ABSTRACT. Leonardo da Vinci’s accomplishments as a polymath are unparalleled: most famous for the Mona Lisa but also developing a theory of fluid dynamics 400 years ahead of its time. Arts oriented polymathy also characterizes a majority of Nobel Laureates, reinforcing the hypothesis that an active artistic imagination facilitates scientific discovery. Statistically testing the hypothesis that polymathy that combines professional pursuits with arts avocations fuels innovation in the population has been challenging as conventional data collections on arts avocations are sparse. This analysis uses an index created from internet traffic to arts avocation websites compiled at the Zip Code level. This arts avocation index provides richer information on the variation in artistic milieu across space. As a proxy for artistic milieu at the Zip Code level, the arts avocation index is used as an independent variable in equations estimating patenting and venture capital success of R&D performing microbusinesses, geolocated at the Zip Code level, available in the Annual Business Survey administered jointly by the National Center for Science and Engineering Statistics and the Census Bureau. This analysis builds on previous work presented at the 2024 NARSC Annual Meeting that investigates alternative specifications using the same exploratory sample. Bayesian county-level random effects are used as an alternative means of controlling creative milieu as an unobserved confound of a Leonardo effect. At the firm level, arts degrees of high-tech firm owners that were assumed to be exogenous twists of fate are modeled as an endogenous treatment to inject artistic imagination into the innovation process. Feedback from this presentation will be used to identify the most productive models to be passed through for confirmatory testing with a holdout sample in order to reduce the likelihood of false discovery.

ABSTRACT. This paper introduces a model of political competition under distinct institutional regimes to trace the economic performance of what we call “techno-creative places.” Specifically, we analyze how political competition in high-tech places that are creative in the sense of Richard Florida affects fiscal (tax) policy and consequent economic outcomes. There are three stylized groups of actors in our analysis: laborers or workers, techno-creative class members or entrepreneurs, and the elites who make the political decisions. We study two broad institutional-economic scenarios. In the first (second) scenario, the likelihood of political power shifting permanently from the elites to entrepreneurs is an increasing (decreasing) function of the net income of a representative techno-creative entrepreneur. Our study addresses the institutional implications of both scenarios and then comments on the implications of these two scenarios for the welfare of the elites and the techno-creative entrepreneurs.

ABSTRACT. This study evaluates the spatial agglomeration of talent across U.S. regions. We iden- tify “talent” based on the academic field of the individuals’ undergraduate education using U.S. Census micro data from 2009 to 2023. Specifically, we investigate the agglomeration propensity of STEM fields relative to other higher education majors. Surprisingly, previous research has not looked into measuring the degree of talent ag- glomeration by urban area, based on higher education fields such as STEM. Recent studies that consider the impact of talent on regional economic development assert that such individuals sort themselves in cities with higher levels of agglomeration but do not measure the extent of and differences among clusters of academic fields. Thus, this study aims to fill the gap in the literature regarding the inter-urban distribution of talent concentration. To measure the revealed comparative advantages in talent agglomeration, we examine location quotients to discern whether there are superstar regions in STEM agglomeration. The regional location quotients for STEM degrees appear to be highly skewed, above all for the computer engineering STEM field. We then regress urban-level STEM agglomeration against population size, employment density, and urban area fixed effects. Examining the fixed effect estimates we clearly identify the talent “superstar” regions. In addition, we test the effect of STEM and non-STEM talent agglomeration on regional productivity (captured by the urban wage level). Our results confirm that STEM agglomeration has a strong positive relationship with urban wages, whereas non-STEM agglomeration does not exhibit any association with regional productivity.