Iranian Journal of Economic Research

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

Introduction

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

Materials and Methods

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

Results and Discussion

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

Conclusion

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

Abstract Companies adjust their voluntary information disclosure based on the volatilities they experience in their cash flows. Focusing on the digital industry segment of the Tehran Stock Exchange during the period 2012–2022, the current study aimed to investigate the effects of news related to risk, ambiguity, and ambiguity aversion on the policies adopted by firms regarding voluntary disclosure of soft and hard information. The analysis employed dynamic panel models to explain the voluntary disclosure behavior by the selected companies. The corporate voluntary disclosure lag was also used to capture disclosure dynamics, along with control variables including cost of capital, financial leverage, and stock liquidity. According to the results, managers of digital industry companies respond differently to news concerning risk, ambiguity, and ambiguity aversion depending on the type of information available for voluntary disclosure—whether disclosed conservatively or non-conservatively. This variation may be attributed to the nature of the disclosed information and its perceived credibility by investors. Furthermore, the findings confirmed that voluntary disclosures in previous periods positively influenced disclosures in subsequent periods, suggesting the presence of inertia in voluntary disclosure policies in the digital industry. Introduction The type of information disclosed by a company can be interpreted differently by participants in the stock market. According to the cheap talk literature, soft information is somewhat informative and can serve as an imperfect substitute for hard information (Kirk & Vincent, 2014). In contrast, hard information is quantitative and more reliable (Stein, 2002). Moreover, the market often interprets the disclosure of hard information as favorable news, whereas soft information is frequently perceived as unfavorable (Bertomeu & Marinovic, 2016). It is thus expected that firm managers employ different response tools—specifically, the disclosure of hard and soft information—when faced with news that affects firm value. Additionally, corporate disclosure environments are characterized by multi-period, multi-dimensional flows of information from the firm to the market (Guttman et al., 2014). Accordingly, it is hypothesized that the disclosure of hard and soft information in one period will encourage increased disclosure in subsequent periods, reflecting the presence of inertia in disclosure policies. According to decision-making theories, investors’ reactions to a company’s information disclosure differ in the presence of ambiguity and risk. As the level of ambiguity increases—assuming investors are ambiguity-averse and risk-averse—stock price volatility rises because ambiguity leads investors to place greater weight on the possibility of an unfavorable future state (Brenner & Izhakian, 2018a; Epstein & Schneider, 2007a). Therefore, the effectiveness of an information disclosure policy under ambiguity may differ from its effectiveness under risk (Billings et al., 2015; Rava, 2022). In this respect, the present study aimed to model the transition of the information environment from risk to ambiguity. Furthermore, theoretical literature emphasizes that both the level of ambiguity and the degree of investors’ aversion or preference toward ambiguity independently influence how firm value is evaluated. Ignoring these independent effects in empirical model specifications can introduce specification errors, as these factors affect managers’ assessment of a firm’s financing costs and, consequently, their decisions regarding the amount of voluntary information disclosure needed to achieve their disclosure objectives. Accordingly, this study also examined the independent effects of changes in the level of ambiguity and investors’ ambiguity aversion on the level of voluntary information disclosure. Materials and Methods The sample consisted of eight companies operating in the digital industry segment of the Tehran Stock Exchange during the period 2012–2022. The detrended state of variables was used to extract news related to ambiguity, ambiguity aversion, and risk. Moreover, the generalized method of moments (GMM) was employed to address potential endogeneity among the research variables and improve the accuracy of coefficient estimates. The empirical model of the study is specified as follows:   In the model above, respectively, the variable ( ) represents the level of voluntary information disclosure by company i at time t in two different categories, that is, hard information and soft information. ( ) captures dynamics of voluntary disclosure for both types of information. ( ) denotes news related to investors’ ambiguity aversion, and ( ) represents