Economics Research

Abstract  This study investigates the impact of ESG performance on the financial sustainability of companies listed on the Tehran Stock Exchange from 2018 to 2024. It analyzes the moderating role of senior managers' characteristics, particularly overconfidence, using a maximum likelihood-based spatial panel regression approach. In emerging economies like Iran, which face environmental, social, and institutional challenges, ESG performance is considered a key tool for reducing financial risks and enhancing long-term firm stability. Global studies suggest that strong ESG performance can lower financing costs and increase resilience against economic shocks, but findings in emerging markets have been inconsistent, often overlooking spatial effects and the role of managers. The statistical population consists of non-financial listed companies, with a final sample of 125 companies (875 observations) selected based on inclusion and exclusion criteria. Hypothesis testing results indicate that ESG performance positively affects firms' financial sustainability, and this relationship is confirmed when accounting for spatial and regional dependencies. Additionally, managers' overconfidence, as a moderating factor, weakens this positive impact and influences spatial spillover effects. The study emphasizes that enhancing ESG performance can improve the financial stability of firms in emerging economies like Iran, but managers' behavioral biases must be managed to maximize sustainability benefits. Introduction In emerging economies like Iran, which face environmental, social, and institutional challenges, Environmental, Social, and Governance (ESG) performance is recognized as a key tool for reducing financial risks and enhancing the long-term stability of firms. However, research findings in this area have been inconsistent, often overlooking spatial effects and the role of managers’ characteristics, particularly overconfidence. This study focuses on companies listed on the Tehran Stock Exchange from 2018 to 2024, examining the impact of ESG performance on financial sustainability and analyzing the moderating role of managers’ overconfidence using an innovative maximum likelihood-based spatial panel regression approach. The study aims to address gaps in the literature by explicitly accounting for spatial dependencies and managers' behavioral biases in the context of Iran's economy. Methods and Material The statistical population comprises non-financial companies listed on the Tehran Stock Exchange. The final sample includes 125 companies (875 observations) selected based on exclusion criteria (e.g., incomplete data) and inclusion criteria (e.g., availability of ESG reports). Financial sustainability was measured using Altman’s Z-score, ESG performance was assessed via a weighted average score of its three dimensions. Managers’ overconfidence was measured using the Malmendier and Tate (2005) method. A spatial panel regression model (SDM) was estimated using the maximum likelihood approach, accounting for spatial dependencies based on a geographical distance-weighted matrix. Data were collected from the Codal system and companies’ annual reports and analyzed using Stata 18 and GeoDa software. Pre-tests (e.g., Moran’s I, L[agrange]M[ultiplier] tests, Hausman test) and robustness checks (e.g., substitution with O-score, removal of outliers) ensured the model’s validity. Results and Discussion The results indicate that ESG performance has a significant positive impact on financial sustainability (total coefficient 0.083, p < 0.01), accounting for 58% of the total effect as direct within-firm effects and 42% as indirect spatial spillover effects. This finding confirms regional dependencies, such as the influence of Tehran-based companies on neighboring provinces. Managers’ overconfidence weakens this positive effect (interaction coefficient = −0.044, p < 0.05), likely through inefficient resource allocation. Control variables such as firm size and profitability showed positive effects, while financial leverage had a negative effect. Robustness tests, including substitution of Z-score with O-score and alteration of the weight matrix, confirmed the stability of the results. Conclusion This study confirms that ESG performance enhances financial sustainability in emerging economies, but managers’ behavioral biases, particularly overconfidence, diminish this effect. Spatial dependencies play a critical role in amplifying ESG effects, particularly in Iran, given regional heterogeneity in environmental and sustainability policies. The findings align with global studies (e.g., Wu et al., 2025) but highlight Iran-specific aspects, such as spatial spillovers. Limitations include limited access to ESG data and the focus on listed companies.

