Cost-Benefit Approach to
Public Support of Private
R&D Activity
Bettina Peters
Centre for European Economic Research (ZEW)
b.peters@zew.de
DIMETIC Doctoral European Summer School
Pecs, July 14, 2010
Motivation
■ Striking empirical evidence that innovation is a key driver for productivity
and employment growth
■ Concern: Level of R&D investments in the economy might be
suboptimal (low) due to three main reasons:
● Financial constraints
• Market failure due to asymmetric information between borrowers and
lenders( especially for young and small firms)
● Investment under uncertainty
• Uncertainty impedes access to external finance
• Firms may delay investment in R&D projects due to outcome
uncertainty (real option theory)
● Positive external effects of R&D
• Market failure due to imperfect appropriability of returns (knowledge
spillovers)
Motivation
■ I will focus only on the third argument: Do such spillovers exist and if
yes to what extent?
 lecture today: part I: Estimating spillovers
■ Negative investment effects due to spillovers may be mitigated by
innovation policies such as public R&D subsidies or by introducing
IPR‘s
● Public subsidies reduce private cost of R&D and therefore close the gap
between social and private equilibrium
Motivation
■ Lisbon-Agenda (2010) & EU2020-Agenda
● Stimulating growth and competitiveness through R&D and innovation
(R&D&I)
● Supporting industrial R&D
• R&D Tax credits
• Technology-specific R&D programmes
● Supporting basic research (allocating more funds to universities,
introduction of ERC, etc.)
● Beyond (traditional) R&D policy: Using other instruments to stimulate R&D
and innovation
• at EU level: Structural/regional funds & EIB
• at national level: stimulating VC investment + technology transfer
● EU2020: broader focus, but one aim is still to foster smart growth through
R&D&I
R&D Spending (GERD) in % of GDP
2008 or latest available year
United Kingdom
2008
1999
France
Germany
Finland
Sweden
EU-15
EU-27
China
Korea
Japan
United States
0,5
Source: OECD MSTI 2009-2
1,0
1,5
2,0
2,5
3,0
3,5
4,0
GERD financed by Private Sector in % of GDP
2008 or latest available year
United Kingdom
2008
1999
France
Germany
Finland
Sweden
EU-15
EU-27
China
Korea
Japan
United States
0,0
Source: OECD MSTI 2009-2
0,5
1,0
1,5
2,0
2,5
3,0
GERD financed by Government in % of GDP
2008 or latest available year
United Kingdom
2008
1999
France
Germany
Finland
Sweden
EU-15
EU-27
China
Korea
Japan
United States
0,0
Source: OECD MSTI 2009-2
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
Motivation
■ Problem of public R&D subsidies: crowding-out of privately financed
R&D investments may occur!
■ Evaluation of crowding out effects of R&D subsidies is necessary

lecture tomorrow, part II:
Econometrics of Evaluation of Public Funding Programmes
■ The existence of social returns to R&D, however, are only a necessary
but not sufficient condition. Public R&D programmes are always
associated with costs which go beyond the pure amount of subsidy.

lecture tomorrow, part III:
A Simple Example of a Cost-Benefit Approach to Public Support of
Private R&D Activity
Part I:
Estimating Spillovers
(Social Returns to R&D)
Content
1. Definition of Knowledge Spillovers
2. Spillover Literature
3. Framework: Production Function Approach
4. Measurement Problems
5. Empirical Evidence
6. Example: Social returns to R&D in German firms (Stata)
7. Conclusion
What a spillover is?
■ Knowledge as a public good (Nelson 1959, Arrow 1962):
● non-excludable
•
•
R&D produces knowledge and something intangible as knowledge
cannot be kept secret.
Knowledge will always spill over to third parties, so that many more
agents will benefit from an R&D investment – not only the initial
inventor
● no rivalry in consumption
•
Knowledge can be used by many firms at the same time without
getting less (but economic value of knowledge might decrease)
■ Spillover is a positive externality due to the public nature of knowledge.
