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Intertemporal effects of consumption and their implications for demand elasticity estimates

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  • Wesley Hartmann

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    Abstract

    Consumption of a good typically diminishes the marginal utility of consuming more, but for how long? This paper adapts a model of consumption capital to allow consumption to have a lasting effect that diminishes the marginal utility of future consumption. Estimates of the model find that it takes the 25th, median and 75th percentile of consumers 19, 32 and 43 days for their marginal utilities to return to pre-consumption levels, and they are forward-looking with respect to these effects. This generates intertemporal substitution of consumption that leads to an overestimate of the own-price elasticity of demand of ten percent when it is estimated using temporary price changes. In addition to these implications consumption effects share with those of durable and storable goods, consumption effects also raise concerns for capacity constrained industries because the timing of consumption affects capacity utilization. In the empirical application in this paper, price variation in one time period generates substantial changes in capacity utilization in that period, but minimal changes in other periods because the intertemporal substitution is spread over many time periods. Copyright Springer Science + Business Media, LLC 2006

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    Bibliographic Info

    Article provided by Springer in its journal Quantitative Marketing and Economics.

    Volume (Year): 4 (2006)
    Issue (Month): 4 (December)
    Pages: 325-349

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    Handle: RePEc:kap:qmktec:v:4:y:2006:i:4:p:325-349

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    Web page: http://www.springerlink.com/link.asp?id=111240

    Related research

    Keywords: Consumption; Discrete choice; Dynamic programming; Random coefficients;

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    Cited by:
    1. Chen, Jiawei & Esteban, Susanna & Shum, Matthew, 2008. "Demand and supply estimation biases due to omission of durability," Journal of Econometrics, Elsevier, vol. 147(2), pages 247-257, December.
    2. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics, Springer, vol. 9(1), pages 25-70, March.
    3. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics, Springer, vol. 7(4), pages 343-376, December.
    4. Bart Bronnenberg & Jean Dubé & Carl Mela & Paulo Albuquerque & Tulin Erdem & Brett Gordon & Dominique Hanssens & Guenter Hitsch & Han Hong & Baohong Sun, 2008. "Measuring long-run marketing effects and their implications for long-run marketing decisions," Marketing Letters, Springer, vol. 19(3), pages 367-382, December.
    5. Aguirregabiria, Victor & Nevo, Aviv, 2010. "Recent developments in empirical IO: dynamic demand and dynamic games," MPRA Paper 27814, University Library of Munich, Germany.
    6. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics, Springer, vol. 10(2), pages 151-196, June.
    7. Wesley Hartmann & V. Viard, 2008. "Do frequency reward programs create switching costs? A dynamic structural analysis of demand in a reward program," Quantitative Marketing and Economics, Springer, vol. 6(2), pages 109-137, June.

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