news concerning the level of investors’ ambiguity. Moreover, ( ) corresponds to news about the firm’s risk. Control variables include ( ) as the weighted average cost of capital, ( ) as the firm’s financial leverage, and ( ) as the stock liquidity of the firm. The model also incorporates ( ) to account for cross-sectional effects, ( ) for time effects, and ( ) as the error term. Results and Discussion At the 95% confidence level, voluntary disclosure of both soft and hard information exhibited persistence over time (Tables 1 and 2). Moreover, when soft information was viewed as a managerial response tool to news affecting firm value, news related to investors’ ambiguity aversion (  variable) had a negative and statistically significant effect on the level of voluntary disclosure of soft information. Specifically, when this news is unfavorable—represented by a positive deviation from the expected trend—and given the negative regression coefficient, managers are expected to reduce the level of soft information disclosure in response to an unexpected increase in investors’ ambiguity aversion, thereby adopting a non-conservative reporting approach. Similarly, news related to the level of investors’ ambiguity ( ) had a negative effect on soft information disclosure. When this news is favorable—indicated by a negative deviation from the expected trend—and given the negative coefficient, managers tend to pursue a non-conservative disclosure policy for soft information in an effort to reduce investors’ ambiguity, and vice versa. In contrast, news concerning the firm’s risk ( ) had a negative but statistically insignificant effect on the voluntary disclosure of soft information. This result may be attributed to the unverifiable nature of soft information for investors and managers’ limited ability to influence investors’ worst-case beliefs under conditions of ambiguity, particularly given the characteristics of the digital industry. As  shown in Table 2, when the firm manager’s response tool is hard information (the dependent variable) and news related to firm risk is unfavorable—indicated by a positive deviation from the expected trend—the negative regression coefficient suggests that managers respond strategically through voluntary disclosure. Specifically, managers adjust their disclosure of hard information in reaction to unfavorable risk-related news, a finding that is consistent with the results of Bertomeu et al. (2011). Table 1. Regression Model Related to Voluntary Disclosure of Soft Information (3) (2) (1)   GMM estimation Fixed effect estimation Pooled estimation Dependent variable: Soft information 0.64 (0.00) 0.52 (0.00) 0.78 (0.00) Lag Soft Information -8.7 (0.00) -0.5 (0.26) -0.26 (0.54) AAN -0.54 (0.01) -0.019 (0.75) 0.035 (0.55) DAN -0.54 (0.44) 0.14 (0.89) 0.038 (0.95) RiskN 0.34 (0.00) 0.005 (0.89) 0.03 (0.34) WACC 4.76 (0.00) 0.86 (0.00) 0.45 (0.02) Leverage 0.33 (0.00) -0.041 (0.45) -0.006 (0.9) Stock Turnover -0.61 (0.01) 0.11 (0.33) 0.07 (0.26) _Cons - 0.80 0.61 R-squared - 3.31 (0.00) - F-Leamer -2.91 (0.00) - - Arellano-Bond test for AR (1) 0.97 (0.33) - - Arellano-Bond test for AR (2) 8.00 (0.71) - - Sargan-Hansen Test The numbers in parentheses show the probability level of each coefficient statistic. Source: Research findings According to the results in Table 2, due to the verifiable nature of hard information for investors, managers can influence investors’ worst-case beliefs by disclosing hard information. In response to unexpected changes in investors’ ambiguity aversion and the level of ambiguity, managers expand the extent of voluntary disclosure. Moreover, given managers’ disclosure behavior in reaction to bad news concerning the level of ambiguity and investors’ ambiguity aversion, it seems that they adopt a conservative reporting approach in their voluntary disclosure policy.                                                     Conclusion Using the disclosure tools available to them, managers of digital industry companies listed on the Tehran Stock Exchange adjust the degree of conservatism in their voluntary information disclosure in response to news related to ambiguity, risk, and investors’ ambiguity aversion. This behavior critically depends on the nature of the information disclosed by the companies.   Table 2. Regression Model Related to Voluntary Disclosure of Hard Information (3) (2) (1)   GMM estimation Fixed effect estimation Pooled estimation Dependent variable: Hard information 0.56 (0.03) 0.33 (0.00) 0.7 (0.00) Lag Hard Information 17.12 (0.05) 0.5 (0.13) 0.16 (0.6) AAN 4.02 (0.04) 0.01 (0.75) 0.005 (0.89) DAN -14.1 (0.05) -0.38 (0.61) -0.61 (0.18) RiskN 0.51 (0.11) 0.02 (0.36) 0.03 (0.19) WACC -0.59 (0.77) -0.06 (0.77) 0.12 (0.36) Leverage 0.32 (0.02) -0.005 (0.89) 0.03 (0.36) Stock Turnover -0.48 (0.27) 0.26 (0.00) 0.13 (0.00) _Cons - 0.65 0.66 R-squared - 2.34 (0.03) - F-Leamer -7.29 (0.00) - - Arellano-Bond test for AR (1) 0.88 (0.37) - - Arellano-Bond test for AR (2) 8.00 (0.88) - - Sargan-Hansen Test The numbers in parentheses show the probability level of each coefficient statistic. Source: Research findings