■ Spillovers arise if firm A can benefit from firm‘s B R&D activity without
sharing the R&D costs of B (Branstetter 1998)
What a spillover is?
■ Different channels through which the outcome of R&D can lead to
positive external effects.
■ Griliches (1979): Knowledge spillovers vs. market spillovers
■ Market (rent) spillovers:
● New products or technologies are usually sold at higher prices. However,
even if they are protected by patents, these prices often do not fully reflect
the quality improvements (e.g. production of a memory chip)
● Market spillover: Increase in consumer welfare which arises when new
(intermediate) goods are not priced to their user value due to quality
improvements
What a spillover is?
■ Knowledge (technological) spillovers:
● View of sender:
•
The non-appropriable part of knowledge produced by a firm’s
innovation effort can be regarded as (outgoing) knowledge spillover.
● View of receiver:
•
“By technological (or R&D) spillovers we mean that a firm can acquire
information created by others without paying for that in a market
transaction” (Grossman & Helpman, 1991)
■ Knowledge spillovers can be used to simply imitate new products (if it is
not protected), to invent around or as input for further own product or
technology developments.
■ More subtile form of knowledge spillovers: e.g. if a market leader
abandon its research in a specific area and therefore signals to others
that this research strand is not promising (Jaffe 1998).
What a spillover is?
■ Knowledge spillovers are expected to be higher for basic research
(Jaffe 1998)
■ Knowledge and market spillovers are hard to distinguish in empirics
(Nadiri 1993).
First attempt: Los and Verspagen 2000
Spillovers: Different Channels
■ Within industry
intra-industrial / horizontal knowledge spillovers
■ Between industries
inter-industrial / vertical knowledge spillovers
■ Geographical proximity / localized knowledge spillovers:
● Anselin, Varga & Acs (2000), Jaffe, Trajtenberg & Henderson (2002);
Audretsch & Feldman (2004), Breschi & Lissoni (2001), Thompson & FoxKean (2005)
■ Trade-related & FDI :
● Grossman & Helpman (1991), Kinoshita (2001).
Problem associated with Spillovers
■ Nelson (1959), Arrow (1962)
■ Research firm cannot appropriate all returns from its initial investment
Private returns
to R&D
+
Social excess
returns to R&D
=
Social returns
to R&D
■ Profit-maximising companies will only account for expected private returns
and private cost but not for (higher) expected social return
■ Socially desirable: all projects for which expected social returns exceed
social cost
■ Thus: underinvestment in R&D from a social point of view
Solutions
■ Solutions, e.g.:
● Public subsidies
● Tax credits
● Fostering formation of R&D cooperations
■ Reduce private cost of R&D and therefore close the gap between social
and private equilibrium
Overview: Spillover Literature
■ Measuring the existence of (localized) knowledge spillovers
● E.g. Jaffe, Trajtenberg & Henderson (2002)
• Patents contain geographic information about their inventors
• Use citation patterns to test the extent of spillover localization
• To the extent that regional localization of spillovers is important, citations should
come disproportionately from the same area as the originating patent.
• Result: Citations to domestic patents are more likely to be domestic, and more
likely to come from the same area as the cited patents. Knowledge spillovers are
significant and quite large. But geographic localization fades over time.
• Problem: Citations often come from the examiner (esp. in Europe)
■ Spillovers and agglomeration effects
● e.g. Audretsch & Feldman (1996)
■ Effect of spillovers on cooperation behaviour
● D’Aspremont & Jacquemin (1988), AER
● Lambertini, Lotti & Santarelli (2004), EINT
● Cassiman & Veugelers (2002), AER (incoming and outgoing spillovers)
Overview: Spillover Literature
■ Effect of spillovers on own R&D / innovation
● Non-tornament models: positive (negative) effect if external knowledge is a
complement (substitute) to own knowledge (Spence 1984, Levin & Reiss
1988).
