Identifying Technology Spillovers
and 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
1
Introduction
Two 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)
2
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)
3
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)
4
Structure
1.
2.
3.
4.
5.
6.
Intro
Analytical Framework
Data
Econometrics
Results
Extensions – Industry heterogeneity,
policy simulations
7. Conclusions
5
2. Analytical Framework
Two 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
6
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.
7
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
8
Table 1. R&D Spillovers and Strategic Rivalry
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
9
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
10
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)
11
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)
12
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
13
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
14
Figure 1: Correlation between SIC and TEC across all firm pairs
Product market
closeness
Technological closeness
15
Examples (High TECH, low SIC):
Computer and chip makers
Table A1
Correlation
IBM
Apple
Motorola
Intel
IBM
SIC
TECH
1
1
0.32
0.64
0.01
0.47
0.01
0.76
Apple
SIC
TECH
1
1
0.02
0.17
0.01
0.47
Motorola
SIC
TECH
1
1
0.35
0.46
Intel
SIC
TECH
1
1
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)
16
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
17
4. Econometric Specifications: Generic
issues across all 4 equations
yit  x'it   uit
• 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
18
(a) Market Value
Griliches (1981)
R&D stock/
Fixed assets
Tobin’s Q (market value

V 
v G  

ln

ln


ln
1


 
  
it
to fixed assets)

 A it

 A it 
6th order series expansion (compare with NLLS)
Should be +ve
 G  
V 
ln           v1 ln SPILLTECH it 1
 A it
  A it 1 
  v 2 ln SPILLSICit 1  Z itv '  v 3   v i   v t  vitv
Should be -ve
Fixed effects
(long T)
Time dummies
19
(b) R&D Equation
R&D expenditures
ln Rit   r Rit 1   r1 ln SPILLTECHit 1
  r 2 ln SPILLSICit 1  Z itr '  r 3   r i   r t  vitr
Test of strategic complementarity (βr2>0) vs. strategic substitutes (βr2<0)
Compare with Blundell Bond (1998, 2000) GMM-SYS approach
20
(c) Patent Count Equation
+ve
1 Dit ln Pit 1   2 Dit   p1 ln SPILLTECH it 1

