Research Paper
Planning and Budget
Samira Ghanbari; Hamid Amadeh; Davood Danesh Jafari; Teymour Mohammadi
Abstract
Today, health insurance is recognized as one of the most important sources of financing in the health sector. In this regard, improving the level of satisfaction of the insured and increasing their access to medical services are among the primary goals of health insurance organizations. Organizations ...
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Today, health insurance is recognized as one of the most important sources of financing in the health sector. In this regard, improving the level of satisfaction of the insured and increasing their access to medical services are among the primary goals of health insurance organizations. Organizations that can provide adequate services without imposing a financial burden on the insured are considered successful. To achieve this, it is crucial to maintain a balance between revenue sources and expenditure in health insurance organizations. The current study examined the budget balance across five funds of the Iran Health Insurance Organization over the period 2008–2019. It also analyzed the factors influencing the budget deficit in these funds, including premiums, coinsurance, treatment, and overhead costs, as well as the number of services purchased. Using a panel vector error correction model, the analysis evaluated these factors in terms of the funds’ mediating role in reimbursement, behavior management, and the purchasing of medical services. The goal was to propose solutions for eliminating the budget deficit. The results showed that, in the long run, coinsurance paid by the insured and the premiums received by the various funds of the Iran Health Insurance Organization had a negative effect on the budget deficit. In contrast, increases in treatment and overhead costs, as well as the number of services purchased by the five funds, worsened the budget deficit.IntroductionHealth systems around the world are under increasing financial pressure due to rising healthcare costs and limited resources. As a result, financing has become a critical function of any health system. One widely adopted strategy is funding through health insurance programs, which promote risk sharing, enhance financial protection, and improve access to services—particularly for the poor and other vulnerable populations. To fulfill their obligations and remain sustainable, insurance organizations must control costs, secure adequate funding, and maintain budgetary balance. Achieving this requires structural reforms in both expenditure and revenue streams to improve service delivery and quality. Insurance organizations play a central role in healthcare financing, and their budget deficits can have significant impact on the broader health system. On this account, the present study aimed to examine the factors contributing to budget deficits in these organizations, with a focus on their intermediary role in service reimbursement and cost management. In this context, the first key factor is the adequacy of financial resources. Health insurance organizations pool funds from various sources, including employee and employer premiums, contributions from the self-employed, government subsidies, and donations. These financial resources are used to reimburse insured individuals for eligible medical services (Hsiao & Shaw, 2007). The second factor is rising expenditures, particularly in treatment and overhead costs. These are influenced by both patient and provider behaviors, including moral hazard, adverse selection, and provider-induced demand (Ekman et al., 2008). The third contributing factor is the number of services purchased by insurance organizations. Expanding benefits packages increases costs and creates a trade-off between the range of covered services and the size of the covered population. It is thus essential to define the benefits package and manage service utilization in order to avoid budget deficits. Furthermore, the insured’s share of treatment costs (coinsurance) can influence patient behavior and overall spending, thereby impacting the organization’s budget balance. Materials and MethodsMany econometric challenges (e.g., analyzing the effects of economic shocks) arise from the difficulty, or even impossibility, of obtaining the long-term data required for traditional time series models. Furthermore, in some cases, the impact of economic variables spills over into other sectors, creating interdependencies across sections. These complexities can be more effectively addressed using panel vector autoregression (PVAR) models, which incorporate both cross-sectional and time-series dimensions. The PVAR model shares the same structure as a standard VAR model, but by incorporating a cross-sectional dimension, it becomes a significantly more powerful tool for analyzing key economic issues. To illustrate the mathematical form of the PVAR model, let represent a (K×1) vector of endogenous variables for unit i, where i=1,…,N. Also, is the (N.K×1) vector of the stacked . Therefore, the equation of the PVAR model can be expressed as follows: (1)where is a (K×1) vector of intercepts, for j=1,…,p and i=1,…,N is a (K×N.K) matrix of slope coefficients. Morover, is a (K×1) vector of possibly contemporaneously correlated reduced-form disturbances (Dees & Guntner, 2014).Results and DiscussionThis section presents the estimation results of the panel vector error correction model (PVECM), highlighting the short-term effects of selected variables on the budget deficit across the five funds of the Iranian Health Insurance Organization.The dependent variable (BD) represented the budget deficit, calculated as the difference between revenues and expenditures. The independent variables were as follows: PR (premiums paid by the insured), CO (coinsurance contributions), TE (treatment costs), OE (overhead costs), and SE (number of