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Transcript of 1 Identifying Technology Spillovers and Product Market Rivalry Nick Bloom (Stanford & CEP) Mark...
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Identifying Technology Spilloversand Product Market Rivalry
Nick Bloom (Stanford & CEP) Mark Schankerman (LSE and CEPR)
John Van Reenan (LSE and CEP)
Conference on Productivity Growth:Causes and Consequences
San Francisco Federal Reserve Bank
18-19 November 2005
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IntroductionTwo main types of R&D “spillover” effects
1. technological spillovers (Griliches, 1992; Keller,2004)2. product market rivalry effect (I.O. models of R&D)
• Direct business stealing effect on firm profits• Strategic effect on R&D decisions
Why try to identify and quantify their separate effects?• Current estimates of technological spillovers may be
contaminated by product market rivalry effects• In order to assess the net effect of spillovers: Is there
over- or under- investment in R&D?• Useful for examining different technology policy analysis
(e.g. R&D subsidies for smaller vs. larger firms)
3
Empirical Identification Scheme• We use separate measures of technological closeness
(through multiple patent classes) and product market closeness (through multiple industry classes)(Bransetter and Sakakibara, 2002)
• We use multiple outcome variables (market value, patents and R&D). Also look at productivity– Identification of technological spillovers comes from
the R&D, patents and value equations. – Identification of strategic rivalry effect comes from
value eq (existence) and R&D equation (direction – complements or substitutes).
(Griliches, Hall and Pakes, 1991)
4
Findings
• Empirical evidence of both technology spillovers and product market rivalry (controlling for unobserved heterogeneity is important)
• Technology spillover effects dominate – social return to R&D over three times the private return
• See this in pooled sample and in analysis for 3 high tech sectors
• Simulation of R&D policy suggests that medium sized firms generate less spillovers than large firms (EU policies)
5
Structure
1. Intro2. Analytical Framework3. Data4. Econometrics5. Results6. Extensions – Industry heterogeneity,
policy simulations7. Conclusions
6
2. Analytical FrameworkTwo stage game. R&D (r) choice at stage 1. Knowledge, k (we will proxy by
patents) then revealed as a function of firms’ R&D. Firms choose price/quantity (x) at Stage 2.
Three firms: 0, τ and m. Firms 0 and m compete in the same product market. Firms 0 and τ operate in same technology area. Can generalise to many firms interacting in both product and technology markets.
Stage 2: Short run competition
• Profit of firm 0 depends on short run decision variable by firm 0 (x0 ), her product market rival (xm) and on the knowledge stock of firm 0 (k0). We assume Nash Equilibrium but make no assumption on what x is (e.g. Cournot or Bertrand).
• The best responses on x0 and xm yield a reduced-form profit function for firm 0 (and firm m) that depends on k0 and km: Π0(k0,km) = π (k0,x*0,x*m)
• Firm 0’s profit increases in k0 and declines in km . The latter is the direct business stealing effect.
• We allow for either strategic substitutability or strategic complementarity in R&D
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Stage 1: Production of knowledge (k)
• k0 is produced with its own (r0) and firm τ’s R&D (rτ): k0 = Φ(r0,rτ). k0 is increasing in both arguments. rτ may increase, reduce or leave unchanged the marginal product of r0 .
• We analyse how rτ and rm affect the best response R&D, r0 , knowledge (patent) k0, and the value of the firm V0.
• Look at this non-tournament model. Compare to a tournament (patent race) model in Appendix A. The main empirical predictions are similar.
