Research Paper
Other
Soraya Jelvezan; Ataaulah Mohammadi Malqarani; Behrooz Shahmoradi
Abstract
Profit, as presented in financial statements, is one of the most important performance metrics and a key determinant of an economic entity's value. The primary objective of this research is to assess product profitability based on a Product Complexity Index (PCI), considering the product's diversity ...
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Profit, as presented in financial statements, is one of the most important performance metrics and a key determinant of an economic entity's value. The primary objective of this research is to assess product profitability based on a Product Complexity Index (PCI), considering the product's diversity and market reach across different markets. The study analyzes a sample of 500 profitable companies listed in the Fortune, spanning the financial years from 2014 to 2018. The analysis employs both panel and pooled data methods. The study aims to estimate the profitability of each product to help investors identify the most profitable products for investment, based on their profitability as determined by the PCI. The findings suggest that adopting a new approach focused on producing high-complexity products, rather than merely selecting product types or business activities, along with measuring the profitability index of each product, can enhance decision-making. By examining the relationship between product complexity and profitability, and introducing an index to forecast product profitability, investors who are key users of financial information can make more informed decisions. These decisions, based on the optimal combination and selection of products, can foster economic growth and contribute to the development of society. The results show a positive and significant relationship between operating profit and the Product Complexity Index, as well as the estimation of profitability based on product diversity and market reach.
Research Paper
Econometrics
Forough Esmaeily Sadrabadi; Maedeh Khanari
Abstract
This study explores the effects of artificial intelligence (AI) investment on total factor productivity (TFP) in Iranian industries from 1997 to 2020, utilizing a comprehensive dataset organized by four-digit International Standard Industrial Classification (ISIC) codes. We employ the generalized method ...
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This study explores the effects of artificial intelligence (AI) investment on total factor productivity (TFP) in Iranian industries from 1997 to 2020, utilizing a comprehensive dataset organized by four-digit International Standard Industrial Classification (ISIC) codes. We employ the generalized method of moments (GMM) approach to address challenges such as endogeneity and collinearity within a dataset comprising over 200 cross-sectional variables.Our results reveal that both physical and intangible investments significantly influence TFP; a 1% increase in physical investment results in a 0.514% rise in TFP, while intangible investment leads to a 0.288% improvement. A key innovation of this research is the introduction of an AI measurement variable in the production function, employing the Corrado, Hulten, and Sichel (CHS) methodology for a clearer assessment of AI's productivity effects.Although AI investment positively correlates with TFP, its current impact is limited, reflecting the gradual adoption of advanced technologies in Iranian industries. This highlights the need for a comprehensive strategy to fully realize the productivity benefits of AI. We recommend policies aimed at facilitating technology integration and workforce specialization, including investing in training, providing incentives for AI adoption, and promoting collaboration between industry and educational institutions to enhance productivity and competitiveness in the global market.
Research Paper
Institutional Economics
Hassan Daliri
Abstract
This study explores the impact of governance indicators, , on economic growth across different income groups. Using dynamic panel data estimation with the one-step system GMM method, we analyze data from low-income (15 country), lower-middle-income (40 country), upper-middle-income (40 country), and ...