Abstract  This study investigates the effect of investor attention on stock excess returns in the Tehran Stock Exchange, using weekly data from 52 companies over a five-year period. The Google Search Volume Index (GSVI) is employed as a proxy for investor attention, and its impact across different return levels is analyzed using quantile regression. This study distinguishes itself from similar domestic research both in its focus on stock excess returns as the dependent variable and in its use of quantile regression methodology. By utilizing the quantile regression approach, it is less sensitive to outlier data and avoids statistical issues such as omitted variable bias. The results indicate that investor attention has an asymmetric effect on stock excess returns. Specifically, for stocks whose excess returns lie within the middle quantiles of the excess return distribution, the effect of investor attention on these stocks exhibits short-term persistence. Conversely, for stocks whose excess returns are in the lower quantiles of the distribution, increased investor attention leads to selling pressure and a decrease in their excess returns in subsequent weeks. Furthermore, for stocks of companies whose excess returns are in the upper quantiles of the excess return distribution, an increase in detrended trading volume has a positive and significant effect on stock excess returns. This study shows that investors' behavioral indicators can be used as a complementary tool alongside well-known classical market variables to predict and achieve higher returns for investors. Introduction and Theoretical Foundations Traditional financial theories, such as the Efficient Market Hypothesis, assume investors are fully rational. However, behavioral finance challenges this, highlighting the role of psychological factors like "attention" as a limited cognitive resource. In today's digital age, online search behavior provides a measurable proxy for this otherwise difficult-to-quantify concept. This study leverages the Google Search Volume Index (GSVI) as a novel proxy for investor attention. The study investigates a central question: How does investor attention asymmetrically affect stock excess returns in an emerging market like the Tehran Stock Exchange (TSE)? The present research is distinct from prior domestic studies by focusing directly on "excess return" and employing a robust quantile regression approach, moving beyond traditional average-based analyses to capture heterogeneous effects across the entire return distribution. Data and Methodology Our empirical analysis utilizes a unique panel dataset of 52 companies listed on the TSE. We collected weekly data over a five-year period (2019-2024), resulting in 11,128 observations. The dependent variable is Stock Excess Return (ER), calculated as the difference between an individual stock's weekly return and the return of the overall market index, adjusted for its beta (systematic risk). The primary independent variable is the change in the standardized Google Search Volume Index (SGSVI) for each company's ticker symbol, which captures the week-over-week shift in investor online attention. To control for market dynamics, we include detrended trading volume (VLMt) and one-week lags of both the dependent variable (ER1) and the independent variables (SGSVI1, VLMt1). The core of our methodological contribution is the use of Panel Quantile Regression. This method was chosen for three crucial reasons: It bypasses normality assumptions: A Shapiro-Wilk test strongly rejected the normality of our ER variable (p < 0.001), making standard OLS regression potentially biased. It is robust to outliers: Financial data is well known to be prone to extreme values, and quantile regression is resistant to their influence. It reveals heterogeneity: Unlike OLS, which only estimates the average effect, quantile regression allows us to examine how the impact of investor attention differs for stocks with low (e.g., Q1, Q2), medium (e.g., Q5), and high (e.g., Q8, Q9) excess returns. We also conducted Pesaran's Cross-sectional Dependency test and second-generation panel unit root tests (CADF) to ensure the validity of our data. Key Findings and Results Our quantile regression results reveal a clear and pronounced asymmetric effect of investor attention on stock excess returns. The coefficients for SGSVI vary significantly across quantiles (confirmed by a Wald test, p < 0.001), which is the central finding of this study. The results can be broken down as follows: For Low-Performing Stocks (Lower Quantiles, e.g., Q1-Q4): The effect of investor attention (SGSVI) is negative and statistically significant. For example, at Q1 (the 10th percentile), an increase in attention correlates with a -0.0068 decrease in excess return. This suggests that heightened online searches for these "unpopular" or underperforming stocks signal investor concern or panic, leading to selling pressure and a further decline in returns in the following weeks. The lagged attention variable (SGSVI1) shows a similarly negative effect, indicating the persistence of this sell-off pressure. For Average-Performing Stocks (Middle Quantiles, e.g., Q5): Here, the effect of current attention (SGSVI) is non-significant (p-value 0.805). However, the lagged dependent variable (ER1) is positive and significant, indicating that these stocks exhibit short-term return persistence driven by their own momentum rather than new attention shocks. Investors seem to react more moderately and rationally to this group. For