services purchased by the five funds of the Iranian Health Insurance Organization). The estimation results indicated a negative relationship between the budget deficit and both premium collections (PR) and coinsurance payments (CO), suggesting that increases in these revenue sources help reduce the budget deficit. In contrast, treatment costs (TE) and the number of services (SE) exhibited a positive relationship with the budget deficit, suggesting that higher spending on healthcare services and treatment places greater financial strain on the organization’s funds. Interestingly, overhead costs (OE) had a negative effect on the budget deficit. This may imply that an increase in overhead costs is associated with improved efficiency or more effective cost management practices, ultimately contributing to a reduction in the deficit. These findings underscore the importance of enhancing revenue streams while managing service-related expenditure to improve the financial sustainability of health insurance funds.Table 1. Panel vector error correction modelDependent variable: D(BD)Independent variableCoefficientt-statisticProbability -0/002352-2/3019490/0216 1/7813952/098410.0000 -0/880473-26/051420.0000 -0/419725-1/3043600/1925 0/3637381/1243620/2612 -0/021947-0/9656720/3345 -0/033938-1/4809050/1391 0/1272261/7081520/0881 -0/098218-1/2830120/1999 -0/069282-1/2401300/2153 -0/064522-1/1628250/2453 41337/741/2786670/2014 -33641/15-1/0389650/2992C7/77e+082/1179710/0345R-squared: 0/99F-statistic: 6729/078Prob(F-statistic): 0/0000Durbin-Watson stat: 2/19 Source: Research findingsConclusionAccording to the findings, in the short term, the Health Insurance Organization can effectively control and reduce its budget deficit by adopting strategies such as increasing premiums, coinsurance contributions, and overhead expenditures, while simultaneously reducing treatment costs and the number of services purchased.
Research Paper
Econometrics
Manijeh Mahmoodi; Mohammad Reza Salehi Rad
Abstract
Modeling plays a crucial role in economic and financial research, forming the foundation for analysis, decision-making, policy development, and planning. Assumptions made during the modeling process are particularly important for estimation and forecasting, as they can significantly influence the results. ...
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Modeling plays a crucial role in economic and financial research, forming the foundation for analysis, decision-making, policy development, and planning. Assumptions made during the modeling process are particularly important for estimation and forecasting, as they can significantly influence the results. One of the most widely used classical time series models is the autoregressive model, in which current values are expressed as a finite linear combination of past values. However, in real-world scenarios, many variables interact with each other. To capture these interdependencies, vector time series models-an important class of multivariate time series models-are employed. The vector autoregressive (VAR) models are commonly used in economic and financial modeling. VAR models are typically formulated assuming that the shocks (or noise terms) follow a normal distribution. However, in economic and financial contexts-particularly in macroeconomics-shocks do not often follow a symmetric distribution. The present article focused on a VAR model in which the shocks follow a multivariate skew normal (MSN) distribution. The expectation conditional maximization (ECM) algorithm were used to estimate the model parameters. Finally, using real-world datasets from Canada and Iran-where the shocks exhibit skewness-the study found that the VAR model with MSN-distributed shocks is more efficient than the VAR model with multivariate normal distribution for shocks.IntroductionThe multivariate normal distribution is commonly used to model shocks in VAR models. However, in fields such as economics, finance, the stock market, and medicine, various factors can introduce skewness (asymmetry) into the shocks, resulting in non-symmetric distributions. In such cases, the normal distribution becomes an inappropriate choice. To address this, the multivariate skew distribution-which accounts for asymmetry-should be used for modeling shocks. Despite its relevance, this approach has received limited attention in previous research. The family of multivariate skew distributions is broad and complex, posing practical challenges. The present study aimed to test the VAR model in which the shocks follow a multivariate skew normal (MSN) distribution, using the real-world datasets from Canada and Iran.Materials and MethodsConsider the VAR model of order p: where , o is location parameter, is the scale parameter, and S is the skew parameter. Its density function is given by: where , , , and denotes density function of MN, while denotes the standard cumulative distribution function.To find the maximum likelihood estimates of the parameters requires derivatives of the log-likelihood function; however, these derivatives do not have closed-form expressions. Therefore, they must be approximated using numerical methods. The maximum likelihood estimators were obtained via the expectation conditional maximization (ECM) algorithm. Based on the hierarchical representation of the multivariate skew normal distribution, we have:,(o, I),The logarithm of the conditional likelihood function in VAR(p) can be formulated as: where . The expectation of the logarithm of the conditional likelihood is denoted by , and the steps for the maximizing are as below:Step 1: Assuming no skewness, estimate the initial values for the coefficients and scale parameters.Step 2 Step 3 Step 4 Step 5: Repeat steps 2 to 5 until the convergence condition of the algorithm is established: Results and DiscussionThe performance of the proposed method was evaluated using two real-world datasets from Canada and