continuity and sustainability of the economic growth path. Finally, the effect of structural changes on GDP growth is also positive and significant. Therefore, alongside traditional growth determinants, structural changes also contribute to increasing economic growth. 

The results of the study show that in the Iranian economy, if structural changes, human capital, and institutional indices grow by 1%, GDP will increase by 2%, 1.06%, and 0.86%, respectively, and the role of structural changes is more prominent than other factors. Additionally, the movements of structural changes in Iran are fluctuating. From the set of these factors, it can be inferred that in terms of structural changes and fundamentals, economic growth in Iran has experienced an uneven and diverse pattern.

Inflation and exchange rate volatility are two very important categories in the Iranian economy that affect each other and overshadow economic growth in Iran. Different views on the relationship between these variables are common in economic literature. Therefore, investigating this issue is very important for policymaking. In the present study, the causal relationship between exchange rate volatility, economic growth, and inflation rate is investigated with a nonlinear approach in the form of Markov regime change vector autoregression method using monthly data from 2011 to 2024. The results of this study indicate that during boom and bust, there is a two-to-two causal relationship between all three variables, except for the causal relationship from the economic growth rate to the inflation rate. In other words, during boom and bust, there is no causal effect from the economic growth rate to the inflation rate, but inflation has a causal effect on economic growth. During recessions, the variables of economic growth rate, exchange rate fluctuations, and inflation rate have a significant effect on each other in pairs, which indicates the high sensitivity of the variables to each other during recessions and turbulence. Since the asymmetric effect of the aforementioned variables is evident under different regimes, it is recommended that policymakers consider this asymmetric effect in policymaking in order to have a better and more accurate understanding of the implementation effects of their economic policies.

This study examines the nonlinear relationship between international entrepreneurship, institutional quality, and economic growth across 45 selected countries (both developed and developing) during 2003–2023. Using panel econometric techniques, including the System‑GMM estimator to control endogeneity and the Panel Threshold Regression (PTR) model to identify structural breaks, the research provides strong evidence of asymmetric effects. Findings show that in developed economies, international entrepreneurship—especially firms with high export intensity—acts as a driver of innovation and competitiveness, exerting a significant positive impact on growth. In contrast, in developing economies, it has a significant negative effect, supporting the “institutional pull paradox” hypothesis: without supportive institutions, bureaucratic frictions and rent‑seeking outweigh the benefits of global market access. Threshold analysis reveals that institutional quality functions as a switching variable; only after surpassing a certain level of good governance does international entrepreneurship become growth‑enhancing. Domestic investment was identified as a stable growth driver in both groups, while financial development showed a negative effect, reflecting risks of inefficient credit allocation without effective oversight. These results imply that for developing countries, policy priorities must focus on fundamental institutional reforms, since trade liberalization and outward entrepreneurship without crossing the institutional threshold may lead to resource misallocation.

First, during periods of crisis, fiscal uncertainty acts as a 'recessionary anchor,' establishing a strong negative causal link with economic growth. Second, the results reveal a 'paradox of prosperity': emerging from a crisis, economic recovery itself becomes a source of future expenditure uncertainty, exposing the weakness of institutions in curbing pro-cyclical policies. Finally, the absence of a long-run relationship implies the dominance of fundamental and structural factors over fiscal policy uncertainty. These findings, which highlight the sensitivity of the results to the choice of measurement proxy, underscore the complexity of uncertainty's transmission channels in the Iranian economy. An understanding of these dynamic patterns offers crucial policy implications for managing fiscal stability, which are discussed in detail in the paper.First, during periods of crisis, fiscal uncertainty acts as a 'recessionary anchor,' establishing a strong negative causal link with economic growth. Second, the results reveal a 'paradox of prosperity': emerging from a crisis, economic recovery itself becomes a source of future expenditure uncertainty, exposing the weakness of institutions in curbing pro-cyclical policies. Finally, the absence of a long-run relationship implies the dominance of fundamental and structural factors over fiscal policy uncertainty. These findings, which highlight the sensitivity of the results to the choice of measurement proxy, underscore the complexity of uncertainty's transmission channels in the Iranian economy. An understanding of these dynamic patterns offers crucial policy implications for managing fiscal stability, which are discussed in detail in the paper.