Iranian Journal of Economic Research

Abstract The present study examines the impact of globalization on Iran’s underground economy over the period 1979–2020. In this regard, the size of the underground economy was estimated using the MIMIC method. Subsequently, the effects of the three main dimensions of globalization—economic, social, and political—as well as the dual components of each dimension (de facto and de jure), were analyzed using the autoregressive distributed lag (ARDL) approach. The findings from the long-run estimations indicate that the economic and social dimensions of globalization have a negative effect on the underground economy, whereas the political dimension exerts a positive effect. Further analysis of the components of each globalization dimension reveals that both the de facto and de jure components of social globalization negatively influence the underground economy. Although the de jure component of economic globalization also has a negative impact, its de facto component does not have a significant effect. Regarding the political dimension, de facto component has a significant positive effect on the underground economy, while the de jure component does not show a meaningful impact. Additionally, the results demonstrate that, in all four estimated models, unemployment has a positive effect and financial deepening has a negative effect on the underground economy. These findings offer useful guidance for policymakers aiming to reduce the size of the underground economy and enhance economic transparency in the country.

Abstract Abstract: The relationship between democracy, bureaucracy, and corruption is a topic that has garnered significant attention in academic and policy-making circles. There are various perspectives on how these three concepts interact, and the inherent complexities often render simple, static analyses inadequate. This research investigates the dynamic relationship between democracy, bureaucracy, and corruption using time-frequency analysis based on the wavelet analysis method over the period from 1984 to 2022. The findings indicate that in the short-term horizon, the causal effect of democracy and bureaucracy on corruption is highly dynamic, with the direction of causality and the type of relationship changing over time. In the medium-term, although the negative causal impact of democracy and bureaucracy on corruption is the dominant phenomenon, there are also periods where corruption has a causal and positive impact on democracy and bureaucracy, suggesting a potential two-way relationship in this range. In the long-term, the relationship between democracy and bureaucracy with corruption is characterized by strong and positive coherence. Finally, this study emphasizes the multifaceted and time-dependent nature of the relationship between democracy, bureaucracy, and corruption. Understanding these dynamics is essential for designing effective anti-corruption strategies that account for both short-term variations and long-term trends. The results of this study suggest the necessity of strengthening democratic institutions, creating efficient and transparent bureaucracies, and addressing fundamental factors such as political will to solve the problem, elite dominance to achieve sustainable progress, and combating corruption through focused transparency.

Abstract Universal subsidies have a limited impact on the welfare of vulnerable groups, while substantially increasing government expenditures and disproportionately benefiting high-income households. This highlights the urgent need to reconsider the mechanisms for subsidy payment and the identification of poor households. One proposed approach is the regionalization of the subsidy system. In this respect, the current study compared national and regional subsidy targeting at the urban level. Using the data from the Urban Household Income and Expenditure Survey (2022/2023), the study measures the poverty line and poverty indices. On the basis of these indices, urban areas across provinces were classified into eight regions using a hierarchical clustering method. Then, a numerical optimization method was employed to compare the outcomes of subsidy targeting at the national and regional levels. According to the results, subsidy targeting based on both individual and combined characteristics of the most vulnerable groups leads to significant differences at national and regional levels. These differences are evident in optimal subsidy amounts, poverty reduction outcomes, efficiency of targeting, and associated errors. Therefore, even when policymakers target households with identical characteristics at both national and regional levels, subsidy payment amounts must still vary across regions and population groups to achieve effective targeting.

Introduction

Untargeted subsidy payments in Iran have not only failed to improve the welfare of vulnerable groups but have also disproportionately benefited higher-income households. This outcome prompted reforms to certain commodity subsidies; however, the cash subsidies introduced as replacements were likewise not targeted. Consequently, universal commodity subsidies were effectively replaced by universal cash subsidies, which had little impact on poverty reduction in Iran. In light of these challenges, the need to reconsider subsidy payment mechanisms and improve the identification of poor households has become increasingly evident. Targeting subsidies by distinguishing the poor from the non-poor can reduce resource waste and increase the share of benefits received by the poor within a fixed budget. However, such benefits depend on the government’s ability to accurately identify poor households. In practice, structural weaknesses in the tax system and social provision institutions limit the government’s capacity to do so (Darvishi et al., 2019). To address this limitation, the current research aimed to propose a numerical optimization method for targeting the poor under conditions of a fixed budget and missing information about household welfare. The study also compared subsidy targeting outcomes in Iran at the national and regional levels.

Materials and Methods

The study relied on three main categories of data: (a) data on per capita household expenditure excluding received subsidies, household size, and household population weights; (b) information on the economic and social characteristics of households; and (c) the poverty line. The data for the first and second categories was obtained from the Household Income and Expenditure Survey for urban and rural areas of the country for the year 1401 (2022/2023). The third category concerned the estimation of the poverty line, for which several points merit clarification. First, a daily intake of 2,300 kilocalories was used as the standard calorie requirement. This decision was based on the calculations that account for the calorie needs of different age and gender groups and their respective population shares. Second, the cost-of-basic-needs (food basket) approach was employed to calculate the poverty line. Third, using household equivalence scales was deemed unnecessary since the 2,300-kilocalorie threshold was derived from population-weighted calorie requirements across age and gender groups.
The Foster–Greer–Thorbecke (FGT) class of poverty indices was employed to measure poverty, which is defined as follows:
 
where z denotes the poverty line, y_i represents the income of the i-th individual, and α reflects society’s degree of poverty aversion. For α = 0, α = 1, and α = 2, the index corresponds to the headcount ratio, the poverty gap, and the squared poverty gap (poverty severity), respectively.
In the next stage, the estimated poverty indices were used to classify urban areas across the country’s provinces using hierarchical clustering methods. Finally, a numerical optimization model was applied to compare subsidy targeting at the national and regional levels. Specifically, drawing on the existing literature, the study adopted a numerical optimization method to target poor households under conditions of a fixed budget and missing information about individual welfare. This approach could determine optimal transfer payments that maximize reductions in any additively decomposable poverty index, such as those in the FGT family of poverty measures.

