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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/31438
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| Title: | Estimating Causal Installed-Base Effects: A Bias-Correction Approach |
| Authors: | Narayanan, Sridhar
Nair, Harikesh S. |
| Keywords: | Contagion, Social Interactions, Installed-base E ects, Homophily,
Correlated Unobservables, |
| Issue Date: | 17-Jan-2012 |
| Series/Report no.: | Working Papers;11_22 |
| 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 fixedeffects 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. |
| URI: | http://hdl.handle.net/2451/31438 |
| Appears in Collections: | NET Institute Working Papers Series
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