Iran. The Dickey-Fuller test was employed to determine the stationarity of the data, while the Akaike Information Criterion (AIC), Hannan-Quinn Criterion (HQC), and Schwartz Bayesian Criterion (BIC) were used to select the order of the VAR model. The Canadian dataset consists of seasonally adjusted employment and unemployment data from 1980 to 2000. According to the Mardia test, the shocks follow a multivariate skew normal (MSN) distribution. Therefore, we estimated the parameters of the VAR(1) model. The AIC and BIC results are presented in Tables 1 and 2, respectively. Table 1. The Estimated Parameters of Model for Stationary Differenced Canadian DataEstimation 0.91030.7094 0.2139-.0149 -0.2018-0.4663 0.33030.0255 -0.0703 -0.1066 0.16460.1595 -0.09106-0.1003 0.09106-0.1003 0.114000.0977Table 2. The AIC and BIC for DataDistributionAICBIC-Log-Like 282.0266286.836139.011 260.6781265.4915128.339The collected data on agriculture, forestry, and fishing (AFF) and employment of women (EW) in Iran span the years 1991 to 2021. According to the Mardia test, the shocks follow a multivariate skew normal (MSN) distribution. The parameter estimates for the VAR(1) model, along with the AIC and BIC values, are presented in Table 3. Table 3. The Estimated Parameters of Model for Stationary Differenced Iranian DataEstimation -0.1104 --0.0798 1.23971.2267 0.27480.2836 0.27480.2836 0.38550.3796AIC146.1145145.9185BIC148.8491148.6531The fitted model can be formulated as follows: where and represent AFF and EW, respectively.According to the AIC and BIC criteria presented in Tables 2 and 3 for the Canadian and Iranian data, the VAR model with MSN-distributed shocks is more appropriate than the VAR model with MN-distributed shocks.ConclusionConsidering the VAR(p) model with shocks following a multivariate skew normal (MSN) distribution, the present study employed the maximum likelihood method and the ECM algorithm to estimate the model parameters. Based on two real-world datasets from Canada and Iran, the findings showed that the VAR model with MSN-distributed shocks provides a better fit than the model with MN shocks when the shocks exhibit skewness.
Research Paper
Financial Economics
Majid Aghaei; Amin Razinataj
Abstract
Given the interconnected nature of financial markets, understanding the relationships among them is essential for investors and traders in selecting optimal portfolios, and for policymakers in adopting appropriate monetary and financial policies. The present study aimed to investigate the interrelationship ...
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Given the interconnected nature of financial markets, understanding the relationships among them is essential for investors and traders in selecting optimal portfolios, and for policymakers in adopting appropriate monetary and financial policies. The present study aimed to investigate the interrelationship between risk and return, as well as their spillover effects, between Iran’s stock market and competing markets-namely the foreign exchange, gold, and housing markets-under varying bullish and bearish market conditions. The analysis relied on monthly data from 2011 to 2022, as well as a multivariate GARCH model. The results showed significant spillover effects of returns and volatility from the foreign exchange market to the stock market during both bullish and bearish phases of the foreign exchange market. In addition, return spillovers from the stock market to the foreign exchange market were also observed across both market conditions, underscoring the strong interdependence between these two markets in Iran. However, the study found no evidence of return spillovers from the stock market to the gold market under either market condition. In contrast, return and volatility spillovers from the gold market to the stock market were confirmed in both bullish and bearish phases of the gold market. The results did not confirm the return and volatility spillover effects from the housing market to the stock market under either bearish or bullish conditions in the housing market. However, a return spillover from the housing market to the stock market was observed during bearish conditions in the stock market. This suggests that investors in Iran’s housing and stock markets likely belong to two distinct spectrums.IntroductionThe existence of strong and efficient financial markets, supported by appropriate and active organizations, plays a critical role in promoting investment, economic growth, and development. In recent years, the analysis of inter-market relationships has gained significant attention from capital market practitioners and researchers. Empirically modeling and examining these relationships is essential for investors seeking to implement effective investment and hedging strategies, particularly for portfolio diversification. According to financial theories and prior research, when two markets are weakly correlated, external shocks in one market have less impact on the other. As a result, investors can reduce their risk by diversifying their portfolios across such markets (Kundu & Sarkar, 2016). Asset prices in financial markets are inherently volatile, influenced by relevant market developments as well as sudden, unexpected changes triggered by domestic and global economic, social, and political events (Kang et al., 2011). This volatility often prompts investors to adjust the composition of their asset portfolios (Attarzadeh et al., 2022). This phenomenon is referred to as volatility spillover (Pandey & Vipul, 2018), which can both exacerbate the turmoil in the crisis-stricken market, and transmit volatilities and shocks to other markets (Khalifa et al., 2014). Consequently, the magnitude of price volatility in a given market is influenced not only by its own historical fluctuations but also by the volatility of other markets (Zhang et al., 2008). This issue has gained increasing