● Tournament models: Additional uncertainty-resolving effect of spillovers, i.e.
spillovers may also function as signals and convey information about the
feasibility of undertaking a given R&D project. (Choi 1991)
● Multistage patent races in which intermediate results are made known after
each stage: If, e.g., a previously lagging firm closes the gap to the leader,
the race may “heat up” in the sense that R&D investment will increase.
(Grossman & Shapiro 1987, Harris & Vickers 1987).
● Empirical studies:
• Positive effect of Spillovers on number of patents (elasticity of 1.1%, Jaffe 1986)
• Positive effect of Spillovers on own R&D (Jaffe 1988: elasticity of 0.2-0.3, Harhoff
2000: elasticity of 0.01-0.14)
■ Effect of spillovers on firm performance („social returns to R&D“)
Effect of spillovers on firm performance
■ Assessing the size, importance and origin of knowledge spillovers due
to R&D
■ Controversial issue, e.g. Krugman (1991, p. 53)
„..knowledge flows, by contrast, are invisible; they leave no paper trail by
which they may be measured and tracked, and there is nothing to prevent
the theorist from assuming anything about them that she likes."
■ Dominant approaches: Construct a measure of external knowledge
(„spillover pool“) and include this as an extra term in a firm‘s
● Production function (Griliches 1979)
● Cost function (e.g. Bernstein 1979, 1997, 1998)
● Profit function (Jaffe 1986, Czarnitzki and Kraft 2008)
■ Lecture focuses mainly on production function approach
Production Function Approach
t




Yit  Ai e Cit Lit KIit KEit e
Y:
Output
C:
Physical capital stock
L:
Labour
uit
KI: Internal knowledge stock
KE: External knowledge stock
:
Exogenous technological change
A:
Scaling parameter (total factor productivity)
u:
error term (unsystematic productivity shocks which are not observed)
Production Function Approach
■ Productivity level (in logs:)
yit  ai t  cit   lit   kiit   keit  uit
Due to log-log specification:
:
output elasticity of own knowledge
percentage change of output when the own knowledge stock
increases by 1 percent

Yit KIit
KI
 it it
KI it Yit
Yit
„Private rate of return“
Private rate of return:
absolute change in output when the own knowledge stock
increases by 1 Euro
Production Function Approach
Due to log-log specification:
θ:
output elasticity of external knowledge
percentage change of output when the external knowledge stock
increases by 1 percent
Yit KEit
KEit

  it
KEit Yit
Yit
„Social excess rate of return“
Social excess rate of return:
absolute change in output when the external knowledge stock
increases by 1 Euro
Production Function Approach
■ Productivity level (per capita)
● Assuming constant returns to scale in conventional inputs (e.g. Mairesse
und Sassenou 1991 ):
yit  lit  ai  t    cit  lit    kiit   keit  uit
 Test on constant returns to scale:
yit  lit  ai  t    cit  lit    kiit   keit     1 lit  uit
● Assuming constant returns to scale in all internal inputs (e.g. Los and
Verspagen 2000):
yit  lit  ai  t    cit  lit     kiit  lit    keit     1 lit  uit
● Assuming constant returns to scale in all inputs:
yit  lit  ai  t    cit  lit     kiit  lit     keit  lit      1 lit  uit
Production Function Approach
■ Productivity growth rates:
● Instead of estimating productivity levels, many authors also use equation in
(first) differences, i.e. productivity growth rates (e.g. Harhoff 2000)
  yit  lit   ai  t     cit  lit    kiit   keit     1 lit  uit
■ Note: So far, output elasticities w.r.t. internal and external knowledge
capital are the same for all firms, respectively.