High-Performing Stocks (Upper Quantiles, e.g., Q7-Q9): The effect dramatically reverses and becomes positive and strongly significant. At Q9 (the 90th percentile), an increase in attention (SGSVI) is associated with a +0.0068 increase in excess return. This finding indicates that for stocks already delivering high returns, increased investor attention acts as a positive reinforcement signal, attracting more buyers, generating buying pressure, and driving prices even higher. Furthermore, detrended trading volume (VLMt) shows a negative effect at the lowest quantiles and a strong positive, significant effect from the median (Q5) upwards. For example, at Q9, a one-unit increase in VLMt leads to a 0.0211 increase in excess return. This suggests that high trading volume accompanies institutional investor inflows for high-performing stocks and signals divestment for underperforming ones. Discussion and Conclusion This study provides robust empirical evidence that investor attention, measured through the GSVI, is a significant and asymmetric driver of stock excess returns in the Tehran Stock Exchange. The central finding is that the effect of investor attention is not inherently positive or negative; it depends entirely on the stock's recent performance context. For high-performing stocks, increased attention reinforces upward return momentum. For underperforming stocks, it accelerates selling pressure and further return decline. These findings carry practical implications. Investors and analysts can incorporate Google Trends data as a real-time behavioral indicator alongside conventional tools such as P/E ratios and technical charts. More specifically, a sudden spike in search volume for a consistently underperforming stock may serve as an early sell signal, while a steady rise in attention toward a high-momentum stock can reinforce a continuation pattern and suggest a buying opportunity. This study has two main limitations. First, Google Trends restricts data extraction to a five-year window. Second, our data does not distinguish between search intent (e.g., buying vs. selling research). Future research could incorporate high-frequency intraday search data or classify search terms (e.g., "company news" vs. "company stock price") to refine the measurement of attention and its causal impact.

Abstract  Financial development plays an important role in economic development and growth. However, the question arises as to what effect financial development has on environmental quality. The aim of this study is to investigate the effect of institution-based and market-based indicators of financial development on Iran's carbon dioxide emissions. Considering the different aspects of market-based and institution-based indicators, a market-based composite financial development index and an institution-based composite financial development indices are constructed using principal component analysis (PCA). The estimation of the nonlinear ARDL models shows that renewable energy significantly leads to emission reduction and improvement of environmental quality. The Environmental Kuznets Curve in Iran is confirmed and short-run GDP dynamics is significant. Positive shocks to the market-based composite index do not lead to a significant increase in carbon dioxide emissions but positive shocks to the institution-based composite index lead to a significant increase in carbon dioxide emissions. Negative shocks to the market-based composite index significantly lead to a reduction in carbon dioxide emissions, but negative shocks to the institution-based composite index do not significantly lead to a reduction in carbon dioxide emissions. Therefore, the quality of the environment in Iran shows a different and asymmetric response to shocks to financial development indicators. Financial development, especially in the banking sector, requires complementary policies, including strengthening the use of renewable energies, to control its negative environmental effects. The development of the stock market can be a way for the sustainable development of Iran. Introduction In recent decades, environmental degradation has become one of the most important global challenges. The increase in greenhouse gas emissions, especially carbon dioxide, has caused global warming and widespread climate change, which has devastating effects on natural ecosystems, water resources, and biodiversity. Against this backdrop, financial development, as one of the main drivers of economic activity, plays a dual and contested role in environmental quality. The main issue is that the mechanisms of financial development's impact on the environment are accompanied by significant theoretical and empirical ambiguity. Iran has been among the ten largest emitters of carbon dioxide in the world in recent years. The fundamental question is whether financial development acts as a driver or a restraint of carbon dioxide emissions in Iran. The aim of this study is to investigate the impact of different financial development indicators on Iran's carbon dioxide emissions in the period 1981-2021. Methods and Materials Given that different and sometimes contradictory results are seen in the literature regarding the impact of financial development on CO2 emissions, an attempt was made to use both groups of institution-based and market-based financial development indicators. The Global Financial Development Database reports two groups of market-based and institution-based financial development indicators. Previous studies have often used unidimensional indicators