importance in today’s economies, where advanced communication systems and the interdependence of financial markets have shaped the nature of markets.Understanding the mechanisms of interdependence and volatility and return spillovers among assets is crucial for several reasons, such as assessing market efficiency, optimizing asset portfolios, managing risk, and regulating the market. In other words, accurate identification of the behavior of asset returns and price volatility-along with their interrelationships-is essential for optimal resource allocation, accurate pricing of financial assets, optimal selection of asset portfolios, and better forecasts of future price movements (Hassan & Malik, 2007; Poon & Granger, 2003). Furthermore, as shown by Fabozzi and Francis (1978), the beta coefficient in the Capital Asset Pricing Model (CAPM) can vary across different market conditions, such as during bullish versus bearish markets or periods of high versus low volatility. A review of developments in Iran’s stock, foreign exchange, gold, and housing markets reveals significant and sudden changes in asset prices and heightened volatility, especially in recent years. Due to the economic stagnation and high inflation, many investors in the Iranian economy have turned to the stock, foreign exchange, and gold markets as alternative investment options. Understanding the spillover of volatility and returns across these markets-under varying market conditions-is essential for assessing market efficiency, selecting asset portfolios, and determining asset pricing. Accurately identifying and analyzing the behavior of price volatility and returns enables policymakers to adopt appropriate regulatory policies. This study aimed to examine the interrelationships of risk and return, as well as their spillovers between the stock market and other competing markets (foreign exchange, housing, and gold) under different market conditions, specifically bullish and bearish phases. This focus on varying market conditions ensures the novelty of the present study in terms of its approach.Materials and MethodsThe current study employed a bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model to investigate the mutual relationship between the return and risk of Iran’s stock market and its competing markets. The multivariate GARCH model is designed to model the simultaneous volatility of two or more variables. The model examines the relationship between the volatilities of two series of variables, in which the conditional variance is modeled as a function of its own lagged value and the lagged value of its error residuals (Suri, 2013). To analyze the spillover effects of volatility and returns across different markets, the study used the VAR-BEKK-GARCH model. First, the vector autoregression (VAR) method was used to estimate the research model. Then, the system of mean and variance equations was constructed and estimated using the VAR(1)-BEKK(1,1) method. Moreover, the Hannan-Quinn, Schwarz-Bayesian, and Akaike selection criteria were used to select the optimal model intervals. Based on these criteria, the VAR(1)-BEKK(1,1) model was selected to estimate all the models. The equations in the VAR-BEKK-GARCH model were divided into two main categories: the mean equation and the variance equation. The mean equation estimated the spillover effects or the contagion rate of market returns, while the variance equation estimated the spillover or contagion rate of volatility and shocks among the variables.Results and DiscussionAccording to the results, during bullish and bearish conditions in the stock and foreign exchange markets, there is a noticeable spillover effect in both returns and volatility between these two markets. This indicates a strong interdependence between the foreign exchange and stock markets. The findings also showed that under normal market conditions, there is no significant spillover of returns or volatility between these markets. However, during periods of bullish or bearish markets, spillover effects become evident. Regarding the relationship between the stock market and the gold market, the study found no significant spillover of returns from the stock market to the gold market under either bullish or bearish stock market conditions. In contrast, during both bullish and bearish conditions in the gold market, there is a clear spillover effect of returns and volatility from the gold market to the stock market. This finding seems logical, given the high correlation between the gold market and the foreign exchange market in Iran. A positive or negative shock in the foreign exchange market is likely to be transmitted to the gold market, causing return and volatility spillover effects between the gold market and the stock market. In the case of the housing market, the study found no evidence of return or volatility spillovers from the housing market to the stock market, under either bullish or bearish housing market conditions. This could be attributed to the differences in the investor base between the two markets in Iran. Considering the records of high housing returns in Iran, housing market investors tend to be long-term participants who are less responsive to short-term volatility. Furthermore, the lower liquidity of the housing market may contribute to the lack of return spillovers to the stock market, especially during bearish housing market conditions. The results also confirmed a spillover effect of returns from the stock market to the housing market during bearish stock market conditions. This suggests that during downturns in the stock market, investors may shift their capital toward investment in the housing sector, which historically has offered high long-term returns in Iran.ConclusionToday, the significance of the gold, foreign exchange, housing, and stock markets is widely recognized due to their vital role in attracting capital and driving the