IDEAS home Printed from https://ideas.repec.org/p/ecm/wc2000/1691.html
   My bibliography  Save this paper

Estimating Product Characteristics and Spatial Competition in the Network Television Industry

Author

Listed:
  • Ronald Goettler

    (Carnegie Mellon University)

  • Ron Shachar

    (Tel Aviv University)

Abstract

Assessing the demand for products with characteristics that are unobservable or difficult to measure is becoming increasingly important with the growing proliferation and value of such products. Analyzing industry performance and firm competition in these sectors is hindered by the failure of traditional empirical methods to estimate demand for the products of these sectors. This paper focuses on the network television industry to present: (a) an empirical analysis of spatial competition, and (b) a structural approach to estimating product characteristics and consumer preferences in such industries, and (c) optimal network programming and scheduling given the estimated demand system. We use maximum simulated likelihood to estimate a structural model of viewer choice, yielding estimates of the latent characteristics of each show, the distribution of consumers' preferences for these characteristics, and the state dependence of choices. Results indicate the attribute space spans four dimensions of horizontal differentiation and one vertically differentiated dimension. Interpretations of these dimensions reflect the traditional show labels. For example, one of the dimensions represents the degree of realism in a show. Furthermore, the clustering of shows based on the estimated characteristics corresponds to traditional show labels. We identify four clusters --- sitcoms for mature viewers, sitcoms for younger viewers, reality based dramas, and fictional dramas. Regarding strategic behavior, our model suggests the networks should use counter-programming (i.e., differentiated products) within each time slot and homogeneous programming through each night. The estimated show locations reveal an extensive use of these strategies, as well as a limited degree of branding. Nonetheless, by unilaterally changing their schedules to increase both counter-programming and homogeneity, ABC, CBS, and NBC are able to increase their weekly ratings by 16%, 12%, and 15%, respectively. In a Nash equilibrium of the static scheduling game, these gains are reduced to 15%, 6%, and 12% increases.

Suggested Citation

  • Ronald Goettler & Ron Shachar, 2000. "Estimating Product Characteristics and Spatial Competition in the Network Television Industry," Econometric Society World Congress 2000 Contributed Papers 1691, Econometric Society.
    • Handle: RePEc:ecm:wc2000:1691
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/RePEc/es2000/1691.pdf
    File Function: main text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wagner A. Kamakura & Rajendra K. Srivastava, 1986. "An Ideal-Point Probabilistic Choice Model for Heterogeneous Preferences," Marketing Science, INFORMS, vol. 5(3), pages 199-218.
    2. McFadden, Daniel & Ruud, Paul A, 1994. "Estimation by Simulation," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 591-608, November.
    3. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    4. Goettler, R., 1999. "Advertising Rates, Audience Composition, and Competition in the Network Television Industry," GSIA Working Papers 1999-28, Carnegie Mellon University, Tepper School of Business.
    5. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    6. John Rust, 1997. "A Comparison of Policy Iteration Methods for Solving Continuous-State, Infinite-Horizon Markovian Decision Problems Using Random, Quasi-random, and Deterministic Discretizations," Computational Economics 9704001, University Library of Munich, Germany.
    7. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74, pages 132-132.
    8. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    9. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
    10. Michael Spence & Bruce Owen, 1977. "Television Programming, Monopolistic Competition, and Welfare," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 91(1), pages 103-126.
    11. Roland T. Rust & Mark I. Alpert, 1984. "An Audience Flow Model of Television Viewing Choice," Marketing Science, INFORMS, vol. 3(2), pages 113-124.
    12. V A Hajivassiliou, 1997. "Some Practical Issues in Maximum Simulated Likelihood," STICERD - Econometrics Paper Series 340, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    13. Terry Elrod, 1988. "Choice Map: Inferring a Product-Market Map from Panel Data," Marketing Science, INFORMS, vol. 7(1), pages 21-40.
    14. James J. Heckman & James M. Snyder, Jr., 1996. "Linear Probability Models of the Demand for Attributes with an Empirical Application to Estimating the Preferences of Legislators," NBER Working Papers 5785, National Bureau of Economic Research, Inc.
    15. Heckman, James J, 1984. "The x[superscript]2 Goodness of Fit Statistic for Models with Parameters Estimated from Microdata," Econometrica, Econometric Society, vol. 52(6), pages 1543-1547, November.
    16. A. Papageorgiou & J. F. Traub, 1996. "New Results on Deterministic Pricing of Financial Derivatives," Working Papers 96-06-040, Santa Fe Institute.
    Full references (including those not matched with items on IDEAS)

    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. Pradeep Chintagunta & Jean-Pierre Dubé & Vishal Singh, 2003. "Balancing Profitability and Customer Welfare in a Supermarket Chain," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 111-147, March.
    2. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    3. Davis, Peter, 2000. "Empirical models of demand for differentiated products," European Economic Review, Elsevier, vol. 44(4-6), pages 993-1005, May.
    4. Inkmann, Joachim, 2000. "Misspecified heteroskedasticity in the panel probit model: A small sample comparison of GMM and SML estimators," Journal of Econometrics, Elsevier, vol. 97(2), pages 227-259, August.
    5. Gould, Brian W. & Dong, Diansheng, 2000. "The Decision Of When To Buy A Frequently Purchased Good: A Multi-Period Probit Model," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(2), pages 1-17, December.
    6. Freyberger, Joachim, 2015. "Asymptotic theory for differentiated products demand models with many markets," Journal of Econometrics, Elsevier, vol. 185(1), pages 162-181.
    7. Lapo Filistrucchi & Tobias J. Klein, 2013. "Price Competition in Two-Sided Markets with Heterogeneous Consumers and Network Effects," Working Papers 13-20, NET Institute.
    8. Igal Hendel, 1994. "Estimating Multiple-Discrete Choice Models: An Application to Computeri-zzation Returns," NBER Technical Working Papers 0168, National Bureau of Economic Research, Inc.
    9. Steven Berry & James Levinsohn & Ariel Pakes, 2004. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 68-105, February.
    10. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    11. Hans G. Bloemen & Arie Kapteyn, 2008. "The estimation of utility-consistent labor supply models by means of simulated scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(4), pages 395-422.
    12. Keane, Michael & Moffitt, Robert, 1998. "A Structural Model of Multiple Welfare Program Participation and Labor Supply," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(3), pages 553-589, August.
    13. Xiaodong Gong & Arthur van Soest, 2002. "Family Structure and Female Labor Supply in Mexico City," Journal of Human Resources, University of Wisconsin Press, vol. 37(1), pages 163-191.
    14. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    15. Daniel McFadden, 2001. "Economic Choices," American Economic Review, American Economic Association, vol. 91(3), pages 351-378, June.
    16. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
    17. Lee, Lung-Fei, 1997. "Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results," Journal of Econometrics, Elsevier, vol. 82(1), pages 1-35.
    18. Jean-Jacques Forneron, 2019. "A Sieve-SMM Estimator for Dynamic Models," Papers 1902.01456, arXiv.org, revised Jan 2023.
    19. Schmidheiny, Kurt, 2006. "Income segregation and local progressive taxation: Empirical evidence from Switzerland," Journal of Public Economics, Elsevier, vol. 90(3), pages 429-458, February.
    20. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.

    More about this item

    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:ecm:wc2000:1691. 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: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.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.