Results and Discussion

According to the results, the characteristics associated with efficient targeting are identical at both the national level and across the eight urban regions—namely household size, the education level of the household head, and the number of children under six years of age. However, substantial differences exist in targeting efficiency as well as in inclusion and exclusion errors between the national and regional approaches. Among the characteristics, household size is the only variable that shows a strong and consistent correlation with poverty across all regions examined. Therefore, it should receive serious consideration in poverty alleviation policymaking, and greater coordination and alignment should be established between population policies, which are aimed at encouraging population growth, and poverty reduction policies.
Moreover, a comparison was made between national and regional targeting outcomes based on individual and combined household characteristics. The former included gender and marital status of the household head, household size, the education level and employment status of the head, housing status, and household demographic composition, such as the number of members aged six years or younger and those aged 65 years or older. Combined household characteristics consisted of the following: 1) divorced women, widows, or married women who are female household heads for any reason; 2) illiterate household heads; 3) households with five or more members; 4) households with at least one member under six years of age; and 5) households with unemployed heads. The comparison revealed substantial differences. These differences are evident in the population shares of target groups, the amount of subsidies paid, targeting efficiency, and the outcomes, particularly poverty reduction. Therefore, even when policymakers seek to target identical households at both the national and regional levels based on individual or combined characteristics, subsidy payment amounts to the same population groups must still vary across regions.

Conclusion

The findings indicated that several necessary conditions exist for replacing national targeting with regional targeting. However, implementing regional targeting requires careful consideration of the political, technical, and migration-related challenges, yet it is also essential to take into account the following key factors. First, a fundamental prerequisite for geographic targeting is the development of poverty maps at the smallest feasible geographic units within the country. Second, because of limitations in the precision of geographic targeting, this approach is rarely used in isolation for transfer payments involving large amounts. Geographic targeting should therefore be combined with other targeting methods to enhance efficiency and reduce errors. Third, it should not be assumed that poverty can be eradicated—or even substantially reduced—solely through subsidy payments. Actually, meaningful poverty reduction requires the implementation of comprehensive social development policies. In this regard, geographic targeting based on poverty mapping is not limited to the allocation of transfer payments; it can also be used to design and implement programs aimed at improving infrastructure and expanding access to social services at the regional level.

Abstract 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 impact of natural resources on economic growth has long attracted the attention of researchers. Drawing on the resource curse hypothesis, some scholars argue that the mismanagement of natural resources can lead to corruption, increased unproductive investment, and rising economic inequality, all of which ultimately hinder economic growth (Yadav et al., 2024). Given the pivotal role of natural resources in encouraging economic growth, numerous studies have examined the validity of the resource curse hypothesis. Empirical evidence suggests that the effect of natural resource rents on economic growth—whether positive or negative—depends on various contextual factors, including financial technology (fintech). Fintech refers to technology-driven financial innovations that affect financial markets, institutions, and service delivery, resulting in the emergence of new business models, products, and applications. On the one hand, fintech can promote economic growth in resource-rich countries by improving households’ and firms’ access to credit and reducing economic uncertainty. On the other hand, fintech can reshape the relationship between natural resource rents and economic growth by fostering exports, enhancing organizational performance, reducing dependence on natural resources, and improving resource management. Therefore, it is essential to examine the role of fintech in the relationship between oil rents and economic growth in countries like Iran. A better understanding of how fintech influences this relationship can help policymakers design more effective strategies for managing oil revenues—mitigating the adverse effects of oil rents and potentially transforming the resource curse into a resource blessing. In this respect, the present study aimed to investigate the threshold effect of fintech on the relationship between oil rents and economic growth in Iran during 2013–2022. Materials and Methods The current study used the models proposed by Gao et al. (2024) and Li et al. (2024) to examine the threshold effect of fintech on the relationship between oil rents and economic growth. The dependent variable—gross domestic product (GDP)—was specified as a function of the interaction term between fintech and oil rent, oil rent, physical capital, labor force, human capital, government size, and a sanctions dummy variable. Fintech was measured by the total value of transactions conducted via the internet and mobile phones for online purchases and bill payments, capturing the payments dimension of fintech. Oil rents were measured as the ratio of the difference between the value of crude oil production and oil production costs to GDP. Human capital was measured by the number of university students in Iran, and the government size was measured as the ratio of government consumption expenditure to GDP. All variables were expressed in log-differenced form, using quarterly data covering the period 2013–2022. The data was obtained from the Central Bank of Iran and the World Bank. Real values were calculated using the consumer price index (CPI), with 2016 as the base year. The model was estimated through a threshold regression approach, in which the interaction terms between oil rents and fintech, as well as between oil rents and government size, would appear in both regimes. Results and Discussion The estimated threshold level of fintech was 0.146, corresponding to 24.01 percent of the fintech index. Once fintech exceeds this threshold, the coefficients of the variables undergo a structural change. The coefficient of oil rents in the first and second regimes was –0.27 and –0.18, respectively. Similarly, the interaction coefficient between oil rents and fintech was –0.02 in the first regime and 0.004 in the second regime. According to the results, oil rents reduce economic growth in both regimes, confirming the presence of the resource curse in Iran. In the first regime, the interaction between oil rents and fintech had a negative effect on economic growth. However, in the second regime, as fintech developed beyond the threshold level, this interaction became positive and growth-enhancing. The findings suggested that oil revenues, by fostering rent-seeking activities, tend to reduce economic growth. In contrast, fintech—by facilitating financial transactions through the internet and mobile phones—enhances financial inclusion. Improved financial inclusion increases entrepreneurs’ access to financial services, which in turn fosters export diversification. Furthermore, digital financial transactions enhance transparency and efficiency in tax collection, thereby reducing tax evasion. Lower levels of tax evasion increase government tax revenues and reduce reliance on oil income. Therefore, the expansion of fintech mitigates the resource curse effect. Government size exhibited a nonlinear relationship with economic growth. In the first regime, government size had a negative and statistically significant impact on growth, whereas in the second regime it exerted a positive and significant effect. This suggests that in the early stages of fintech development, an expansion in government size may hinder economic growth due to inefficiencies. However, as fintech advances, a larger government—through improvements in social and economic infrastructure—can contribute positively to economic growth. The results also indicated that growth in physical capital, labor force, and human capital all had positive and statistically significant effects on economic growth. Physical capital and labor are fundamental factors of production: the former enhances growth by expanding production capacity, while the latter contributes through division of labor and specialization. Human capital improves individual skills and productivity, thereby promoting economic growth. Finally, sanctions have a negative and significant effect on economic growth, as increased sanctions restrict access to international markets. Conclusion The findings indicated that in the lower regime—prior to reaching the threshold level—fintech remains underdeveloped and is therefore unable to mitigate the adverse effects of oil revenues on economic growth. However, once fintech surpasses the threshold, its continued expansion through the adoption of digital technologies improves firms’ access to financial services, particularly in the non-oil sector. Enhanced access to finance strengthens the capacity of non-oil firms to foster innovation and competitiveness, thereby reducing the dominant role of oil in the economy. Diminishing the centrality of oil also lowers the economy’s vulnerability to oil price volatility and geopolitical risks. Furthermore, by expanding access to financial services for households and entrepreneurs, fintech facilitates investment in human capital and contributes to higher employment levels. In addition, greater transparency in digital financial transactions reduces opportunities for corruption. Overall, by weakening the economy’s reliance on oil, promoting trade diversification, reducing dependence on oil exports, increasing employment, and curbing corruption, fintech development helps alleviate the resource curse in Iran.