Abstract The present study addressed the question of why countries exhibit substantial disparities in tax revenue performance, using a comprehensive multilevel meta-analysis of 48 empirical studies (799 effect sizes). After accounting for publication bias and relevant moderator variables, the analysis identified the key determinants of tax revenue performance. The findings indicated that gross domestic product (GDP), international trade, inflation, industrial sector value added, and the lagged value of tax revenue exerted significant positive effects on tax revenues. In contrast, agricultural sector value added and corruption had significant negative effects. Foreign direct investment (FDI), however, did not exhibit a statistically significant relationship with tax revenues. Moreover, how tax revenue is measured—whether including or excluding social security contributions—critically shapes the estimated relationships between these determinants and tax revenue. The analysis also demonstrated that methodological choices (e.g., model specification and estimation techniques), the study period, and control variables (e.g., population size and institutional quality) significantly contributed to the heterogeneity observed across prior empirical findings.

Introduction

The persistent disparities in tax-to-GDP ratios across countries pose a critical challenge for policymakers and economists. Numerous empirical studies have examined the determinants of tax revenue, highlighting factors such as economic size, trade openness, sectoral composition, inflation, and institutional quality. However, their findings remain fragmented and, at times, contradictory. These inconsistencies largely arise from differences in methodological approaches, data sources, model specifications, and contextual moderators. To address this gap, the present study aimed to conduct a comprehensive multilevel meta-analysis to synthesize the existing evidence, identify the core determinants of tax revenue, and explain the sources of heterogeneity in prior empirical findings.

Materials and Methods

Adopting a meta-analysis method, the present study systematically identified and synthesized quantitative evidence from 48 empirical studies, yielding 799 effect sizes. The analysis was centered on a meta-regression framework that models reported effect sizes as a function of their standard errors and a vector of moderator variables. This approach enabled the correction for publication bias and the systematic consideration of methodological and contextual heterogeneity across studies.
The empirical model was specified as follows:
 
where   is the reported effect on tax revenue,  ​ is its standard error,  ​ represents moderators, and   is the error term. A multi-level framework was also employed to address dependencies arising from multiple effects per study.

Results and Discussion

The meta-analysis yielded robust, synthesized findings on the key drivers of tax revenue, with the moderator analysis providing critical contextual insights. Among the positive and significant determinants, GDP (Effect Size = 0.20) emerged as a primary driver, confirming that economic scale plays a central role in revenue generation. This effect was the strongest in panel, static, and fixed/random effects models. International trade (Effect Size = 0.068) also exerted a positive and significant influence, indicating that trade openness enhances revenue performance. The effect was particularly pronounced when tax revenue was measured excluding social security (TRISSC), and it remained consistent in static and fixed/random effects models. Industrial value added (Effect Size = 0.079) demonstrated a stable positive impact, with its influence reinforced in model specifications that controlled for GDP, trade, and corruption. The strongest predictor was lagged tax revenue (Effect Size = 0.528), highlighting strong persistence in revenue collection over time. This effect was consistently observed across nearly all model specifications and definitions.
In contrast, several determinants exhibited negative and significant relationships with tax revenue. Agricultural value added (Effect Size = -0.185) significantly constrained revenue mobilization, suggesting that a larger agricultural share in the economy hampers tax collection capacity. This negative relationship became even stronger in models that controlled for inflation, population, and corruption. Corruption (Effect Size = -0.156) also consistently undermined tax revenue performance, with the effect most pronounced in panel and static models. Foreign direct investment (FDI), by contrast, did not display a statistically significant relationship with tax revenue. This finding suggests that its overall impact may be neutral or highly context-dependent, making it difficult to detect a systematic average effect across studies. A key insight from the moderator analysis is that the definition of the tax base matters profoundly. The relationship between several determinants (e.g., trade and agriculture) and tax revenue varies significantly depending on whether social security contributions are included (TRESSC) or excluded (TRISSC) from the revenue measure.
*Table 1. Meta-Analysis Results With Key Moderators*