8
Some basic Predictions (Table 1)
• With any SIPM (strategic interaction in product market) market value (V) falls with rival R&D (rm)
• With any technology spillovers market value rises with technological neighbor’s R&D (rτ)
• Knowledge, k0 (patents) increases with technological neighbor’s R&D rτ if’f there are tech spillovers
• Knowledge is unaffected by rm
• If strategic interaction in product market own R&D (r0) rises with rival R&D (rm) if strategic comps and falls with rm if strategic subs
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Comparative static prediction
Empirical counterpart
No Technological Spillovers
No Technological Spillovers
Some Technological Spillovers
Some Technological Spillovers
Strategic complements
Strategic Substitutes
Strategic complements
Strategic Substitutes
∂V0/∂rτ Market value with SPILLTECH
Zero Zero Positive Positive
∂V0/∂rm Market value with SPILLSIC
Negative Negative Negative Negative
∂k0/∂rτ Patents with SPILLTECH
Zero Zero Positive Positive
∂k0/∂rm Patents with SPILLSIC
Zero Zero Zero Zero
∂r0/∂rτ R&D with SPILLTECH
Zero Zero Ambiguous Ambiguous
∂r0/∂rm R&D with SPILLSIC
Positive Negative Positive Negative
Table 1. R&D Spillovers and Strategic Rivalry
10
Note: Ambiguity of effect of tech neighbor’s R&D on own R&D
• ∂r*0/∂rτ
• Depends partly on sign impact of neighbours R&D on marginal product of own R&D (∂2Φ/ ∂ r0∂
rτ)
– Could be positive if own marginal productivity of R&D increasing in neighbour’s R&D
– Could be negative if “fishing out” of ideas (own marginal productivity of R&D decreasing in neighbour’s R&D)
• Sign also depends on diminishing returns to knowledge production. Will be negative if these are very strong
11
3. Data
• Compustat (all listed US firms) for R&D, Tobin’s Q, capital, labor and distribution of sales by SIC’s (Compustat Line of Business files)– Sample period: 1980-2001– Sample period for sales by 597 4-digit SIC classes: 1993-2001.
Average number of classes per firm is 5.2 (range is 1 to 36)
• US Patents and Trademarks Office (USPTO) for patent counts, patent citations and distribution of patents by tech classes (Jaffe and Trajtenberg, 2002)– We keep firms with at least one patents between 1968 and1999.– Patents assigned to 426 technology classes. Average number
of patent classes 33 (range is 1 to 320)
12
Technology Spillovers
• Following Jaffe (1986) we compute technological closeness by uncentred correlation between all firm pairings of patent distributions
• Define Ti = (Ti1, Ti2 ,, ……, Ti426) ; where Tik is the % of firm i’s total patents in technology class k (k = 1,..,426) averaged 1968-1999. e.g. if all patents in class 1, Ti= (1,0,0,…..0)
• TECHi,j = (Ti T’j)/[(Ti Ti’)1/2(Tj T’j)1/2]; ranges between 0 and 1 for any firm pair i and j
• Technology spillovers are defined as:SPILLTECHit = Σj,j≠iTECHi,jGjt
where Gjt is the R&D stock of firm j at time t (Gjt = Rjt + (1-δ) Gjt-1)
13
Product Market Spillovers
Analogous construction of product market “closeness”
Define Si = (Si1, Si2 ,, ……, Si597) where Sik is the % of firm i’s total sales in 4 digit industry k
(k = 1,…,597)
SICi,j = (Si S’j)/[(Si Si’)1/2(Sj S’j)1/2]
Product market “spillovers” from R&D are
SPILLSICit = Σj,j≠i SICi,j Gjt
14
Identification of product market and technological spillovers
• How distinct are TECH and SIC?• TECH,SIC Correlation is only 0.47 (see
figure 1)• SPILLTECH, SPILLSIC correlation is 0.42