economic growth and development of countries. Ensuring the proper functioning and coordination of these markets-by strengthening their interconnections and improving their resilience and flexibility-can not only enhance capital attraction and allocation but also serve as a defensive shield against economic shocks. This, in turn, helps mitigate the impact of spillover risks and increases the overall resilience of the economy to shocks. Given the importance for market practitioners and policymakers to understand the relationships among key markets of interest to investors in the Iranian economy, the current study investigated the spillover effects of returns and volatilities between the stock market and its major competing financial markets in Iran-namely the foreign exchange, gold, and housing markets. The analysis focused on both bullish and bearish market conditions, using the VAR-BEKK-GARCH model and monthly data from the period 2011 to 2022. The results indicated a significant interdependence and spillover effect of returns and volatilities between the foreign exchange and stock markets under both bullish and bearish conditions. However, this relationship was not evident under normal market conditions. In the gold market, spillover effects of returns and volatilities toward the stock market were observed only during bullish and bearish phases, but the reverse was not confirmed. As for the housing market, no significant spillover from this market to the stock market was detected, which may be attributed to differences in the investor base across the two markets. Nonetheless, during bearish phases in the stock market, a portion of capital flows into the housing market, reflecting investors’ preference for real estate during stock market downturns. In light of the findings, it is recommended that policymakers work to strengthen economic stability and manage foreign exchange market volatility in order to limit the transmission of shocks across markets. In addition, maintaining investor confidence in the stock market through appropriate incentives is crucial. Special attention should also be given to the correlation between the foreign exchange and gold markets, improvement in the liquidity in the housing market, and development of market forecasting systems.
Research Paper
Institutional economy
Yavar Ahmadpour Torkamani; Parviz Mohammadzadeh
Abstract
Over the past three decades, the repeated failure of structural adjustment policies in Iran has cast doubt on the effectiveness of imported shock therapy models in addressing the country’s economic and political challenges. The current study, drawing on the art of economics as the methodological ...
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Over the past three decades, the repeated failure of structural adjustment policies in Iran has cast doubt on the effectiveness of imported shock therapy models in addressing the country’s economic and political challenges. The current study, drawing on the art of economics as the methodological framework, aimed to explore the economic origins of the persistent implementation of the failed shock therapy policies. It evaluated their impact through the lens of eight key factors that shape the emergence and consolidation of democracy: civil society, crises and shocks, income sources, intergroup inequality, political institutions, the middle class, globalization, and the Internet. The analysis revealed that shock therapy undermines democratic capacities and facilitated undemocratic decision-making. The study argued that such policies endure only within undemocratic governance structures. To support a transition toward participatory economic governance, this research concluded by recommending a set of strategic measures, such as recognizing and responding to crises, fostering community-based policymaking, learning critically from international experiences, and empowering civil society.IntroductionIn Iran, proponents of economic shock therapy have consistently advocated for structural adjustment programs as the ultimate solution to the country’s economic, social, and even political issues. Characterized by market liberalization, privatization, subsidy removal, and trade deregulation, these programs have often been implemented without sufficient regard for the local context. Consequently, they have contributed to poverty, unemployment, stagflation, and social unrest. Despite repeated failures, the continued adoption of these policies over the past three decades suggests that they are deeply rooted in Iran’s institutional and ideological decision-making structures. In this regard, the present study tried to answer the following research question: Why are failed economic policies repeatedly adopted despite substantial evidence of their failure? The analysis aimed to explore the institutional, ideological, and structural origins of such undemocratic policies, with a particular focus on how the economic outcomes of shock therapy impact democratic development, and on the institutional conflicts and barriers that hinder democratic transition in Iran.Materials and MethodsThis study used the art of economics as the methodological framework. The art of economics is a problem-oriented approach that emphasizes contextual and institutional analysis rather than abstract quantitative modeling. Unlike the traditional dichotomy between positive and normative economics, the art of economics focuses on the practical application of theory to real-world problems in a given sociocultural context. In addition, the research relied on a combination of secondary data and empirical observations with theoretical insights from key scholars in political economy and institutional development—including Douglass North, Robert Dahl, Joseph Stiglitz, Manuel Castells, and others. Their theories converge around eight core factors that influence the emergence and consolidation of democratic