فصلنامه مطالعات اقتصادی کاربردی ایران
پیاپی 48 (زمستان 1402)

Demographic discussions as well as studies in the field of economic growth are among the oldest economic topics. Despite a wide range of theoretical and empirical research, economists and demographers have not yet reached a single point of view on the relationship between economic growth and population. Countries with rapidly growing populations tend to have low economic growth rates. This negative relationship is usually not shown in different studies. What social welfare theory can argue that the population of Iran is under or overhead of optimal value? In literature, most studies focus specifically on exogenous population changes. Endogenous population changes in an endogenous growth model have been hypothesized only in restricted studies. The production function in the Ramsey model is generally a function of capital, labor and technology (knowledge level). In the developed model, the population is assumed to be endogenous in the Ramsey model. Also we assumed the technology level is a function of the population as a sigmoid function. So in this study, technology is endogenous. The dynamic problem is solved (Bellman function formed and optimal solution of this objective function was found) and optimal population growth rate is calculated in extended Ramsey growth model with simultaneous endogenous population growth rate and technology level. The optimal population growth rate for the Iranian economy is 2%. However, according to the Iran Statistics Center, the population growth rate was 1.1 percent in 2018. A lower population size than optimal can have negative effects on social welfare in the long run. Therefore, the government should implement appropriate population policies to achieve the optimal population growth rate of 2%.

Investigating the investment trend of the past decade in the country shows that the average growth of investment in those years has been decreasing. Factors such as the high attractiveness of the country’s non-productive markets, the high cost of the business environment, currency fluctuations, and the problems caused by the embargo, etc, have led to a decrease in investment and created a volatile environment with uncertainties in this variable. Due to the importance of investment in the macro economy, unfavorable conditions of the country and resulting insecurities will lead to a decrease in investment and economic growth. Therefore, this research aims to simulate the uncertainty variables of private and government investment due to the base years of 1997 and 2011, from 1961 to 2021. we performed This simulation using the Vasichek mean reversion stochastic differential equations method, which has an advantage due to the examination of the assumptions of competing models (neural network, ARIMA, etc.) [1, 2]. According to the simulation results, in all the years except 1963, Private investment uncertainties were more than government investment uncertainties. Also, the highest amount of private investment uncertainties are related to 2014, 2015, and 2013, respectively, and the lowest amount is related to 1961, 1963, and 1962, respectively. Meanwhile, the highest amount of government investment uncertainties are related to 1978, 1979, and 1980, respectively, and the lowest amount is related to 1961, 1962, and 1964, respectively.

The budget Imbalance can lead to the reduction of social and economic welfare programs such as healthcare, education and social security. This can negatively affect the quality of life of citizens and can lead to an increase in poverty and inequality. In this regard, the aim of the present study is to investigate the role of budget imbalance on economic welfare in Iran for the period of 1978-2021. For this purpose, the research model has been estimated using the non-linear (asymmetric) Autoregressive Distributed lag method in two models. Also, IEWB comprehensive index has been used to calculate economic welfare. The movement trend of the economic welfare index shows that it fluctuated during the period under review; so that the average welfare index in the first plan has decreased compared to the previous period; While it has had an upward trend in the following periods and has an average of 128.89; Also, the trend of total Imbalance and operating and capital Imbalance has a fluctuating trend. The long-term results in the first model indicate that a decrease in the budget balance deficit has an inverse and significant effect on economic welfare, and an increase in the budget Imbalance does not have a significant effect on economic welfare. In the second model, the long-term results indicate that the increase and decrease in the capital Imbalance has direct effects on welfare. An increase in the operating Imbalance has an inverse effect on welfare, and a decrease in the operating Imbalance does not have a significant effect on welfare. Also, in both models, per capita income and economic growth have a direct and significant effect on welfare. According to the results, it is suggested that the government should adopt similar policies in the financial foundations to improve the security of the society, create the necessary infrastructure to maintain the total income tax, eliminate non-targeted tax exemptions and credit priorities in order to reduce the imbalance budget. It is also recommended that the government focus more on increasing the share of the capital component of the public budget and reduce the operational imbalance of the budget in the long-run.