Abstract The expansion and deepening of the financial sector-one of the most critical sectors of any economy-can influence tax evasion. The present study aimed to examine the effect of the deepening of financial institutions and markets on tax evasion in Iran. First, the multiple indicators multiple causes (MIMIC) method was used to estimate the relative size of tax evasion in Iran, revealing an average rate of 8.1% in Iran’s economy. Then, relying on the indicators published by the International Monetary Fund (IMF) for the period 1980–2022, the study used the autoregressive distributed lag (ARDL) approach to examine the effect of the deepening of financial institutional and markets on tax evasion. The long-run estimates indicated that both financial institutional deepening and financial market deepening have a negative effect on tax evasion. In terms of size (absolute value), the effect of financial institutional deepening on reducing tax evasion is greater than that of financial market deepening. Among the control variables, the tax burden exhibits an inverted U-shaped relationship with tax evasion, while oil rents have a positive effect on it. It was also noted that tax evasion significantly decreased during the post-JCPOA period (2017–2022).

Introduction

Addressing tax evasion in Iran is of critical importance, particularly given the government’s long-standing dependence on oil revenues-a concern frequently emphasized by economists and policy experts. Reducing the dependence through oil rents and shifting toward tax-based revenue through the expansion of the tax base and the reduction of tax exemptions can be considered a vital step toward accelerating economic development and societal well-being. While the scholarly literature on tax evasion has examined various aspects of this hidden segment of the economy, most studies have focused on estimating its size and scope. Few have investigated the effects of the deepening of financial institutions and financial markets on tax evasion separately. To address the gap, the present research aimed to examine the separate effects of the deepening of financial institutions and financial markets on tax evasion in Iran. The research questions are as follows: Do the deepening of financial institutions and financial markets have a significant effect on tax evasion? And if so, in what way?

Materials and Methods

The study estimated the relative size of tax evasion by using the multiple indicators and multiple causes (MIMIC) method over the period from 1980 to 2022. The autoregressive distributed lag (ARDL) model was also employed to examine the effect of financial institutions and markets on tax evasion. The analysis used the Financial Development Index published by the International Monetary Fund (IMF). This index ranks countries based on the depth, efficiency, and accessibility of their financial institutions and markets, on a scale from 0 (lowest) to 100 (highest). Concerning the research model, TaxEva was the dependent variable representing the level of tax evasion as a proportion of GDP. FID and FMD denoted the financial deepening indicators for financial institutions and financial markets, respectively. OilRR referred to oil rents as a percentage of GDP, calculated as the difference between the value of crude oil production at global market prices and total production costs. Moreover, TaxB represented the total tax burden, defined as the ratio of total direct and indirect taxes to GDP; its squared term was also included in the model. The research model was explained based on the specified variables as follows:
 

Results and Discussion

The results of the estimation of the long-run model confirmed several key points. First, both the deepening of financial institutions and the deepening of financial markets have a negative effect on tax evasion. Second, in terms of size (absolute value), the inverse effect of financial institution deepening on tax evasion is greater than that of financial market deepening. According to theoretical foundations, the deepening of financial institutions and markets reflects the broader development of a country’s financial sector. This development narrows the gap between lenders and borrowers, reduces information asymmetry, and decreases the financial requirements of companies-all of which contribute to a decrease in tax evasion. Another important finding is the inverse U-shaped relationship between the tax burden and tax evasion. Specifically, up to a threshold of 4.203% of GDP, an increase in the tax burden leads to higher tax evasion. Beyond this point, however, further increases in the tax burden are associated with a reduction in tax evasion. Finally, the results showed that oil rent has a positive effect on tax evasion, providing empirical support for the resource curse hypothesis. This suggests that reliance on resource revenues can weaken the government’s tax income.
Table 1. Results of Estimated Long-Run Coefficients




 


Variable


Coefficient


Std.Error


T-Statistic


Prov.




