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Theatre allocation as a distributor’s strategic variable over movie runs

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Abstract

The main objective of this paper was to examine the relationship between the type of distributor and its influence on the theatre allocation process. In doing so, we study the differences in the theatre elasticity of box-office revenues between distributors, once the other determinants of movie box-office revenues and unobserved film characteristics have been controlled for. The empirical exercise involves estimating a panel for the weekly box-office revenue in the US motion picture market during the 2002–2009 period. Given the dynamic nature of our data and the endogeneity problems that some of the independent variables may present due to the presence of unobserved individual effects of movies, we use the Hausman–Taylor estimator. Regarding theatre allocation, we find evidence of similar, but not homogeneous, behaviour among the so-called Majors, which, in turn, differs from that observed for non-Majors regarding theatre allocation to films. These differences are greater when we consider a selection of more successful movies that are exhibited for a longer run, maybe due to the rise in the amount of uncertainty for this sample. Our results provide indirect evidence of the differences in the market power of distributors.

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Notes

  1. Advertising is another important strategic variable, especially in the opening week. Several studies have analysed the impact of advertising on box-office revenues, such as Prag and Casavant (1994) and Zufryden (1996). Elberse and Eliashberg (2003) have additionally studied its indirect influence on audiences, through the impact of screens allocated to a movie in its opening week.

  2. See also Basuroy et al. (2006), Fernandez-Blanco et al. (2013), and Hadida (2009).

  3. As McKenzie (2012) points out, very few researchers have actually had access to exhibition contracts for empirical analysis, Filson et al. (2005) and Gil (2007) being among the exceptions. Furthermore, as explored by Gil (2013), there is plenty of informal contracting and renegotiation.

  4. One exception is Moul (2008). He estimates elasticities to find indirect evidence on horizontal collusion between Majors (distributors) regarding rental-rates and advertising strategies by observing the consequences downstream (i.e. on the exhibitors’ behaviour).

  5. Disney, Fox, Paramount, Sony, Universal, and WB. We consider Major studios including all their distribution brands.

  6. For the properties of the distribution of motion picture profits and its implications, see De Vany and Walls (2004). Concentrating on the top films in terms of generated revenue is usual in this literature. For instance, Brewer et al. (2009), De Vany and Walls (1997), and Moul (2008) also use samples restricted to high-performing films.

  7. Current prices, as recorded in the original dataset, are deflated using the Consumer Price Index (CPI) for each of those years, published by the U.S. Bureau of Labour Statistics (BLS).

  8. Eliashberg et al. (2006), Hofmann (2013), and McKenzie (2012) provide overviews of models and the previous results.

  9. Rating systems have proven to be a relevant determinant of box-office revenues (see for instance Walls 2005; Chen et al. 2013).

  10. The reference category is no star in the cast. Star_1 measures whether the film has an international star who has not won an Oscar, and Star_2 whether it has at least one international star who has won an Oscar.

  11. Int.Award_1 identifies films that have obtained the main awards at minor festivals or minor awards at the major festivals (such as an Oscar in minor categories). Int.Award_2 indicates films with a major award at an international film festival (e.g. Cannes, San Sebastian, Venice, or Berlin). Int.Award_3 is for films with at least one Oscar award in a major category.

  12. Stars, awards, sequels, and budget are signals (Basuroy et al. 2006; Deuchert et al. 2005; De Vany and Walls 1999; Ginsburgh 2003) and ways of extending established brand names to new products (Ravid 1999).

  13. The timing of entry is among the most important factors of success for short life-cycle products (Calentone et al. 2010; Elberse and Eliashberg 2003; Gutierrez-Navratil et al. 2014). Alternative factors may be the decision between nationwide or platform releases (Chen et al. 2013) or product differentiation across local competitors (Collins et al. 2009).

  14. The major peaks in the average weekly box-office revenues coincide with all the celebrations nationwide. For instance, the maximum peaks in the USA are recorded on Memorial Day and at Christmas. We observe other significant peaks on the birthday of Martin Luther King, Presidents’ Day, Independence Day, and Veterans Day. Einav (2009) identifies four windows using a different technique.

  15. Einav (2007) identifies two sources of seasonality: seasonality in the underlying demand and seasonality due to the endogenous industry reaction in terms of the timing and quality of releases.

  16. Furthermore, it can be seen that the average revenue drops sharply from the second week onwards. In fact, weekly revenues after week 12 represent, on average, less than two per cent of the box-office revenue in the opening week.

  17. When the models are just-identified, in the sense that the number of time-varying exogenous variables equals the number of time-invariant endogenous variables, then the coefficients of the time-varying variables estimated by Hausman–Taylor are the same as those estimated by fixed-effects.

  18. Brewer et al. (2009) also obtain evidence that variables, such as genre and MPAA rating, cease to be relevant after the release. After a given number of weeks, more information is available to consumers, and there is evidence of increasing returns to information and a non-linear effect of the ‘star power’. In a cross-sectional study, those authors determine that indicators of approval of the industry in terms of awards (or nominations), word-of-mouth, and praise from movie goers outweigh other signals.

  19. These results are in line with the evidence obtained by, among others, Ainslie et al. (2005), Basuroy et al. (2006), and Gutierrez-Navratil et al. (2014).