to measure financial development that are unable to separate the effects of "financial institutions" from "financial markets". Subsequently, drawing on the diversity of institution-based and market-based indicators, composite indices for each group are constructed using principal component analysis. Finally, the impact of positive and negative financial development shocks on carbon dioxide emissions is examined separately using the nonlinear autoregressive distributed lag (NARDL) model. Also, this model examines the possibility of analyzing the short-run and long-run effects of financial development on environmental quality to provide a more accurate picture of the dynamics of this relationship over time. Results and Discussion The estimation of the nonlinear ARDL model shows that renewable energies significantly lead to emission reduction and improvement in environmental quality, and the environmental Kuznets curve is confirmed in Iran. Positive shocks to the market-based composite financial development index do not lead to a significant increase in carbon dioxide emissions, but negative shocks to the market-based composite financial development index significantly lead to a decrease in carbon dioxide emissions. Positive shocks to the institution-based composite financial development index lead to a significant increase in carbon dioxide emissions, but negative shocks to the institution-based composite financial development index do not significantly lead to a decrease in carbon dioxide emissions. These results indicate that the impact of financial development on environmental quality in Iran is asymmetric, varying significantly between institution-based and market-based indicators. Conclusion It is predicted that stock market development will not have a significant impact on carbon dioxide emissions and therefore provides a platform for moving towards sustainable development and a low-carbon economy. Banking sector development as one of the main pillars of the financial system leads to a significant increase in carbon dioxide emissions. Therefore, financial development, especially in the banking sector, requires complementary policies, including strengthening the use of renewable energies, in order to control its negative environmental effects. The development of financial institutions without expanding the use of renewable energy makes it difficult to achieve sustainable development. In addition to expanding banking activities, policymakers need to prioritize investing in renewable energies, improving energy efficiency, and making bank facilities conditional on compliance with environmental standards. In contrast, financial market development is expected to play a prominent role in achieving sustainable development in Iran and be a tool for transitioning to a low-carbon economy. Strengthening the role of the capital market in financing low-carbon projects, issuing green bonds, and creating tax incentives for green companies can contribute to sustainable development.

Abstract  Recent policy discussions on public utility pricing have increasingly focused on the provision of free or fully subsidized utility services for low-consumption households as a mechanism to improve affordability, support vulnerable groups, and promote sustainable resource use. Such policies, particularly in electricity, gas, and water services, can be analyzed within the framework of a three-part tariff (3PT), consisting of a fixed fee, an initial free-consumption block, and a positive marginal price above a specified consumption threshold. Although these tariff structures are often justified on social and environmental grounds, their implications for efficiency, equity, cost recovery, and sustainability remain insufficiently examined. Introduction This study investigates the analytical properties, policy implications, and limitations of 3PT in pursuing multiple objectives in public utility pricing. The paper argues that relatively simple and transparent tariff structures, such as uniform pricing or tariffs with fewer consumption blocks, may offer important advantages over more complex pricing schemes in terms of implementation, consumer understanding, and policy effectiveness. Nevertheless, the performance of the 3PT in achieving objectives beyond efficiency depends critically on the calibration of key design parameters, including the fixed fee, the size of the free-consumption threshold, and the marginal price. The study first develops a theoretical framework to examine the efficiency properties of 3PT. The analysis demonstrates that, under a purely efficiency-oriented objective, the optimal free-consumption threshold converges to zero. In other words, the existence of a free initial consumption block cannot be justified solely on efficiency grounds. The introduction of free consumption therefore requires additional policy objectives, such as redistribution, affordability, minimum access guarantees, demand management, or political feasibility. Methods and Material To evaluate the broader performance of 3PT, the paper employs a simulation model based on the framework developed by Céline Nauges and Dale Whittington (2017). The simulations include 5,000 households whose utility consumption follows a log-normal distribution correlated with household income. Two alternative correlation scenarios between income and consumption (0.1 and 0.8) are examined, together with multiple combinations of tariff parameters. To distinguish the contribution of this study, it should be noted that while Nauges and