 


 


-0.079


0.033


-2.42


0.025




 


-0.037


0.007


-5.06


0.000




 


 


-8.120


1.044


-7.77


0.000




 


 


0.966


0.127


7.63


0.000




 


 


0.093


0.007


12.79


0.000




^*Source: Research estimates

Conclusion

In light of the findings, it is recommended that policymakers adopt policies to increase the deepening of financial institutions and markets in Iran. These policies may include reformulating and amending the laws governing financial institutions and markets to improve transparency, as well as establishing appropriate mechanisms for monitoring. In addition, the development of information and communication technology (ICT) infrastructure is essential to increase access to financial services across the country. Given the observed negative impact of oil rent on tax evasion, it is also advisable to adopt policies to reduce the government’s dependence on oil rents. Instead, greater emphasis should be placed on the role of the tax system as a source of public revenue. The present study has several limitations. These include the simplicity of the model used, constraints related to data availability over time, and the limitations inherent in the chosen estimation method.

Abstract Abstract
Economic growth and development, as the primary goals of any country, play a crucial role in improving living standards and promoting sustainable development. Efforts to achieve these goals, and consequently increase per capita income, can ensure the enhancement of economic and social well-being of a nation. However, natural and political crises can pose significant obstacles to achieving such objectives. Natural disasters and economic sanctions, in particular, can have devastating effects on economic growth and development, leading to a decline in per capita income. Using the Dynamic Ordinary Least Squares, the present study aimed to examine the effect of economic sanctions and natural disasters on non-oil per capita income in Iran from 1980 to 2022. The findings showed that, in the long-term, increases in natural disasters and economic sanctions had contributed to a decline in per capita income in Iran. Additionally, environmental innovation and the interaction between innovation and natural disasters positively influenced per capita income. The results also indicated that factors such as the labor force, physical capital, and trade openness had contributed to improvements in per capita income. In light of the findings, it is recommended that Iran implement effective plans and policies to mitigate the effects of sanctions and natural disasters, promote environmental innovations, and strengthen the development of fixed capital and the labor force, aimed at ensuring the continued growth of per capita income.

Introduction

In recent years, Iran has become a prominent case study and focal point in discussions about sanctions within global research and academic circles. This attention stems from Iran’s status as a target of both multilateral and unilateral sanctions campaigns, which have had adverse effects on its economy. The sanctions have led to currency devaluation; severe budgetary, commercial, and financial deficits; reduced foreign investment, skyrocketing inflation, and rising poverty rates. Natural disasters, meanwhile, are large-scale catastrophic events that intermittently strike, causing extensive human and infrastructural damage that impacts societies and economies alike. Natural disasters inflict significant damage on infrastructure, property, and industries, leading to reduced production, business disruptions, damage to manufacturing facilities, and interruptions in transportation systems. Due to their unpredictability, natural disasters have a substantial impact on the economy. The current study aimed to explore whether natural and political disasters pose genuine obstacles to economic growth and development in Iran. The primary research question is: What are the effects of economic sanctions, natural disasters, and environmental innovation on per capita income in Iran’s economy?

Materials and Methods

To meet the objectives, the study used an experimental model as defined in logarithmic form based on Equations (1) and (2) below.




 


(1)




 


(2)




Per capita income (Y) was considered as the dependent variable, while the independent variables included economic sanctions (ES), natural disasters (ND), environmental innovation (EI), physical capital (K), labor force (L), trade openness (TO), and an interaction variable (ND×EI). In line with the research objectives, time series data spanning from 1980 to 2022 were utilized. The research model was analyzed using the Dynamic Ordinary Least Squares (DOLS) estimator in EViews software.

Results and Discussion

To analyze the results, a unit root test was first conducted to evaluate the reliability of the data. The results showed that the variables of trade openness (TO) and economic sanctions (ES) were at a stationary level, while the variables of per capita income (Y), physical capital (K), labor force (L), environmental innovation (EI), and natural disasters (ND) could be stationary with one time difference. Next, the Bayesian Information Criterion (BIC) was used to determine the optimal lag length. The presence of long-term relationships among the variables was then tested using the Augmented Engle-Granger cointegration test and the Cointegrating Regression Durbin-Watson (CRDW) test, both of which indicated at least one long-term relationship among the variables. The DOLS method was then employed to estimate the research model (see Table 1). The findings revealed that economic sanctions (ES) had a significant and negative effect on per capita income (Y) in Iran’s economy. Specifically, a one percent increase in ES in Models 1 and 2 reduces non-oil per capita income by 0.108% and 0.063%, respectively. Additionally, the frequency of severe natural disasters (ND) had a significantly negative correlation with non-oil per capita income. A one percent increase in ND results in a reduction of 0.161% and 0.158% in non-oil per capita income. Conversely, environmental innovation (EI) had a significantly positive effect on per capita income, with a one percent increase in EI leading to a 0.032% rise in non-oil per capita income. The interaction variable (ND×EI) was also positive and significant, where a one percent increase in this variable results in a 0.025% increase in non-oil per capita income (Y). Furthermore, physical capital (K) had a significantly positive effect on non-oil per capita income. In this case, a one percent increase in K is associated with an increase in Y by 0.185% and 0.25% in Models 1 and 2, respectively. Labor force (L) also had a positive and significant effect on non-oil per capita income, with a one percent increase in L leading to an increase in Y by 0.611% and 0.518% in Models 1 and 2, respectively. Finally, trade openness (TO) had a positive effect on per capita income, as a one percent increase in TO results in a rise of 0.253% and 0.208% in non-oil per capita income.
Table 1. Estimation Results of Research Models




Variables


Model 1


Model 2




Coefficients


t statistic


Coefficients


t statistic




K


0.185


2.483^**


0.250


5.140^***




L


0.611


3.887^***


0.518


2.257^***




TO


0.253


4.462^***


0.208


5.516^***




ES


-0.108


-2.119^**


-0.063


-1.883^*




ND


-0.161


-4.946^***


-0.158


-8.704^***




EI


0.032


2.359^**


-


-




ND×EI


-


-


0.025


4.745^***




C


3.533


2.028^*


4.296


3.994^***




Note: ***, ** and * represent significance levels of 1%, 5% and 10%, respectively.
Source: Research results

Conclusion

The research findings suggested that repeated political and natural disasters could drive a country and its economy into a period of stagnation. On the one hand, these events lead to the destruction of physical, human, and natural resources. On the other hand, they disrupt trade processes, halting the transfer of technology through imports and impeding the modernization of domestic industries. As a result, both outcomes contribute to a decline in per capita income. Moreover, the study showed that adopting environmental innovations could not only increase per capita income but also positively influence the relationship between natural disasters and per capita production, thus helping to mitigate the impact of such disasters. Therefore, investing in research, development, and innovation will strengthen the country’s ability to cope with and adapt to such challenges, while reducing risk levels. In conclusion, this study demonstrated that the political and natural disasters observed during the analyzed period hah negatively impacted the country’s economic growth and development, leading to a decrease in Iran’s per capita income.

