IDEAS home Printed from https://ideas.repec.org/p/net/wpaper/1122.html
   My bibliography  Save this paper

Estimating Causal Installed-Base Effects: A Bias-Correction Approach

Author

Abstract

New empirical models of consumer demand that incorporate social preferences, observational learning, word-of-mouth or network effects have the feature that the adoption of others in the reference group - the Òinstalled-baseÓ - has a causal effect on current adoption behavior. Estimation of such causal installed-base effects is challenging due to the potential for spurious correlation between the adoption of agents, arising from endogenous assortive matching into social groups (or homophily) and from the existence of unobservables across agents that are correlated. In the absence of experimental variation, the preferred solution is to control for these using a rich specification of fixed-effects, which is feasible with panel data. We show that fixed-effects estimators of this sort are inconsistent in the presence of installed-base effects; in our simulations, random-effects specifications perform even worse. Our analysis reveals the tension faced by the applied empiricist in this area: a rich control for unobservables increases the credibility of the reported causal effects, but the incorporation of these controls introduces biases of a new kind in this class of models. We present two solutions: an instrumental variable approach, and a new bias-correction approach, both of which deliver consistent estimates of causal installed-base effects. The bias-correction approach is tractable in this context because we are able to exploit the structure of the problem to solve analytically for the asymptotic bias of the installed-base estimator, and to incorporate it into the estimation routine. Our approach has implications for the measurement of social effects using non-experimental data, and for measuring marketing-mix effects in the presence of state-dependence in demand, more generally. Our empirical application to the adoption of the Toyota Prius Hybrid in California reveals evidence for social influence in diffusion, and demonstrates the importance of incorporating proper controls for the biases we identify.

Suggested Citation

  • Sridhar Narayanan & Harikesh S. Nair, 2011. "Estimating Causal Installed-Base Effects: A Bias-Correction Approach," Working Papers 11-22, NET Institute.
  • Handle: RePEc:net:wpaper:1122
    as

    Download full text from publisher

    File URL: http://www.netinst.org/Narayanan_Nair_11_22.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bun, Maurice J.G. & Carree, Martin A., 2005. "Bias-Corrected Estimation in Dynamic Panel Data Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 200-210, April.
    2. Bart J. Bronnenberg & Vijay Mahajan, 2001. "Unobserved Retailer Behavior in Multimarket Data: Joint Spatial Dependence in Market Shares and Promotion Variables," Marketing Science, INFORMS, vol. 20(3), pages 284-299, October.
    3. Munshi, Kaivan & Myaux, Jacques, 2006. "Social norms and the fertility transition," Journal of Development Economics, Elsevier, vol. 80(1), pages 1-38, June.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Marianne Bertrand & Erzo F. P. Luttmer & Sendhil Mullainathan, 2000. "Network Effects and Welfare Cultures," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(3), pages 1019-1055.
    6. Esther Duflo & Emmanuel Saez, 2003. "The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence from a Randomized Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 815-842.
    7. Alan T. Sorensen, 2006. "Social learning and health plan choice," RAND Journal of Economics, RAND Corporation, vol. 37(4), pages 929-945, December.
    8. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    9. Nerlove, Marc, 1971. "Further Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross Sections," Econometrica, Econometric Society, vol. 39(2), pages 359-382, March.
    10. Mohammad Arzaghi & J. Vernon Henderson, 2008. "Networking off Madison Avenue," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(4), pages 1011-1038.
    11. Giorgio Topa, 2001. "Social Interactions, Local Spillovers and Unemployment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 68(2), pages 261-295.
    12. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    13. Puneet Manchanda & Ying Xie & Nara Youn, 2008. "The Role of Targeted Communication and Contagion in Product Adoption," Marketing Science, INFORMS, vol. 27(6), pages 961-976, 11-12.
    14. Ram C. Rao, 1986. "Estimating Continuous Time Advertising-Sales Models," Marketing Science, INFORMS, vol. 5(2), pages 125-142.
    15. Alan T. Sorensen, 2006. "Social learning and health plan choice," RAND Journal of Economics, The RAND Corporation, vol. 37(4), pages 929-945, December.
    16. Bruce Sacerdote, 2001. "Peer Effects with Random Assignment: Results for Dartmouth Roommates," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(2), pages 681-704.
    17. Shlomo Kalish & Gary L. Lilien, 1983. "Optimal Price Subsidy Policy for Accelerating the Diffusion Of Innovation," Marketing Science, INFORMS, vol. 2(4), pages 407-420.
    18. H. Leibenstein, 1950. "Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 64(2), pages 183-207.
    19. David Bell & Sangyoung Song, 2007. "Neighborhood effects and trial on the internet: Evidence from online grocery retailing," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 361-400, December.
    20. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    21. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
    22. Jan Kratzer & Christopher Lettl, 2009. "Distinctive Roles of Lead Users and Opinion Leaders in the Social Networks of Schoolchildren," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 36(4), pages 646-659, December.
    23. Scott K. Shriver, 2010. "Network Effects in Alternative Fuel Adoption: Empirical Analysis of the Market for Ethanol," Working Papers 10-20, NET Institute.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. John Beshears & James J. Choi & David Laibson & Brigitte C. Madrian & Katherine L. Milkman, 2015. "The Effect of Providing Peer Information on Retirement Savings Decisions," Journal of Finance, American Finance Association, vol. 70(3), pages 1161-1201, June.
    2. Scott K. Shriver & Harikesh S. Nair & Reto Hofstetter, 2013. "Social Ties and User-Generated Content: Evidence from an Online Social Network," Management Science, INFORMS, vol. 59(6), pages 1425-1443, June.

