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on Post Keynesian Economics |
By: | Julia M. Puaschunder (Columbia University, USA) |
Abstract: | One of the hallmarks of macroeconomics is the Keynesian multiplier. John Maynard Keynes described the multiplying effect of new investments in the economy to have multifaceted influences on the overall wellbeing of nations. The notion that investments drive economic activity and growth is consolidated with many empirical findings in different domains. Interestingly, hardly any account exists on intertemporal aspects of Keynes’ multiplier. The discounting and temporal elements of multiplying effects and the time-lag for investments to bloom in the economy are – to this day – not captured. Behavioral economics offers ample account on discounting. People are found to focus on the present rather than discounting for future instances properly. Integrating a temporal element into the Keynesian multiplier effect offers opportunities to understand the long-term benefits of green investments. Environmentally-conscientious finance has seen an advent in most recent decades. To this day, however, there is no clear account of the performance of green funds. Temporal aspects in Keynes’ multiplier may help understand the difficulty in determining the long-term advantages of green investments. Adding information on the long-term benefits of green funds may also serve contemporary endeavors to capture wealth in nature. This article is organized as follows: First, an introduction describes Keynes’ multiplier and temporal discounting. Then the need for integrating temporal aspects into Keynes’ multiplier is outlined. The application of temporal Keynes’ multiplier aspects in the green investment domain is provided. The discussion closes with a prospect for future research avenues. |
Keywords: | behavioral economics, economics, finance, green funds, green investments, Keynes’ multiplier, sustainability, temporal discounting |
Date: | 2024–05 |
URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0380 |
By: | Chavis, Larry (Institute for American Indian Arts); Wheeler, Laurel (University of Alberta, Department of Economics) |
Abstract: | We contribute to the effort to build a more inclusive discipline by offering lessons and teaching strategies derived from the Indigenous peoples of North America. Our proposed relational approach to teaching provides a framework that accommodates many practices already gaining traction in economics. Drawing on the literature on inclusive teaching practices as well as personal narratives from the classroom, we propose a set of principles of Indigenous-influenced economics courses, and we talk about how to translate those principles into applied teaching strategies. We believe borrowing from Indigenous pedagogies can build belonging and community in our classrooms, thereby contributing to a discipline that is more welcoming of a broader range of students. |
Keywords: | teaching economics; Indigenous; Native American; First Nations; diversity; inclusion |
JEL: | A20 |
Date: | 2024–09–16 |
URL: | https://d.repec.org/n?u=RePEc:ris:albaec:2024_005 |
By: | Tavishi Choudhary (Greenwich, Connecticut, United States of America) |
Abstract: | 53% of adults in the US acknowledge racial bias as a significant issue, 23% of Asian adults experience cultural and ethnic bias, and more than 60% conceal their cultural heritage after racial abuse (Ruiz 2023). AI models like ChatGPT and Google Bard, trained on historically biased data, inadvertently amplify racial and ethnic bias and stereotypes. This paper addresses the issue of racial bias in AI models using scientific, evidence-based analysis and auditing processes to identify biased responses from AI models and develop a mitigation tool. The methodology involves creating a comprehensive database of racially biased questions, terms, and phrases from thousands of legal cases, Wikipedia, and surveys, and then testing them on AI Models and analyzing the responses through sentiment analysis and human evaluation, and eventually creation of an 'AI-BiasAudit, ' tool having a racial-ethnic database for social science researchers and AI developers to identify and prevent racial bias in AI models. |
Keywords: | data bias, digital law, diversity, ethical artificial intelligence, ethnic bias, inequality, racial bias, sentiment analysis |
Date: | 2024–07 |
URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0400 |