Abstract The utilization of natural resources, particularly energy, is essential for economic well-being. However, the increasing consumption of economic resources raises concerns about sustainable development. This study aimed to investigate the dynamic decoupling of economic growth, energy consumption, and pollution in Iran from 2000 to 2020, employing the method proposed by Tapio (2005) and factor analysis on three levels of energy consumption (i.e., primary, final, and useful). The findings revealed that economic growth is often associated with negative decoupling, with this negative decoupling being more pronounced in useful and final energies compared to primary energy. Decomposing energy consumption further confirmed negative decoupling in various energy components. Additionally, the study confirmed weak decoupling between energy consumption and pollution (CO₂ emissions), with stronger negative decoupling observed at lower energy levels. Furthermore, the decoupling of economic growth and pollution closely mirrors the decoupling of economic growth and energy consumption. The negative decoupling can be attributed to the inefficiency in energy consumption, limited access to new technologies, and the lack of appropriate structures due to the absence of a specific strategy for sustainable development. The research recommends the prioritization of energy efficiency across different energy levels as well as the investment in infrastructure and energy technology.

Introduction

Economic growth is intricately linked to the consumption of natural resources, with these scarce and costly resources serving as the primary catalyst for the development and acceleration of economic growth process in modern societies (Song et al., 2019; Song et al., 2020; Zhang et al., 2018). Meanwhile, the production and consumption of energy resources are associated with significant social costs and diminished welfare (Feng et al., 2020a; Feng et al., 2020b; Li et al., 2018; Rjoub et al., 2021; Wang et al., 2020). The world grapples with the challenge of balancing economic development and energy consumption (Bradshaw, 2010). Despite the looming threat of global warming, many countries, particularly developing nations, have prioritized economic development over environmental conservation (Shah et al., 2016). Consequently, decoupling energy consumption from economic growth is widely recognized as a significant achievement in the global effort to combat climate change and mitigate adverse environmental effects. The experience of developed countries instill hope for overcoming resource scarcity and growth limitations, as well as fostering green and sustainable economic growth. While relative decoupling has been achieved in numerous countries, absolute decoupling remains challenging and seemingly unattainable (Hickel & Kallis, 2020). In this respect, the present study aimed to scrutinize the decoupling dynamics of economic growth, energy consumption, and pollution in Iran from 2000 to 2020, employing the method proposed by Tapio (2005) as well as factor analysis across three energy levels.

Materials and Methods

The study followed the method proposed by Tapio (2005) in order to calculate the decoupling between energy consumption and economic growth. First, the decoupling elasticity coefficient was calculated as outlined below:




 


(1)




e(E) is the elasticity coefficient of decoupling between economic growth and energy consumption. ∆E represents changes in energy consumption during the time period under study. E (t-1) indicates energy consumption in the base year. ∆G refers to changes in GDP per capita during the time period, and G (t-1) indicates the GDP per capita in the base year (Wang & Zhang, 2021). In the method proposed by Tapio (2005), eight decoupling states can be distinguished (Figure 1).
Figure 1. Decoupling states
 
The present study conducted a more comprehensive analysis of decoupling by using factor analysis at various energy levels. In this line, the consumption across three energy levels (primary, final, useful) was divided into three distinct effects: activity (production rate), structural (change of economic structure), and intensity (technology effect). The logarithmic mean division method and each of these effects were used as follows:




 


(2)




 


(3)




 


(4)




 


(5)




The study also divided economic activities into several categories: agriculture, services, industry, residential, and transportation. This categorization aligns with the most feasible separation based on the available data and statistical classifications within domestic data sources. In Iran’s energy balance, although household, public, and commercial sectors are categorized under one group, these sectors were individually reported, and the residential sector was distinguished from the commercial and public sector (as the service sector).

Results and Discussion

Figure 2 presents the decoupling dynamics of Iran’s economic growth, energy consumption, and carbon dioxide emissions during 2000–2020. The figure is divided into two parts focused on various energy levels for different components: the first part depicts the decoupling of economic growth and energy consumption, while the second part shows the decoupling of energy consumption from carbon dioxide emissions. As show in Figure 2, the decoupling of economic growth and carbon dioxide follows a pattern similar to and influenced by the decoupling process between economic growth and fossil energy consumption. The decoupling of economic growth and fossil energy consumption aligns with changes in decoupling at different energy levels (primary, final, and useful), reflecting the significant share of fossil energy in Iran’s overall energy consumption. Figure 2 also highlights the weak decoupling between fossil energy consumption and carbon dioxide, which can be attributed to the nature of fossil fuel pollution. Consequently, the decoupling of economic growth from carbon dioxide is influenced by fossil energy consumption.
The first part of Figure 2 reveals various forms of negative decoupling (expansive negative, weak negative, and strong negative) concerning economic growth and energy consumption. Correspondingly, the second part indicates a generally weak decoupling for different energy levels and carbon dioxide emissions. Within the energy consumption components, the intensity component exhibits strong decoupling, though it fluctuates, sometimes displaying positive decoupling (weak, recessive, and strong) and occasionally negative decoupling (expansive and strong negative)—which can be caused by the drop in technology. This finding aligns with the second part of Figure 2, where the decoupling of the intensity component and carbon dioxide experiences fluctuations. Notably, the structural component in the first part of Figure 2 exhibits the strongest negative decoupling from economic growth, signifying a change in Iran’s economic structure that has exacerbated the decoupling between energy consumption and economic growth. However, the decoupling of the structural component and carbon dioxide, as depicted in the second part of Figure 2, remains within the range of weak but fluctuating decoupling.
Finally, the first part of Figure 2 indicates that economic growth is often associated with negative decoupling (expansive and strong negative) from total energy consumption. Despite weak decoupling in initial periods and subsequent fluctuations, the last two years show strong decoupling between total energy consumption and carbon dioxide. Overall, Figure 2 illustrates a fluctuating trend in the decoupling of economic growth and energy consumption over time, predominantly featuring negative decoupling, which corresponds to the decoupling trend between energy consumption and carbon dioxide. Among the components of energy consumption, the intensity component exhibits strong negative decoupling, while the structural component displays weak decoupling, both characterized by fluctuating patterns. This fluctuation may stem from the absence of a specific plan and strategy to decouple economic growth, energy consumption, and carbon dioxide.
Figure 2. Decoupling of economic growth, energy consumption, and carbon dioxide emission in Iran during 2000–2020
 