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This study explores the impact of governance indicators, , on economic growth across different income groups. Using dynamic panel data estimation with the one-step system GMM method, we analyze data from low-income (15 country), lower-middle-income (40 country), upper-middle-income (40 country), and high-income economies (52 country) in 2007-2022. The findings suggest that governance indicators have varying effects on economic growth depending on the income group. The analysis reveals that the impact of governance indicators on economic growth varies significantly across income groups. In low-income economies, "Control of Corruption" and "Regulatory Quality" have the strongest positive effects, emphasizing the critical role of governance improvements in fostering growth in these settings. For lower-middle-income economies, the "Rule of Law" and "Government Effectiveness" are key drivers, reflecting the importance of legal frameworks and efficient public services during economic transitions. In upper-middle-income economies, "Government Effectiveness" and "Voice and Accountability" are significant, though the moderate coefficients suggest structural and external constraints limit governance's role in driving growth. For high-income economies, "Regulatory Quality," "Rule of Law," and "Political Stability" are essential for sustaining growth, highlighting the role of efficient, stable, and innovation-friendly institutions. These findings underscore the evolving importance of governance indicators across development stages and the need for tailored institutional priorities to maximize growth potential. Interestingly, COVID-19 had a significant negative impact on economic growth across all groups, though its magnitude varied. The results show that the negative impact of the Corona shock on economic growth has increased as countries' income levels have decreased
Research Paper
Monetary economics
Reza Taheri Haftasiabi; Ameneh NaderI; Yousef Mohammadzade; Akbar Zavari Rezai
Abstract
Self-employment plays an important role in the Iranian economy and understanding the factors affecting it is of great importance. This study aimed to investigate the impact of private sector credit on self-employment in Iran using artificial intelligence techniques such as artificial neural networks, ...
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Self-employment plays an important role in the Iranian economy and understanding the factors affecting it is of great importance. This study aimed to investigate the impact of private sector credit on self-employment in Iran using artificial intelligence techniques such as artificial neural networks, deep neural networks and machine learning algorithms to identify nonlinear relationships and complex patterns in the data. Also, spatial econometric models SAR, SEM and SDM were used to consider spatial dependencies between provinces and to examine the spatial spillover effects of use for the period 1990-2023 at the provincial level of the country.The findings indicate a negative and significant relationship between the ratio of microfinance to GDP and the self-employment rate. Also, the negative coefficients of the economic openness index and the capital formation rate indicate their negative effect on self-employment. In contrast, the variables of total factor productivity and educational expenditure have a positive and significant effect on the self-employment rate. The results of the spatial models also indicate the dependence of the self-employment rate in different regions of the country on each other. Therefore, this study found a negative relationship between the two, which could be due to inefficiency in the provision of microfinance and its insufficient focus on creating sustainable and productive jobs. Increased economic openness and higher rates of capital formation also have a negative effect on self-employment by intensifying foreign competition and encouraging investment in larger economic sectors.
Research Paper
Other
Malihe Hadadmoghadam; Pedram Davoudi
Abstract
The phenomenon of child labor and street children is one of the pressing issues in most contemporary large cities around the world. The prevalence of this phenomenon has become so significant that it engages both developed and developing societies equally. Child labor refers to any form of employing ...
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The phenomenon of child labor and street children is one of the pressing issues in most contemporary large cities around the world. The prevalence of this phenomenon has become so significant that it engages both developed and developing societies equally. Child labor refers to any form of employing children in activities that are mentally, physically, socially, or ethically hazardous and deprive them of their childhood and continuous participation in education. In this study, an attempt was made to present an overview of the situation of these children in Iran using available data related to child labor (microdata from the labor force survey conducted between the years 2016 to 2020 has been used). Subsequently, by applying logistic regression, the individual and household factors influencing child labor were examined. In the overall model, migration (both international and domestic), lack of education among household heads, and rural living were identified as the most significant environmental factors contributing to child labor participation. In urban areas, the most influential environmental factors affecting child participation, in order of importance, were migration (both international and domestic) and household head unemployment. In rural areas, the key environmental variables increasing child participation included the education level of the household head, migration, and household head unemployment. Analyzing urban and rural patterns separately, while avoiding aggregation errors from the overall model, underscores the high impact of economic factors on child labor.
Research Paper
Monetary economics
Mohammad Amin Shojaeenia; Ahmad Barkish; Abolmohsen Valizadeh
Abstract
This study examines how exchange rate growth and liquidity growth impact the relationship between consumer price index “CPI” inflation and producer price index “PPI” inflation in Iran from 2005 to 2023, using monthly data. We employ continuous wavelet transformation to capture ...
