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Title: Creativity and Prosperity: An Unsupervised Machine Learning Approach to Modeling Creative Human Capital
Citation Type: Dissertation/Thesis
Publication Year: 2023
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Abstract: Innovation and the creativity that underlies it are the driving forces behind regional prosperity. Moretti (2012), an economist, highlights that employment in innovative occupations portends high wages and associated multiplier effects. Conversely, Sawyer (2012), a psychologist, articulates that the lack of creativity in occupations dampens wages or eliminates employment outright. In concert, they highlight the importance of a workforce prepared for innovative or creative production. The widespread attention to and endurance of the Creative Class Hypothesis (Florida, 2002) suggests that there are salient aspects of the theory meriting continued attention and articulation. The salient aspects are those that elicit the comparison to, and failure of some to distinguish between, what is called creative capital and human capital, more generally. The distinction is not conceptual but rather procedural, where human capital is construed too narrowly and typically confined to education, training, and work experience (using worker age as a proxy). This dissertation uses insights from psychology literature to help define a creativity metric using unsupervised learning, compares it to existing measures of creative or knowledge workers, and quantitatively explore its association with wages, the wage distribution, and inter-metropolitan migration. The ultimate purpose is to determine if a rationale can be identified for public intervention in promulgating a creative workforce.
Url: https://www.proquest.com/docview/2806825179?pq-origsite=gscholar&fromopenview=true
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Authors: Yemen, Cory Robert
Institution: Rutgers, The State University of New Jersey
Department: School of Graduate Studies
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Pages: 1-326
Data Collections: IPUMS USA
Topics: Labor Force and Occupational Structure, Methodology and Data Collection
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