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Demand for Performing Arts: The Effect of Unobserved Quality on Price Elasticity

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Listed:
  • Alina R. Buzanakova

    (National Research University Higher School of Economics)

  • Evgeniy M. Ozhegov

    (National Research University Higher School of Economics)

Abstract

This paper studies behavior patterns among theater attendees in the process of ticket purchasing. Since the theater attempts to balance between a high occupancy and affordable prices, the purpose of the study is to reveal the effects of changes in prices on attendance. This project is conducted conjointly with the Perm Tchaikovsky Opera and Ballet Theater. Data are taken from the sales information system of the theater for four seasons 2011-2012/2014-2015. The data are disaggregated to the level of the seating area and performance and consist of the attendance rate, the set of prices and the performance characteristics. The research explores the determinants of demand using a censored quantile regression which accounts for the heterogeneity of effects on different levels of attendance rates and censoring. We estimate the parameters of the demand function and show that the aggregated demand is elastic by price, at the same time the elasticity varies across different seating areas. Moreover, demand for the more popular seats and performances is less elastic

Suggested Citation

  • Alina R. Buzanakova & Evgeniy M. Ozhegov, 2016. "Demand for Performing Arts: The Effect of Unobserved Quality on Price Elasticity," HSE Working papers WP BRP 156/EC/2016, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:156/ec/2016
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    References listed on IDEAS

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    1. Trine Hansen, 1997. "The Willingness-to-Pay for the Royal Theatre in Copenhagen as a Public Good," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 21(1), pages 1-28, March.
    2. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    3. Henry Hansmann, 1981. "Nonprofit Enterprise in the Performing Arts," Bell Journal of Economics, The RAND Corporation, vol. 12(2), pages 341-361, Autumn.
    4. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    5. Marta Zieba, 2009. "Full-income and price elasticities of demand for German public theatre," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 33(2), pages 85-108, May.
    6. Gapinski, James H, 1984. "The Economics of Performing Shakespeare," American Economic Review, American Economic Association, vol. 74(3), pages 458-466, June.
    7. José Grisolía & Kenneth Willis, 2012. "A latent class model of theatre demand," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 36(2), pages 113-139, May.
    8. K. Willis & J. Snowball, 2009. "Investigating how the attributes of live theatre productions influence consumption choices using conjoint analysis: the example of the National Arts Festival, South Africa," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 33(3), pages 167-183, August.
    9. Louis Lévy-Garboua & Claude Montmarquette, 1996. "A microeconometric study of theatre demand," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 20(1), pages 25-50, March.
    10. Seaman, Bruce A, 2006. "Empirical Studies of Demand for the Performing Arts," Handbook of the Economics of Art and Culture, in: V.A. Ginsburgh & D. Throsby (ed.), Handbook of the Economics of Art and Culture, edition 1, volume 1, chapter 14, pages 415-472, Elsevier.
    11. Jenkins, Stephen & Austen-Smith, David, 1987. "Interdependent decision-making in non-profit industries: A simultaneous equation analysis of English provincial theatre," International Journal of Industrial Organization, Elsevier, vol. 5(2), pages 149-174.
    12. Jacques Wolff, 1972. "Decazeville : expansion et déclin d'un pôle de croissance," Revue Économique, Programme National Persée, vol. 23(5), pages 753-785.
    13. Jörg Schimmelpfennig, 1997. "Demand for Ballet: A Non-Parametric Analysis of the 1995 Royal Ballet Summer Season," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 21(2), pages 119-127, June.
    14. Throsby, David, 1994. "The Production and Consumption of the Arts: A View of Cultural Economics," Journal of Economic Literature, American Economic Association, vol. 32(1), pages 1-29, March.
    15. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    16. Daniel Urrutiaguer, 2002. "Quality Judgements and Demand for French Public Theatre," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 26(3), pages 185-202, August.
    17. Jani-Petri Laamanen, 2013. "Estimating demand for opera using sales system data: the case of Finnish National Opera," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 37(4), pages 417-432, November.
    18. Amil Petrin & Kenneth Train, 2003. "Omitted Product Attributes in Discrete Choice Models," NBER Working Papers 9452, National Bureau of Economic Research, Inc.
    19. Günther Schulze & Anselm Rose, 1998. "Public Orchestra Funding in Germany – An Empirical Investigation," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 22(4), pages 227-247, December.
    20. Whitney K. Newey, 2013. "Nonparametric Instrumental Variables Estimation," American Economic Review, American Economic Association, vol. 103(3), pages 550-556, May.
    21. Jonathan Corning & Armando Levy, 2002. "Demand for Live Theater with Market Segmentation and Seasonality," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 26(3), pages 217-235, August.
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    Cited by:

    1. Ziwei Cong & Jia Liu & Puneet Manchanda, 2021. "The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest," Papers 2107.01629, arXiv.org, revised Sep 2022.

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    More about this item

    Keywords

    performing arts; demand; price elasticity; heterogeneity; censoring; quantile regression.;
    All these keywords.

    JEL classification:

    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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