TAX revenues are considered one of the best and most reliable sources of income for governments, and are one of the most fundamental tools for the economic development of any country. The stability and continuity of TAX collection leads to stability in the government's planning to provide the services needed by the country in various fields. Hence, in order to plan as well as possible to increase TAX revenues, it is necessary to know about the TAX EFFORTs of the provinces of the country. TAX EFFORT indicates the extent or limits in which the government has been able to discover existing TAX bases and utilize its TAX capacity. Therefore, the main purpose of this study is to calculate the TAX EFFORT of Iran's provinces. In this regard, first the TAX ratio model of Iran's provinces has been estimated using spatial econometric methods. After estimating the model, the TAX capacity of the country's provinces was calculated and used to compute the TAX EFFORT of each province. The results of this study show that during the study period, the TAX EFFORT index of all provinces of Iran except Tehran province is less than 0.84, indicating very low TAX EFFORT of the country's provinces. Tehran province's TAX EFFORT index was also between 0.85 and 1.24. Therefore, it can be said that all provinces of the country except Tehran province have low TAX performance and there are many unused TAX capacities in these provinces.

The significance of TAX revenue as the primary source of government finance underscores the importance of accurately measuring TAX EFFORTs using unbiased methodologies. This study employs a state-space model and the Kalman filter algorithm to estimate TAX EFFORT as an unobservable variable within the TAX revenue equation in Iran from 1970 to 2021.The findings reveal a nuanced relationship between various factors and the TAX ratio. Per capita income exhibits a positive impact, while the agriculture share in GDP exerts a negative influence. Interestingly, the coefficients of openness and monetization initially have negative elasticity but transition to positive after reaching a certain threshold, indicating a dynamic relationship with the TAX ratio. Conversely, the services and industry share in GDP demonstrate a positive effect on the TAX ratio before reaching a peak, after which their squared coefficients turn negative.TAX EFFORT in Iran, throughout the studied period, has never been more than 0.25 highlighting a significant disparity between actual and potential TAX revenue and underscores inefficiencies within the TAX system. Introduction Due to dependence on oil and structural problems, attention to important TAX indexes has been neglected in Iran’s Economy. Therefore, in order to achieve more accurate results, a new approach has been taken by the research to estimate TAX EFFORT as an indicator that shows the ability of the government to enhance TAX revenues. Traditionally, TAX EFFORT is calculated by dividing actual TAX revenue by potential TAX revenue. However, this method is inherently biased as it fails to account for the influence of economic, social, and political factors on TAX revenue collection, alongside TAX EFFORT itself. To address this limitation, the study employs the Kalman Filter estimation technique, which treats TAX EFFORT as an unobservable variable within the TAX revenue function, alongside other economic variables. Methods and Material In literature, TAX EFFORT is calculated by estimating the following equation:   (1) F1 =  =   = where T is TAX share, F is TAX EFFORT, Z is a vector of other factors affecting TAX share and ω is an error term. It is evident that the estimator F1 is a biased estimator for TAX EFFORT F. Considering the effect of TAX EFFORT on TAX revenue, the index should be included as a dependent variable in the TAX revenue function. Referring to the study of Kim (2007) and in order to overcome the bias, the research suggests a state-space approach and Kalman Filter Algorithm. The structural time series method allows TAX EFFORT to be taken into consideration in the TAX revenue function as an unobservable variable. In this context, the TAX revenue function considered a linear form incorporating variables such as per capita income, the share of agriculture, services and industry, openness, and monetization . However due to the low coefficient of determination and the results of Ramsey Reset test, adopting a quadratic function  became imperative. Consequently,the final equation was changed as follows:   (3) Results and Discussion In econometric analysis, the  stationary test of data typically examined. However, According to Harvey, the stationary test holds less signifcance in the structural time series model.. The analysis of variables confirms that all variables exibite statistical normality. The results of estimating equation 3 are reported in Table 1, which shows that the variables are significant at one percent level. Table 1. The results of estimating the square function of TAX revenue using the STSM method Prob t-statistic RMSE Coefficients Variables 0.0090 -2.7836 0.0647 -0.1801 Level break 1998 0.0000 