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A Cross-Section Analysis of Factors Affecting Interstate 11 Migration in the United States
Get PDFThis paper investigates the factors influencing interstate migration in the United States of America. This study incorporates political affiliations and income and property tax policy into an existing migration determinants model. This is done to examine the influence of policy or potential future policies on an individual’s decision-making in addition to household factors. The greatest factors influencing an individual\u27s decision to move are the average January temperature, the income tax rate, and the number of hazardous waster sites in the state
The Role of Institutional Quality Factors on Inequality in Upper-Middle Income Countries in Latin America
Get PDFThis paper investigates the influence that institutional quality factors have on the power or spread of inequality across low-income countries, as opposed to high income countries. As such, an empirical study and analysis will be conducted to measure the impact of the following institutional quality factors: control of corruption, government effectiveness, political stability and absence of violence/terrorism, regulatory quality, rule of law, and voice and accountability. Specifically, cross-sectional or panel data from the World Bank will be utilized to look at upper-middle income nations in Latin America. The factors will be simultaneously measured against the Gini Coefficient. These countries include Brazil, Argentina, the Dominican Republic and Peru from 2008 – 2022. Results from the panel data will show that a variety of institutional quality factors have significant influence over inequality factors and many variables contribute to this disparity, including Foreign Direct Investment and Global Competitiveness
Artificial Intelligence and Music: Analysis of Music Generation Techniques Via Deep Learning and the Implications of AI in the Music Industry
Get PDFThe use of artificial intelligence (AI) is quickly gaining relevancy in creative fields, and its emergence into the music industry comes with many unique implications. This paper examines the technical processes of creating music with AI and machine learning, the relationship between music and emotion, and finally the implications and ethical considerations for AI generated music in creative industries. As part of this project, a generative deep learning model (Music Variational Autoencoder) is explored and applied to generate music using a pre-trained training set of piano rolls. The AI reconstructions are based on self-made 4 measure electronic instrumental tracks. 46 machine learning students then take a survey to blindly compare the human generated tracks with the AI generated tracks to see if they can tell the difference. There are two trials of this survey, with a presentation on MusicVAE given in between the two trials. Exploratory analysis indicates that music experience is correlated with increased ability to distinguish AI generated music. Chi-Square Tests are then conducted for each set in each trial with a null hypothesis stating that the chance of guessing if a song is AI generated is equal to 50%. These results indicate that the null hypothesis cannot be rejected for the first trial, but that it is rejected for the second trial with the addition of the presentation in between
Understanding the Public Reaction to Major United States Environmental Policies Through Twitter
Get PDFAn increased focus on access to general data as well as a continued lack of usable environmental data have resulted in an odd phenomenon where the public does not have the opportunity to understand their environment on a deep level. The goal of this research is to understand, as a result, how people both talk and feel about certain environmental changes, particularly those in the realm of politics. Through word clouds and sentiment analysis performed with historical Twitter data collected between 2010 and 2022, we can identify the general trends in both conversation and feeling as they relate to a digital public space and its interaction with the environmental and political worlds around it. Eleven major environmental policies were identified to help conduct the analysis. The research resulted in several major findings: political figures, such as presidents, land at the forefront of most relevant conversations; the environmental aspects that come with the policies at hands are rarely discussed; conversations do change to reflect new policy enactment, and they do so for an extended period of time; negative emotions are typically the most expressed in this context; the general reaction to new policy enactment was found to be significantly positive; sentiment tends to change quickly and will do so regardless of how the public feels about a given policy
Factor Exposures of Environmental, Social, and Governance (ESG) Indexes
No full textWe examine the factor exposures of Environmental, Social, and Governance (ESG) indexes used as benchmarks by the largest ETFs with social responsibility (SR) focus. Our sample includes 31 ESG/SR indexes benchmarked by ESG/SR ETFs with the largest assets under management (AUM) as of January 31, 2020. We pay special attention to quality factors and use robustness of operating profitability and investment characteristics as proxies. Our findings indicate these indexes mostly act as expected in terms of size and value factors. Momentum factor, for the most part, is not significant. About half (14/31) exhibit a statistically significant positive quality tilt. However, there are some statistically significant negative tilts on proxies used for quality that can be explained by index composition. We also attempt to isolate the factor exposures due to the specific characteristics of the ESG indexes by pooling the individual ESG indexes by their segment benchmarks and analyzing ESG index returns in excess of non-ESG segment benchmark returns. Most exposures stay intact, though some reverse. However, the positive quality tilts persist. As the move from active to passive investment accelerates in the ESG space, understanding the factor exposures of these indexes is essential for investors, financial advisors, and fund managers
Edmund “Ted” Shallcross III
No full textEdmund “Ted” Shallcross III is the President and Chief Executive Officer of Amica Mutual Insurance Company. Before joining Amica in 2007, Shallcross spent 12 years in KPMG’s Financial Services Practice, where Amica was one of his clients. A dedicated and active philanthropist, Shallcross serves on the board of several nonprofit institutions, including the CEO Partnership for Rhode Island, the Greater Providence Chamber of Commerce, and the American Cancer Society’s New England Chapter of CEOs Against Cancer
