On the Information Content of Different Measures of Q

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Abstract
Tobin's q is widely accepted as proxy for an underlying "true" q, which is assumed to characterize a firm's incentive to invest. Researchers have developed numerous methods for computing q. This paper assesses the measurement quality of different proxies for q. We adapt the measurement-error consistent estimators in Erickson and Whited (2002) to estimate the extent to which variation in true unobservable q explains variation in different proxies for q. We find most proxies for q are poor: careful algorithms for calculating q do little to improve measurement quality. However, using elaborate algorithms depletes the number of usable observations and possibly introduces sample selection bias.