IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2310.12272.html
   My bibliography  Save this paper

Peer Effects in Consideration and Preferences

Author

Listed:
  • Nail Kashaev
  • Natalia Lazzati
  • Ruli Xiao

Abstract

We develop a general model of discrete choice that incorporates peer effects in preferences and consideration sets. We characterize the equilibrium behavior and establish conditions under which all parts of the model can be recovered from a sequence of choices. We allow peers to affect only preferences, only consideration, or both. We show that these peer-effect mechanisms have different behavioral implications in the data. This allows us to recover the set and the type of connections between the agents in the network. We then use this information to recover the preferences and the consideration mechanisms of each agent. These nonparametric identification results allow for general forms of heterogeneity across agents and do not rely on the variation of either exogenous covariates or the set of available options (menus). We apply our results to model expansion decisions by coffee chains and find evidence of limited consideration. We simulate counterfactual predictions and show how limited consideration slows down competition.

Suggested Citation

  • Nail Kashaev & Natalia Lazzati & Ruli Xiao, 2023. "Peer Effects in Consideration and Preferences," Papers 2310.12272, arXiv.org, revised Jan 2024.
  • Handle: RePEc:arx:papers:2310.12272
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2310.12272
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fabian Dunker & Stefan Hoderlein & Hiroaki Kaido, 2017. "Nonparametric identification of random coefficients in endogenous and heterogeneous aggregate demand models," CeMMAP working papers CWP11/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Pietro Bonaldi & Ali Hortaçsu & Jakub Kastl, 2015. "An Empirical Analysis of Funding Costs Spillovers in the EURO-zone with Application to Systemic Risk," NBER Working Papers 21462, National Bureau of Economic Research, Inc.
    3. Kashaev, Nail & Aguiar, Victor H., 2022. "A random attention and utility model," Journal of Economic Theory, Elsevier, vol. 204(C).
    4. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    5. Jeremy T. Fox & Natalia Lazzati, 2017. "A note on identification of discrete choice models for bundles and binary games," Quantitative Economics, Econometric Society, vol. 8(3), pages 1021-1036, November.
    6. Crawford, Gregory S. & Griffith, Rachel & Iaria, Alessandro, 2021. "A survey of preference estimation with unobserved choice set heterogeneity," Journal of Econometrics, Elsevier, vol. 222(1), pages 4-43.
    7. Jason Abaluck & Abi Adams-Prassl, 2021. "What do Consumers Consider Before They Choose? Identification from Asymmetric Demand Responses," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(3), pages 1611-1663.
    8. Agranov, Marina & Elliott, Matt & Ortoleva, Pietro, 2021. "The importance of Social Norms against Strategic Effects: The case of Covid-19 vaccine uptake," Economics Letters, Elsevier, vol. 206(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roy Allen & John Rehbeck, 2020. "Identification of Random Coefficient Latent Utility Models," Papers 2003.00276, arXiv.org.
    2. Siddhartha Chib & Kenichi Shimizu, 2023. "Scalable Estimation of Multinomial Response Models with Uncertain Consideration Sets," Papers 2308.12470, arXiv.org, revised Oct 2023.
    3. Wang, Ao, 2023. "Sieve BLP: A semi-nonparametric model of demand for differentiated products," Journal of Econometrics, Elsevier, vol. 235(2), pages 325-351.
    4. Wang, Ao, 2020. "Identifying the Distribution of Random Coefficients in BLP Demand Models Using One Single Variation in Product Characteristics," The Warwick Economics Research Paper Series (TWERPS) 1304, University of Warwick, Department of Economics.
    5. Wang, Ao, 2021. "A BLP Demand Model of Product-Level Market Shares with Complementarity," The Warwick Economics Research Paper Series (TWERPS) 1351, University of Warwick, Department of Economics.
    6. Victor Aguirregabiria & Alessandro Iaria & Senay Sokullu, 2023. "Identification and Estimation of Demand Models with Endogenous Product Entry and Exit," Papers 2308.14196, arXiv.org.
    7. Victor H. Aguiar & Maria Jose Boccardi & Nail Kashaev & Jeongbin Kim, 2023. "Random utility and limited consideration," Quantitative Economics, Econometric Society, vol. 14(1), pages 71-116, January.
    8. Allen, Roy & Rehbeck, John, 2022. "Latent complementarity in bundles models," Journal of Econometrics, Elsevier, vol. 228(2), pages 322-341.
    9. Nail Kashaev, 2018. "Identification and estimation of multinomial choice models with latent special covariates," Papers 1811.05555, arXiv.org, revised Mar 2022.
    10. 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.
    11. Fu Ouyang & Thomas T. Yang, 2023. "Semiparametric Discrete Choice Models for Bundles," Papers 2306.04135, arXiv.org, revised Nov 2023.
    12. Diwanji, Vaibhav S. & Cortese, Juliann, 2020. "Contrasting user generated videos versus brand generated videos in ecommerce," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    13. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    14. Shijie Lu & Xin (Shane) Wang & Neil Bendle, 2020. "Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry," Management Science, INFORMS, vol. 66(5), pages 2140-2162, May.
    15. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    16. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
    17. Greg Lewis & Bora Ozaltun & Georgios Zervas, 2021. "Maximum Likelihood Estimation of Differentiated Products Demand Systems," Papers 2111.12397, arXiv.org.
    18. Zhang, Jason Q. & Craciun, Georgiana & Shin, Dongwoo, 2010. "When does electronic word-of-mouth matter? A study of consumer product reviews," Journal of Business Research, Elsevier, vol. 63(12), pages 1336-1341, December.
    19. Goyal, Sanjeev & Heidari, Hoda & Kearns, Michael, 2019. "Competitive contagion in networks," Games and Economic Behavior, Elsevier, vol. 113(C), pages 58-79.
    20. Francis X. Diebold & Kamil Yilmaz, 2016. "Trans-Atlantic Equity Volatility Connectedness: U.S. and European Financial Institutions, 2004–2014," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 81-127.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2310.12272. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.