      Expansive negative decoupling


      decoupling Expansive


Weak decoupling


Strong decoupling




Recessive decoupling


       Recessive coupling


     Weak negative decoupling


       Strong negative decoupling





Conclusion

Using the method proposed by Tapio (2005) and factor analysis across three energy levels, the present study investigated the dynamics of decoupling economic growth, energy consumption, and pollution in Iran during 2000–2020. The findings underscored challenges faced by the policy aimed at reducing energy consumption, which is primarily due to the dependency of Iran’s economy on energy. Specifically, the research showed the dependency of Iran’s economy on energy on energy consumption across all three levels: primary energy, final energy, and useful energy. Moreover, the results highlighted a low degree of energy efficiency, particularly at higher energy levels (secondary and useful). Considering the relation between environmental pressure and restrictions on economic growth, there is a pressing need to address energy intensity and energy efficiency to strike a balance between economic growth and energy consumption. The observed negative decoupling in structural, intensity, and activity effects suggests a lack of a specific strategy in Iran’s economy concerning the decoupling and balance between energy consumption and economic development. In light of these findings, it is imperative to focus on enhancing energy consumption efficiency across diverse energy levels. Additionally, the study recommends prioritizing more effective decoupling in sustainable development policies concerning energy consumption, economic growth, and pollution.
 

Abstract This paper investigates the effectiveness of monetary policy in recession and expansion periods of business cycles in Iran. It uses the distribution of price changes over time using micro-data of producer and consumer price indices from March 2004 to March 2007 and March 1990 to March 2017. Results show that the observed distribution price changes at the producer and consumer levels change significantly over time. Whereas price flexibility (or, similarly, price stickiness) is closely related to the impact of monetary policy, the variable distribution of price changes over time suggests that the effectiveness of monetary policy should also change over time. We estimated the related parameters using the Ss model and the observed facts from the distribution of price changes, the price flexibility index, which shows how prices react to a monetary policy shock. The correlation coefficient and regression analysis results showed that the price flexibility index is counter-cyclical; this means that during periods of economic recession, the index of price flexibility increases. Therefore, the impact of monetary policy on real output decreases. However, during periods of economic expansion, the impact of monetary policy increases.

Abstract Human capital is a hidden variable. In different economic studies, various proxies have been used as a proxy for human capital, including the average literacy index, the number of graduates or the average number of years of schooling. This study will review the economic literature first, and then the three pillars of human capital index including education variables, skills and health will be analyzed for the Iranian economy. In addition, by using fuzzy approach and Mamdani Fuzzy Inference System, the human capital index in the Iranian economy during the 1981-2019 period will be estimated. The results of this calculation shows that during the period under study, the human capital index has continuously grown; in 1981, the index was estimated at 0.13 and 0.59 in 2019. On this basis, it can be stated that human capital in the Iranian economy during the 1981 to 2019 period has grown significantly. This accumulation of human capital can be seized in the production processes, leading to increase in production and productivity..

Abstract The aim of this paper is to investigate the responses of stock, gold and foreign exchange markets in Iran, with an emphasis on the spillover volatility effects. For this purpose, the rate of return of variables is calculated by using the daily data of Tehran Stock Exchange price index, exchange rate and gold price during the period of 25 March 2009 to 18 July 2018. The estimated model investigates volatility spillovers in the markets using the VAR-BEKK-GARCH approach. The impulse-response functions are estimated by including the possibility of the asymmetry of the coefficients of the cross terms of the errors in MGARCH-type equations. The results show two-way volatility spillover between foreign exchange and stock markets, one-way volatility spillover from the foreign exchange to gold markets and one-way volatility spillover from the gold to stock markets. Moreover, the findings obtained from the impulse-response functions confirm the spread of uncertainty among the financial markets in Iran.

Abstract Financial crises, along with the negative and destructive effects of the debt stocks on the economy of countries with the national debt, have caused the "economic effects of the public debt stocks problem," and has become a controversial issue in the public sector economics literature. Using a Smooth Transition Regression (STR) model, this paper investigates the asymmetric impact of the size of government debt - the ratio of government debt to the central bank to GDP - on economic growth in Iran during 1973-2017. The findings showed that the size of government debt to the central bank in a two-regime structure, with two thresholds, affected economic growth by 4.40% and 28.98%. At low levels of debt (years that the size of government debt to central bank is less than 4.4%) and high levels of debt (years that the size of government debt to central bank is greater than 4.40% and less than 28.98%), government borrowing from the central bank has had a negative and positive effect on economic growth, respectively.  Finally, contrary to the expectations, during the period 1980-1991 (years that the size of government debt to the central bank is greater than 28.98%), the amount of government debt to the central bank has positively affected economic growth. This positive impact can be due to the specific features of the revolution and war periods in Iran, such as reducing crowding-out effects, the significant gap between real and potential production, and the more efficient cost management during years.