Variable


Overall effect


TRISSC (Tax Excl. SSC)


TRESSC (Tax Incl. SSC)


Panel models


Static models


With corruption control




GDP


0.20***


0.002


0.194***


0.20***


0.239***


0.09***




Trade


0.068***


Insignificant


0.062***


0.053***


0.066**


0.055***




Agriculture


-0.185**


-0.361


-0.18***


-0.147***


-0.186***


-0.089***




Industry


0.079***


0.024


0.091***


0.071***


0.117***


0.09***




Inflation


0.03**


0.133


0.003


0.005


-0.013


0.023***




Corruption


-0.156**


0.038


-0.154***


-0.16***


-0.355***


-




Tax (t-1)


0.528**


0.53*


0.675*


0.566*


0.491*


0.381*




FDI


-0.019


-


-


-


-


-




*Note: *p<0.1, ** p<0.05, *** p<0.01. SSC = Social Security Contributions.*




Source: Results Research

Conclusion

This study offered a comprehensive synthesis of the determinants of tax revenue, demonstrating that economic structure (i.e., sectoral composition), the macroeconomic environment (e.g., trade and inflation), and institutional quality (especially corruption), play pivotal roles in shaping revenue outcomes. Importantly, the impact of these factors is not fixed or absolute; rather, it is significantly moderated by methodological choices and by how the tax base is defined. The findings can be translated into several clear policy implications. Governments should promote trade openness through well-designed, trade-liberalizing policies, as it can enhance revenue both directly and indirectly by expanding and formalizing economic channels. At the same time, agricultural taxation requires rationalization. Efforts should focus on formalizing the agricultural sector and reassessing blanket tax exemptions that may unintentionally create loopholes and narrow the tax base. In addition, a strategic emphasis on industrial development is also essential. Policies that support industrialization, particularly those enabling small and medium enterprises to scale up, can help create a more easily taxable economic base. Finally, combating corruption and strengthening institutions must remain a central priority. Improving governance and curbing corruption are indispensable for improving tax compliance and for increasing the overall efficiency of revenue collection.