■ Private and social rates of returns can be indirectly calculated (indirect
approach)
25
Production Function Approach
■ Direct approach I:
  yit  lit   ai  t     cit  lit    kiit   keit     1 lit  uit
  yit  lit   ai t     cit  lit   
KIit
KEit
kiit  
keit     1 lit  uit
Yit
Yit
● Assuming that depreciation rates on knowledge capital are zero:
kiit  log( KIit )  log( KIi ,t 1 ) 
KIit  KIi ,t 1
KIi ,t 1

RDIit   KIi ,t 1
KIi ,t 1
● We approximately get:
  yit  lit   ai t     cit  lit   
RDIit
RDEit

    1 lit  uit
Yit
Yit
26
Production Function Approach
■ Advantage:
● Not necessary not calculate knowledge stock
● Direct estimates of private returns and social excess returns to R&D
● However, rates of return not directly comparable to indirect approach since
depreciation on knowledge has been neglected („gross rate of return“)
■ Direct approach II (Goto and Suzuki 1989)
  yit  lit   ai t     cit  lit   
KIit
KEit

    1 lit  uit
Yit
Yit
● Direct estimates of net private returns and net social excess returns to R&D
27
Measurements Problems
■ Internal and external capital stock: Key ingredients in estimation
● How to define these concepts ?
● How to measure them ?
Internal Knowledge Stock:
Data Issues and Solutions
■ Nessary to have a long time period
■ How to get initial kapital stock in t=1?
● Permanent growth approximation
● Assumptions:
• capital accumulation process has been going on for a sufficiently long time
• R&D expenditure has been growing at constant rate g in a pre-sample
period (many studies simply assume 5 %)
• Knowledge has depreciated at a constant rate d
KIi1  RDIi1 /  g  d 
● Note: Many studies delete the first observation in regression (e.g. Harhoff
1998, Los and Verspagen 2000)
Internal Knowledge Stock:
Data Issues and Solutions
■ Depreciation rate?
● Ideally: Depreciation rate should reflect whether knowledge is measured by
inputs (R&D  depreciation on components of R&D such as investments or
material) or outputs (patents  loss in value of patent) (Bernstein 1988)
● Obviously, depreciation depends on competition (Schumpeter inspired
method; Binzer and Stephan 2002)
● However, in empirical studies it is common to assume a depreciation rate of
15 percent (Hall and Mairesse 1995)
● Sensitivity analysis: results are quite robust to assumed depreciation rate
(Bernstein 1988, Hall and Mairesse 1995)
● At industry level: knowledge often depreciates faster physical capital
(Mansfield 1973, Pakes and Schankerman 1984)
Internal Knowledge Stock:
Data Issues and Solutions
■ Deflator for R&D?
● Not available in official statistics
● Solutions:
• Many papers use GDP deflator (e.g. Los and Verspagen 2000)
• Others use deflator for investments (e.g. Harhoff 2000)
• Most appropriate:
composite index using a deflator for labour costs, material and
investments and the (average) share of labour, materials and
investments of R&D expenditure (Bernstein 1988, Peters et al. 2009)
Internal Knowledge Stock:
Data Issues and Solutions
■ Double-Counting
● R&D expenditure mainly consists of cost for labour and capital which is
already counted in L and C (Schankerman 1981, Cuneo und Mairesse 1983)
● Solution: Substract from labour the number of R&D employees and from
investments the R&D investments
● Problem: Many data sets do not have this information
● Most studies simply ignore this problem and hence underestimate (!) the
private return to R&D
• coefficient of internal capital reflects the excess effect of R&D
employees/investments
• „excess gross private return to R&D“
● Correcting for double-counting lead to a significant increase in the estimated
return (Schankermann 1981, Hall and Mairesse 1995, Harhoff 2000, Peters et
al. 2009)
External Knowledge Stock:
Data Issues and Solutions
■ Measurement of external knowledge stock is quite heterogenous in
literature (which knowledge is included?)
■ Same measurement problems as for internal knowledge stocks arise for
external knowledge stocks + ….
■ Simple measures for spillover pools:
● Sum of all R&D expenditure of all other firms
● Sum of R&D expenditure of all other firms in the same industry (except firm
i!), e.g. Bernstein (1988)
• Intra-industrial knowledge spillovers / horizontal spillovers
● Sum of R&D expenditure of all other firms in all other industries
• Inter-industry spillovers / vertical spillovers
● Sum of R&D expenditure of all other firms in the same region/country (e.g.