in cross section and 0.17 in growth rates (latter relevant for empirics which control for fixed effects)
• Examples
15
Figure 1: Correlation between SIC and TEC across all firm pairs
Technological closeness
Product market closeness
16
Examples (High TECH, low SIC): Computer and chip makers
Correlation IBM Apple Motorola Intel
IBM SICTECH
11
0.320.64
0.010.47
0.010.76
Apple SICTECH
11
0.020.17
0.010.47
Motorola SICTECH
11
0.350.46
Intel SICTECH
11
IBM, Apple, Motorola and Intel all close in TECH (sample mean .13)But a) IBM close to Apple in product market (.32, computers; sample mean=.05) b) IBM not close to Motorola or Intel in product market (.01)
Table A1
17
Other examples (high SIC, low TEC)
• Gillette corp. and Valance Technologies compete in batteries (SIC=.33, TECH=.01). Gillette owns Duracell but does no R&D in this area (mainly personal care products). Valence Technologies uses a new phosphate technology to improve the performance of standard Lithium ion technology
• High end hard disks. Segway with magnetic technology Philips with holographic technology
• HDTVs
18
4. Econometric Specifications: Generic issues across all 4 equations
• Unobserved heterogeneity (correlated fixed effects)
• Endogeneity (use lagged x’s, but also experiment with IV/GMM approach)
• Dynamics – allow lagged dependent variable• Demand controls – time dummies, industry sales• Newey-West corrected standard errors
ititit uxy '
19
(a) Market Value
it
vit
it A
G
A
V 1lnlnln
Griliches (1981)
vitt
vi
vvvitit
v
itv
itit
vZSPILLSIC
SPILLTECHA
G
A
V
312
11
1
'ln
lnln
6th order series expansion (compare with NLLS)
R&D stock/Fixed assets
Fixed effects(long T)
Time dummies
Tobin’s Q (market valueto fixed assets)
Should be +ve
Should be -ve
20
(b) R&D Equation
ritt
ri
rrritit
r
itr
itr
it
vZSPILLSIC
SPILLTECHRR
312
111
'ln
lnln
R&D expenditures
Test of strategic complementarity (βr2>0) vs. strategic substitutes (βr
2<0)
Compare with Blundell Bond (1998, 2000) GMM-SYS approach
21
(c) Patent Count Equation
p
ittp
ippp
ititp
itp
itititititit
vZSPILLSIC
SPILLTECHDPDPXPE
312
112111
'ln
lnlnexp),|(
• Note: Own R&D stock in Z; D=1 if P>0, D=0, otherwise (MFM) • Allow for overdispersion via Negative Binomial model
• Use Blundell, Griffith and Van Reenen (1999) control for fixed effectswith weakly exogenous variables through pre-sample mean patents (1968-1984)
• Compare with Linear Feedback model estimated by GMM
• Compare with citation weighted patents as dependent variable
+ve
0
22
(d) Production Function
yitt
yi
yyyitit
y
ity
it
vZSPILLSIC
SPILLTECHY
312
11
'ln
lnln
Deflated sales
Own R&D, labour, capital, etc.
• problem of firm specific prices• Allow different coefficients on factor inputs in different industries• Compare with Blundell Bond (1998, 2000) GMM-SYS approach and Olley-Pakes (1995) extension
+ve
0
23
Reflection Problem
Manski (1991) - Identifying endogenous social effect (R&D spillovers)
Main concerns: technological opportunity (supply) and demand shocks that are common to firms.
To address we include:• Parametric determination of who are the “neighbors” (cf. Slade,
2003)• Industry sales variables: constructed using firm’s distribution of
sales across SIC’s• Fixed firm effects• Value function w.r.t. rival R&D robust to this critique (but R&D
equation most problematic)
There remains an issue: Do we identify technology spillovers or supply shocks?