Abstract The issue of underdevelopment in Iran has long been marked by a paradoxical situation. On the one hand, there is broad consensus on the necessity of development to achieve social welfare; on the other hand, there is no clear theoretical or political agreement on how to realize it. Contemporary theories of underdevelopment identify inequality as the primary factor underlying development failures, emphasizing that power imbalances between the state and society play a central role in exacerbating inequality. In this respect, the present study examined the role of power relations between elites and non-elites in reproducing, mitigating, or constraining unjustified inequalities in Iran. The analysis relied on an institutional approach and employed a combined theoretical framework derived from main scholarship in the field. According to the findings, inequality in the distribution of power, wealth, and social status largely results from the absence—or weakness—of a stable and institutionalized balance of power between the state and society. Periods characterized by political consolidation and weakened accountability mechanisms are associated with rising inequality, whereas deviations from political homogenization tend to coincide with, albeit limited, reductions in inequality. The study argued that moving beyond the status quo and advancing toward sustainable development would require profound institutional restructuring and the establishment of the shackled Leviathan—a state that is capable of effective policy implementation, committed to the rule of law, and subject to democratic accountability. Such a configuration, conceptualized as a strong state–strong society equilibrium, constitutes a necessary condition for reducing inequality and achieving inclusive growth in Iran. Introduction The issue of development in Iran, despite broad public consensus on its necessity, remains mired in ambiguity and failure. A key driver of this failure is the persistence of unjustified inequalities. In this regard, the present research aimed to address the following questions: what constitutes the primary determinant of inequality in Iran? And how can it be resolved to enable meaningful development? The analysis adopted an institutional approach to examine how the structure of political power distribution (i.e., the mode of interaction between the state and society) influences the state’s capacity to implement policies aimed at reducing inequality, and ultimately shapes wealth distribution. The study covered the period of 2005–2021, as it encompassed various phases of political consolidation and changes in governance structures, thus providing an ideal context for the current inquiry. Leading theoretical contributions in development studies identify the imbalance of power and wealth between elites and non-elites as the core of the crisis. Accordingly, the current analysis merits attention as it aimed to identify the fundamental factor generating inequality in Iran and outline the mechanisms for addressing it. Materials and Methods The present study adopted a descriptive–analytical method to address the research problem. The data was primarily drawn from macroeconomic indicators for the period 2005–2021. Indicators such as budget compliance, the democracy index, and the rule of law index were used to measure the state’s executive capacity. In addition, the misery index, corruption index, and the dollar value of the monthly wage were analyzed in order to assess the state of power balance. Finally, the Gini coefficient and net job opportunities were examined to evaluate the impact of different types of Leviathan on inequality. Given the complexity of the topic, a single theoretical model could not fully account for the analysis required. Therefore, a combined theoretical model was used to derive explanation, prediction, and prescription. It was first necessary to examine the political order prevailing in the country during the period under investigation. To this end, the theoretical framework of Douglas North was employed, which explains the transition from a limited-access order to an open-access order—a transition that requires the rule of law. Moreover, Fukuyama’s tripartite model (effective state, the rule of law, and democratic accountability) was incorporated to assess the state’s executive capacity. Acemoglu and Robinson, while recognizing the importance of state executive capacity, argue that the core of power balance formation lies in the creation of a shackled Leviathan. In a shackled Leviathan, power is balanced both among elite interest groups and between elites and non-elites. Finally, by examining changes in power between the state and society, this study analyzed the persistence of unjustified inequalities in Iran. Results and Discussion According to the findings, Iran’s political economy during the period under review was strongly shaped by fluctuations in the balance of power between the state and society. During periods characterized by power consolidation and the weakening of oversight institutions, the government tended to expand its share of power and control over wealth. The results indicated that any erosion of the balance of power is directly linked to the reproduction of economic inequality and increased pressure on the middle and lower strata of society. Conversely, during periods of reduced power consolidation, improvements in the balance of power were evident, which led to a limited reduction in inequality. The findings suggest that the persistence of unjustified inequalities in Iran’s political economy stems not solely from the structural characteristics of the economy, but primarily from the failure to establish a stable and institutionalized balance of power between the state and society. When power shifts toward strengthening accountable institutions and upholding the rule of law, the scope for the unjust distribution of resources diminishes. Conclusion Development in Iran requires profound institutional reform and a transition toward the strong state–strong society model. This model, equivalent to the shackled Leviathan in institutional literature, envisions a state capable of effectively implementing policies (a strong state) while being tightly constrained by democratic accountability mechanisms and the rule of law (shackled). Such a balance is essential for reducing inequality and achieving inclusive growth.

