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Repeat Purchase amid Rapid Quality Improvement: Structural Estimation of Demand for Personal Computers

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  • Jeffrey T. Prince

Abstract

This paper estimates a structural model of demand for the personal computer (PC) by repeat purchasers. Taking advantage of a large data set on household‐level PC purchases, the econometric model uses variation in PC holdings among PC owners to identify households' marginal values of quality improvements. The analysis only requires data on a cross‐section of households along with observed PC offerings over time, and accounts for stock effects, forward‐looking behavior, and large amounts of household heterogeneity. The estimates allow us to measure sensitivity to long‐term and short‐term price and technology changes, as well as consumer welfare changes from technological improvements. The results show a large variation in marginal values for PC quality across households, and that failing to account for forward‐looking behavior results in biased estimates and a poorer fit to the data. Incorporating stock effects proves especially important because, for the data used here, the model's parameters are not only biased but also virtually impossible to pin down without them. The results also show that price elasticity is approximately 25% higher in the short term compared to the long term, and technology elasticity is approximately 35% higher in the short term compared to the long term. Furthermore, welfare measurements are significantly underestimated when using a model that does not account for forward‐looking behavior. Finally, the model is extended to include first‐time purchasers. The results show similar patterns, but should be interpreted with much caution owing to the likely presence of significant unobserved heterogeneity between new purchasers and repeat purchasers.

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  • 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, March.
    • Handle: RePEc:bla:jemstr:v:17:y:2008:i:1:p:1-33
    DOI: 10.1111/j.1530-9134.2008.00169.x
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    References listed on IDEAS

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    Cited by:

    1. Takeshi Fukasawa, 2022. "The Biases in Applying Static Demand Models under Dynamic Demand," Discussion Paper Series DP2022-18, Research Institute for Economics & Business Administration, Kobe University, revised Jul 2022.
    2. Gautam Gowrisankaran & Marc Rysman, 2012. "Dynamics of Consumer Demand for New Durable Goods," Journal of Political Economy, University of Chicago Press, vol. 120(6), pages 1173-1219.
    3. Victor Stango & Jonathan Zinman, 2014. "Limited and Varying Consumer Attention: Evidence from Shocks to the Salience of Bank Overdraft Fees," Review of Financial Studies, Society for Financial Studies, vol. 27(4), pages 990-1030.
    4. Jeffrey Prince & Shane Greenstein, 2014. "Does Service Bundling Reduce Churn?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 23(4), pages 839-875, December.
    5. Jean-Pierre H. Dubé & Günter J. Hitsch & Pradeep K. Chintagunta, 2010. "Tipping and Concentration in Markets with Indirect Network Effects," Marketing Science, INFORMS, vol. 29(2), pages 216-249, 03-04.
    6. Ronald L. Goettler & Brett R. Gordon, 2011. "Does AMD Spur Intel to Innovate More?," Journal of Political Economy, University of Chicago Press, vol. 119(6), pages 1141-1200.
    7. Jeffrey Prince & Shane Greenstein, 2017. "Measuring Consumer Preferences for Video Content Provision via Cord‐Cutting Behavior," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 26(2), pages 293-317, June.
    8. Prince, Jeffrey T., 2009. "How do households choose quality and time to replacement for a rapidly improving durable good?," International Journal of Industrial Organization, Elsevier, vol. 27(2), pages 302-311, March.
    9. Brett R. Gordon, 2009. "A Dynamic Model of Consumer Replacement Cycles in the PC Processor Industry," Marketing Science, INFORMS, vol. 28(5), pages 846-867, 09-10.
    10. Das Nilotpal & Falaris Evangelos M & Mulligan James G, 2009. "Vintage Effects and the Diffusion of Time-Saving Technological Innovations," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 9(1), pages 1-37, June.
    11. De los Santos, Babur, 2018. "Consumer search on the Internet," International Journal of Industrial Organization, Elsevier, vol. 58(C), pages 66-105.
    12. Schleife, Katrin, 2008. "Regional Versus Individual Aspects of the Digital Divide in Germany," ZEW Discussion Papers 06-085 [rev.2], ZEW - Leibniz Centre for European Economic Research.
    13. Schleife, Katrin, 2010. "What really matters: Regional versus individual determinants of the digital divide in Germany," Research Policy, Elsevier, vol. 39(1), pages 173-185, February.
    14. Victor Stango & Jonathan Zinman, 2013. "Borrowing High vs. Borrowing Higher: Sources and Consequences of Dispersion in Individual Borrowing Costs," NBER Working Papers 19069, National Bureau of Economic Research, Inc.
    15. Choi, Hyunhong & Koo, Yoonmo, 2023. "New technology product introduction strategy with considerations for consumer-targeted policy intervention and new market entrant," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    16. Paul Chwelos & Ronald Ramirez & Kenneth L. Kraemer & Nigel P. Melville, 2010. "Research Note ---Does Technological Progress Alter the Nature of Information Technology as a Production Input? New Evidence and New Results," Information Systems Research, INFORMS, vol. 21(2), pages 392-408, June.

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