: How Do They Interact? Jim Goldman University of Toronto Joel Peress INSEAD, CEPR 1
An example: Barr Laboratories 90 25 80 70 UNTIL 1997 Pure generic drugs manufacturer FROM 1997 Starts to develop own proprietary drugs 20 $m 60 50 40 30 20 10 15 10 5 Nb. of analysts 0 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 R&D expenditures Analyst coverage 2
In a Large Sample 3
Overview A model of financial development and technological progress Main insight: Feedback loop between financial analysis and firm innovation 1. When financiers are better informed about innovation: Entrepreneurs expect to receive more funding if innovation is successful Entrepreneurs innovate more 2. Conversely, when entrepreneurs innovate more: Fin. anticipate a higher return on capital if they manage to identify successful firms Financiers collect more information about innovation Comes from complementarity between productivity A and capital K in production: Y = AK Evidence supporting 1. and 2. from two experiments that affect a consistent set of firms Economically sizeable effect: indirect effect of R&D policy change, operating through analysts response, is about one third of the size of its total effect 4
Technologies Intermediate goods are produced by a continuum of projects indexed by n [0, 1] K n : project n s capital stock à n : project n-specific random productivity shock α [0, 1] : determines the degree of returns to scale Final good producers use intermediate goods as inputs Y is the total output of all intermediate goods: 5
Agents: Entrepreneur Risk-neutral agents derive utility from the consumption of a final good 0.5 Entrepreneur creates intermediate technologies Conceives a continuum of projects Chooses the productivity of tech. in both states (, ) = innovation effort But cannot influence probability of success (= 0.5) Innovation effort has a cost e A ( + ) (increasing and convex in A) Chooses same effort for all projects (prevents entrepreneur from focusing on one project only) 0.5
Agents: Financier Financier invests his wealth (endowed) Allocates K n units of capital to project n Learns quality of project thanks to imperfect signal S With proba q, signal reveals successful project accurately (but 1 q of being wrong) q : the learning effort chosen by the financier Learning effort has a cost e q (q) (increasing and convex in q)
Timing Period 1 First: entrepreneur and financier choose cooperatively Research effort: A Learning effort: q Then: financier observes signal S and invests across projects K n Period 2 Goods are produced and agents consume 8
Equilibrium Concept 1. Market clearing in the intermediate good market: 1. Capital allocation: 2. First-best determination of effort levels: Entrepreneur & financier choose efforts cooperatively (before the signal S is observed): 9
Learning and Research In equilibrium, learning and research efforts are the unique solutions to the system: The financier allocates: units of capital to projects deemed successful by its signal and to those deemed unsuccessful The research effort is increasing in the learning effort. Conversely, the learning effort is increasing in the research effort 10
Interaction Between Learning and Research Knowledge about technologies and technological knowledge feed on each other q in A: Each unit of capital is more productive the larger the innovation effort A A in q : An invention can be applied on a larger scale the larger K Effect comes from complementarity between productivity A n and capital K n in production of intermediate goods: Quality of the match between projects and K Growth rate of income (in OLG model): 11
Summary of Main Predictions The entrepreneur performs more research when the financier learns more where ε * A = 1+elast. of e (A) w.r.t. A The financier learns more when the entrepreneur does more research where ε * q = 1+elast. of e (q) w.r.t. q Auxiliary predictions on the mechanism An increase in learning effort lead to a more dispersed distribution of capital across projects An increase in learning or research effort lead to a more dispersed distribution of return on capital across projects 12
Empirical Strategy Measurement: Proxies for learning and research efforts Research effort proxied by firm s level of R&D expenditures ( R&D ) Learning effort proxied by number of analysts following the firm ( Coverage ) Endogeneity: Simultaneity and possible omitted variables Two sets of plausibly exogenous shocks 1. R&D shocks: passage of US States R&D tax credits 2. Learning shocks: loss of analysts due to mergers and closures of brokerage houses 13
R&D Shock: US States R&D Tax Credits Allow firms to reduce their state tax liability by deducting a portion of R&D expenditures from their state tax bill Followed the implementation of federal tax credits in 1981 Minnesota started in 1982, 32 other states followed (as of 2006) TC rates range from 3% (NE, SC) to 20% (AZ, HI). 14
Learning Shock : Brokerage Closures and Mergers Closures of and mergers between brokerage houses which lead to reduction in analyst coverage: Closures that lead to the removal of analysts who are not re-hired by a new broker Mergers that lead to the dismissal of redundant analysts who follow the same stocks as analysts working for the other merging entity 52 events from Derrien and Kecskes (2013) 15
Sample Construction To properly estimate interaction effect, we use a common sample for both experiments Compustat firms Manufacturing firms for which R&D is material to the business (R&D>0) Followed (and shocked) over 1990 2006 In the learning experiment, treatment affects mainly large firms To improve accuracy, restrict to firms with sufficient overlap on covariates Estimate propensity score for firms in each experiment using industry, sales, profitability variables Keep firms with score between 0.1 and 0.9 for both experiments End up with around 1000 firms Large: median average revenue of $640m Investing in R&D: median R&D/assets = 4.4% 16