    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. Bryan Bollinger & Kenneth Gillingham, 2012. "Peer Effects in the Diffusion of Solar Photovoltaic Panels," Marketing Science, INFORMS, vol. 31(6), pages 900-912, November.
    2. Bhatia, Tulikaa & Wang, Lei, 2011. "Identifying physician peer-to-peer effects using patient movement data," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 51-61.
    3. Yueming Qiu & Shuai Yin & Yi David Wang, 2016. "Peer Effects and Voluntary Green Building Certification," Sustainability, MDPI, vol. 8(7), pages 1-15, July.
    4. Khim Yong, Goh & Kai-Lung, Hui & I.P.L., Png, 2008. "Social Interaction, Observational Learning, and Privacy: the "Do Not Call" Registry," MPRA Paper 8225, University Library of Munich, Germany.
    5. John Beshears & James J. Choi & David Laibson & Brigitte C. Madrian & Katherine L. Milkman, 2015. "The Effect of Providing Peer Information on Retirement Savings Decisions," Journal of Finance, American Finance Association, vol. 70(3), pages 1161-1201, June.
    6. Mugerman, Yevgeny & Sade, Orly & Shayo, Moses, 2014. "Long term savings decisions: Financial reform, peer effects and ethnicity," Journal of Economic Behavior & Organization, Elsevier, vol. 106(C), pages 235-253.
    7. Grant Miller & A. Mushfiq Mobarak, 2015. "Learning About New Technologies Through Social Networks: Experimental Evidence on Nontraditional Stoves in Bangladesh," Marketing Science, INFORMS, vol. 34(4), pages 480-499, July.
    8. Park, Minjung, 2019. "Selection bias in estimation of peer effects in product adoption," Journal of choice modelling, Elsevier, vol. 30(C), pages 17-27.
    9. Leonardo Bursztyn & Florian Ederer & Bruno Ferman & Noam Yuchtman, 2012. "Understanding Peer Effects in Financial Decisions: Evidence from a Field Experiment," NBER Working Papers 18241, National Bureau of Economic Research, Inc.
    10. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    11. Leonardo Bursztyn & Florian Ederer & Bruno Ferman & Noam Yuchtman, 2014. "Understanding Mechanisms Underlying Peer Effects: Evidence From a Field Experiment on Financial Decisions," Econometrica, Econometric Society, vol. 82(4), pages 1273-1301, July.
    12. Liu, Hong & Sun, Qi & Zhao, Zhong, 2014. "Social learning and health insurance enrollment: Evidence from China's New Cooperative Medical Scheme," Journal of Economic Behavior & Organization, Elsevier, vol. 97(C), pages 84-102.
    13. Morey, Edward R. & Kritzberg, David, 2012. "It's not where you do it, it's who you do it with?," Journal of choice modelling, Elsevier, vol. 5(3), pages 176-191.
    14. Adriaan R. Soetevent, 2006. "Empirics of the Identification of Social Interactions; An Evaluation of the Approaches and Their Results," Journal of Economic Surveys, Wiley Blackwell, vol. 20(2), pages 193-228, April.
    15. Jae Young Lee & David R. Bell, 2013. "Neighborhood Social Capital and Social Learning for Experience Attributes of Products," Marketing Science, INFORMS, vol. 32(6), pages 960-976, November.
    16. Bryan Bollinger & Kenneth Gillingham & A. Justin Kirkpatrick & Steven Sexton, 2022. "Visibility and Peer Influence in Durable Good Adoption," Marketing Science, INFORMS, vol. 41(3), pages 453-476, May.
    17. Bing Jing, 2011. "Social Learning and Dynamic Pricing of Durable Goods," Marketing Science, INFORMS, vol. 30(5), pages 851-865, September.
    18. Fishman, Arthur & Fishman, Ram & Gneezy, Uri, 2019. "A tale of two food stands: Observational learning in the field," Journal of Economic Behavior & Organization, Elsevier, vol. 159(C), pages 101-108.
    19. Machikita, Tomohiro, 2006. "Are Job Networks Localized in a Developing Economy? Search Methods for Displaced Workers in Thailand," IDE Discussion Papers 84, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    20. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.

    More about this item

    Keywords

    Contagion; Social Interactions; Installed-base Effects; Homophily; Correlated Unobservables; Diffusion; Product Adoption; Toyota Prius;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • L00 - Industrial Organization - - General - - - General
    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General

    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:net:wpaper:1122. 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: Nicholas Economides (email available below). General contact details of provider: http://www.NETinst.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.