Abstract
Using detailed data for 2001–2009 from the sales system of the Finnish National Opera, we estimate the determinants of demand for opera tickets. We find that operas in their premiere season are more popular than reprises. Demand is lower for classical operas and higher for domestic operas and for performances with a famous opera singer. Press reviews and the overall popularity of the opera piece have the expected effects. There is also evidence of seasonal effects. By excluding temporarily discounted tickets, controlling for performance characteristics and quality and using a method that takes into account capacity constraints, we are able to credibly estimate the price elasticity of demand. The overall elasticity is close to unity: on average, a 1 % increase in prices would result in 1.16 % decrease in demand.
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Notes
His cross-section model of attendance frequency gives an income elasticity estimate of 1.03, which is larger than the estimates from his aggregated data.
Source: Annual report of 2011 and the website of FNO.
This variable was taken from website http://www.operabase.com. We added one to the variable before taking the logarithm because there were plays with zero performances.
Unfortunately, theatre component of CPI could not be used because prices of the FNO are included in it.
In the substitute price data, there were two extreme outliers (the other one clearly being an error) which we corrected (more information available upon request). Corrections changed the estimated coefficient of the substitute price variable, but in the way that it now corresponds to the coefficient estimate obtained by estimating the model without the time periods with outliers. We find this result more likely to be reliable than the one with the variable including outliers.
This is likely to be also the case with other population-level socioeconomic variables, such as education, age structure and professional structure. To identify the effects of these factors, micro-data or data from multiple institutions in different locations would be needed.
We have also tried calculating full-income and price-of-leisure measures (see Zieba 2009), but these are almost perfectly correlated with annual time trend and with each other. Thus, including income and price-of-leisure variables in our model does little more than control for a linear time trend and, at the same time, causes a severe multicollinearity problem. However, there is some nonlinear variation in the components of these variables that measure employment (the unemployment rate, employment rate and working hours per employee). We use these components and a linear time trend to check the robustness of our results to inclusion of macroeconomic variables. A linear time trend and one of the three measures of employment at a time were included in the model. The majority of our results appear to be qualitatively robust to these tests.
We use the R function crq (in the quantreg package) and the Powell method with fixed censoring. The standard errors have been calculated using the associated bootstrapping algorithm.
This definition of word-of-mouth effect is different from that of Grisolía and Willis (2011), who allow word-of-mouth to be positive or negative. This kind of variable was not available for our analysis.
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Acknowledgments
An earlier version of this paper was written in collaboration with Emmi Martikainen. We are very grateful for her contributions to the text and data collection. We would like to thank Sirkka Hämäläinen, Jari Hännikäinen, Päivi Kärkkäinen, Otto Kässi, the executive board of the Finnish National Opera and seminar participants in Jyväskylä and Vaasa for helpful comments and discussions. We thank Veera Laiho for excellent research assistance and Tiina Romar for help with the data.
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Laamanen, JP. Estimating demand for opera using sales system data: the case of Finnish National Opera. J Cult Econ 37, 417–432 (2013). https://doi.org/10.1007/s10824-012-9190-6
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DOI: https://doi.org/10.1007/s10824-012-9190-6