Abstract Migration is one of the most significant challenges facing the modern world, as it generates various social, economic, and political complexities within host regions. The primary objective of this study was to examine the impact of Afghan migrants on Iran’s regional economic development over a ten-year period. It relied on a descriptive–analytical methodology and a spatial econometric modeling approach. First, the TOPSIS multi-criteria decision-making technique was used to construct a composite index of regional development based on twelve economic and social indicators. Then, the spatial Durbin model (SDM) with fixed spatial effects was employed to analyze spatial relationships and assess the direct and indirect effects of migration-related variables. The model was estimated using the maximum likelihood estimation (MLE) method to capture both local and spatial spillover effects among neighboring provinces. The empirical results indicated that spatial spillovers significantly affect adjacent regions and that the economic participation rate of Afghan migrants has a positive and statistically significant impact on regional economic development in Iran. Moreover, foreign direct investment (FDI) exhibited a positive local effect but a negative spatial effect, reflecting interprovincial competition for capital attraction. Introduction Migration is a key driver of regional economic and social transformation, particularly in developing countries characterized by labor market imbalances and uneven regional development. Iran, as one of the world’s largest host countries of Afghan migrants, offers a unique context for examining the regional development effects of migration. Over several decades, Afghan migrants have predominantly settled in border and less-developed provinces, actively participating in labor markets, establishing small-scale enterprises, and engaging with local institutions, thereby influencing regional economic dynamics. Despite their considerable presence and economic involvement, the regional development effects of Afghan migration in Iran have not yet been systematically examined through spatial econometric methods. Amid persistent structural challenges—including infrastructure deficits, international sanctions, environmental pressures, inflation, and social inequalities—along with the recent surge in Afghan migration following political changes in Afghanistan, migration management has become as a critical national policy issue in Iran. In this context, assessing the role of migrants in regional economic development is both timely and essential. The present study aimed to examine the impact of Afghan migrants on regional development in Iran. It tried to construct a composite regional development index based on twelve socioeconomic indicators using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Relying on a spatial panel dataset covering 31 provinces and estimating a fixed-effects spatial Durbin model (SDM) through maximum likelihood estimation (MLE), the analysis sought to capture both the direct and indirect spatial effects of migration-related variables, including education, economic participation, employment, investment, and international aid. By integrating spatial theory with applied migration economics in a developing-country setting, this study can contribute to the existing literature and offer policy insights for migration governance, labor market planning, and regional development strategy. Materials and Methods Anchored in the regional development framework, the current study focused on place-based factors, spatial interdependencies, and labor mobility, reflecting the shift in migration research from linear causality toward interregional and network-based perspectives. This approach highlights the importance of coordinated, multilevel, and region-oriented policymaking and justifies the application of spatial econometric methods to capture the complex spatial dynamics underlying migration and regional development. Spatial econometric modeling provides a robust analytical framework for examining geographic dependence and regional inequalities by identifying spatial structures and distributional patterns in socioeconomic indicators. Models such as the spatial Durbin model (SDM), spatial autoregressive model (SAR), and spatial error model (SEM) allow for the estimation of both direct and indirect (spillover) effects of migration-related variables, thereby offering a more comprehensive understanding of migration impacts that extend beyond regional boundaries (Benedetti et al., 2021). Within this