The unemployment rate and Gross Domestic Product (GDP) are among the most important economic indicators that understanding their current and future trends can help policymakers and decision-makers adopt appropriate solutions to prevent crises and improve the country’s economic situation. Accurate prediction of these two indicators can be useful in future planning and improving the country’s economy and people’s livelihoods. In recent years, artificial intelligence techniques and tools, given their many capabilities, can play a very important role in predicting important economic indicators. Therefore, given the high importance of the two indicators of unemployment rate and GDP on the economy of our country Iran, this article intends to first predict these two indicators separately and then predict the rate of GDP growth based on the unemployment rate using artificial intelligence techniques. For this purpose, in this research, seasonal data related to GDP and its components and the unemployment rate for the years 1976-2022 have been used. Also, machine learning models based on regression have been used for prediction. The results show that the predictions of the mentioned models have an appropriate accuracy in terms of evaluation criteria such as root mean square error, mean absolute error, mean absolute percentage error, which indicates.

Electric energy is one of the most significant production inputs in various industries, especially energy-intensive industries. As an incentive for industrial activity, this input is delivered to Iranian industries at prices below their cost. In one sense, this type of pricing has made energy-intensive industries profitable, while on the other hand, it has reduced investment in electricity generation industries. The current study uses published data from 2003 to 2019 of the country’s industries to identify energy-intensive industrial groups. Their production function estimate using dynamic, per capita panel model. On the basis of the marginal production value of these industries, the economic value of electric energy input was calculated. This study indicates that in the energy-intensive industry of “Manufacture of chemicals and chemical products”, the short-term and long-term economic value of electric energy input is approximately 5.6 to 6.5 times the average price paid for it. This ratio is approximately 1.8 to 2 in the “Manufacture of other non-metallic mineral products” and 1.4 to 1.7 in the “Manufacture of basic metals” energy-intensive industries. The average of this ratio is about 2.9 to 3.5 for entire Iranian industry ranges.

The purpose of this article is to investigate the effect of the financial stress index (FSI) on the returns of mutual funds (separately including mutual funds and mixed mutual funds) in Iranian capital market during 2011-2021 on a monthly basis. For this purpose, the principal component analysis method is used to construct the financial stress index, and the effect of financial stress on the returns of mutual funds in two regimes of high return and low return was investigated using the Markov switching model. Results show that the financial stress in the high return regime has a negative effect on the return of investment funds in stocks and mixed. In a regime with low return, financial stress has had a positive effect on the return of investment funds in stocks and mixed. Despite the same direction of the effect of financial stress on the performance of both types of studied funds, this situation indicates the asymmetry information in the effect of financial stress on the return of investment funds.

In recent years, the increasing significance of creative industries has made them a topic of discussion in global economics. This has profound ramifications for the economic structures and employment of societies as the creative industries swiftly emerge as potent engines of economic growth and development. Creative industries utilize the creativity, innovation, skills, and aptitude of individuals to generate employment and have a high expansion potential. Using GMM the current study investigates the impact of creative industries on employment in 98 selected countries from 2011 to 2020. The results of this study indicate a positive and statistically significant effect of “creative industries” on employment in our three different models. The coefficient of creative industries in developed countries (0.3539) is greater than its value in developing countries (0.2992), as indicated by the results obtained. Consequently, it is anticipated that the influence of artistic, cultural, and creative productions on employment in these nations will be greater than in developing nations. Also, the results has demonstrated that inflation has an adverse effect on employment, while human capital, gross domestic product, investment, and the level of economic openness all have a positive influence on employment.

The purpose of this article is to analyze the impact of open economy indicators including economic openness and foreign direct investment on income distribution in Iran during the years 1996 to 2018. For this purpose, we use fuzzy regression with asymmetric coefficients. The reason for using it is very high flexibility in analysis. For each income decile, an optimal model is estimated that shows the effect of open economy indicators on the Gini coefficient of income deciles. The results show that the average effect of foreign direct investment on the Gini coefficient of income deciles is zero or very small and reduces inequality. But the maximum impact of foreign direct investment on the Gini coefficient of income deciles has increased, which leads to inequality in income distribution. This is due to the lack of appropriate programs and policies in the economy, so that we can use the opportunity of foreign direct investment to reduce inequality. Also, the results show that the maximum effect of the degree of economic openness on the Gini coefficient of income deciles is zero, which means that the degree of economic openness does not increase inequality. In order to strengthen or weaken foreign relations. Therefore, the amount of subsidy paid to each of the income deciles provides the infrastructure needed to attract foreign investment and provides measures for the treasury to tax the income deciles.