Summit 2016
April 1-2, Washington DC

Session 1: Friday 11:45
Moderator: Bruce Ronkin

Courtney Blankenship
Assistant Professor, Director of Music Business, School of Music
Western Illinois University

Stan Renard
Assistant Professor, Coordinator of the Music Marketing Program
University of Texas at San Antonio

Pop Songs on Political Platforms

The presentation reviews the use of music in the campaigns of current presidential hopefuls. Many candidates remain in the presidential race and each campaign has its unique sonic identity. The authors will analyze certain criteria for each candidate in order to assess the presence of any correlation between them, including their target demographics, the candidate’s age, amount/selection of music (with title, tune, composer and lyricist, publisher, copyright year), the genre of music, related songs, relevant information about their party, and the resulting success in polls. In addition, the authors will proceed with an event study to consider whether copyright infringement of music negatively affects the candidates’ polls.

David Bruenger
Director - Music, Media, & Enterprise Program
The Ohio State University

Music Scene Mechanics: Toward a Predictive Process Model

Music happens somewhere. The importance of local music communities, where performers, listeners, and institutions come together to support and synergize musical creation, production, and public presentation has long been recognized. The roles and relationships of Sun Records in Memphis, Motown in Detroit, and SubPop in Seattle, to name but three, have been amply explored. But much of this work focuses on historical narrative rather than critical or empirical analysis. While understanding what happened is of great value, the economic and social mechanisms of the scenes in question—the how and why of them—is less clear.

In 2008, Richard Florida and Scott Jackson’s study, Sonic City, applied economic metrics and social demography to illustrate how traditional 20th century music scenes differed from those emerging in the early 21st century. They were particularly concerned with the movement and geographic concentration of musicians in North America as indicators of music scene development. Florida and Jackson identified three essential trends that defined music scenes on the cusp of the 21st century: a concentration of musicians in a given place, a decline in the importance of cities that were transportation and manufacturing centers as music scenes, and an increase in those that were home to large universities and/or high tech industries.

While providing an elegant descriptive framework, the Sonic Cities model suffers the same limitation that Florida’s earlier Creative Class model did: it was not a reliable predictor of success. In other words, much as communities that struggled to attract the “creatives” of the creative class with appropriate infrastructure and amenities often found that it did not necessarily work to spark local economies, so too did cities attempting, for example, to “seed” a local music scene find that neither creativity or opportunities for monetization increased. While the elements of success could be precisely defined, the process for achieving it was not.

In 2015 Dell introduced the Future Ready Economies Model, based on the Strategic Innovation Summit: Enabling Economies for the Future at Harvard University. The Dell model uses three primary indicators: human capital, commerce, and infrastructure. In a broad sense, these align with the Sonic Cities description of music scenes as places that “provide the diversity of people and the institutional and social infrastructure required to commercialize cultural products like music.” But, while comparable to the Sonic Cities framework, the Dell model is a specifically predictive tool, designed to use the three broad indicators (plus 23 sub-indicators) to assess cities in terms of “how closely they are structured to optimal future readiness.”

The presentation will explore how Florida’s Sonic Cities framework can be combined with Dell’s Future Ready model to move beyond descriptive analysis and provide a basis to develop an adaptive and predictive model for nascent and emerging music scenes.