structures, namely civil society, crises and shocks, income sources, intergroup inequality, political institutions, the middle class, globalization, and the Internet. Drawing on this composite framework, the study examined how structural adjustment and shock therapy policies undermine the democratic foundations in Iran. The objective was to demonstrate how the repeated application of such policies contributes to the weakening of democratic capacities and the entrenchment of undemocratic structures.Results and DiscussionThe findings indicated that the implementation of structural adjustment and shock therapy policies in Iran significantly undermine the core foundations of democratic development. These policies have weakened civil society, deepened income inequality, eroded the middle class, and constrained participatory political institutions-ultimately steering the country away from a democratic path. The resulting economic crises (e.g., inflation, unemployment, and stagnation) have intensified public dissatisfaction, yet have largely led to social disillusionment and lack of trust rather than organized collective action. The analysis of the impact of such policies on the eight core determinants of democracy revealed several critical issues. Civil society suffers from a lack of institutional support and limited capacity for mobilization, while shocks and crises have led to societal fragmentation rather than unity. Income sources have been captured by unproductive rentier elites, reinforcing extractive systems that deepen inequality. In addition, the widening intergroup inequality has exacerbated social conflict. Political institutions remain monopolized by elites, excluding the broad participation of different groups and individuals. The middle class has been marginalized, losing its vital mediating role. Furthermore, globalization-in the lack of effective state-led protection-has intensified both inequality and instability. Finally, the Internet and digital platforms, subjected to censorship and political control, have failed to become vehicles for democratic mobilization. Overall, the findings suggest that shock therapy has not only failed to deliver economic success but has also functioned within-and reinforced-an undemocratic political structure. This reproduction of authoritarian policymaking poses a significant barrier to democratic governance and participatory reform.ConclusionThis study, grounded in the framework of the art of economics, critically examined the continued implementation of structural adjustment and shock therapy policies in Iran. It argued that the persistence of these failed approaches is not merely a technical error but is rooted in the country’s undemocratic institutional and political structures. Such policies have systematically undermined eight core determinants of democracy, thereby hindering the country’s democratic development. Nonetheless, meaningful reform remains possible, provided there is a shift in policy orientation and an attempt to foster participatory institutional foundations. Recommended pathways include recognizing and responding to crises, fostering community-based policymaking, learning critically from international experiences, and empowering civil society to overcome the free-rider problem. Furthermore, any successful transition must be grounded in a nuanced understanding of the logic of natural states and limited access orders. Only by addressing these structural constraints can a more participatory, sustainable, and equitable model of governance emerge.
Research Paper
Financial Economics
Mahdiyeh Rezagholizadeh; Hossein Jafari; Narges Kafi
Abstract
The resource curse hypothesis, as an important area of research, addresses the complex relationship between natural resource revenues and financial development. The impact of natural resource revenues on the financial development of any country can be influenced by factors such as technological innovation, ...
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The resource curse hypothesis, as an important area of research, addresses the complex relationship between natural resource revenues and financial development. The impact of natural resource revenues on the financial development of any country can be influenced by factors such as technological innovation, financial market risk, and institutional quality. It is believed that these factors can influence whether natural resources act as a blessing or a curse. In this line, the present study examined the impact of natural resource revenues on financial development, the development of financial institutions, and the development of financial markets. Applying the fully modified ordinary least squares (FMOLS) model to data from 2000 to 2021, the analysis focused on the role of technological innovation, financial risk, and institutional quality in a selected group of developing countries with rich natural resources. According to the findings, natural resource revenues contribute to the development of financial markets, with technological innovation and institutional quality enhancing this positive effect, while financial market risk diminishes it. The findings showed that although the resource curse hypothesis is supported regarding the impact of natural resource revenues on overall financial development and the development of financial institutions, technological innovation and institutional quality mitigate this negative effect, thereby undermining the resource curse hypothesis. However, financial market risk intensifies the resource curse hypothesis.IntroductionNatural resources serve as a foundation for a country’s economic growth and development. The countries endowed with natural resources should leverage them to achieve sustainable economic growth. In other words, natural resources are viewed as a driving force for transforming developing and emerging economies into developed ones. Many economists