24
5. Main Results
• Tobin’s Q equation
• Patents equation
• R&D equation
• Productivity equation
25
Table 3: Tobin’s QDependent variable:
Ln (V/A)(1) (2) (3) (4)
No individual Effects
Fixed Effects Fixed Effects (drop SPILLSIC)
Fixed Effects (drop
SPILLTEC)
Ln(SPILLTECHt-1)
Technology spillovers
-0.040(0.012)
0.240(0.104)
0.186(0.100)
Ln(SPILLSICt-1)
Product market rivalry
0.038(0.007)
-0.067(0.031)
-0.047(0.031)
Ln(Industry Salest) 0.434(0.068)
0.294(0.044)
0.298(0.044)
0.299(0.044)
Ln(Industry Salest-1) -0.502(0.067)
-0.170(0.045)
-0.176(0.045)
-0.164(0.043)
Note: Sixth-order terms in ln(R&D/Capital) and time dummies also included. NT=10,011
26
Quantification of value eq
• $1 of own R&D associated with $1.18 higher V (cf. Hall, Jaffe, Trajtenberg, 2005, $0.86)
• $1 of SPILLTECH associated with 4.32 cents higher V
• $1 of SPILLTECH is worth 3.6 percent as much as own R&D
• $1 of SPILLSIC associated with 4.36 cents lower V
• Industry sales growth raises value, conditional on R&D variables
27
Table 4: Patent Model (1) (2) (3) (4) Dependent variable: Patent Count
No initial conditions: Static
Initial Conditions: Static
Initial Conditions: Dynamics
Initial Conditions: Dynamics
Ln(SPILLTECH)t-1
Technological spillovers
0.403 (0.086)
0.282 (0.046)
0.192 (0.037)
0.194 (0.037)
Ln(SPILLSIC)t-1 Product market rivalry
0.044 (0.032)
0.049 (0.031)
0.024 (0.019)
Ln(R&D Stock)t-1 0.495 (0.044)
0.282 (0.046)
0.105 (0.027)
0.104 (0.027)
Ln(Sales)t-1 0.338 (0.052)
0.258 (0.047)
0.138 (0.027)
0.140 (0.027)
Ln(Patents)t-1 0.550 (0.026)
0.550 (0.026)
Pre-sample fixed effect 0.450 (0.049)
0.175 (0.028)
0.174 (0.028)
Over-dispersion (alpha)
0.954 (0.067)
0.814 (0.046)
0.402 (0.029)
0.402 (0.029)
Note: Time dummies and four-digit industry dummies included. Negative binomial model; NT=9,122.
28
Quantification of patent eq
• $1 of R&D associated with 0.007 extra patents (“marginal cost” of patent is about $125,000)
• $1 of SPILLTECH associated with 0.00022 extra patents.
• So $1 of SPILLTECH is worth (in terms of patents) about 3% as much as own R&D
• SPILLSIC does not affect patents
• Higher firm sales associated with more patenting, conditional on R&D (makes patenting more valuable, given innovation)
29
Table 5: R&D Equations (1) (2) (3) (4)
Dependent variable: ln(R&D)
No Effects Fixed Effects Fixed Effects + Dynamics
Fixed Effects + Dynamics
Ln(SPILLTECH) t-1
Technology spillovers 0.224
(0.017) 0.115
(0.071) 0.039
(0.039)
Ln(SPILLSIC) t-1
Product market rivalry
0.291 (0.012)
0.110 (0.026)
0.025 (0.014)
0.030 (0.013)
Ln(Sales) t-1 0.797 (0.009)
0.801 (0.017)
0.218 (0.015)
0.217 (0.015)
Ln(R&D) t-1 0.695 (0.015)
0.695 (0.015)
Ln(Industry Sales) t 0.698 (0.083)
0.133 (0.030)
0.133 (0.022)
0.134 (0.022)
Ln(Industry Sales) t-1 -0.879 (0.083)
-0.085 (0.031)
-0.110 (0.023)
-0.108 (0.022)
Note: Time dummies included; NT=8,565.
30
Quantification of R&D eq
• SPILLSIC and own R&D are positively correlated (implies strategic complementarity). Including sales and firm fixed effects lowers the SPILLSIC effect but it remains significant.
• With fixed effects and dynamics, SPILLTECH does not affect the own R&D decision.
