This dissertation explores the reciprocal relationship between the arts and employment. This relationship is characterized by a combination of a “multiplier effect” in which one additional arts job attracts many jobs in other industries, and an “audience effect” in which several jobs in other industries are necessary in order to form an audience large enough to attract additional artists. Using the County Business Patterns dataset from the US Census Bureau, this dissertation explores how employment in the arts affects the non-arts industries and vice versa in 481 urban areas from 1998 to 2016. The main statistical methods used in this research are cross-lagged regressions, followed by fixed-effect meta-analysis. When comparing the arts to non-arts industries in general, results indicate that in both the short and long terms, the multiplier/audience effects hold. When comparing the arts to business services and high-tech industries individually, results showed a much stronger relationship between the arts and business services than for arts and high-tech. As a relatively young industry, high-tech does not yet present an arts multiplier, but it does present higher audience effects than the business services industries, indicating that while artists are not yet attracting high-tech jobs, high-tech jobs are strongly attracting the arts. In all three analyses, the multiplier/audience effects hold better for larger urban areas than for medium, followed by smaller sized urban areas. In addition, this dissertation proposes data selection and transformation methods by overlapping the urban areas and ZIP code maps in order to make the official data units into geographically and time consistent hexagons.Datasets: US Census Bureau County Business Pattern (yearly from 1998 to 2016); 2000 and 2010 US Census Decennial Population data; 2010 Census Urban Area Reference map; and the 2009 and 2018 ZIP Code maps. Methodologies: cross-lagged regression, ordinary least squares, sampling methods, fixed effects meta-analysis, hexagonal tessellation, GIS, and mapping.