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

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  • Narayanan, Sridhar

    (Stanford University)

  • Nair, Harikesh S.

    (Stanford University)

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 "installed-base" - 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.

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Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number 2076.

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Date of creation: Mar 2011
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Handle: RePEc:ecl:stabus:2076

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  1. Hsiao,Cheng, 2003. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9780521818551, Fall.
  2. repec:bla:restud:v:75:y:2008:i:4:p:1011-1038 is not listed on IDEAS
  3. 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.
  4. Mohammad Arzaghi & J. Vernon Henderson, 2008. "Networking off Madison Avenue," Review of Economic Studies, Oxford University Press, vol. 75(4), pages 1011-1038.
  5. Topa, Giorgio, 2001. "Social Interactions, Local Spillovers and Unemployment," Review of Economic Studies, Wiley Blackwell, vol. 68(2), pages 261-95, April.
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Cited by:
  1. John Beshears & James J. Choi & David Laibson & Brigitte C. Madrian & Katherine L. Milkman, 2011. "The Effect of Providing Peer Information on Retirement Savings Decisions," NBER Working Papers 17345, National Bureau of Economic Research, Inc.
  2. Shriver, Scott K. & Nair, Harikesh S. & Hofstetter, Reto, 2011. "Social Ties and User-Generated Content: Evidence from an Online Social Network," Research Papers 2083, Stanford University, Graduate School of Business.

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