THE BOND MARKETS qTHOMAS PHILIPPONI propose an implementation of the q-theory of investment using bond pricesinstead of equity prices. Credit risk makes corporate bond prices sensitive to futureasset values, and q can be inferred from bond prices. With aggregate U.S. data,the bond markets q fits the investment equation six times better than the usualmeasure of q, it drives out cash flows, and it reduces the implied adjustment costsby more than an order of magnitude. Theoretical interpretations for these resultsare discussed.I. INTRODUCTIONIn his 1969 article, James Tobin argued that “the rate ofinvestmentthe speed at which investors wish to increase thecapital stockshould be related, if to anything, to q, the valueof capital relative to its replacement cost” (Tobin 1969, p. 21).Tobin also recognized, however, that q must depend on “expecta-tions, estimates of risk, attitudes towards risk, and a host of otherfactors,” and he concluded that “it is not to be expected that theessential impact of . . . financial events will be easy to measurein the absence of direct observation of the relevant variables (q inthe models).” The quest for an observable proxy for q was thereforerecognized as a crucial objective from the very beginning.Subsequent research succeeded in integrating Tobins ap-proach with the neoclassical investment theory of Jorgenson(1963). Lucas and Prescott (1971) proposed a dynamic model ofinvestment with convex adjustment costs, and Abel (1979) showedthat the rate of investment is optimal when the marginal cost ofinstallment is equal to q 1. Finally, Hayashi (1982) showed that,under perfect competition and constant returns to scale, marginalq (the market value of an additional unit of capital divided byits replacement cost) is equal to average q (the market valueof existing capital divided by its replacement cost). Because av-erage q is observable, the theory became empirically relevant.This paper was first circulated under the title “The y-Theory of Investment.”I thank Robert Barro (the editor), three anonymous referees, Daron Acemoglu,Mark Aguiar, Manuel Amador, Luca Benzoni, Olivier Blanchard, Xavier Gabaix,Mark Gertler, Simon Gilchrist, Bob Hall, Guido Lorenzoni, Sydney Ludvigson,Pete Kyle, Lasse Pedersen, Christina Romer, David Romer, Ivan Werning, ToniWhited, Jeff Wurgler, Egon Zakrajsek, and seminar participants at NYU, MIT, theSED 2007, London Business School, Ente Einaudi (Rome), University of Salerno,Toulouse University, Duke University, and the NBER Summer Institutes 2006 and2007. Peter Gross provided excellent research assistance.C2009 by the President and Fellows of Harvard College and the Massachusetts Institute ofTechnology.The Quarterly Journal of Economics, August 200910111012 QUARTERLY JOURNAL OF ECONOMICSUnfortunately, its implementation proved disappointing. The in-vestment equation fits poorly, leaves large unexplained residualscorrelated with cash flows, and implies implausible parameters forthe adjustment cost function (see Summers 1981 for an early con-tribution, and Hassett and Hubbard 1997 and Caballero 1999for recent literature reviews).Several theories have been proposed to explain this failure.Firms could have market power, and might not operate underconstant returns to scale. Adjustment costs might not be convex(Dixit and Pindyck 1994; Caballero and Engle 1999). Firms mightbe credit-constrained (Fazzari, Hubbard, and Petersen 1988;Bernanke and Gertler 1989). Finally, there could be measurementerrors and aggregation biases in the capital stock or the rate ofinvestment. None of these explanations is fully satisfactory, how-ever. The evidence for constant returns and price-taking seemsquite strong (Hall 2003). Adjustment costs are certainly not con-vex at the plant level, but it is not clear that it really matters in theaggregate (Thomas 2002; Hall 2004), although this is still a con-troversial issue (Bachmann, Caballero, and Engel 2006). Gomes(2001) shows that Tobins q should capture most of investmentdynamics even when there are credit constraints. Heterogeneityand aggregation do not seem to create strong biases (Hall 2004).In fact, an intriguing message comes out of the more recentempirical research: the market value of equity seems to be the cul-prit for the empirical failure of the investment equation. Gilchristand Himmelberg (1995), following Abel and Blanchard (1986),use VARs to forecast cash flows and to construct q, and they findthat it performs better than the traditional measure based onequity prices. Cumins, Hasset, and Oliner (2006) use analystsforecasts instead of VAR forecasts and reach similar conclusions.Erickson and Whited (2000, 2006) use GMM estimators to purgeq from measurement errors. They find that only 40% of observedvariations are due to fundamental changes, and, once again, thatmarket values contain large “measurement errors.”Applied research has therefore reached an uncomfortable sit-uation, where the benchmark investme