5.3930 0.0586 0.3159 Level break 2005 0.0000 -5.8246 0.0569 -0.3314 Level break 2000 0.0000 6.4744 0.9254 5.9920 LPY 0.0000 -6.0820 0.0611 -0.3719 LPY^2 0.0031 -3.2082 2.4578 -7.8851 LMO 0.0030 3.2130 0.2998 0.9631 LMO^2 0.0449 -2.0900 0.5520 -1.1536 LOP 0.0626 1.9312 0.0766 0.1480 LOP^2 0.0000 5.9359 3.2214 19.1223 LIND 0.0000 -6.1448 0.4668 -2.8682 LIND^2 0.0007 3.7434 7.1427 26.7385 LSEV 0.0004 -3.9563 0.8990 -3.5566 LSEV^2 0.0106 -2.7203 0.1102 -0.2997 LAGR Reference: Research calculations and software output There were breaks in 1998, 2000, and 2005. The cause of these breaks can be attributed to the Asian financial crisis, the dot-com bubble, and oil fever, respectively. "In addition to examining elasticities, the squared coefficients of variables hold significance in the analysis. Despite the elasticity of the agricultural sector share being -0.29, its squared coefficient was omitted from the model due to its low explanatory power. Notably, the TAX exemption status of the agricultural sector in Iran contributes to a negative impact on TAX revenues. Regarding per capita income, its elasticity is positive, yet its squared coefficient is negative. Initially, an increase in per capita income enhances TAX revenues, but subsequently leads to a decline in the TAX ratio. This phenomenon arises because governments can only collect a specific portion of per capita income as TAXes. Continued TAXation may result in TAXpayer resistance, consequently leading to a reduction in TAX revenue.". The share of industry and services, both, have negative elasticity and squared coefficient. Initially, an increase in these variables leads to a rise in TAX revenue, followed by a subsequent decrease where the negative effect predominates. Notably, only in cases where production is efficient, the industry can generate a significant TAXable surplus.  Therefore, the inefficient industry sector will not result in higher TAX revenue in Iran. Due to the lack of a full database and since some economic activities in Iran are unregistered and consequently untraceable, a significant percentage of TAX evasion occurs in the services sector. Hereupon, the increase of this sector in Iran will not lead to more TAX revenue. Both openness and monetization exibit positive quadratic coefficients. The effect of these variables on the TAX ratio is negative at first and becomes positive after the minimum point. The negative elasticity of these two variables is respectively caused by the government's policies such as lower tariffs for essential goods and the adverse effects of inflation on monetization and TAX revenue as a result. The TAX EFFORT trend is shown in Figure 1. The unevenness of the trend is caused by the fluctuation in oil revenues in Iran. TAX EFFORT in the last 50 years has always been lower than 0.25, which indicates the misutilization of TAX capacities. Figure 1. TAX EFFORT Trend in Iran during the years 1970-2021   Reference: Research Findings Conclusion Recognizing the significance of TAXes as a primary source of government revenue, this research employs the Kalman Filter algorithm and a state-space model to calculate TAX EFFORT in a novel manner. In this approach, TAX EFFORT, treated as an unobservable variable, is incorporated into the TAX revenue function alongside six other variables. Given the low coefficient of determination and the outcomes of the Ramsey reset test, the linear model was deemed unsuitable. Consequently, the quadratic form of the function was adopted to better capture the complex relationship between TAX EFFORT and TAX revenue."The estimations showed that the effect of per capita income on TAX ratio is positive due to the increase in the potential of citizens to pay TAXes, and the impact of agriculture share is negative due to TAX exemptions. The elasticity of monetization is negative owing to high inflation in Iran and its adverse effects on sales TAX. The effect of this variable on TAX ratio is initially negative, but after the minimum point, it becomes positive due to the compliance of TAXpayers with inflationary conditions. Openness also has a negative elasticity due to the negative effect of import promotion policies. The share of industry and services have a positive effect on the dependent variable before reaching the maximum point. However, due to a high rate of TAX evasion within this sector and production inefficiency in manufacturing, this effect reverses after surpassing the maximum point.. The discrepancy in the signs of the elasticities for some variables can be attributed to utilization of  different approaches in estimating the TAX EFFORT. The low TAX EFFORT in Iran reveals the necessity to make changes in government TAX policies to make the most of TAX capacities. For the purpose of enhancing TAX revenue, some measures should be taken to reduce TAX evasion and increase the TAX potential of economic sectors. Eliminating unnecessary TAX exemptions can also improve TAX performance. The exemptions should be gradually phased out until they are completely eliminated,as long-term TAX exemptions in Iran create non-competitive structures.

