Advertisement

Quantitative Marketing and Economics

, Volume 14, Issue 3, pp 157–193 | Cite as

Costly search and consideration sets in storable goods markets

  • Tiago PiresEmail author
Article

Abstract

Costly search can result in consumers restricting their attention to a subset of products–the consideration set–before making a final purchase decision. The search process is usually not observed, which creates econometric challenges. I show that inventory and the availability of different package sizes create new sources of variation to identify search costs in storable goods markets. To evaluate the importance of costly search in these markets, I estimate a dynamic choice model with search frictions using data on purchases of laundry detergent. My estimates show that consumers incur significant search costs, and ignoring costly search overestimates the own-price elasticity for products more often present in consideration sets and underestimates the elasticity of frequently excluded products. Firms employ marketing devices, such as product displays and advertising, to influence consideration sets. These devices have direct and strategic effects, which I explore using the estimates of the model. I find that using marketing devices to reduce a product’s search cost during a price promotion has modest effects on the overall category revenues, and decreases the revenues of some products.

Keywords

Search costs Consideration set Information Storable goods Dynamic discrete-choice models 

JEL Classification

D12 D83 L81 

Notes

Acknowledgments

I am especially indebted and grateful to Aviv Nevo for his advice, guidance and support. I am also indebted and grateful to Igal Hendel and Robert Porter for valuable comments and discussions. I would like to thank Alberto Salvo, Brian McManus, Andre Trindade, Mike Abito, Guillermo Marshall, Agnieszka Roy, Arkadiusz Szydlowski, Sergio Urzua, Esteban Petruzello, Fernando Luco, Eric Anderson, Robin Lee, Song Yao, David Henriques, Tiago Botelho, Claudia Alves, Ernesto Freitas, MathisWagner, Mike Powell, David Miller, Jose Espin-Sanchez, Maja Kos, and seminar participants at Northwestern University, University of Toronto, Northeastern University, Tilburg University, Einaudi Institute for Economics and Finance, Banco de Portugal, University of North Carolina, Oklahoma University, Bates White, and Compass Lexecon for their suggestions. I am thankful to IRI and particularly Mike Kruger for generously supplying the data. Financial support from Fundaca̧o para a Cîencia e Tecnologia under the scholarship SFRH/BD/43857/2008 is also gratefully acknowledged. This paper is a revised chapter from my dissertation at Northwestern University. All errors are my responsibility.

Supplementary material

11129_2016_9169_MOESM1_ESM.pdf (189 kb)
(PDF 192 KB)

