Estimating Causal Installed-Base Effects: A Bias-Correction Approach
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.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Marianne Bertrand & Erzo F.P. Luttmer & Sendhil Mullainathan, 1999.
"Network Effects and Welfare Cultures,"
JCPR Working Papers
62, Northwestern University/University of Chicago Joint Center for Poverty Research.
- Bertrand, M. & Luttmer, E.F.P. & Mullainathan, S., 1998. "Network Effects and Welfare Cultures," Papers 201, Princeton, Woodrow Wilson School - Public and International Affairs.
- Marianne Bertrand & Erzo F. P. Luttmer & Sendhil Mullainathan, 1999. "Network Effects and Welfare Cultures," Working Papers 9903, Harris School of Public Policy Studies, University of Chicago.
- Sendhil Mullainathan & Marianne Bertrand & Erzo F.P. Luttmer, 1998. "Network Effects and Welfare Cultures," Working papers 98-21, Massachusetts Institute of Technology (MIT), Department of Economics.
- Marianne Bertrand & Erzo F.P. Luttmer & Sendhil Mullainathan, 1998. "Network Effects and Welfare Cultures," NBER Working Papers 6832, National Bureau of Economic Research, Inc.
- H. Leibenstein, 1950. "Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand," The Quarterly Journal of Economics, Oxford University Press, vol. 64(2), pages 183-207.
- J. Vernon Henderson & Mohammad Arzaghi, 2005.
"Networking Off Madison Avenue,"
05-15, Center for Economic Studies, U.S. Census Bureau.
- Topa, Giorgio, 1997.
"Social Interactions, Local Spillovers and Unemployment,"
97-17, C.V. Starr Center for Applied Economics, New York University.
- Giorgio Topa, 2001. "Social Interactions, Local Spillovers and Unemployment," Review of Economic Studies, Oxford University Press, vol. 68(2), pages 261-295.
- 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.
- Tom Doan, "undated". "LSDVC: RATS procedure to estimate a dynamic FE model with correction for bias," Statistical Software Components RTS00111, Boston College Department of Economics.
When requesting a correction, please mention this item's handle: RePEc:net:wpaper:1122. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nicholas Economides)
If references are entirely missing, you can add them using this form.