framework, diagnostic tools and indicators—including dispersion measures, Moran’s I, and multi-criteria decision-making techniques such as TOPSIS and entropy-based weighting—serve as essential instruments in regional economic analysis Results and Discussion The fixed-effects SDM revealed heterogeneous and statistically significant effects of migration-related variables on Iran’s regional development index. The model estimation accounted for unobserved provincial heterogeneity, while spatial diagnostic tests—including Moran’s I and Lagrange multiplier tests—confirmed the presence of significant spatial autocorrelation, thereby validating the use of the SDM framework. The results underscored the central role of spatial effects in regional economic development, indicating that region-specific characteristics alone are insufficient to explain development outcomes. Afghan migrants positively contribute to regional development by enhancing productivity and investment; however, spatial competition among neighboring provinces may generate adverse spillover effects. These findings highlight the need for policies that support productive integration of migrants while promoting coordinated, spatially balanced regional development strategies to mitigate negative spillovers. Table 1. Results of Estimating the Coefficients of the Fixed-Effects SDM Probability range 0.95 Probability z-statistic Standard deviation Coefficients Variables -1.25e-07 0.917 0.10 6074e-08 2.03e-09 Hr .003953 0.000 6.24 .0004819 .0030084 Epr -.0037513 0.000 -7.08 .0004149 -.0029381 Er -.002185 0.048 -1.98 .00065533 -.0012951 Lr -3.62e-07 0.743 -0.33 1.58e07 -5.19e-08 Iaid -2.51e-08 0.002 -3.14 2.32e-08 -6.68e-08 Fdi -.333908 0.068 -1.83 .0881603 -.1611171 Spatial rho .0020443 0.000 12.42 .0001953 .0024272 Variance         0.3941 Mean of Fixed- Effects         492.6575 Log-likelihood Source: Research findings The results (Table 2) showed heterogeneous indirect effects of Afghan migrants on Iran’s regional development. While the migrant employment rate generated positive spatial spillovers, strengthening economic linkages across neighboring provinces, economic participation, and foreign direct investment exhibited negative indirect effects, likely reflecting institutional constraints and interregional competition. Other migration-related variables primarily exhibited localized impacts. Overall, Afghan migrants contributed to regional development through increased labor supply, productivity gains, entrepreneurial activity, and human capital transfer, with their effects extending beyond provincial boundaries via spatial spillovers. These findings underscore the importance of coordinated, spatially-informed policies and appropriate legal frameworks to manage migrant employment and investment, thereby promoting balanced and sustainable regional development. Table 2. Direct and Indirect Effects of Afghan Migrants on Regional Development Direct variables Meaningfulness Coefficients Interpretation Spatial lag Indirect effect Elasticity Hr 0.382 9.13e-08 Insignificant wlx_hr -0.0000 -0.0755 Epr 0.000 0.0032948 Significant wlx_epr -0.0005 -0.0260 Er 0.000 -0.0028494 Significant wlx_er 0.0007 0.2739 Lr 0.735 -0.000348 Insignificant wlx_lr 0.0000 0.0509 Iaid 0.688 -1.18e-07 Insignificant wlx_iaid -0.0000 -0.0022 Fdi 0.192 4.61e-08 Insignificant wlx_fdi -0.0000 -0.0513 Source: Research findings Conclusion Using spatial panel data and a spatial Durbin model (SDM), this study found that Afghan migration exerts statistically significant and spatially interdependent effects on Iran’s regional economic development. The results revealed strong spatial dependence across provinces, indicating that migration-related economic activities in one region could influence development outcomes in neighboring areas. While migrants’ economic participation positively contributes to regional development, weak or negative effects associated with employment and literacy reflect legal, institutional, and structural constraints in labor market integration. The presence of spatial spillovers further suggests that regional competition and capacity limitations may lead to uneven distribution of migration benefits. Overall, the findings highlighted the necessity of coordinated, spatially informed migration and development policies, strengthened legal and institutional frameworks, and interprovincial cooperation. Adopting a development-oriented, multilevel policy approach—beyond a narrow security-oriented perspective—is essential for leveraging migration as a driver of balanced and sustainable regional development in Iran.

