Journal of Economic Modeling Research

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The main purpose of this paper is to measure the multidimensional inequality index. To achieve this goal and answer of what trend inequality has gone through during the study period, using the data of the household expenditure income plan of the statistical center of Iran and also using the Bourguignon index, inequality was measured in several dimensions for the period 1984-2018. In addition, it should be noted that in this study, household expenditures were initially adjusted based on age composition and number of household members by calculating the equivalence scale. This adjustment was made possible by estimating the share of expenditures of different commodity groups by considering its functional form in the the quadratic almost ideal demand system (QUAIDS). Then, using data mining techniques and Principal Component Analysis(PCA), the weight of the studied dimensions in the analysis (income, education and health) was calculated and when measuring inequality, the degree of social aversion of inequality was considered in the form of two scenarios, zero and one. the results of this study indicate that the magnitude of multidimensional inequality per zero value for both the degree of social aversion of inequality parameter and the degree of substitution parameter based on the Bourguignon, index is between 0.28 and 0.41 in urban areas and between 0.26 and 0.41 in rural areas. fluctuations in the Bourguignon index and the Gini index of income have not necessarily been similar. The findings of this study also showed that the size of multidimensional inequality in rural areas is lower than urban areas in most of the years studied. There is an approximate similarity between its trend in urban and rural areas. Inequality in the 1980s was higher than in other periods (despite higher oil revenues than in the previous and subsequent periods and more government shares transferred than in previous periods), in the early 1390s, declined, and then increased again. Finally, the research findings indicate the failure of the egalitarian goals of development programs and thus emphasize the need for a fundamental review of the forthcoming programs with more attention to distribution by the market institution rather than the government.

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After the victory of the Islamic Revolution and the capture of the spy nest, the West, and especially the United States, in addition to pursuing other tools, has also used the tools of sanctions and has implemented many sanctions against Iran. One type of sanctions is oil sanctions, which were imposed to force Iran to join the international community. The US and its allies' embargo on Iranian oil affects the variables of the Iranian and world economies. For this reason, a computable five-zone global trade model (GTAP) is used to calculate the implications of the game tree between the three independent actors of the United States, the European Union, and Iran. The closing of the GTAP model has been changed according to the assumptions used. The results show that the US, Iran and major oil buyers from Iran are damaged by the sanctions. This damage is exacerbated by increasing oil restrictions. With the escalation of sanctions, the European Union is also gaining negative welfare. In the Nash equilibrium, the United States and the European Union will choose weak sanctions, and Iran will try to circumvent the sanctions. Due to the economic costs of oil sanctions against Iran, the lack of full understanding between the United States and Europe, and Iran's efforts to circumvent sanctions, it seems that the United States will not be able to reduce Iran's oil exports to zero.

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In the Iranian economy, which has experienced various sanctions, it was necessary to anticipate macroeconomic variables when imposing new sanctions. On the other hand, in the context of sanctions, it is possible to make a more accurate assessment of economic policies in order to be able to respond in a timely manner to these shocks and the need for appropriate planning and security against them. Therefore, in the present study, a macroeconomic model with Mixed-frequency data sampling  has been used,While having a high accuracy in prediction, it is possible that when new information about multivariate variables is obtained, based on it, the previous prediction for the dependent variable of the pattern is revised. The model consists of 27 behavioral equations, 8 communication equations and 33 definitional and union relations and the parameters of the model are estimated using time series data in the period 1338 to 1396. Predictive results show that the use of new observations in high frequency variables in the model has led to improved accuracy in predicting the endogenous variables of the model.

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In recent decade in developing countries, lack of government budget or lack of access to modern technology, persuade governments to attract private sector participation in the economy. One of the most common methods is Public-Private Partnership agreements. The real implementation of this type of partnership needs to set contracts that satisfies preferences of both parties. This research aims to solve this problem using the solutions available in the Contracts Therory Knowledge. Theoretical modeling with analyzing public-private partnership model, provide an optimal model for BOT contract. We use library method to explain the basic contract and mathematical modeling  by MATLAB software with Particle Swarm Optimization to specify the parameters of utility functions and to provide optimal contract. 
The simulation results for an optimal contract were calculated using the supposed parameters (life time, incom, costs, future incoms discount rate, salvage value of project costs) 38 years (project utilization time), 78% (principal participation after transfer time), 45% (principal participation during the operation), 7% (riskes to the principal).

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Purpose: The insurance penetration rate is one of the most important indicators used to evaluate the insurance industry of a country. This ratio is also a measure to compare the performance of the insurance industry between developed and developing countries. The aim of this research is to compare the insurance penetration rate and the factors affecting it in high and low income countries.

Methodology: The current research examines the effect of variables such as inflation rate, education, labor productivity, dependency ratio and income on the insurance penetration rate in the period 2011-2021 and using PMG and ARDL methods to derive short-term and long-term equations in 18 countries with income High and low income and the country of Iran pays.

Findings: The results obtained from the estimation of long-term PMG models in high-income countries indicate a positive effect of dependency ratio, income level and fertility level on the insurance penetration rate, as well as a negative effect of inflation rate and labor productivity on the dependent variable, also in selected countries with high income. All the variables, except for education and dependency ratio, which had a positive and significant effect on the insurance penetration rate, are statistically meaningless.   On the other hand, the findings from the estimation of the long-term ARDL model in Kesho Iran show the negative impact of the inflation rate on the insurance penetration rate and the positive impact of the education level, income level and dependency ratio on the insurance penetration rate.

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Purpose: The aim of this study is to identify and classify insurance customers in order to identify the target population for increasing the profitability of insurance companies, achieving a balance in premium payments, and examining the health questionnaire as an indicator of policyholders' preferences. Moreover, designing a marketing strategy to optimize advertising efficiency.
Method: In this paper, five machine learning algorithms, namely Decision Tree, Random Forest, Support Vector Machine, Naive Bayes, and Logistic Regression, are used to classify customers into two categories: profit-generating and loss-generating. Data from a private insurance company is utilized, consisting of 2,897 observations collected from December 1400 to December 1401.
Findings: By utilizing machine learning methods and focusing on the target population, the chances of success can be increased. The presence of a small number of individuals who significantly reduce the profitability of insurance companies is evident. The pre-existing medical conditions of individuals have a considerable impact on their insurance usage and the damage caused to insurance companies.
Conclusion: Machine-learning methods can provide a comprehensive understanding of insurance customers and their needs. By identifying the target population, insurance companies can increase their profitability and satisfy their customers by addressing their specific demands