Abstract Improving food security status through  socio-economic ‎determinants is always important at the household level. In this study, ‎after assessing the food security level of households in urban and rural ‎areas of Khuzestan province, associated factors including economic, social, and racial with food security were identified in 1397. To achieve the goals, 1876 and ‎‎1495 questionnaires were collected in urban and rural areas ‎respectively. The logistic regression model was used to identify effective ‎factors. The results showed that 63 % and ‎‎68 % of households in urban and rural areas face food insecurity respectively. Hamidiyeh county with 18 %, Omidieh 25 % ‎, and Dezful 28 % had the least percent of food secured households in the urban areas of Khuzestan province, respectively. Also, ‎the cities of Shadegan with 13 %, Izeh with 15 %, and Mahshahr port with ‎‎18 % had the least percent of food security households in ‎rural areas, respectively. The results of the quantitative estimated model in the present study showed that employment of the head of the household, income, number of rooms and personal car ownership were significantly and directly associated with food security in urban and rural areas of Khuzestan province. Therefore, due to the weakness of income policies which are applying as the only ways to ameliorate food security status in Iran, paying close attention to socio-economic factors related to improving the level of household food security before any intervention is necessary.

Abstract  One important aspect of financial development in oil-exporting countries is how to allocate oil income during periods of oil price fluctuations. Financial development in these countries affects their current accounts in two ways: directly through the impact on savings and investment and indirectly through the impact on the relationship of oil prices and current account. Considering the importance of this issue, this study investigates the role of financial development in the relationship between oil price and current account using a nonlinear Smooth Transition Regression model (STR) during the period of 1978-2016 in Iran. Based on the relevant tests, it is concluded that there is a nonlinear relationship between the current account and world oil price. Financial development is chosen as the best transition variable and the nonlinear Smooth Transition Regression model with a two-regime logistic transition function (LSTR1) has been chosen as the best model for this relationship. The results of estimation show that the oil price appearing in the form of a two-regime structure with a threshold level (1.5386) in the first regime (when financial development is less than it’s threshold value (1.5386)) has a positive and significant effect on Iran’s current account; The intensity of this positive effect increases by crossing the threshold level entering to the second regime (when financial development is higher than its threshold value (1.5386).

Abstract Tax evasion constitutes a major component of underground activities and development of financial sector -as one of the most important sectors in every country can affect its size. Considering the importance of this issue, this study tries to investigate the relationship between financial development and tax evasion and provide an answer to this question: can financial development in Iran reduce tax evasion? This study estimates the volume of tax evasion in Iran by using multiple-indicators -multiple causes (MIMIC) model and maximum likelihood method in AMOS software, for the period of 1978-2016. Then the effect of financial development on tax evasion is investigated by using ARDL Bounds test method. The results show that despite some fluctuations, volume of tax evasion has been generally increasing over the underlying period. The results of the estimation of the effect of financial development on the tax evasion indicate that financial development in Iran in long-run and short-run (with one lag) has a negative and significant impact on the tax evasion. Also, findings show that an increase in inflation, increases tax evasion and increase in GDP reduces tax evasion.

Abstract From a financial perspective, decentralization is a transfer of resources from the central government to local governments. Fiscal decentralization policies can lead to more FDI attraction by increasing the share of provincial government funding to local infrastructure.Accordingly, the major purpose of this study is to evaluate the long run and short run impact of fiscal decentralization on FDI in Iran during the period 1992-2014. For this purpose the three indicators of fiscal decentralization of revenue, fiscal decentralization of expenditures, fiscal decentralization of autonomy power and also some control variables including inflation, exchange rate fluctuations, and degree of trade openness have been used.In order to estimate the model, the Johansen-Juselius method and Vector Error Correction Model (VECM) have been applied. Based on the results of the model, all three fiscal decentralization indicators increase FDI in the long run and in the short run. Therefore, providing the necessary conditions for the expansion of fiscal decentralization can help to promote the FDI level in Iran.Also, both in the long run and the short run, inflation and exchange rate fluctuations have a negative effect, and the degree of trade openness has a positive effect on FDI.

Abstract Attitude towards the role of government and reasons for the existence of government have experienced several changes and revisions during the last century. Attitude changes alter the duties and responsibilities assigned to the government and thus change the size and composition of public expenditure. In the context of these attitudes, there are factors that could explain the changes in the size and the growth of government and consequently the government intervention in the economy over time and among different countries. This study investigates the relationship between government size and macroeconomic variables including economic growth, growth of oil revenues, growth of tax revenues, inflation in Iran using seasonal data during the period of 1990:1 – 2014:4 by applying Markov Regime Switching model. The results show that in the selected model consisting of two regimes with different government sizes, economic growth has a significant negative impact on government size in both regimes of zero and one. But inflation has different effects on government size: it has a negative effect in the regime zero (smaller government) and a positive effect in the regime one (bigger government). Moreover, the growth of oil revenues has a positive effect in both regimes, but the growth of tax revenues has a positive effect only in the regime one. Also, the results indicate that the government size in Iran has often been in the regime one with bigger government size and it is predicted that bigger government will be more sustainable than smaller government.

Abstract Due to the historical importance of inflation in the Iranian economy and its serious effects on the society, the present study has explored the impacts of international sanctions and other effective factors on the inflation rate in Iran during 1981-2014. To this end, this paper has specified an econometric model of inflation rate, which has been estimated by the ARDL method using relevant time series data including the above period. The empirical results obtained indicate that the international sanctions have had direct and significant effects on the inflation rate through changes in exchange rate and budget deficit. Additionally, exchange rate, money liquidity and deposit interest rate have had positive and significant effects on the inflation rate, while oil revenues and tax earnings have influenced indirectly and significantly Iran’s inflation rate over the period.

