Beyond the Endogeneity Bias: The Effect of Unmeasured Brand Characteristics on Household-Level Brand Choice Models
Abstract
We investigate the role of potential weekly brand-specific characteristics that influence consumer choices, but are unobserved or unmeasurable by the researcher. We use an empirical approach, based on the estimation methods used for standard random coefficients logit models, to account for the presence of such unobserved attributes. Using household scanner panel data, we find evidence that ignoring such time-varying latent (to the researcher) characteristics can lead to two types of problems. First, consistent with previous literature, we find that these unobserved characteristics may lead to biased estimates of the mean price response parameters. This argument is based on a form of price endogeneity. If marketing managers set prices based on consumer willingness to pay, then the observed prices will likely be correlated with the latent (to the researcher) brand characteristics. We resolve this problem by using an instrumental variables procedure. Our findings suggest that simply ignoring these attributes may also lead to larger estimates of the variance in the heterogeneity distribution of preferences and price sensitivities across households. This could overstate the benefits from marketing activities such as household-level targeting. We resolve the problem by using weekly brand intercepts, embedded in a random coefficients brand choice model, to control for weekly brand-specific characteristics, while accounting for household heterogeneity. Overall, our results extend the finding on the endogeneity bias from the mean of the heterogeneity distribution (i.e., the price effect) to include the variance of that distribution.Download Info
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Article provided by INFORMS in its journal Management Science.
Volume (Year): 51 (2005)
Issue (Month): 5 (May)
Pages: 832-849
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Keywords: brand choice; choice models; econometric models; targeting; endogeneity; instrumental variables;References
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Eric Bradlow & Bart Bronnenberg & Gary Russell & Neeraj Arora & David Bell & Sri Duvvuri & Frankel Hofstede & Catarina Sismeiro & Raphael Thomadsen & Sha Yang, 2005. "Spatial Models in Marketing," Marketing Letters, Springer, vol. 16(3), pages 267-278, December.
- Sergio Meza & K. Sudhir, 2006. "Pass-through timing," Quantitative Marketing and Economics, Springer, vol. 4(4), pages 351-382, December.
- Abe Dunn, 2010.
"Drug Innovations and Welfare Measures Computed from Market Demand: The Case of Anti-Cholesterol Drugs,"
BEA Working Papers
0057, Bureau of Economic Analysis.
- Abe Dunn, 2012. "Drug Innovations and Welfare Measures Computed from Market Demand: The Case of Anti-cholesterol Drugs," American Economic Journal: Applied Economics, American Economic Association, vol. 4(3), pages 167-89, July.
- Christopher T. Conlon & Julie Holland Mortimer, 2010.
"Demand Estimation Under Incomplete Product Availability,"
Boston College Working Papers in Economics
799, Boston College Department of Economics, revised 07 Aug 2012.
- Christopher T. Conlon & Julie Holland Mortimer, 2008. "Demand Estimation Under Incomplete Product Availability," NBER Working Papers 14315, National Bureau of Economic Research, Inc.
- Nair, Harikesh, S., 2006.
"Intertemporal Price Discrimination with Forward-Looking Consumers: Application to the US Market for Console Video-Games,"
Research Papers
1947, Stanford University, Graduate School of Business.
- Harikesh Nair, 2007. "Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games," Quantitative Marketing and Economics, Springer, vol. 5(3), pages 239-292, September.
- Sugawara, Shinya, 2012. "A nonparametric Bayesian approach for counterfactual prediction with an application to the Japanese private nursing home market," MPRA Paper 42154, University Library of Munich, Germany.
- Jean-Pierre Dubé & Günter Hitsch & Puneet Manchanda, 2005. "An Empirical Model of Advertising Dynamics," Quantitative Marketing and Economics, Springer, vol. 3(2), pages 107-144, June.
- Bradlow, E. & Bronnenberg, B.J.J.A.M. & Russell, G.J. & Arora, N. & Bell, D. & Deepak, S.D. & Hofstede, F. ter & Sismeiro, C. & Thomadsen, R. & Yang, S., 2005. "Spatial models in marketing," Open Access publications from Tilburg University urn:nbn:nl:ui:12-332173, Tilburg University.
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