support the idea that economic development significantly depends on resource abundance. However, a growing body of empirical evidence suggests that countries rich in natural resources often experience lower economic growth compared to those without such resources. This paradoxical relationship between resource abundance and slower economic progress was first introduced by Auty (2002) as the resource curse hypothesis. Despite numerous studies on natural resource wealth, a definitive answer has yet to be found regarding whether natural resources are ultimately a blessing or a curse. In the literature on the resource curse, the impact of natural resource revenues on financial development remains particularly complex. Researchers highlight several key factors-such as technological innovation (TIN), financial market risk (FMR), and institutional quality (IQ)-as critical in determining whether natural resources are a blessing or a curse. The present study aimed to examine the impact of natural resource revenues on financial development.Materials and MethodsThis study aimed to examine the impact of natural resource revenues on financial development, with a particular focus on testing the resource curse hypothesis. A fully modified ordinary least squares (FMOLS) model was applied to analyze the data from 2000 to 2021. The analysis focused on a group of resource-rich developing countries, including Iran, Russia, Saudi Arabia, China, Brazil, Argentina, Mexico, Chile, the Democratic Republic of the Congo, the Republic of the Congo, Nigeria, Egypt, India, Indonesia, Malaysia, Qatar, and Vietnam. The modeling approach is based on three key factors—technological innovation, financial market risk, and institutional quality—that may influence the relationship between natural resource revenues and financial development. These variables were incorporated into the model through interaction terms. This allows for an assessment of how each variable shapes the relationship between natural resource revenues and financial development. Based on the proposed theoretical framework and previous research, the general form of the panel data model used in this study is as follows:(1) (2) (3) Moreover, three financial development indicators were considered as dependent variables across Models (1) to (3). In Model (1), the dependent variable FD represents the overall financial development index; in Model (2), FI denotes the financial institutions development index; and in Model (3), FM stands for the financial markets development index. Other variables were defined as follows: NRR for natural resource revenues, TIN for technological innovation, FMR for financial market risk, IQ for institutional quality, and GDP for gross domestic product.Results and DiscussionThe results of the FMOLS model estimation indicated that natural resource revenues had a negative impact on overall financial development and the development of financial institutions in a selection of resource-rich developing countries, thereby confirming the resource curse hypothesis in these two areas. However, natural resource revenues positively influenced the development of financial markets. Furthermore, the FMOLS estimates for the technological innovation showed a positive relationship with all three indicators: financial development, financial institution development, and financial market development. This suggests that technological innovation can help reduce production costs and increase production efficiency through improved natural resource management. As a result, it enhances the profitability of manufacturing firms and the financial sustainability of businesses, thus facilitating economic and financial development. Regarding financial market risk, the FMOLS model showed a negative relationship with all three dimensions of financial development. This indicates that components of financial market risk-such as external debt, exchange rate volatility, debt service, capital account, and international liquidity-create economic uncertainty, which discourages investment and participation in financial markets. Consequently, this weakens overall financial development, financial institutions, and financial markets. Finally, institutional quality was found to have a positive effect on financial development and financial markets, but a negative effect on the development of financial institutions. This may be explained by the idea that the stronger rule of law is likely to enhance the efficiency of natural resource management within an economy.ConclusionIn sum, a lack of technological innovation, high financial risk, and weak institutional quality-along with factors such as policy imbalances and low levels of human development-contribute to the occurrence of the resource curse hpothesis in resource-rich countries.
Research Paper
Financial Economics
Ahmadreza Ahmadi; Mohammad Boushehri
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) ...
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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).IntroductionAddressing 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 MethodsThe 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 DiscussionThe 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 VariableCoefficientStd.ErrorT-StatisticProv. -0.0790.033-2.420.025 -0.0370.007-5.060.000 -8.1201.044-7.770.000 0.9660.1277.630.000 0.0930.00712.790.000*Source: Research estimatesConclusionIn 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.
Research Paper
Regional Planning
Seyed Amin Mansouri; Seyed Morteza Afghah; Masood Khodapanah; Fateme Mombeini
Abstract
Measuring the level of economic development in the counties of Khuzestan Province is essential due to the region’s cultural, economic, and environmental significance. Such an evaluation can aid policymakers, researchers, and local communities in making informed decisions about policy design, resource ...