Whittington (2017) provide the simulation framework, they do not evaluate the performance of 3PTs. this paper extends Nauges and Whittington's analysis by incorporating a theoretical examination of 3PTs, thereby providing an analytical foundation for examining the conditions under which such tariff structures may be justified and their implications for efficiency and redistribution. Tariff performance is evaluated along two principal dimensions: (1) equity, measured by the share of subsidies allocated to lower-income households, and (2) economic efficiency, measured by welfare losses relative to a benchmark uniform tariff. The performance of 3PTs is also compared with conventional increasing block tariff (IBT) structures commonly used in utility pricing. Findings The findings indicate that 3PT can substantially improve the distributional targeting of subsidies toward lower-income households. In several simulation scenarios, approximately 42 percent of total subsidies accrue to the lowest income quintile, whereas the corresponding share under IBT structures remains below 15 percent. The redistributive effect becomes stronger as the free-consumption threshold increases and the fixed fee remains moderate. Under these conditions, higher-consumption households effectively finance part of the minimum consumption needs of lower-income groups. However, these distributional gains are accompanied by significant efficiency losses. Expanding the size of the free-consumption block increases welfare losses by distorting marginal consumption incentives and reducing allocative efficiency. The simulations further demonstrate that the trade-off between equity and efficiency is highly sensitive to tariff design. Relatively small adjustments in the fixed fee or threshold level can substantially alter both the distribution of subsidies and overall welfare outcomes. Conclusion The study concludes that 3PTs may serve as a flexible policy instrument for balancing equity, affordability, efficiency, and sustainable consumption in public utility sectors, particularly in contexts where alternative instruments for achieving redistributive objectives are limited or unavailable. Nevertheless, the performance of 3PT requires gradual adjustment and careful monitoring of policy outcomes following changes in tariff parameters. Overall, the findings suggest that free-consumption thresholds are justifiable only when policymakers explicitly prioritize broader social and distributional objectives alongside economic efficiency and do not have access to lower-cost policy alternatives. Even in such cases, careful tariff design remains essential, as neglecting the trade-off between equity and efficiency may result in substantial welfare losses and compromise the achievement of policy objectives.

Abstract  The challenge of unregulated credit creation and the diversion of facility consumption in underbanked regions has persistently undermined the effectiveness of distributive justice policies in Iran's banking system. Despite various policy interventions over the past decade, the lack of systematic post-disbursement supervision mechanisms has led to continuous imbalances, with liquidity growth reaching 24.3 percent in 2023 and non-performing loans increasing from approximately 420 thousand billion tomans in 2019 to about 780 thousand billion tomans in 2023. This situation indicates that credit resources are not effectively directed toward productive activities, which from the perspective of Islamic transactional jurisprudence constitutes a violation of the prohibitions against uncertainty (gharar), unjust appropriation of wealth (akl māl bi-al-bāṭil), and the obligation to fulfill contractual commitments (wafāʾ bi-al-ʿahd). The persistence of facility disbursement without effective mechanisms to ensure consumption aligns with agreed-upon objectives transforms the credit relationship from a legitimate commutative obligation into a process of resource transfer lacking contractual substance. Introduction This study aims to propose and analytically validate a locally developed, justice-oriented smart supervision model for post-disbursement credit management that integrates technical efficiency with Islamic transactional jurisprudence requirements within the institutional context of Iran's banking system. It also attempts to identify operational data-driven mechanisms capable of detecting and controlling credit diversion in near real-time, and to demonstrate how Islamic transactional jurisprudence rules can be transformed from normative principles into practical, implementable indicators within banking supervision processes. Methods and Materials A mixed-methods exploratory research design was employed. The statistical population consisted of experts in banking, monetary policy, and Islamic transactional jurisprudence. Theoretical saturation was approached after sixteen interviews; sampling was extended to twenty participants to strengthen coverage of technical, institutional, and jurisprudential dimensions. Data collection instruments included a semi-structured interview (researcher-made, 2023) in the qualitative phase and a fuzzy Delphi questionnaire in the quantitative phase. The qualitative data were analyzed using three-step thematic analysis consisting of open coding, axial coding, and selective coding. The quantitative phase employed the fuzzy Delphi technique with calculation of the fuzzy consensus index (λ), where only components achieving λ ≥ 0.7 were retained in the final model. Validity was established through expert