• Both firm and industry sales correlated with own R&D decision
31
Table 6: Production Function (1) (2) (3) Dependent variable: Ln(Sales)
No Fixed Effects
Fixed effects Fixed effects
Ln(SPILLTECH) t-1
Technology spillovers -0.038 (0.009)
0.104 (0.046)
0.111 (0.045)
Ln(SPILLSIC) t-1
Product market rivalry -0.008 (0.004)
0.009 (0.012)
Ln(Capital) t-1 0.291 (0.009)
0.164 (0.012)
0.165 (0.012)
Ln(Labour) t-1 0.646 (0.012)
0.628 (0.015)
0.627 (0.015)
Ln(R&D Stock) t-1 0.059 (0.005)
0.045 (0.007)
0.045 (0.007)
Ln(Industry Sales) t 0.208 (0.040)
0.197 (0.021)
0.198 (0.021)
Ln(Industry Sales) t-1 -0.105 (0.040)
-0.040 (0.022)
-0.040 (0.022)
Note: Time dummies and industry deflators included. NT=10,092
32
Quantification of prod eq
• No fixed effects has negative SPILLSIC (omitted firm specific prices?)
• But with fixed effects only SPILLTEC is significantly and positively related to productivity
• SPILLSIC insignificant
33
Table 7: Theory vs. empirics (under technology spillovers and strategic complementarity)
Partial correlation of: Theory Empirics Consistency?
∂V0/∂rτ Market value with SPILLTECH
Positive .240* Yes
∂V0/∂rm Market value with SPILLSIC
Negative -.067* Yes
∂k0/∂rτ Patents with SPILLTECH
Positive .192* Yes
∂k0/∂rm Patents with SPILLSIC Zero .024 Yes
∂r0/∂rτ R&D with SPILLTECH
Ambiguous .039 -
∂r0/∂rm R&D with SPILLSIC Positive .025* Yes
*significant at 5% level in preferred specifications
34
6. Further investigations
• Industry Heterogeneity – look at 3 high tech sectors (computer hardware, pharma, telecommunications equipment)
• Quantification of spillovers
• Policy Simulations
35
Table 9A. Computer Hardware
A. Computer Hardware (1) (2) (3) (4) (5) Dependent variable Tobin’s Q Patents Cite-
weighted patents
R&D Real Sales
Ln(SPILLTECH)t-1 1.302 (0.622)
0.151 (0.090)
0.338 (0.146)
0.263 (0.199)
0.685 (0.213)
Ln(SPILLSIC)t-1 -0.476 (0.145)
-0.005 (0.153)
0.157 (0.342)
0.039 (0.026)
-0.092 (0.085)
Lagged dependent variable
0.717 (0.065)
0.427 (0.084)
0.684 (0.056)
Observations 358 279 279 390 343
36
Table 9B. Pharmaceuticals
(1) (2) (3) (4) (5) Dependent variable Tobin’s Q Patents Cite-
weighted patents
R&D Real Sales
Ln(SPILLTECH)t-1 1.628 (0.674)
-0.273 (0.326)
1.056 (0.546)
0.407 (0.225)
0.445 (0.208)
Ln(SPILLSIC)t-1 -1.342 (0.612)
-0.106 (0.194)
-0.087 (0.174)
-0.395 (0.452)
-0.391 (0.227)
Lagged dependent variable
0.218 (0.091)
0.269 (0.089)
0.590 (0.147)
Observations 334 265 265 381 313
37
Table 9C. Telecommunications Equipment
(1) (2) (3) (4) (5) Dependent variable Tobin’s Q Patents Cite-
weighted patents
R&D Real Sales
Ln(SPILLTECH)t-1 2.255 (0.870)
0.368 (0.202)
0.658 (0.368)
0.140 (0.246)
0.526 (0.304)
Ln(SPILLSIC)t-1 -0.087 (0.446)
0.036 (0.110)
-0.010 (0.217)
0.033 (0.118)
0.147 (0.156)
Lagged dependent variable
0.590 (0.063)
Observations 405
353 353 429 390
38
Summary on econometric case studies for 3 main high tech sectors
• Evidence of technological spillover effects in all sectors
• Evidence for product market rivalry in computers and pharma, but not in telecom equipment
• Private returns to R&D similar to overall sample ($1.18) in telecom ($1.23), lower in computers ($0.77) and much higher in pharma ($3.65)
• Less clear evidence of strategic complementarity