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Measuring the level of economic development in the counties of Khuzestan Province is essential due to the region’s cultural, economic, and environmental significance. Such an evaluation can aid policymakers, researchers, and local communities in making informed decisions about policy design, resource management, and planning. It also helps identify the underlying causes of economic disparities and promotes efforts to enhance the region’s economic resilience and sustainability. By identifying underperforming sectors, measuring economic development guides the formulation of targeted policies aimed at fostering a more diversified and resilient economy. The present research used the numerical taxonomy method and urban economic components in order to measure and compare the levels of development across the counties of Khuzestan Province in 2017 and 2020. A total of 65 indices were used, spanning a wide range of development dimensions, including agriculture, housing and welfare services, industry, education, health and medical services, macroeconomic performance, infrastructure and physical assets, and urban economy metrics. In 2017, Abadan, Ahvaz, and Shushtar ranked as the most developed counties, while Andika, Shadegan, and Haftkel ranked lowest. By 2020, Ahvaz and Abadan maintained their economic leadership; Behbahan and Dezful excelled in education; Mahshahr led in industry; and Dezful stood out in agriculture. Abadan was also the pioneer in healthcare services. These findings highlighted both progress in certain counties and the need for increased attention and support in others.IntroductionThe Human Development Report on Iran highlights significant regional disparities among provinces in terms of human development levels. Contrary to the convergence theory-which predicts a reduction in regional imbalances-these disparities have not diminished. In fact, the coefficient of dispersion among the studied units has increased. Measuring the level of economic development in the counties of Khuzestan Province is a critical scholarly endeavor with far-reaching implications for regional policy, resource allocation, and the overall well-being of its population. Located in southwestern Iran, Khuzestan Province is notable for its rich cultural heritage, economic significance, and environmental diversity. As such, a comprehensive assessment of the province’s economic development is essential for various stakeholders, including policymakers, researchers, and local communities. First, measuring economic development in Khuzestan provides a foundation for informed policymaking and effective resource management. By systematically collecting data on key economic indicators (e.g., employment rates and industrial growth), local and national governments can more effectively prioritize and implement development plans. This enables them to address inequalities and allocate resources to areas most in need. Moreover, assessing the economic development of Khuzestan’s counties is crucial for identifying the causes of deep economic disparities. It also plays a pivotal role in improving the resilience and sustainability of the region’s economy. Khuzestan Province faces specific challenges, including dependency on the oil industry, water resource management, and industrial diversification. Assessing economic development helps pinpoint vulnerable sectors and informs the formulation of targeted policies aimed at creating a more diverse and resilient economy. In general, measuring the economic development of Khuzestan’s counties is a multifaceted and essential task that can help informed decision-making, equitable economic growth, sustainability, and enhances the overall well-being of the population. In this respect, the current research aimed to assess the development levels of Khuzestan’s counties in 2017 and 2020. Materials and MethodsThis study used the numerical taxonomy method and urban economic components in order to conduct a comparative assessment of the levels of development across the counties of Khuzestan Province in 2017 and 2020. Results and DiscussionThe ranking results from 2017 indicated that Ahvaz was the most developed county in terms of education, healthcare, urban economy, and infrastructure. Mahshahr ranked highest in industry and economy, while Dezful was the most developed in agriculture. Other counties such as Behbahan, Abadan, Ramhormoz, Masjed Soleyman, and Shushtar also showed significant development in some specific indicators. By 2020, there were notable changes in the development rankings. Ahvaz and Abadan emerged as leaders in economic development, while Behbahan and Dezful led in education. Mahshahr maintained its leading position in the industrial sector, and Dezful continued to be the top county in agriculture. In terms of housing, welfare services, and infrastructure, Abadan, Behbahan, Dezful, Mahshahr, and Ahvaz ranked highest. Abadan took the lead in healthcare development. Regarding urban economy, Dezful, Ahvaz, Abadan, Shushtar, Ramhormoz, and Behbahan were among the leading counties. Furthermore, Shushtar, Ramhormoz, Ahvaz, Behbahan, Abadan, Khorramshahr, Izeh, Dezful, Omidieh, Mahshahr, and Masjed Soleyman showed notable development in infrastructure and physical indices. This analysis reflects the shifts, progress, and in some cases, regression in development indicators across the counties between 2017 and 2020. ConclusionCounties such as Ahvaz and Abadan, which have performed well in the economic and healthcare sectors, can capitalize on these strengths to promote development in other areas. Infrastructure development plays a key role in regional development; counties that are already developed in this regard can serve as examples for those that are less developed. Strategic investments in infrastructure can lead to improvements across other development indicators as well. Education is another critical area where improvement can yield significant benefits. In counties that are underperforming educationally, implementing educational programs and expanding access to educational resources can be beneficial. Similarly, enhancing healthcare services in counties with poor performance in this sector can directly contribute to a better quality of life and improved public welfare. Supporting agriculture and local industries is also essential. Counties with agricultural or industrial potential can boost their productivity and development outcomes through financial aid and technical support. Finally, achieving balanced development is crucial. Addressing existing inequalities and planning for balanced development across all counties can reduce disparities and improve the overall regional development.AcknowledgementsWe hereby express our gratitude to the Vice-Chancellor for Research Affairs at Shahid Chamran University of Ahvaz, who assisted the authors in conducting this research.Conflict of interestThe authors of the article declare that there is no conflict of interest in publishing the presented article.FundingThis article is part of Fatemeh Mombeini’s master’s thesis in economics, conducted under the supervision of Dr. Seyed Amin Mansouri and Dr. Seyed Morteza Afghah at Shahid Chamran University of Ahvaz. It was sponsored by the Vice-Chancellor for Research Affairs at Shahid Chamran University of Ahvaz under Grant No. SCU.EE1403.30460.
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