Descriptive Statistics 25th 50th 75th N 50th in Compu. Coverage 3.80 7.50 14.00 1,011 1.70 R&D ($m) 6.59 16.32 48.94 1,011 2.26 R&D/assets 1.79 4.41 10.21 1,011 3.12 Sales ($m) 193.65 637.33 2,630.64 1,011 76.92 ROA 4.74 9.33 13.76 1,011 4.01 Note: one observation per firm (the average over time) 17
Difference-in-differences ; panel regression Regression estimated in first-differences Accommodates repeated shocks Removes firm fixed effects Symmetric specifications Specification R&D shock: Tax credit, s is state Learning shock: Broker event, b is broker For both shocks, X includes ln(sales) and loss dummy Standard errors clustered at industry level 18
Shocks Validation Δln(rd) Δln(rd) Δln(rd) Δln(cov) Δln(cov) Δln(cov) TC+ t+1 0.016 [0.017] TC+ t 0.036** 0.045** 0.042** [0.015] [0.017] [0.018] TC+ t-1-0.013 [0.011] AN- t+1 0.015 [0.018] AN- t -0.105*** -0.087*** -0.066*** [0.023] [0.024] [0.019] AN- t-1-0.025* [0.014] Year FE Yes Yes Yes Yes Yes Yes Controls No Yes Yes No Yes Yes N 9,953 8,337 7,248 9,953 8,337 7,248 19
Effect of Learning on Innovation and of Innovation on Learning Δln(rd) Δln(rd) Δln(rd) Δln(cov) Δln(cov) Δln(cov) TC+ t+1 0.002 [0.018] TC+ t 0.038** 0.052*** 0.056*** [0.019] [0.019] [0.019] TC+ t-1 0.002 [0.016] AN- t+1-0.011 [0.019] AN- t -0.039*** -0.025** -0.035** [0.015] [0.012] [0.013] AN- t-1-0.004 [0.015] Year FE Yes Yes Yes Yes Yes Yes Controls No Yes Yes No Yes Yes N 9,953 8,337 7,248 9,953 8,337 7,248 20
Auxiliary Predictions of the Model Distribution of new equity proceeds S.D. Mean Before (2 yr average) R&D tax credit 0.692 0.049 After (2 yr average) R&D tax credit 0.860-0.055 F-stat for Variance Ratio Test, V(before)/V(after) p-value 0.65*** 0.003 Distribution of return on assets (RoA ) S.D. Mean Before (2 yr average) R&D tax credit 0.136 0.024 After (2 yr average) R&D tax credit 0.158-0.007 Before (2 yr average) broker event 0.118 0.015 After (2 yr average) broker event 0.101-0.017 F-stat for Variance Ratio Test, V(before)/V(after) p-value 0.74*** 0.007 1.34** 0.033 21
Quantification of the Indirect Effect of Learning on R&D Sensitivity of R&D to coverage (analyst shock): Δln(coverage) = -0.087 x AN- ; Δln(rd) = -0.025 x AN- Δln(rd)/Δln(coverage) = -2.5%/-8.7% = 28.7% Δln(rd) = 28.7% x Δln(coverage) Indirect effect of tax credit (R&D shock): Δln(coverage) = 0.052 x TC+ Indirect effect of TC, operating through analysts response: Δln(rd)* = 28.7% x 5.2% = 1.5% Total effect of tax credit : Δln(rd) = 0.045 x TC+ Compare indirect effect to total effect of TC+: Indirect effect = 1.5%/4.5% = 33.3% of total effect of tax credit 22
Calibration Income speed of convergence: Calibrate the model to assess the importance of the parameter. Determine 4 parameters (α, β, ε a, ε q ) to compute γ and its components. 23
Calibration α measures how profits are shared between firms: α=1 to generate the most skewed distribution of profits. ε a and ε q are derived from e a and e q as estimated in our reduced-form regressions: Assume that the economy is initially in steady state and that it is perturbed by a rescaling shock (changes in R&D tax credits or in broker closures) during period T. The perturbed economy then converges toward a new steady state. Use the model to compute the change in the learning and research efforts from period T to the next, T + 1. β measures share of capital in total income: A range of values in [1/3, 2/3]. Result: Interaction s contribution to income growth represents a third of the total contributions of learning and R&D. 1/3 24
Calibration 25
Summary A model of financial development and technological progress Knowledge about techs. and technological knowledge feed on each other Financiers are better informed about inventions Inventors expect to receive more funding if successful They innovate more Conversely, inventors innovate more Financiers anticipate a higher return on their capital They collect more information about inventions Qualitatively: Effect supported by the combination of two experiments Magnitude: Indirect effect of R&D policy change, operating through analysts response, is about 1/3 of the size of its total effect 26
APPENDIX 27
Stylized Facts Motivating the Model Fin. dev. stimulates investments in R&D, R&D contributes to TFP, and TFP contributes to economic growth (Carlin & Mayer (2003), Griliches (1988)) Fin. dev. also enhances TFP by improving capital efficiency Countries with more developed financial sectors allocate capital more efficiently (Wurgler (2000), Bertrand et al. (2005), Galindo et al. (2005), Chari & Henry (2006)) A more efficient distribution of capital at the micro level translates into higher TFP (Jeong & Townsend (2006), Restuccia & Rogerson (2003) and Hsieh & Klenow (2006)) Fin. dev. improves capital efficiency by alleviating informational frictions (Rajan & Zingales (1998), Wurgler (2000), Carlin & Mayer (2003)) Countries that are sufficiently developed tend to specialize and the degree of specialization is positively related to fin. dev. (Imbs & Wacziarg (2003), Kalemli-Ozcan, Sorensen & Yosha (2003)) 28
Related Literature Finance & growth theory shows how frictions limit the efficient use of resources, e.g.: Incomplete information (Greenwood & Jovanovic (1990) ) Project indivisibilities (Acemoglu & Zilibotti (1997)) Moral hazard (Bhattacharya & Chiesa (1995), De la Fuente & Marin (1996), Acemoglu et al. (2004)) This paper: Focus on selection (ex ante info) rather than monitoring (ex post info) Anticipation that capital will be efficiently allocated encourages innovation Empirical literature on finance and innovation Effect of finance on innovation (Derrien and Kecskes (2013), Amore et al. (2013), Hombert and Matray (2014)) This paper: Empirically evaluate the interplay between R&D and financial analysis 29