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

Abstract Economics examines the optimal allocation of scarce resources in the face of unlimited demands. This requires access to reliable and relevant information to support effective prioritization. In the development literature, particular emphasis is placed on the capacity and relative position of regions within the national production system, as these factors constitute pillars of balanced development. The present study aimed to rank Iranian provinces and counties based on their shares in value added. Adopting a descriptive–analytical approach, the research selected the statistical population comprising 457 counties of the country for the period 2017–2020 (corresponding to 1396–1399 in the solar Hijri calendar). Given the comprehensive scope of the study, no sampling was undertaken, and the analysis was conducted using a census method. The VIKOR multi-criteria decision-making technique was applied, and Shannon entropy was employed to determine criterion weights. The input variable was each county’s share of value added relative to the national total in the corresponding sector. The data was obtained from statistical tables (published by the Statistical Center of Iran) and regional accounts. The results indicated that Tehran, Rey, and Mashhad counties achieved the highest rankings in both years, whereas Margoun, Karkheh, and Angut ranked the lowest. Provinces were also ranked using both direct and indirect approaches. Under the direct method, Tehran, Khuzestan, and Bushehr ranked highest, while Ilam, Chaharmahal-and-Bakhtiari, and North Khorasan were positioned at the lower end of the ranking. In the indirect method, Qom, Tehran, and Bushehr occupied the top positions, whereas Chaharmahal-and-Bakhtiari, South Khorasan, and Sistan-and-Baluchestan ranked lowest. Overall, the findings revealed a significant concentration of production in a limited number of regions, highlighting the necessity of targeted regional policies to promote balanced development. The results can provide valuable guidance for policymakers in designing resource allocation strategies and regional economic planning initiatives. Introduction Under current national economic conditions, there is broad consensus—particularly among economists, experts, and policymakers—that Iran’s level of economic growth and production does not align with the capacity of its human and natural resources. A substantial portion of economic potential remains underutilized, leading to high unemployment of resources—especially labor—and an excessive reliance of national economic growth on oil revenues, which are inherently volatile. This dependence has contributed to instability in economic planning and key macroeconomic variables. These factors have reduced overall productivity in the national economy, thus highlighting an urgent need for structural reforms aimed at the more efficient utilization of economic resources and potential. An examination of Iran’s regional economy reveals significant disparities in performance, with some regions achieving higher-than-average levels of economic growth. Owing to differences in regional potential, levels of development across provinces are uneven in the industrial, agricultural, and service sectors. Failure to adequately recognize and utilize regional capacities leads to misaligned investments and the persistence of underdevelopment, despite the implementation of numerous national and regional development programs. These programs have largely been unable to reduce economic, social, and spatial inequalities. As a result, severe poverty in certain regions, unequal employment opportunities, uneven access to facilities, and migration continue to pose major development challenges. Identifying the factors that influence regional economic growth enables more informed policymaking at both the national and local levels. In light of the long-term objectives set out in the Twenty-Year Vision Document—particularly the goal of attaining a leading economic position in the region—continuous monitoring of economic indicators is essential. One of the most important indicators in this regard is sectoral value added at the provincial level. However, the absence of county-level accounts represents a significant informational gap. The current study sought to address this gap by ranking provinces and counties according to their shares of value added across different economic sectors, using constant prices to eliminate the effects of inflation. The study tried to answer the following research questions: How does each province and county in Iran rank in terms of their share of value added across different economic sectors? And what is the difference between a province’s direct ranking and its indirect ranking (calculated based on the average rank of its counties)? Materials and Methods As a quantitative research based a descriptive–analytical approach, the present study relied on library-based documentary analysis and field survey data. Value-added indicators for 18 economic subsectors, classified according to ISIC Rev.4, were calculated at the county level. They were weighted using Shannon entropy, and ranked using the VIKOR method. The data was sourced from official national, provincial, and county-level accounts published by the Statistical Center of Iran, ensuring full consistency across spatial levels. The VIKOR method, grounded in multi-criteria optimization, was chosen for its ability to rank alternatives under conflicting criteria based on their proximity to the ideal solution. Results and Discussion Table 1. Comparison of Direct and Indirect Rankings of Provinces in 2019 No. Province Direct rank Indirect rank Rank difference 1 East Azerbaijan 7 16 −9 2 West Azerbaijan 9 11 −2 3 Ardabil 25 25 0 4 Isfahan 4 9 −5 5 Alborz 14 6 8 6 Ilam 29 29 0 7 Bushehr 3 3 0 8 Tehran 1 2 −1 9 Chaharmahal and Bakhtiari 30 28 2 10 South Khorasan 28 30 −2 11 Razavi Khorasan 5 24 −19 12 North Khorasan 31 26 5 13 Khuzestan 2 5 −3 14 Zanjan 26 15 11 15 Semnan 27 17 10 16 Sistan and Baluchestan 16 31 −15 17 Fars 6 19 −13 18 Qazvin 12 4 8 19 Qom 22 1 21 20 Kurdistan 23 13 10 21 Kerman 10 21 −11 22 Kohgiluyeh and Boyer-Ahmad 15 23 −8 23 Kermanshah 24 27 −3 24 Golestan 20 20 0 25 Gilan 11 12 −1 26 Lorestan 21 14 7 27 Mazandaran 8 7 1 28 Markazi 17 18 −1 29 Hormozgan 13 22 −9 30 Hamedan 19 10 9 31 Yazd 18 8 10 Source: Results Research By utilizing newly released county accounts (published in 2021) and analyzing 457 counties over multiple years, this study addressed a significant gap in subprovincial economic analysis in Iran. The results indicated that from 2017 to 2020, the counties of Tehran, Rey, and Mashhad consistently ranked first to third nationwide. In 2020, their VIKOR index values were 0, 0.8817, and 0.8877, respectively, reflecting a strong proximity to the ideal solution. In contrast, Margun (Kohgiluyeh and Boyer-Ahmad Province), Karkheh (Khuzestan Province), and Angut (Ardabil Province) were ranked the lowest, with VIKOR values approaching one. A notable finding is the pronounced spatial concentration of value added. Among the top 25 ranking positions during the period, 16 were occupied by counties in Tehran Province, underscoring the heavy concentration of economic activity in the capital region. Furthermore, Pearson correlation coefficients between the VIKOR index and population or land area were relatively weak. The strongest correlation (–0.152) was observed with urban population, which exerted roughly twice the influence of rural population. Figure 1. Map of Indirect Ranking of Provinces in 2010   Figure 2. Map of Direct Ranking of Provinces Using the VIKOR Method in 2010     Source: Results Research Sectoral analysis showed that in 2020, more than half of the national value added was generated by mining, real estate, industry, and wholesale and retail trade. Tehran County alone contributed over 16 percent of the national value added and dominated knowledge-intensive services, including information and communication, financial and insurance activities, and professional and scientific services. This structure differs markedly from the national pattern, reflecting the concentration of financial and technological infrastructure in the capital. Conclusion Iran’s development policies are largely centralized, resulting in unequal wealth distribution, rural-to-urban migration, rising unemployment, and the decline of local economic activities. The growing gap between major metropolitan areas—such as Tehran, Isfahan, Mashhad, and Shiraz—and other provincial centers highlights the urgent need for place-based regional policies tailored to local economic structures and capacities to promote more balanced and sustainable development.