Bernstein 1998)
• Localized knowledge spillovers
External Knowledge Stock:
Data Issues and Solutions
■ Griliches (1979): Firms that are closely related benefit more from each
other than firms that are more distant.
■ Use proximity or similarity metrics to weight the R&D expenditure of
other firms/industries/countries in the first place (spilloverpool)
Sit   j i wijt RD jt
n
■ Use Spilloverpool S to construct external knowledge capital stock KE
using the perpetual inventory method
External Knowledge Stock:
Data Issues and Solutions
■ Several weighting schemes have been used in the literature:
■ Input-output flows (industry matrix):
● Idea: traded goods transfer knowledge between industries
● Terleckyi (1974), Sveikauskas (1981), Wolff &Nadiri (1987), Odagiri (1985)
■ Patent flows across industries
● Industry matrix in which patents are classified according to their industry of
manufacture and industry of use
● Scherer (1982), Griliches & Lichtenberg (1984), Sterlaccini (1989), Mohnen
& Lepine (1991), Putnam & Evenson (1994), Los & Verspagen (2000)
■ Import shares, esp. for international spillovers
● Coe & Helpman (1995), Lichtenberg & van Pottelsberghe (1998), Naidiri &
Kim (1996), Jacobs, Nahuis & Tang (2002)
External Knowledge Stock:
Data Issues and Solutions
■ Technology distance measured by patents
● Jaffe (1986), Guellec & van Pottelsberghe (2001, 2004), Park (1995, 2004),
Hanel & St. Pierre (2002), Bloom, Schankerman & van Reenen (2007),
Capron & Cincera (2001), Aldieri & Cincera (2009), Peters et al. (2009)
■ Technology distance measured by research fields
● Many R&D surveys include information on the devision of R&D expenditure
according to product areas
● Goto and Suzuki (1997), Harhoff (2000), Adams (1997), Peters et al. (2009)
Technological Distance (Jaffe, 1986)
■ Characterizes the technological position of firms.
■ Suppose there are K “technological areas” in which firms can invest in
R&D / patent.
■ Then firm’s i position in the technological space can be summarized as
Fi   PAi ,1 , PAi ,1 ,
, PAi , K 
where PAik is either
● Share of patents in a specific IPC class k
● Share of R&D budget allocated to a specific product area / technology field k
37
Technological Distance (Jaffe, 1986)
■ Example (k=2)
1
Orthogonal firms
Close firms
1
■ Jaffe technology distance index (uncentered correlation of Fi and Fj):
wij  Tij 
Fi Fj
 F F  F F 
i
i
j
j
,
0  Tij  1
Nice properties:
1 for identical firms
0 for orthogonal firms
Not directly affected by length of F
38
Empirical Evidence of Spillovers
■ Studies at the country level
● Coefficient of national capital stock:
• Can be used to derive the social domestic rate of return: Sum of private rate of
return of researching firm and social excess returns to other domestic firms (within
and between industries)
● Coefficient of international capital stock:
• Can be used to derive the foreign social excess rate of returns: profit gains by
domestic firms due to knowledge generated by foreign firms
● In many studies social domestic rate of return is smaller than international
social rate of return, but not in all
e.g. Coe & Helpman (1995), Guellec & van Pottelsberghe (2001, 2004) / Nadiri & Kim (1996), Park
(1995, 2004)
● Social domestic rate of return varies in these studies between 20% and 145%
(Coe & Helpman 1994, Lederman & Maloney 2003, Lichtenberg & van Pottelsberghe 1998, Park
1995, 2004)
● Social domestic rate of return is larger in G7 countries (Coe & Helpman 1995)
● International spillovers are larger in more open countries (Coe & Helpman 1995)
● Evidence for spillovers from public R&D: mixed (Park 1995, Guellec & v. Pott. 2001)
Empirical Evidence of Spillovers
■ Studies at the industry level
● Coefficient of own industry capital stock:
• Can be used to derive the industry‘s private rate of returns to R&D: Sum of
private rate of return of researching firm and social excess returns to other
domestic firms within the industry industry (intra-industry spillovers)
● Coefficient of capital stock by other industries:
• Can be used to derive the social excess rate of returns to other industries
(inter-industry knowledge spillovers)
● Range of private rate of returns at industry level: 10% to 80%
Goto & Suzuki 1989, Bernstein & Nadiri 1991, Griffith et al 2003, Hanel 1988, Jacobs et al 2002,
Link 1978, Mohnen & Lepine 1988, Mohnen et al. 1986, Odagiri 1985, Rouvinen 2002b, Scherer
1982, 1984, Sterlacchini 1989, Sveikauskaus 1981, Terleckyi 1974, Wolff & Nadiri 1987.