  20. It is easy to see a direct link between the theatre elasticity of revenues (η BO,thtrs ) and the occupancy rate of the theatres (occ t ). If, at a particular week t, BO t defines the box-office revenues that week, which are equal to the price (P) times the attendance (att t ), and the attendance is equal to the average seating capacity of the theatres (cap) multiplied by the number of theatres (thtrs t ) and their occupancy rate:

    \(BO_{t} = P*att_{t} = P*\left[ {cap*thtrs_{t} *occ_{t} \left( {thtrs_{t} } \right)} \right]\)

    Given this expression and the assumption that the occ t negatively depends on the number of screens, the theatre elasticity of revenues, at a particular week t, is defined as:

    \(\begin{aligned} n_{BO, thtrs} & = \frac{{\partial BO_{t} }}{{\partial thrts_{t} }} \frac{{thrts_{t} }}{{BO_{t} }} \\ & = \left[ {cap \, P\left( {occ_{t} \left( {thtrs_{t} } \right) + thtrs_{t} \frac{{\partial occ_{t} \left( {thtrs_{t} } \right)}}{{\partial thtrs_{t} }}} \right)} \right]\frac{{thtrs_{t} }}{{P\left[ {cap \, thtrs_{t} occ_{t} \left( {thtrs_{t} } \right)} \right]}} \\ & = 1 + \frac{{\partial occ_{t} \left( {thtrs_{t} } \right)}}{{\partial thtrs_{t} }}\frac{{thtrs_{t} }}{{occ_{t} \left( {thtrs_{t} } \right)}} = 1 + n_{occ, thtrs} \\ \end{aligned}\)

    Therefore, we expect a theatre elasticity of revenues lower than 1, the difference being equal to the theatre elasticity of occupancy rate. If we explicitly consider differences in capacity and assume that theatres added overtime are usually smaller and less appealing than the opening ones, this could reinforce (but complicate) the effect on occupancy.

  21. As suggested by one of the anonymous referees, theatre heterogeneity could be an important issue, so we also run Model 1 just for movies in the lower and upper budget quartiles. If movies with a similar budget are exhibited in similar theatres, screen heterogeneity should not be a problem within these two samples. Again, we find differences among the theatre elasticities of Majors and that these elasticities are statistically lower than those of non-Major elasticities. Results are available upon request.

  22. In our sample, the average number of opening screens is 2,658, 2,647, and 2,562 for Sony, WB and Paramount, respectively, and 2,221 for the other three Majors.

  23. A lower rental ratio would be consistent with the observed lower theatre elasticity, since exhibitors would have a greater incentive to allocate screens to those distributors, even if the total revenue by screen is not as high as it could be for movies distributed by other companies.

  24. This is consistent with the market shares of WB and Sony over the years analysed, as the two largest distributors (see www.boxofficemojo.com and www.the-numbers.com).

  25. As stated above, we have defined restricted samples by budget quartiles and the estimated results show a similar pattern between Majors and non-Majors.

  26. Whereas the financial performance of a film is found to be nearly impossible to forecast in the ‘nobody knows’ environment, Hand (2002) finds that the level of admissions can be foreseen, at least in the short term.

  27. Some data reflect plenty of informal contracting and renegotiation. Using evidence from a Spanish exhibitor, Gil (2013) reports that nearly one-half of formal exhibition contracts are renegotiated, and that informal contracts are extensively used (about 34 % of the contracts the author analyses). This is explained in terms of deviations from the expected performance of the film, providing evidence that exhibitors learn during the film’s run and accommodate their choices to maximise revenue. As distributors make the contract proposal, they should also anticipate the subsequent decisions made by exhibitors.

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Acknowledgments

We are grateful to the editor and two anonymous referees for their very useful comments, which we found very constructive and helpful to improve our manuscript. This research has been funded with support from the Spanish Ministry of Economy and Competitiveness (projects #ECO2011-27896, #ECO2010-17590 and #ECO2012-35820), the BETS Group (UFI11/46) of the University of the Basque Country (UPV/EHU), and from the Basque Government (SAIOTEK–SAI 12/149–S-PE12UN088, IT-793-13, and PREDOC-2010 grants). Fernanda Gutierrez-Navratil was awarded a mobility grant financed by the Campus of International Excellence of the University of Oviedo that allowed her to visit the Johns Hopkins Carey Business School during the research period. Victoria Ateca-Amestoy thanks the Department of Economics of the Universidad Autónoma de Madrid (Spain) for the hospitality during the research period in which this paper was written. The authors are members of the “PUCK; Assessing Effective Tools to Enhance Cultural Participation” project, funded by the European Commission (EU project 2012-0298/001-001 CU7PAG07). This work reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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Authors and Affiliations

  1. Department of Economics, University of Oviedo, Avda. Del Cristo, s/n, Oviedo, Spain

    Juan Prieto-Rodriguez & Fernanda Gutierrez-Navratil

  2. Department of Economic Analysis II, Universidad del País Vasco/Euskal Herriko Unibertsitatea, Avda. Lehendakari Aguirre, 83, Bilbao, Spain

    Victoria Ateca-Amestoy

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  1. Juan Prieto-Rodriguez
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  2. Fernanda Gutierrez-Navratil
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Correspondence to Juan Prieto-Rodriguez.

Appendix

Appendix

Table 3 Withdraw rates, average number of theatres, and average revenue by week
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Prieto-Rodriguez, J., Gutierrez-Navratil, F. & Ateca-Amestoy, V. Theatre allocation as a distributor’s strategic variable over movie runs. J Cult Econ 39, 65–83 (2015). https://doi.org/10.1007/s10824-014-9220-7

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