review, and reliability was confirmed through response stability assessment in the final Delphi round. An analytical-scenario evaluation was also conducted using aggregated facility data and supervisory indicators extracted from official Central Bank reports to examine the internal consistency of the model and analyze the direction and magnitude of component effects, without claiming quantitative prediction or empirical generalization. Findings The findings indicated that three mechanisms achieved the required fuzzy consensus index. The first mechanism, allocation of credit to purpose-specific accounts, received a consensus score of 0.82 and was identified as the most strongly endorsed component. This mechanism prevents premature liquidity circulation by restricting the use of credit resources to pre-specified accounts and expenditure patterns. The second mechanism, phased fund release based on the Economic Credit Effectiveness Index, achieved a consensus score of 0.78. This index measures the impact of credit on the real economy by distinguishing productive fund flows from non-productive or speculative transactions. The third mechanism, transaction graph analysis with real-time anomaly detection, achieved a consensus score of 0.75. This mechanism enables ongoing transaction graph analysis to identify emerging deviations in near real-time and trigger early warning alerts. Two proposed components, i.e., machine learning-based prediction of diversion and connection to national credit databases, did not achieve the required consensus threshold, receiving scores of 0.68 and 0.66 respectively, primarily due to expert concerns regarding institutional feasibility, data accessibility, and privacy considerations. The three accepted mechanisms demonstrated significant alignment with the three core rules of Islamic transactional jurisprudence: fulfillment of contracts (wafāʾ bi-al-ʿahd) through continuous monitoring of compliance with agreed expenditure patterns, avoidance of uncertainty (gharar) through purpose-specific accounts and phased release conditional on verification of prior stage completion, and prohibition of unjust appropriation (akl māl bi-al-bāṭil) through the Economic Credit Effectiveness Index which ensures funds are released only upon realization of verifiable economic benefit. Conclusion It was concluded that the proposed framework integrates data-driven supervisory efficiency with jurisprudential legitimacy, providing analytical capacity to reduce credit diversion from approximately baseline levels to significantly lower rates, as demonstrated by scenario-based simulations using aggregated supervisory evidence. The framework serves as a complementary mechanism within the banking supervision system, not as a substitute for initial credit assessment mechanisms or a guarantee of comprehensive social justice realization. The main limitation of this study is its analytical nature and reliance on aggregated data, as the scenario evaluation was conducted solely to examine direction of effects and internal consistency, not to provide predictive accuracy or empirical generalizability. Future research should include field-based empirical testing of the framework within real institutional contexts. The contribution of this paper lies not in presenting an operational or prescriptive formula, but in redefining justice as an institutionally measurable and analyzable concept within the banking system and advancing the analytical frameworks related to post-disbursement supervision.

Abstract  Population health is one of the most important indicators of human development, and the mortality rate, as a key measure, is influenced by health-related, medical, and social factors. The aim of this study is to examine the spatial effects of government health expenditures on health outcomes (mortality rate) across the provinces of Iran during the period 2011–2021. To this end, provincial-level data and a spatial weight matrix are employed to identify spatial dependence among provinces. First, the existence of spatial autocorrelation in mortality rates is confirmed using appropriate diagnostic tests. Then, the Spatial Durbin Model is applied to analyze the impact of government health expenditures on health outcomes. The estimation results indicate that mortality rates exhibit significant spatial dependence, such that changes in mortality rates in one province can affect neighboring provinces. Moreover, government health expenditures, the number of physicians, and the number of hospital beds have a negative and statistically significant effect on mortality rates, highlighting the important role of expanding health infrastructure and services in reducing mortality. In addition, the level of education shows a significant negative effect on mortality rates, confirming the importance of socio-economic factors in improving health outcomes. Based on these findings, the design and implementation of health policies require a regional and spatial approach, so that through efficient allocation of resources and consideration of spatial spillover effects, a sustainable reduction in mortality rates can be achieved at the national level. Introduction Human capital has long been interpreted narrowly in economic thought, but it has become increasingly clear that labor resources cannot be understood primarily in quantitative terms. This has led to a completely new approach to the field of economic activity. According to this broader approach, the qualitative dimensions of human capital have gained increasing