Abstract
Intense competitive pressure in a local geographic market may diminish an organization’s revenues in that market but increase its innovation output and revenue opportunities in other geographic markets. This trade-off between local and traded market performance determines an organization’s combined revenues; yet, it has not been conceptually articulated or empirically examined at the organization level. Using data from the performing arts sector, we isolate the differential effects of local competition on local and traded market revenue. The results offer compelling evidence that competitive density in the local market has a negative effect on local market performance but a positive effect on innovation and traded market performance. The implication is that organizations can overcome the negative effects of local competition through innovation and through the exploration of opportunities for traded revenues. We further find that smaller organizations are more likely to innovate in response to competitive density, but that they lack the resources and capabilities required to translate innovation into increased revenues.
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Notes
Various terms describing competitive colocation appear in the literature, including agglomeration (Gaubert, 2018; Liu et al., 2018; Shaver & Flyer, 2000), clustering (Frenken et al., 2015; Porter 2003), locational differentiation (Iyer & Seetharaman, 2008), and market structure or size (Davis & Dingel, 2019). Following Ciccone and Hall (1996), we focus on the geographic density of economic activity and use the term competitive density.
Park and Voss (2022) estimate distance-based attenuation for arts patronage using box office data for 84 arts organizations in five major US markets, featuring purchase activity for 2.3 million households over five years. Every household was geolocated and patronage likelihood was estimated as a function of distance, applying an exponential power decay function, with parameters \(\alpha >0\) and \(\beta >0\): \(f\left(d\right)={e}^{-\alpha {d}^{\beta }}\)(e.g., Halás et al., 2014), which produced the α = .91, β = .46 parameters.
Recall that breakthrough innovations are summed for the current (t = 0) and previous (t = -1) season, and independent variables predicting breakthrough innovations are lagged one year (t = −1). Breakthrough innovations and all other lagged independent variables predict traded market revenue and local market revenue in the current (t = 0) year. We explored different operationalizations of breakthrough innovation (e.g., one year or three years), which produced similar results. We lag geographic market variables because Census estimates correspond to the calendar year and the majority of organizations in our sample report fiscal years ending between June and September, which implies that nearly half the activity and most decisions for fiscal year t = 0 likely occur during calendar year t = −1.
Following Silva & Tenreyro (2011), we also conducted robustness checks for the breakthrough innovations and traded market revenue analyses using the Poisson pseudo-likelihood regression with multiple levels of fixed effects (Stata PPMLHDFE), which replicated all hypothesis test results (see Appendix Table A2).
The Sobel test statistic is the product of the competitive density coefficient in the innovation equation of Table 3 (= 0.248, std error 0.024), multiplied by the innovation coefficient in the traded revenue equation of Table 4 (= 0.604, std. error .078), divided by the standard error of that product (0.025). This comes out to t = 6.076, with p < 0.001.
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Acknowledgements
The authors wish to acknowledge SMU DataArts, Theatre Communications Group, and the League of American Orchestras for providing access to their data. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors report there are no competing interests to declare.
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Appendix
Appendix
Figures 4 and 5; Tables 5 and 6.
Geographic dispersion of total expenses mapped against total population of competitors*
Geographic dispersion of traded market revenue mapped against total population of competitors. *In each panel, background color coding corresponds to the number of performing arts organizations (i.e., total population) in each county. The size of the red circles corresponds to the relative percentage of the total expenses and traded revenue for our sample, respectively, aggregated to the county level
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Briesch, R., Haruvy, E., Voss, G.B. et al. The countervailing effects of spatial competition in the performing arts: examining local versus traded market performance. J Cult Econ (2024). https://doi.org/10.1007/s10824-024-09510-4
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DOI: https://doi.org/10.1007/s10824-024-09510-4