فصلنامه تحقیقات اقتصادی
پیاپی 139 (تابستان 1401)

The purpose of this study is to prioritize the economic activities in Esfahan Province, with a sustainable development approach and an emphasis on the importance of water scarcity in economic activities. In the first stage, the SFLQ method was used to prepare an input-output table for Esfahan Province. Then, the economic activities in the province were prioritized by using six criteria including the intensity of interdepartmental communication and value-added (economic dimension), job creation (social dimension) as well as energy intensity, water intensity and pollution (environmental dimension). Moreover, a combination of an input-output model and the TOPSIS method was applied. According to the results of this study, among the activities in the province, "transportation" proved to have the highest priority. It was also found that the ignorance of water use as an important criterion can lead to significantly different prioritization results.For example, in the prioritization of economic activities taking into account the criteria of water intensity the "Agriculture" sector is in the 20th place, while in the prioritization without taking into account the criteria of water intensity, it has been promoted to the first place. Also, in the "Food, Beverage and Tobacco Industries" and "Clothing Manufacturing, Tanning and Leather Finishing" in the prioritization taking into account the water intensity criterion, it is ranked 17th and 11th respectively, while the ranking of these sectors in the prioritization without considering Water intensity criterion has been upgraded to seventh and fifth rank.

The present study aims to investigate the impact of technology, innovation, and globalization on income inequality in three groups of low-income countries (19 countries), middle-income countries (63 countries), and high-income countries (41 countries) in the period 2008-2019 using a panel data quantile regression model. To achieve this aim, the following indices have been used: The Gini index as a measure of income inequality; the technology and innovation index and its sub-indices, including the information and communication technology index, technological skills index, research and development index, industrial capacity index, and index of private sector access to financial resources; and the economic globalization index and its sub-indexes, including trade globalization and financial globalization. The results of this study illustrate that technology and innovation in all quantiles of the three income groups have a significant negative impact on income inequality. Moreover, the economic globalization index has a significant positive effect on income inequality in low-income countries in the lower quantiles, however, this effect is significantly negative in the middle and higher quantiles. Nevertheless, economic globalization has a significant negative impact on income inequality in middle- and high-income countries in all quantiles. According to the research results, it is suggested to policymakers, especially in low-income countries, to prioritize technology and investment in this field. On the other hand, according to the effects of globalization, policymaking should be done based on the economic structure of the countries.

This paper shows that the quasi-money (QM) demand function and thus the M2 demand function are unstable, which may arise from the innovations in the banking system. Since the early 90s, the deposit interest rate has increased and innovations in the banking system have changed long-term deposits to an instrument for saving and modified the nature of QM. As a result, QM became a direct function of interest rate (yield), and it has distinguished itself from money as a medium of exchange. However, M1 demand is stable and an inverse function of interest rate, which emphasizes it as the demand for the medium of exchange.These results have important implications for designing the monetary policy framework and prerequisites for its success. The substantial difference between M1 and QM precludes M2 as an appropriate money definition. Therefore, despite its widespread use in analyses, M2 growth is misleading, especially as an inflation predictor. The rise in QM growth because of an interest rate increase is an issue outside the scope of monetary policy, and it should be examined from the financial stability perspective. In other words, QM growth is not a risk to price stability, as long as the banking system can withstand the interest rate risk. If the banking system is stable financially, the central bank could successfully implement a rate-based monetary policy framework.

The study analyzed inequality in Iran's provinces using the Atkinson index, Zanga index, and Gini coefficient based on occupational groups. Reducing inequality is an essential economic and social issue, and regional disparities impede growth and development of any region. Focusing on each province's regional economy can lead to better development planning. Therefore, the calculation of inequality is required. Considering that the Gini coefficient and the Zanga index are statistical tools for measuring inequality and the Atkinson index is derived from the social welfare of individuals; therefore, the effects of income distribution in society in two dimensions have been measured. For this purpose, the study used cost and income data of urban households in Iran from 2013-2019. The Location Quotient approach is used to determine each province's target occupational group to understand regional inequality better and improve it as much as possible. Accordingly, the "managers" and "employees" have a better income distribution than other occupational groups in most provinces. On the other hand, compared to other occupational groups, in most provinces, the "operators and assemblers of machinery, and drivers" and "simple workers" have a more challenging situation. Thus, they should prioritize the target occupational groups to improve income distribution.

Identifying the connection between different economic sectors is pivotal to policy-making and portfolio management, particularly in a developing country such as Iran. This study incorporates the high-frequency data of the daily returns in the Iranian stock market sector (four clusters, including 12 sectors that constitute over 70% of the stock market capitalization) from November 2009 to October 2022 to estimate the total and sectoral static and dynamic connectedness indices using the vector autoregression model (VAR) with time-varying parameter (TVP) and Diebold-Yilmaz connectedness index (DYCI). The findings indicate that 56% of the forecast error variance can be attributed to cross-sectoral innovations within the network, demonstrating a fairly strong co-movement across different sectors. Also, the connectedness between sectoral performances varies significantly over time. The strongest connectedness and spillovers have been observed in recent years when the stock market experienced extraordinary ups and downs, reaching its peak of 85% in the total connectedness index in early 2022. It was also found that the base metal industry and investment sector have acted as permanent transmitters of shocks, and the sugar and ceramic sectors were the permanent receptors of volatilities. This finding confirms the existence of the lead-lag effect in the Iranian stock market. Lastly, the strong pairwise connectedness, especially between “base metal and metal ore sectors” and “food and sugar industries,” indicates that shocks are transferred from downstream to upstream industries in the studied clusters.

Iran's economy has suffered many economic fluctuations for many years. Therefore, stabilization studies about the country's economy must have a significant contribution. One of the ways of economic stabilization is taxation. Among the types of taxes, automatic stabilization taxes are especially considered. This research aims to measure the effects of taxes and take a look at automatic stabilizer taxes especially. Economic data from 1372:1 to 1397:4 was used to conduct the paper. The SVAR model has also been used to estimate the research model. The results showed that the best automatic stabilizer tax is the Income tax.