This paper aims at comparing TAX EFFORT in Iran with that of 14 developing countries including Jordan, Algeria, Malaysia, Congo, Nicaragua, India, Sri Lanka, Paraguay, Tunisia, Peru, Venezuela, Philippine, and South Africa. A TAX ratio model is developed for this purpose. Seemingly Unrelated Regression (SUR) method and panel data for 1994-2002 periods are utilized to estimate the model. The results indicate that the TAX ratio is positively affected by the shares of industry, service and foreign sector value added in GDP. However, we found that the ratio of foreign debt to GDP, the share of agricultural value added in GDP and inflation rate have negative impacts on the TAX ratio.Estimated TAX shares are then used to calculate TAX EFFORT for the sample of countries under investigation. The results indicate that Iran has the least TAX EFFORT as compared with other countries under investigation. This implies that there is substantial room for improvement in TAX EFFORT in this country.

TAXes have been the most significant income sources of governments from a long time ago and one of the main concerns of governors and policymakers is optimal collecting TAXes of whole existing capacities. The aim of this paper is to estimate TAX capacity and to calculate TAX EFFORT among middle- income countries. Panel data method, data from 1995-2009 and Stata software have been used to estimate model coefficients and then TAX EFFORT has been measured by using these coefficients. The results show that Iran obtains the least amount of TAX EFFORT indicator among others.

Thise study examines the TAX capacity and TAX EFFORT of 149 countries, utilizing data envelopment analysis (DEA) combined with hierarchical clustering for differentiation. TAX capacity refers to the maximum potential TAX revenue a country can achieve. While TAX EFFORT indicates the actual TAX revenue collected relative to this potential. By categorizing countries into clusters, we uncover diverse levels of development and highlight strategies for improvement. Our methodology follows a four-step process involvingcorrelation analysis, transformation of undesirable indicators, efficiency calculation, and rankings through the Anderson-Petersen method. Results reveal that economic indicators like GDP per capita influence TAX capacities differently across nations. Some countries like Nepaland Mozambique outperform expectations inTAX EFFORT despite lower economic indicators, while others need significant policy reforms to optimize TAX collection

Getting rid of volatile and unreliable revenues from the sale of crude oil and financing government expenditures through TAX revenues is one of the key goals of the TAX system in the Iranian economy. To achieve this goal, increasing the efficiency of the TAX system and increasing TAX revenues can be effective and efficient as a suitable solution to reduce the dependence of the country's economy on oil revenues. This study examines the affecting factors on TAX EFFORT in Iran during the period 1978 to 2016. To doing the research used multiple linear regression model and for this purpose, TAX capacity functions were determined to determine the TAX capacity and then the amounts of TAX EFFORT. The ratio of TAX revenue to GDP as a dependent variable and from the ratio of value added of agricultural sector to GDP, ratio of value added of services to GDP, ratio of value added of industry to GDP, inflation rate, urban population have been used as model independent variables in GDP with Oil and non-oil separate models. The research data was extracted from the Internet system of the Central Bank, Iran Statistics Center and other sites of international organizations such as the World Bank, the International Monetary Fund and the United Nations and its related sub-organizations. The results showed that the variable of the ratio of the agricultural sector to GDP has a negative relationship with TAX capacity and TAX EFFORT,the reason is the existence of extensive TAX exemptions in this sector. Also, the variables of the ratio of the industry and services to GDP have a positive relationship with TAX capacity and EFFORT.