E ( Pit | X it , Pit 1 )  exp 

  p 2 ln SPILLSICit 1  Z itp '  p 3   p i   p t  vitp 
0
• 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 effects
with 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
21
(d) Production Function
Deflated sales
+ve
ln Yit   y1 ln SPILLTECHit 1
  y 2 ln SPILLSICit 1  Z ity '  y 3   y i   y t  vity
0
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
22
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?
23
5. Main Results
•
•
•
•
Tobin’s Q equation
Patents equation
R&D equation
Productivity equation
24
Table 3: Tobin’s Q
Dependent variable:
Ln (V/A)
(4)
(1)
(2)
(3)
No individual
Effects
Fixed Effects
Fixed Effects (drop
SPILLSIC)
-0.040
(0.012)
0.240
(0.104)
0.186
(0.100)
0.038
(0.007)
-0.067
(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)
Ln(SPILLTECHt-1)
Fixed Effects
(drop
SPILLTEC)
Technology spillovers
Ln(SPILLSICt-1)
-0.047
(0.031)
Product market rivalry
Note: Sixth-order terms in ln(R&D/Capital) and time dummies
also included. NT=10,011
25
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
26
Table 4: Patent Model
Dependent variable:
Patent Count
Ln(SPILLTECH)t-1
Technological spillovers
Ln(SPILLSIC)t-1
Product market rivalry
Ln(R&D Stock)t-1
Ln(Sales)t-1
(1)
No initial
conditions:
Static
(2)
Initial
Conditions:
Static
(3)
Initial
Conditions:
Dynamics
(4)
Initial
Conditions:
Dynamics
0.403
(0.086)
0.282
(0.046)
0.192
(0.037)
0.194
(0.037)
0.044
(0.032)
0.049
(0.031)
0.024
(0.019)
0.495
(0.044)
0.338
(0.052)
0.282
(0.046)
0.258
(0.047)
0.450
(0.049)
0.105
(0.027)
0.138
(0.027)
0.550
(0.026)
0.175
(0.028)
0.104
(0.027)
0.140
(0.027)
0.550
(0.026)
0.174
(0.028)
0.814
(0.046)
0.402
(0.029)
0.402
(0.029)
Ln(Patents)t-1
Pre-sample fixed effect
Over-dispersion
(alpha)
0.954
(0.067)
Note: Time dummies and four-digit industry dummies included.
Negative binomial model; NT=9,122.
27
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)
28
Table 5: R&D Equations
Dependent variable :
ln(R&D)
Ln(SPILLTECH) t-1
Technology spillovers
Ln(SPILLSIC) t-1
Product market rivalry
Ln(Sales) t-1
(1)
No Effects
(2)
Fixed Effects
(3)
Fixed Effects
+ Dynamics
(4)
Fixed Effects
+ Dynamics
0.224
(0.017)
0.291
(0.012)
0.115
(0.071)
0.110
(0.026)
0.039
(0.039)
0.025
(0.014)
0.030
(0.013)
0.797
(0.009)
0.801
(0.017)
0.698
(0.083)
-0.879
(0.083)
0.133
(0.030)
-0.085
(0.031)
0.218
(0.015)
0.695
(0.015)
0.133
(0.022)
-0.110
(0.023)
0.217
(0.015)
0.695
(0.015)
0.134
(0.022)
-0.108
(0.022)
Ln(R&D) t-1
Ln(Industry Sales) t
Ln(Industry Sales) t-1
Note: Time dummies included; NT=8,565.
29
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
30
Table 6: Production Function
Dependent variable :
Ln(Sales)
Ln(SPILLTECH) t-1
Technology spillovers
Ln(SPILLSIC) t-1
Product market rivalry
Ln(Capital) t-1
Ln(Labour) t-1
Ln(R&D Stock) t-1
Ln(Industry Sales) t
Ln(Industry Sales) t-1
(1)
No Fixed Effects
(2)
Fixed effects
(3)
Fixed effects
-0.038
(0.009)
-0.008
(0.004)
0.291
(0.009)
0.646
(0.012)
0.059
(0.005)
0.208
(0.040)
-0.105
(0.040)
0.104
(0.046)
0.009
(0.012)
0.164
(0.012)
0.628
(0.015)
0.045
(0.007)
0.197
(0.021)
-0.040
(0.022)
0.111
(0.045)
0.165
(0.012)
0.627
(0.015)
0.045
(0.007)
0.198
(0.021)
-0.040
(0.022)
Note: Time dummies and industry deflators included. NT=10,092
31
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
32
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
33
6. Further investigations
• Industry Heterogeneity – look at 3 high
tech sectors (computer hardware, pharma,
telecommunications equipment)
• Quantification of spillovers
• Policy Simulations
34
Table 9A. Computer Hardware
A. Computer Hardware
(1)
Dependent variable Tobin’s Q
Ln(SPILLTECH)t-1
Ln(SPILLSIC)t-1
Lagged dependent
variable
Observations
1.302
(0.622)
-0.476
(0.145)
358
(2)
Patents
0.151
(0.090)
-0.005
(0.153)
0.717
(0.065)
279
(3)
Citeweighted
patents
0.338
(0.146)
0.157
(0.342)
0.427
(0.084)
279
(4)
R&D
(5)
Real Sales
0.263
(0.199)
0.039
(0.026)
0.684
(0.056)
390
0.685
(0.213)
-0.092
(0.085)
343
35
Table 9B. Pharmaceuticals
Dependent variable
Ln(SPILLTECH)t-1
Ln(SPILLSIC)t-1
Lagged dependent
variable
Observations
(1)
Tobin’s Q
(2)
Patents
1.628
(0.674)
-1.342
(0.612)
-0.273
(0.326)
-0.106
(0.194)
0.218
(0.091)
265
334
(3)
Citeweighted
patents
1.056
(0.546)
-0.087
(0.174)
0.269
(0.089)
265
(4)
R&D
(5)
Real Sales
0.407
(0.225)
-0.395
(0.452)
0.590
(0.147)
381
0.445
(0.208)
-0.391
(0.227)
313
36
Table 9C. Telecommunications
Equipment
Dependent variable
Ln(SPILLTECH)t-1
Ln(SPILLSIC)t-1
Lagged dependent
variable
Observations
(1)
Tobin’s Q
(2)
Patents
2.255
(0.870)
-0.087
(0.446)
0.368
(0.202)
0.036
(0.110)
(3)
Citeweighted
patents
0.658
(0.368)
-0.010
(0.217)
405
353
353
(4)
R&D
(5)
Real Sales
0.140
(0.246)
0.033
(0.118)
0.590
(0.063)
429
0.526
(0.304)
0.147
(0.156)
390
37
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
38
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
39
Table 8:
Impact of a 1% increase in R&D
Variable
Amplification Mechanism
Autarky Effect
Amplification
Effect
Total Effect
(amplification +
Autarky)
1
0.098
(0.053)
1.098
(0.053)
1
R&D
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)
40
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
41
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
42
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
43
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).
44
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
45
Our basic 3 equations
46
Re-write in terms of R&D flows
47
First order Taylor series expansion of SPILLTEC term
48
First order Taylor series expansion of SPILLSIC term
49
50
Final “reduced form” of R&D equation
51
Deriving effect of R&D increase on patents
52
Final reduced form for steady state impact on patents
From spillover terms in R&D equation
From patent equations
53