Yahya Soleimanimagham, Younes Nademi, Mehdi Chegeni,
Volume 11, Issue 42 (12-2020)
Abstract
Crime is a phenomenon that exists in all societies and affects the useful functioning of different parts of a country. Also, Iranian society is not safe from the harms of this phenomenon. Given the destructive effects of crime in society, recognizing the factors affecting it makes it possible to fight it more effectively. For this purpose, this study has investigated the effect of misery index on the rate of theft in 30 provinces of the country during the years 2008-2018. In order to achieve this goal, the Panel generalized method of moment (GMM) has been used. The findings of this study have shown that the misery index has an increasing effect on the crime of theft. In other words, the misery index through the two channels of inflation and unemployment has destructive effects on people's living standards and puts them on the path of committing crimes such as theft.
Dr. Younes Nademi, Dr. Ramin Khochiani, Dr. Reza Maaboudi,
Volume 16, Issue 59 (5-2025)
Abstract
Objective:Artificial Intelligence (AI), as an emerging and transformative technology, is still in its early stages of development, and many aspects—particularly its economic and social dimensions—remain underexplored. Given the critical importance of eliminating absolute poverty as the first of the United Nations Sustainable Development Goals (SDGs), the present study aims to investigate the effects of investment in artificial intelligence on poverty and identify the main channels through which this impact occurs in countries leading in AI technologies.
Materials and Methods: This study empirically employs panel data from 20 selected countries during the period 2017–2023 using the generalized method of moments (GMM). The main variables include investment in AI technologies as the explanatory variable, and both income-based and multidimensional poverty indicators as dependent variables. Additionally, the study analyzes the effects of control variables including economic growth, income inequality, health index, and human capital.
Results: Empirical results indicate that investment in AI technologies significantly reduces both income-based and multidimensional poverty. AI contributes to poverty alleviation by enhancing economic growth, improving agricultural productivity, enabling financial inclusion, facilitating access to educational and healthcare services, and increasing the precision of targeted subsidies. Furthermore, economic growth and improvements in health indices reduce poverty, whereas increased income inequality exacerbates poverty.
Conclusion:The study emphasizes the importance of investing in legal and technological infrastructure to effectively leverage the potential of artificial intelligence for poverty reduction. Accordingly, policymakers in developing countries, including Iran, could benefit from developing supportive policies and strengthening necessary infrastructure to harness AI capabilities for poverty alleviation and economic well-being.
Originality:This research is among the first comprehensive empirical studies to examine the impacts of investment in artificial intelligence on both income-based and multidimensional poverty, identifying key channels of impact within countries leading in AI technology. The findings provide valuable insights for formulating technology-driven anti-poverty policies.