References

  1. Baye, M. R., Morgan, J., & Scholten, P. (2007). Information, search and price dispersion. Handbook of Economics and Information Systems, I.Google Scholar
  2. Blattberg, R., & Neslin, S. (1990). Sales promotions. Englewood Cliffs. New Jersey: Prentice Hall.Google Scholar
  3. Boizot, C., Robin, J., & Visser, M. (2001). The demand for food products: An analysis of interpurchase times and purchased quantities. Economic Journal, 111, 391–419.CrossRefGoogle Scholar
  4. Bronnenberg, B., & Vanhonacker, W. (1996). Limited choice sets, local price response, and implied measures of price competition. Journal of Marketing Research, 33, 163–173.CrossRefGoogle Scholar
  5. De los Santos, B., Hortacsu, A., & Wildenbeest, M. (2012). Testing models of consumer search using data on web browsing and purchasing behavior. American Economic Review, 102, 2955–2980.CrossRefGoogle Scholar
  6. Dinerstein, M., Einav, L., Levin, J., & Sundaresan, N. (2014). Consumer price search and platform design in internet commerce. Mimeo: Stanford University.CrossRefGoogle Scholar
  7. Draganska, M., & Klapper, D. (2011). Choice set heterogeneity and the role of advertising: an analysis with micro and macro data. Journal of Marketing Research, 48, 653–669.CrossRefGoogle Scholar
  8. Dreze, X., Hoch, S., & Purk, M. (1994). Shelf management and space elasticity. Journal of Retailing, 70, 301–326.CrossRefGoogle Scholar
  9. Dube, J., Hitsch, G., & Rossi, P. (2010). State dependence and alternative explanations for consumer inertia. RAND Journal of Economics, 41, 417–445.CrossRefGoogle Scholar
  10. Eliaz, K., & Spiegler, R. (2011). Consideration sets and competitive marketing. Review of Economic Studies, 78, 235–262.CrossRefGoogle Scholar
  11. Erdem, T., Imai, S., & Keane, M. (2003). Brand and quantity choice dynamics under price uncertainty. Quantitative Marketing and Economics, 1, 5–64.CrossRefGoogle Scholar
  12. Gentry, M. (2012). Displays, sales and in-store search in retail markets. Mimeo: London School of Economics.Google Scholar
  13. Goeree, M. (2008). Limited information and advertising in the U.S. personal computer industry. Econometrica, 76, 1017–1074.CrossRefGoogle Scholar
  14. Hartmann, W., & Nair, H. (2010). Retail competition and the dynamics of demand for tied goods. Marketing Science, 29, 366–386.CrossRefGoogle Scholar
  15. Hauser, J., & Wernerfelt, B. (1990). An evaluation cost model of consideration sets. Journal of Consumer Research, 16, 393–408.CrossRefGoogle Scholar
  16. Hendel, I., & Nevo, A. (2006a). Sales and consumer inventory. The RAND Journal of Economics, 37, 543–561.Google Scholar
  17. Hendel, I., & Nevo, A. (2006b). Measuring the implications of sales and consumer inventory behavior. Econometrica, 74, 1637–1673.Google Scholar
  18. Hoch, S., & Deighton, J. (1989). Managing what consumers learn from experience. Journal of Marketing, 53, 1–20.CrossRefGoogle Scholar
  19. Hong, H., & Shum, M. (2006). Using price distributions to estimate search costs. RAND Journal of Economics, 37, 257–275.CrossRefGoogle Scholar
  20. Honka, E. (2014). Quantifying search and switching costs in the U.S. auto insurance industry. RAND Journal of Economics, 45, 847– 884.CrossRefGoogle Scholar
  21. Kim, J., Albuquerque, P., & Bronnenberg, B. (2010). Online demand under limited consumer search. Marketing Science, 29, 1001–1023.CrossRefGoogle Scholar
  22. Koulayev, S. (2014). Search for differentiated products: Identification and estimation. RAND Journal of Economics, 45, 553–575.CrossRefGoogle Scholar
  23. Manski, C. (1977). The structure of random utility models. Theory and Decision, 8, 229–254.CrossRefGoogle Scholar
  24. Mehta, N., Rajiv, S., & Srinivasan, K. (2003). Price uncertainty and consumer search: A structural model of consideration set formation. Marketing Science, 22, 58–84.CrossRefGoogle Scholar
  25. Moraga-Gonzales, J., Sandor, Z., & Wildenbeest, M. (2009). Consumer search and prices in the automobile market. Mimeo: Kelley School of Business, Indiana University.Google Scholar
  26. Pesendorfer, M. (2002). Retail sales: A study of pricing behavior in supermarkets. Journal of Business, 75, 33–66.CrossRefGoogle Scholar
  27. Pires, T. (2013). Empirical studies of search frictions and consumer choices: Northwestern University Doctoral Dissertation, Department of Economics, Northwestern University.Google Scholar
  28. Pires, T. (2014). Measuring the effects of search costs on equilibrium prices and profits. Mimeo: University of North Carolina.Google Scholar
  29. Roberts, J., & Lattin, J. (1991). Development and testing of a model of consideration set composition. Journal of Marketing Research, 28, 429–440.CrossRefGoogle Scholar
  30. Rust, J. (1987). Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher. Econometrica, 55, 999–1033.CrossRefGoogle Scholar
  31. Seiler, S. (2013). The impact of search costs on consumer behavior: A dynamic approach. Quantitative Marketing and Economics, 11, 155–203.CrossRefGoogle Scholar
  32. Stigler, G. (1961). The economics of information. Journal of Political Economy, 69, 213–225.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.University of North CarolinaChapel HillUSA

Personalised recommendations