IDEAS home Printed from https://ideas.repec.org/p/cue/wpaper/awp-05-2016.html
   My bibliography  Save this paper

Demand for performing arts: the effect of unobserved quality on price elasticity

Author

Listed:
  • Alina Buzanakova

    (National Research University, Higher School of Economics, Department of Economics and Finance, Lebedeva 27, Perm, Russia)

  • Evgeniy M. Ozhegov

    (National Research University, Higher School of Economics, Department of Economics and Finance, Lebedeva 27, Perm, Russia)

Abstract

This paper studies the behavior patterns among the theatre’s attendants in the process of ticket’s purchase. Since the theatre attempts to balance between the high rate of occupancy and the affordable prices for the spectator, the purpose of the study is to reveal the effects of changes in prices on attendance rate on different levels of attendance. This project is conducted conjointly with the Perm Tchaikovsky Opera and Ballet Theater. Data is taken from the sales information system of the theater for four seasons 2011-2012/2014-2015. The data is disaggregated to the level of the seating area and performance and consists of the attendance rate, the set of prices and the performance characteristics. The research explores the determinants of demand using censored quantile regression that accounts for the heterogeneity of effects on different levels of attendance rates and censoring. We have estimated the parameters of demand function and revealed 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 expectedly turns out to be the less elastic.

Suggested Citation

  • Alina Buzanakova & Evgeniy M. Ozhegov, 2016. "Demand for performing arts: the effect of unobserved quality on price elasticity," ACEI Working Paper Series AWP-05-2016, Association for Cultural Economics International, revised Jun 2016.
    • Handle: RePEc:cue:wpaper:awp-05-2016
    as

    Download full text from publisher

    File URL: http://files.culturaleconomics.org/papers/AWP-05-2016.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Henry Hansmann, 1981. "Nonprofit Enterprise in the Performing Arts," Bell Journal of Economics, The RAND Corporation, vol. 12(2), pages 341-361, Autumn.
    5. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    6. Jacques Wolff, 1972. "Decazeville : expansion et déclin d'un pôle de croissance," Revue Économique, Programme National Persée, vol. 23(5), pages 753-785.
    7. 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.
    8. 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.
    9. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    10. 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.
    11. 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.
    12. 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.
    13. Gapinski, James H, 1984. "The Economics of Performing Shakespeare," American Economic Review, American Economic Association, vol. 74(3), pages 458-466, June.
    14. Amil Petrin & Kenneth Train, 2003. "Omitted Product Attributes in Discrete Choice Models," NBER Working Papers 9452, National Bureau of Economic Research, Inc.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ozhegova, A. & Ozhegov, E., 2018. "Estimation of Demand Function for Performing Arts: Empirical Analysis," Journal of the New Economic Association, New Economic Association, vol. 37(1), pages 87-110.
    2. Evgeniy M. Ozhegov & Alina Ozhegova, 2018. "Segmentation of Theatre Audiences: A Latent Class Approach for Combined Data," HSE Working papers WP BRP 198/EC/2018, National Research University Higher School of Economics.
    3. Ozhegova, Alina & Ozhegov, Evgeniy M., 2020. "Segmentation of theatre audiences: A latent class approach for combined data," Journal of choice modelling, Elsevier, vol. 37(C).
    4. Alina Ozhegova & Evgeniy M. Ozhegov, 2018. "Heterogeneity in demand for performances and seats in the theatre," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(3), pages 131-145, June.
    5. Evgeniy M. Ozhegov & Alina Ozhegova, 2017. "Regression Tree Model for Analysis of Demand with Heterogeneity and Censorship," HSE Working papers WP BRP 174/EC/2017, National Research University Higher School of Economics.
    6. 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.
    7. Junlong Wu & Keshen Jiang & Chaoqing Yuan, 2019. "Determinants of demand for traditional Chinese opera," Empirical Economics, Springer, vol. 57(6), pages 2129-2148, December.
    8. 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.
    9. Wiśniewska Aleksandra, 2019. "Quality attributes in the non-market stated-preference based valuation of cultural goods," Central European Economic Journal, Sciendo, vol. 6(53), pages 132-150, January.
    10. Evgeniy M. Ozhegov & Alina Ozhegova, 2020. "Regression tree model for prediction of demand with heterogeneity and censorship," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 489-500, April.
    11. Aleksandra Wiśniewska, 2019. "‘Quality food’ for cultural policies. Quality attributes in the non-market stated-preference based valuation of cultural goods," Working Papers 2019-03, Faculty of Economic Sciences, University of Warsaw.
    12. Andrea Baldin & Trine Bille & Raghava Rao Mukkamala & Ravi Vatrapu, 2024. "The impact of social media activities on theater demand," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 48(2), pages 199-220, June.
    13. Kristien Werck & Bruno Heyndels & Benny Geys, 2008. "The impact of ‘central places’ on spatial spending patterns: evidence from Flemish local government cultural expenditures," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 32(1), pages 35-58, March.
    14. van der Ploeg, Frederick, 2006. "The Making of Cultural Policy: A European Perspective," 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 34, pages 1183-1221, Elsevier.
    15. Aleksandra Wiśniewska & Mikołaj Czajkowski, 2015. "Utilizing the Discrete Choice Experiment Approach for Designing a Socially Efficient Cultural Policy: The case of municipal theaters in Warsaw," Working Papers 2015-36, Faculty of Economic Sciences, University of Warsaw.
    16. Andrea Baldin & Trine Bille, 2018. "Modelling preference heterogeneity for theatre tickets: a discrete choice modelling approach on Royal Danish Theatre booking data," Applied Economics, Taylor & Francis Journals, vol. 50(5), pages 545-558, January.
    17. Kristien Werck & Bruno Heyndels, 2007. "Programmatic choices and the demand for theatre: the case of Flemish theatres," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 31(1), pages 25-41, March.
    18. Victor Nyatefe & Mawussé Nézan Komlagan Okey, 2020. "Analyse de la consommation des biens culturels au Togo," African Development Review, African Development Bank, vol. 32(1), pages 80-95, March.
    19. José M. Grisolía & Kenneth G. Willis, 2016. "Consumer choice of theatrical productions: a combined revealed preference–stated preference approach," Empirical Economics, Springer, vol. 50(3), pages 933-957, May.
    20. K. Willis & J. Snowball & C. Wymer & José Grisolía, 2012. "A count data travel cost model of theatre demand using aggregate theatre booking data," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 36(2), pages 91-112, May.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cue:wpaper:awp-05-2016. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Paul Crosby (email available below). General contact details of provider: https://edirc.repec.org/data/aceiiea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.