This study seeks to model TAX evasion and identify how effective factors affect TAX evasion in the Iranian economy. Recent models show the failure of traditional models; Models do not have enough ability to model hidden variables such as TAX evasion. The present study considers this failure in identifying explanatory variables and experimental model design. To achieve this, the Bayesian averaging method has been used in the period 1370 to 1397 in MATLAB 2021. In this study, 62 variables affecting TAX evasion were entered into the model, and using the Bayesian averaging model approach, 1 to 12 non-fragile variables affecting TAX evasion were identified. The results show that inflation is the most fragile variable affecting TAX evasion. As a result, in TAX evasion modeling, inflation has the highest direct relationship with TAX evasion; Also, based on the results, the most important variables affecting TAX evasion in modeling TAX evasion in order of priority are inflation, exchange rate, TAX culture, unemployment, TAX fairness, TAX EFFORT, budget deficit, TAX morale, privatization, TAX complexity, value-added ratio. The GDP services sector, fair TAX system. According to the results, both political and cultural, and economic factors have a significant impact on TAX evasion.

In this paper, the TAX capacity and EFFORT and its relationship with oil revenue of 6 selected countries member in OPEC such as Iran, Kuwait, United Arab Emirates, Venezuela, Algeria and Saudi Arabia are considered. The study has been applied by panel data within 1990-2008. The variables affecting the TAX capacity including per capita income, economic openness (import and export ratio to the gross domestic product), and oil revenue ratio to gross domestic product and TAX capacity with once pause were considered. The summary of the analysis indicated that the relationship between the per capita income, oil revenue to gross domestic product ratio and TAX capacity with once pause, and dependent variable is positive and significant. Whilst the economic openness grade in the oil countries has negative and significant relationship with the TAX capacity.

TAXation as a main source of revenue for the government has always provoked some sensitivities in different societies. TAX revenues are considered to play an important role, through income redistribution, in the readjustment of income and wealth, improvement of economic and social justice and prevention of inequalities. Therefore, TAX revenues are seen as a reliable and influential instrument to carry out government policies in response to economic crises. An efficient TAX system is the one able to maximize TAX collections. This is not going to be as easy as it appears, since a country's TAXable capacity is a challenging issue. By definition, the TAXable capacity is the TAX that the society is able to pay. Estimating TAXable capacity can, on the one hand, represent the TAX EFFORTs exerted in creating TAX revenues and, on the other hand, it might be of help in determining the extent to which the TAX authority can maximize their revenues. In order to capture the factors involved in TAXable capacity and to present a method for measuring regional TAX potentials, this research has confined itself only to Yazd Province. The variables such as "the share of value added of different economic sectors in the provincial GDP" and "the provincial rate of literacy" have been considered as the influential factors in the regional TAXable capacity of this province and their impacts have been tested through an econometric method. By simulating the general form of "Luffer" curve, the researchers have shaped a second order function of TAX revenues, TAX ratio, and an estimation of the coefficients thereof, in order to determine the TAX ratios that maximize the revenues and to calculate the maximum TAX level to be collected. Research findings indicate that the variables "the shares of value added of different economic sectors in the provincial GDP" and "the share of the industry & mining sector in the regional GDP" can affect the overall regional TAX ratio. The results also show that the optimal TAX ratio in the region under discussion is 4.7 percent.

Estimating TAX capacity of a region or of the whole country provides the information needed for responding to financial and executive problems resulted from the implementation of economic policies. The present study aims at estimating TAX capacities of the two TAX categories, “business and profession income TAX” and “corporate income TAX” in the Iranian provinces through making use of the data of some effective variables. To this end, we have explored the variables involved in TAX revenues of the provinces, and by using data of 28 different provinces within the time span 1379-1385 (2000-2006), we have resorted to SFA method to estimate the efficiency of each TAX category in question in any of the Iranian provinces. According to the findings obtained, as for the business and profession income TAX, the average TAX efficiency of developed provinces (excluding Tehran Province) for the period in question has been 72.3% while it has been 66.5% in less developed provinces. And as for corporate TAX income, the average TAX efficiency of developed provinces (excluding Tehran Province) has been 47.8% while it has been 72.4% in less developed provinces. The present research has calculated the “TAX inefficiency” of each province as the gap between the potential and realized legal capacities. As for TAX EFFORT indices, the results show that over time, the average TAX EFFORT has decreased for business and profession income TAX but it has increased for corporate income TAX.