● Range of social rate of returns: 15% to 150% and in most studies larger than
the private rate (exception: Mohnen & Lepine 1991)
● High variation (5% to 200%) of private rates to return across industries (Mohnen
& Lepine 1991)
● Industries with higher R&D intensities benefit more from intraindustrial
spillovers (Bernstein 1988)
Empirical Evidence of Spillovers
■ Studies at the firm level
● Coefficient of own capital stock:
• Can be used to derive the private domestic rate of return
● Coefficient of external capital stock:
• Can be used to derive the social excess rate of return (national,
horizontal, vertical, international depending on the measurement of the
external capital stock)
Example: Social Returns to R&D in Germany
■ Peters, Kladroba, Licht & Crass (2009)
■ Data: German R&D survey 1991, 1993, 1995, 1997, 1999, 2001, 2003,
2005
■ In contrast to CIS data R&D surveys (ideally) cover the total population
of all R&D doing firms
■ Firm-level data
■ Production Function Framework
■ Weights to measure technological distance
● Based on research fields (40 categories)
● Based on patents classifications (30 IPC categories)
OLS and Fixed Effects Estimates
Dependent Var.
Method
Estimation
Physical Capital (C)
Labour Productivity
OLS
(1)
(2)
0.082***
0.082***
(7.536)
(7.538)
0.142***
0.142***
(11.322)
(11.272)
0.005
(0.327)
-
(3)
(4)
0.072***
0.055***
(6.812)
(3.159)
own knowledge stock
0.034*
0.096***
(KI)
(1.839)
(4.014)
external knowledge
0.097** *
stock (KI)
(4.952)
KI*KE
0.026***
(6.552)
Labour
0.078***
0.083***
0.097***
-0.202***
(10.317)
(5.420)
(6.321)
(-5.355)
East
-0.371***
-0.371***
-0.364***
(-15.934)
(-15.935)
(-26.440)
Constant
-1.375***
-1.410***
-1.935***
-0.891***
(-12.495)
(-9.673)
(-20.097)
(-5.503)
R²
0.416
0.420
0.429
0.263
Rho
0.908
W_Time
0.000
0.000
0.000
0.000
Stata:
W_internal
- east time,
0.000
OLS:
reg lnlp lnCpe lnKEpe
lnKIpe lnL
robust cluster(id)
W_external
0.000fe robust cluster(id)
FE:
xtreg lnlp lnCpe lnKEpe
lnKIpe lnL
east time,
W_knowledge
0.000
-
FE
(5)
0.056***
(3.193)
0.095***
(3.867)
0.007
(0.377)
-0.195***
(-4.705)
-
(6)
0.053***
(3.058)
-0.013
(-0.345)
0.114***
(3.481)
0.027***
(3.711)
-0.167***
(-4.643)
-
-0.958***
-1.569***
(-3.809)
(-6.026)
0.263
0.276
Supports absorptive
0.908
0.907
capacity
hypothesis,
0.000
0.000
cf.- Cohen Levinthal
0.000
1989,
Klette 0.001
1994,
Harhoff
20000.000
-
Labour
East
Constant
R²
Rho
W_Time
W_internal
W_external
W_knowledge
Obs
Firms
Mean
Median
25% Percentile
75% Percentile
Mean