Abstract This study examines the hysteresis effect of currency substitution in Iran using a model of money-in-the-utility function with two currencies (home and foreign). In this respect, Divisia monetary aggregates has been used for calculating the dollars in circulating. The bounds testing approach to the analysis of level relationship proposed by Pesaran et al. (2001) and Autoregressive Distributed Lag (ARDL) model have been used for Iranian quarterly data during 1990- 2014. The results indicate that there is evidence supporting the existence of the hysteresis effect of currency substitution in Iran. In other words, currency substitution process is irreversible. Therefore, it is suggested that the central bank consider the effect of the hysteresis of dollarization phenomenon on monetary policy and the purpose of controlling inflation and reducing exchange rate volatility as the main reasons of currency substitution, continues to be a priority for economic policy.

Abstract In this research, the absolute poverty line based on the food poverty line is estimated using mathematical programming approach concerning urban area prices during February 2015. We first have determined a basic food basket including 76 common and important food items. Second, we have identified the basic human nutrients needs for the survival. Then we have considered the amount of nutrients in each 100 grams of the 76 food items. After that, we have collected the prices of each 100 grams of the food items using a categorized sampling covering urban areas of Mazandaran province. Mathematical Programme is constructed using the total cost function as the target function along with constraints of calorie and eight nutrients needs.  The model has been solved by Simplex method and, the food poverty line is estimated. Then, using the Orshansky method the absolute poverty line is calculated. The results show that the food poverty line and the absolute poverty line for a household with four people in Mazandaran urban areas during February 2015 were about 311,000 and 1,305,000 Tomans(10 Rials), respectively.

Abstract One of the main approaches to achieve food security is redistribution measures such as the subsidy reform program with the aim of social justice and improved welfare of low-income groups. In this study, we analyzed the effect of the implementation of targeted subsidies program on food security in Iran during 2005-2012. A model of food security was estimated after calculating the adult’s energy intake through household’s consumption information. We found an inverse relationship between the subsidy reform program and food security among Iranian households. Due to this program, food prices increased and households’ real income decreased. The results of these changes are the increased cost of living and decreased share of income spent on food.   

Abstract Economic theory has emphasized the important role of human capital on national and regional economic growth. The present study aimed to analyze the effect of the composition of human capital on economic growth in Iranian provinces. We estimated a Spatial Dynamic Panel Data model by using Generalized Method of Moments technique for 28 Iranian provinces over the period 2001-2011. The results indicated that tertiary and primary and secondary education had positive and significant effects on economic growth. Also, the human capital structure had an inverse-U-shape effect on economic growth. In other words, growth is increasing in the human capital structure at low levels of the human capital structure, but the relation turns negative once the human capital structure exceeds a critical value.

Abstract Exchange rate pass-through is one of the most important issues in the international economic studies. Determining the degree of exchange rate pass-through can be used to define the effectiveness of foreign policy, market structure and exporters behavior. The main objective of this study is to investigate the exchange rate pass-through to export prices of Iranian pistachios during the period 1961-2011. In the previous studies, the exchange rate pass-through was assumed to be fixed during different years. This assumption is not consistent with reality, because many factors can influence the exchange rate pass-through. In this study, sensitivity analysis in the framework of artificial neural network is used to address this shortcoming. The results shows that exchange rate pass-through to Iran's pistachio export prices has been more than 70 percent, and its trend has been periodic. Furthermore, the results showed that there is a direct relationship between exchange rate fluctuations and Iranian pistachio export prices. Thus, by reducing exchange rate volatility, it is possible to supply pistachio with lower prices to the world markets. Considering the high elasticity of demand for the Iranian pistachio prices, a reduction in prices would increase revenues of exporters. Incidentally, given the high elasticity of export demand for Iran's pistachio, the revenues from the export of this product can be increased by reducing the cost of pistachio production.

Abstract Low and stable inflation with sustainable growth is the first objective of any monetary authority. To achieve this prime goal, reliable forecast of macroeconomic variables play an important role. This paper investigates the forecasting performance of BVAR models with different priors for Iranian economy.  For this purpose we use BVAR approach with Gibbs sampling for quarterly data of the Iranian economy from 1989:Q1 to 2007:Q4. The main advantage of this paper is using Gibbs Sampling to estimate BVAR models and use of Quasi BVAR models with Normal Wishart and Minnesota  priors in order to compare forecast accuracy of the macroeconomic variables. Comparison of the BVAR with Gibbs Sampler and Quasi BVAR models in this experience shows that the value of MSFE in predicting macroeconomic variables for the four ahead period forecasts in BVAR model with Gibbs algorithms is less than Quasi BVAR models. Generally BVAR model with Gibbs sampling algorithms performs better than Quasi BVAR models in forecasting.  

Abstract The Impact of micro-credits on household’s poverty gap is considered as one of the most important social and economic issues. In this research, the socio- economic characteristics of the borrowing households are identified by using a Logit model, based on the urban and rural household budget survey data for 2011. Then, the impact of micro-credits on poverty gap of poor households is examined by estimating regression models. The result of the Probit analysis indicates that the coefficients of the age of household head, employment of the household head, household size, and urbanization are significantly positively related to households’ access to credit. However, the coefficient of household expenditure is negatively related to households’ access to credit. This implies that the low-income household is more likely to have not access to micro-credits. Also, the regression analysis shows that the access to micro-credits is not a significant explanatory variable for poverty gap of the poor households. This indicates the micro-credits has no impact on the poverty reduction of the poor households.
 

Abstract Since the 1970s, international political economic theorists have emphasized on the role of domestic factors, such as domestic active groups, policies and macro-economic indicators, to explain the  trade protection trends. Empirical studies have often verified this view. This paper  examines the determinants of tariff protection in Iran’s manufacturing industries by using panel data for the period 2001-2007.This research also investigates  the  effects of intra industry trade on tariff protection. The results indicate that value added and the ratio of production to import affect the tariff protection in Iran’s manufacturing industries. Also, intra-industry-trades and their  type, i.e. horizontal and vertical intra-industry trade, have negative effects on the protection. Based on the obtained results, we suggest that domestic industries activities in both domestic and foreign markets as well as intra-industry trade and competitiveness of trade  should be increased.