On the Accuracy of Different Measures of q

Authors


  • We would like to thank Bill Christie (the Editor), an anonymous referee, Matt Billett, Jon Garfinkel, Tom George, Vojislav Maksimovic, and seminar participants at the University of Illinois for helpful comments. Gönül Çolak provided excellent research assistance.

Abstract

Tobin's q is widely accepted as a 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 article 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. Using elaborate algorithms, however, depletes the number of usable observations and possibly introduces sample selection bias.

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