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This study examines how exchange rate growth and liquidity growth impact the relationship between consumer price index “CPI” inflation and producer price index “PPI” inflation in Iran from 2005 to 2023, using monthly data. We employ continuous wavelet transformation to capture the dynamic relationship between CPI and PPI across different frequency bands. Additionally, we use a vector auto-regressive with exogenous variables model to validate our findings and utilized the Granger causality test. In this study, CPI and PPI indices are divided into three categories: CPI and PPI of goods, CPI and PPI of services, and total CPI and PPI, which is the weighted mean of the two priors. Our models are applied separately to each category of CPI and PPI inflation. The results indicate that the relationship between CPI inflation and PPI inflation for goods is stronger and more reliable than for services. Also, we demonstrate how liquidity growth and exchange rate growth contribute to inflation through demand-pull and cost-push mechanisms, respectively. Finally, we highlighted that this relationship is more dependent on exchange rate growth than liquidity growth, particularly in recent years. This indicates that inflation in Iran during the studied period is predominantly driven by cost-push factors rather than demand-pull forces.
Research Paper
Energy Economics
Hamidreza Panah; Seyed Nematollah Mousavi; Bahaaldin Najafi
Abstract
Oil has a large share of the world's total energy consumption. For this reason, it is obvious that any fluctuations in demand, supply, price and other variables affecting this sector have many effects on the economy of oil producing and consuming countries. Any factor that causes a disruption in the ...
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Oil has a large share of the world's total energy consumption. For this reason, it is obvious that any fluctuations in demand, supply, price and other variables affecting this sector have many effects on the economy of oil producing and consuming countries. Any factor that causes a disruption in the supply or demand of oil and the subsequent market will in most cases lead to a change (decrease or increase) in the price. These price changes have had effects on the behavior of energy producers and consumers due to the price determining factor on both supply and demand sides of the energy market. This study examines the inter-variable time-varying conditional correlation between the world oil price and the return of industrial production index (real GDP) in oil exporting countries (Iran, Saudi Arabia, UAE) and OECD countries. Therefore, in this study, using the monthly data of world oil prices, the efficiency of the industrial production index of the Middle East and OECD countries from 2000-2021, and using the Oxmetrics software, the time-varying conditional correlation of the global oil price and the efficiency of the production index Industry in selected countries has been analyzed by CDCC-GARCH method. The results showed that the time-varying conditional correlation with the global oil price and GDP is real and the changes in the final oil price have caused significant changes in the dynamic correlation between the variables in the form of uncertainty.
Research Paper
International Economics
Mohammad Rahimi Ghasemabadi; Reza Zeinalzadeh; Zeinolabedin Sadeghi; Mohsen Zayandehroodi
Abstract
In the modern era, heightened awareness of environmental conservation has spurred countries and corporations to adopt ecological initiatives aimed at improving environmental performance. This study examines the environmental and socioeconomic costs tied to carbon emissions from activities related to ...
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In the modern era, heightened awareness of environmental conservation has spurred countries and corporations to adopt ecological initiatives aimed at improving environmental performance. This study examines the environmental and socioeconomic costs tied to carbon emissions from activities related to Iran’s exports. By focusing on the financial damages caused by air pollution, the research employs monetary values to comparisons these damages with the economic effects of trade, pinpoint industries that contribute to pollution, and calculate trade balance indicators from a more comprehensive viewpoint. Using the input-output tables, pollution levels in different industries were calculated. The findings reveal that the losses caused by international trade are significant and cannot be ignored. Iran’s 2015 economic data indicate that importing goods avoided 2,432million USA $ in damages, while export caused 3,448 million USA $ in damages. Had imports been produced domestically, 2,439 million USA $ damages and 2,049 million USA $ value added would have been created. Net damages generated by the trade amounted to $1008 million, which accounts for 0.84 % of the net value added created by the trade of Agriculture. This implied that the net effect of trade was a $1016-million increase in damages caused by CO2 in 2015. Furthermore, the results show that every $1 million of net value added generated by trade caused emission-related net damages of $0.321 million overall.
