IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Does Service Bundling Reduce Churn?

  • Jeffrey T. Prince

    (Department of Business Economics and Public Policy, Indiana University Kelley School of Business)

  • Shane Greenstein

    (Department of Management and Strategy, Kellogg School of Management, Northwestern University)

We examine whether bundling in telecommunications services reduces churn using a series of large, independent cross sections of household decisions. To identify the effect of bundling, we construct a pseudo-panel dataset and utilize a linear, dynamic panel-data model, supplemented by nearest-neighbor matching. We find bundling does reduce churn for all three "triple-play" services. However, the effect is only "visible" during times of turbulent demand. We also find evidence that broadband was substituting for pay television in 2009. This analysis highlights that bundling helps with customer retention in service industries, and may play an important role in preserving contracting markets.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Paper provided by Indiana University, Kelley School of Business, Department of Business Economics and Public Policy in its series Working Papers with number 2011-05.

in new window

Date of creation: Nov 2011
Date of revision:
Handle: RePEc:iuk:wpaper:2011-05
Contact details of provider: Postal:
1309 East Tenth Street, Room 451, Bloomington, IN 47405-1701

Phone: 812-855-9219
Fax: 812-855-3354
Web page:

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Rosston Gregory L. & Savage Scott J & Waldman Donald M, 2010. "Household Demand for Broadband Internet in 2010," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-45, September.
  2. Seung‐Hyun Hong, 2013. "Measuring The Effect Of Napster On Recorded Music Sales: Difference‐In‐Differences Estimates Under Compositional Changes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 297-324, 03.
  3. Schmalensee, Richard, 1982. "Commodity Bundling by Single-Product Monopolies," Journal of Law and Economics, University of Chicago Press, vol. 25(1), pages 67-71, April.
  4. Shane Greenstein & Ryan C. McDevitt, 2010. "Evidence of a Modest Price Decline in US Broadband Services," NBER Working Papers 16166, National Bureau of Economic Research, Inc.
  5. Jeffrey T. Prince, 2008. "Repeat Purchase amid Rapid Quality Improvement: Structural Estimation of Demand for Personal Computers," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 17(1), pages 1-33, 03.
  6. Michael D. Whinston, 1989. "Tying, Foreclosure, and Exclusion," NBER Working Papers 2995, National Bureau of Economic Research, Inc.
  7. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
  8. Yannis Bakos & Erik Brynjolfsson, 1997. "Bundling Information Goods: Pricing, Profits and Efficiency," Working Paper Series 199, MIT Center for Coordination Science.
  9. Salinger, Michael A, 1995. "A Graphical Analysis of Bundling," The Journal of Business, University of Chicago Press, vol. 68(1), pages 85-98, January.
  10. Jay Pil Choi, 2004. "Tying and innovation: A dynamic analysis of tying arrangements," Economic Journal, Royal Economic Society, vol. 114(492), pages 83-101, 01.
  11. Verbeek, M.J.C.M. & Vella, F., 2002. "Estimating dynamic models from repeated cross-sections," Econometric Institute Research Papers EI 2002-05, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  12. Jose Cuesta & Hugo Ñopo & Georgina Pizzolitto, 2011. "Using Pseudo‐Panels To Measure Income Mobility In Latin America," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 57(2), pages 224-246, 06.
  13. Barry Nalebuff, 2004. "Bundling as an Entry Barrier," The Quarterly Journal of Economics, Oxford University Press, vol. 119(1), pages 159-187.
  14. R. Preston McAfee & John McMillan & Michael D. Whinston, 1989. "Multiproduct Monopoly, Commodity Bundling, and Correlation of Values," The Quarterly Journal of Economics, Oxford University Press, vol. 104(2), pages 371-383.
  15. Moffitt, Robert, 1993. "Identification and estimation of dynamic models with a time series of repeated cross-sections," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 99-123, September.
  16. McKenzie, D.J.David J., 2004. "Asymptotic theory for heterogeneous dynamic pseudo-panels," Journal of Econometrics, Elsevier, vol. 120(2), pages 235-262, June.
  17. Joseph Farrell & Carl Shapiro, 1988. "Dynamic Competition with Switching Costs," RAND Journal of Economics, The RAND Corporation, vol. 19(1), pages 123-137, Spring.
  18. William James Adams & Janet L. Yellen, 1976. "Commodity Bundling and the Burden of Monopoly," The Quarterly Journal of Economics, Oxford University Press, vol. 90(3), pages 475-498.
  19. Carbajo, Jose & de Meza, David & Seidmann, Daniel J, 1990. "A Strategic Motivation for Commodity Bundling," Journal of Industrial Economics, Wiley Blackwell, vol. 38(3), pages 283-98, March.
  20. Gregory Crawford, 2008. "The discriminatory incentives to bundle in the cable television industry," Quantitative Marketing and Economics (QME), Springer, vol. 6(1), pages 41-78, March.
  21. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
  22. Chen, Yongmin, 1997. "Equilibrium Product Bundling," The Journal of Business, University of Chicago Press, vol. 70(1), pages 85-103, January.
  23. Armstrong, Mark, 1996. "Multiproduct Nonlinear Pricing," Econometrica, Econometric Society, vol. 64(1), pages 51-75, January.
  24. Dolores Collado, M., 1997. "Estimating dynamic models from time series of independent cross-sections," Journal of Econometrics, Elsevier, vol. 82(1), pages 37-62.
  25. Mark Israel, 2005. "Tenure Dependence in Consumer-Firm Relationships: An Empirical Analysis of Consumer Departures from Automobile Insurance Firms," RAND Journal of Economics, The RAND Corporation, vol. 36(1), pages 165-192, Spring.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:iuk:wpaper:2011-05. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Rick Harbaugh)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.