39
Quantification of spillovers• Calculate long-run equilibrium response of all variables to an
exogenous increase in R&D (Appendix D)• Complex because of multiple linkages between firms through
SPILLTECH and SPILLSIC• Consider first a 1% increase in R&D of all firms and examine
responses in equilibrium values of all variables (Value, Pat, R&D, productivity)
• Distinguish between “autarky” (effects solely from firm changing its own R&D) and “amplification” (include the effects of SPILLTECH and SPILLSIC)
• Main amplification impacts on patents and productivity via SPILLTECH
• From productivity results we see that “social returns” to R&D about 3.5 times higher than private returns
40
Table 8: Impact of a 1% increase in R&D
Variable Amplification Mechanism Autarky Effect Amplification Effect
Total Effect (amplification +
Autarky)
1 R&D 1 0.098(0.053)
1.098(0.053)
2 Patents TECH, SIC and R&D 0.231(0.028)
0.502(0.091)
0.734(0.119)
3 Market Value TECH, SIC and R&D 0.728(0.161)
0.270(0.112)
0.998(0.212)
4 Productivity TECH, SIC and R&D 0.050(0.007)
0.123(0.049)
0.173(0.049)
41
Policy Simulations
• Baseline: 1% R&D shock to all firms• Policy 1: Existing US R&D tax credit• Policy 2: target smaller/medium sized
firms (many EU programs do this)• Policy of targeting small firms yields lower
returns. • Small firms tend to be “less connected”/in
less populated part of productivity space therefore generate less spillover benefits
42
Table 10A: “Policy” simulations (1) (2) (3)
Target Group Total R&D Stimulus, $m
Total R&D Spillovers, $m
Total Productivity Spillovers, $m
1. All Firms 870 95.0 2,717
2. US R&D Tax Credit (firms eligible in median year)
870 94.9 2,747
3. Smaller Firms (smallest 50%) 870 91.2 1,581
4. Larger Firms (largest 50%) 870 95.1 2,767
43
Table 10B, Descriptive statistics. Smaller firms in more niche technology areas
(1) (2) (3)
Target Group % firms SIC TEC
1. All Firms 100 0.046 0.127
2. US R&D Tax Credit (firms eligible in median year)
40 0.052 0.131
3. Smaller Firms (<50%) 50 0.041 0.074
4. Larger Firms (>50%) 50 0.050 0.130
44
7. Conclusions and Extensions• We find evidence of both technological spillovers and
product market rivalry effects of R&D. Results are consistent with predictions of simple analytical framework with strategic complements and technological spillovers.
• Using both technology and product market closeness measures, AND multiple outcome indicators, can help to identify the different effects.
• Useful for analysing impacts of policies – e.g. alternative forms of R&D subsidies
• Extensions – particular sectors; international dimension; specific equilibrium model (Aghion et al, 2005).
45
Back up slides
• Industry heterogeneity (allow all coefficients to vary in PF; case study of computer hardware)
• Deriving the steady state impact on R&D of a shock
46
Our basic 3 equations
47
Re-write in terms of R&D flows
48
First order Taylor series expansion of SPILLTEC term
49
First order Taylor series expansion of SPILLSIC term
50
51
Final “reduced form” of R&D equation
52
Deriving effect of R&D increase on patents
53
From spillover terms in R&D equation From patent equations
Final reduced form for steady state impact on patents