Innovation and Market Value

1. Innovation and Market Value Bronwyn H. Hall UC Berkeley and U of Maastricht

2. Innovation • Definitions: • the first attempt to put a new product or process into practice (Fagerberg, Mowery, and Nelson, Oxford Handbook of Innovation, Chapter 1) • the introduction of a new product or process to the market • commercialization of an invention • Empirical proxies: R&D investment, patents issued, new product counts DIME-London

3. Market Value • Conceptually • the total value of outstanding claims on a firm’s assets • Theoretically • the expected present discounted value of future cash flows generated by the firm’s assets • Empirically - the sum of • common equity (price*shares) • long term debt (at market value) • other securities DIME-London

4. Why are they related? • Investments in innovation generate intangible assets for the firm • Knowledge capital • Human capital, not all of which is captured by wages • These assets yield cash flows in the future, so they add to market value DIME-London

5. How are they related? Efficient markets and optimal (ex ante) investment decisions => Market value = Book value Tobin: MV>BV – invest to make more of the asset and increase book to market MV<BV – stop investing, allow depreciation to reduce book to market Although MV≠BV in general, “on average” they will be equal. DIME-London

6. How are they related? (2) • Two versions of the model: • Theoretical– value function from firm’s dynamic program as a function of state variables (capital, R&D, etc.) • Hedonic– regress the value of a set of goods traded on a single market that have a lower-dimensional vector of characteristics on those characteristics • yields a measure of current shadow value of the assets (not stable over time) DIME-London

7. Theoretical Q model (1) Tobin’s original Q= ratio of the market value Vof a (unique) asset to its replacement cost A Q>1=> invest to create more of the asset Q<1 => disinvest to reduce asset Q=1in equilibrium Hayashi (1982) - the asset is a firm derived Qfrom the firm’s dynamic program gave conditions under which marginal Q (dV/dA) equal to average (V/A) Hayashi-Inoue (1991) and Wildasin (1984) developed the theory with more than one capital DIME-London

8. Theoretical Q model (2) Using the capital aggregator approach of Hayashi-Inoue, can show that ptI(1-δI)AtandptR(1-δR)Kt are the end of period replacement values of the two assets Aand K. Φ(Kt,,At) is the capital aggregator index under constant returns, constructed using the costs of the two capitals stis the exogenous shock process (a vector of prices, demand, the macro economy, etc.) Q(st) is an index that summarizes the shocks DIME-London

9. Hedonic regression for market value Vit(Ait,Kit) = bt [Ait + γKit] Nonlinear: log(Vit/Ait) = logQit = log bt + log(1+γtKit/Ait) Linear approximation: log Qit = log bt + γt Kit/Ait Interpretation: • Qit =Vit /Aitis Tobin’s qfor firm i in year t • bt= overall market level (approximately one). • γt= relative shadow value of K assets • (γ = 1 if depreciation correct, investment strategy optimal, and no adjustment costs => no rents). DIME-London

10. Compare theory and hedonics Transform the value function to Q, dividing by end of period tangible assets: Compare the empirical model: DIME-London

11. Implementation • Market value V – as defined • Valuing debt (marking to market) the only difficulty – use average bond prices • Book value A • recorded value of physical capital and inventories • Book value K – replacement cost? • Add up past investments (R&D) • How should they be depreciated? • Use patents as an indicator of the underlying knowledge asset • Note that patents imply the presence of rents, in principle • Control variables • log sales (size proxy) – very small effects • year dummies • industry dummies DIME-London

12. R&D input measurement • Deflation • No good measure of “real” costs of R&D • With time dummies, little bias from deflation • Stock computation If Rgrows at a constant rate g over time, Kt ≈ Rt /(δ+g) Example:Kt ≈ Rt /(0.15+0.05) = 5Rt • Low coefficient on Kor Rmay imply δ >>0.15 DIME-London

13. Estimating equation logQit = log(1 + γtKit/Ait + ….) + ind dums + time dums Used kernel estimation to check functional form: For K/A<.01, coefficient is zero For K/A>1, curve flattens out (as implied by log form) For K/A on (.01,1), relation roughly log-linear – most of the data is in this range DIME-London

14. Kernel regression of logQ on K/A - semilog DIME-London

15. Kernel regression of logQ on K/A – log-log plot DIME-London

16. Extracting depreciation rate • Strong assumptions: • Equilibrium in R&D • Market efficiency • Negligible adjustment costs • Only mismeasurement in Kis using wrong depreciation rate to construct it DIME-London

17. Market value estimates – US manufacturing sector Nonlinear least squares estimates with robust s.e.s, year and industry dummies included DIME-London

18. Estimated depreciation of R&D for selected sectors Others: Chem=19%, Elec=36%, Mach=32%, Misc=21% DIME-London

19. Returns to R&D • What do we expect? • In equilibrium, equal to cost of capital • But could be higher • Risk premium • Asymmetric information and moral hazard (lemons premium) • Unexpected demand shock ex post • …or lower • Unexpected demand shock ex post • Innovation failure ex post • Excess entry, not anticipated DIME-London

20. Some issues • Accounting data can be very heterogeneous and noisy • need cleaning or robust estimators to avoid excess influence from outliers • Low R-squared does not necessarily mean a small impact • Other inputs vary • Level of vertical integration affects the relationship to firm size DIME-London

21. Marianna’s questions • What do we expect to find in the relation between mkt value, returns and innovation • Are we surprised by results? When are growth and value not linked with innovation? • Is persistence of innovation correlated with persistence of other variables DIME-London