importance. Healthcare is one of the most vital sectors in any national economy. Improving health status significantly increases human capital through greater productivity and longer working lives through reduced illness, leading to improved production and consumption, as well as returns on investment. Health studies have shown that health spending and investment in health systems are determinants of population health status and mortality rates. Health spending usually includes financial resources spent on medical services, preventable diseases, primary and specialized care, vaccinations, and health insurance. The aim of this study is to investigate the spatial effects of government health spending on health outcomes (mortality rates) in the country's provinces during the period 2011–2021. For this purpose, provincial data and a spatial weight matrix were used to identify spatial dependencies between provinces. Methods and Material This study considers the relationship between health expenditures and health development based on Grossman's (1972) health production function and the "social determinants of health" by the World Health Organization, a macro health production function model similar to Zhao et al. (2024) as equation (1):   (1)  The spatial units in this study comprise 31 provinces of the country during the period 2011–2021. To investigate and measure potential spatial effects, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) are employed. The SDM is derived from the integration of the SLM and SEM. The SLM captures spatial dependence by incorporating a spatially lagged dependent variable into the regression framework. The SEM, on the other hand, assumes that spatial correlation among variables is reflected in the regression error term rather than through the inclusion of a lagged dependent variable. In contrast, the SDM not only accounts for spatial spillover effects of the dependent variable across neighboring regions, but also incorporates the influence of independent variables in adjacent regions on the dependent variable of a given region. Consequently, the SDM is widely used for empirical testing of spatial spillover effects (Elhorst, 2017). To determine which spatial econometric model is more appropriate, Elhorst (2017) proposed a testing procedure. Specifically, two hypotheses of the panel SDM are examined and summarized using the Wald test and the likelihood ratio (LR) test based on the restrictions θ = 0 and θ + ρβ = 0. If both null hypotheses are rejected, the panel SDM should be employed. If θ = 0 and both the LM test and the robust LM test indicate spatial dependence in the dependent variable, the SDM reduces to an SLM. If θ + ρβ = 0 and the LM and robust LM tests suggest that the residuals exhibit spatial autocorrelation, the SDM simplifies to an SEM. Results and Discussion Each variable consists of 341 observations. The average population mortality rate, used as a health indicator, is 4.5 percent, with minimum and maximum values of 3.4 percent and 6.7 percent, respectively. Health expenditures, measured as a percentage of provincial gross domestic product, have an average value of 5.76 percent, with minimum and maximum values of 4.65 percent and 7.98 percent, respectively. The Moran’s I index for each variable is significantly positive, indicating the presence of spatial autocorrelation in health status, health expenditures, physicians per capita, hospital beds per capita, and education per capita across the 31 provinces of the country. The coefficient of the spatial model is significantly positive under all three spatial weight matrices. This finding indicates the existence of strong spatial spillover effects in provincial health outcomes in Iran. Therefore, the relationship between health status and government health expenditures should be examined from a spatial economics perspective. Investment in health services improves access to medical care, enhances disease prevention, and raises the quality of treatment. Increased health spending is generally associated with earlier disease detection, better management of chronic conditions, and a reduction in preventable mortality. In contrast, insufficient health expenditures may weaken the health system and lead to higher mortality rates, particularly among vulnerable population groups. Conclusion This study employed spatial econometric models to examine the effects of health expenditures, physicians per capita, hospital beds per capita, and education level on provincial mortality rates. The results reveal a significant spatial dependence in mortality rates across provinces, indicating that the health and socioeconomic conditions of each province affect not only its own outcomes but also those of neighboring provinces. This finding underscores the necessity of adopting spatial approaches in public health analyses. The estimations further show that increased health expenditures are significantly associated with lower mortality rates, highlighting the importance of targeted investment in health infrastructure and services. Moreover, increases in the number of physicians and hospital beds exert a significant negative effect on mortality, emphasizing the critical role of physical and human access to healthcare services. Finally, education level, as a key socioeconomic determinant, exhibits a significant negative impact on mortality rates, suggesting that improvements in education contribute to reduced mortality through healthier behaviors, greater awareness, and more effective utilization of healthcare services.