Median
25% Percentile
75% Percentile
0.097***
0.083***
0.078***
(6.321)
(5.420)
(10.317)
-0.364***
-0.371***
-0.371***
(-26.440)
(-15.935)
(-15.934)
-1.935***
-1.410***
-1.375***
(-20.097)
(-9.673)
(-12.495)
0.429
0.420
0.416
0.000
0.000
0.000
0.000
0.000
0.000
6665
6665
6665
1635
1635
1635
Estimated Output elasticity of own knowledge KI
0.148
0.142
0.142
0.152
0.112
0.189
Estimated Output elasticity of external knowledge KE
0.006
0.005
0.007
-0.013
0.026
-0.202***
(-5.355)
-
-0.195***
(-4.705)
-
OLS and Fixed Effects Estimates
(II)
-0.958***
-0.891***
-0.167***
(-4.643)
-1.569***
(-6.026)
0.276
0.907
0.000
0.000
0.001
0.000
6665
1635
(-5.503)
0.263
0.908
0.000
6665
1635
(-3.809)
0.263
0.908
0.000
6665
1635
0.096
0.095
0.105
0.109
0.068
0.148
-
0.007
0.020
0.021
0.000
0.040
Heterogenous effects,
more dispersed for
external knowledge
First Difference Estimates
Dependent Var.
Method
Estimation
Physical Capital (C)
own knowledge stock
(KI)
external knowledge
stock (KI)
KI*KE
Labour
Constant
R²
W_Time
W_internal
W_external
W_knowledge
Obs
Growth Rate Labour Productivity
2-Year-Growth Rate
4-Year-Growth Rate
OLS
(1)
(2)
(3)
(4)
(5)
0.045***
0.045***
0.041***
0.042**
0.043**
(2.884)
(2.909)
(2.659)
(2.421)
(2.450)
0.053**
0.052**
-0.086**
0.081***
0.079***
(2.303)
(2.243)
(-2.241)
(3.559)
(3.442)
0.012
0.147***
0.013
(0.622)
(4.230)
(0.680)
0.034***
(4.550)
-0.396***
-0.385***
-0.335***
-0.269***
-0.257***
(-9.833)
(-8.503)
(-8.324)
(-6.483)
(-5.613)
0.145***
0.144***
0.150***
0.041***
0.040***
(11.349)
(11.196)
(11.580)
(2.731)
(2.628)
0.228
0.228
0.244
0.208
0.208
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
5030
5030
5030
3399
3399
Estimated Output elasticity of own knowledge KI
(6)
0.040**
(2.358)
-0.053
(-1.440)
0.144***
(4.297)
0.033***
(4.454)
-0.214***
(-5.772)
0.047***
(3.223)
0.230
0.000
0.000
0.020
0.000
3399
external knowledge
stock (KI)
KI*KE
Labour
Constant
R²
W_Time
W_internal
W_external
W_knowledge
Obs
Mean
Median
25% Percentile
75% Percentile
Mean
Median
25% Percentile
75% Percentile
-
0.012
(0.622)
-
0.147***
(4.230)
0.034***
(4.550)
-0.396***
-0.385***
-0.335***
-0.269***
(-9.833)
(-8.503)
(-8.324)
(-6.483)
0.145***
0.144***
0.150***
0.041***
(11.349)
(11.196)
(11.580)
(2.731)
0.228
0.228
0.244
0.208
0.000
0.000
0.000
0.000
0.000
0.000
0.000
5030
5030
5030
3399
Estimated Output elasticity of own knowledge KI
0.053
0.052
0.063
0.081
0.068
0.016
0.117
Estimated Output elasticity of external knowledge KE
0.012
0.028
0.030
0.003
0.054
-
0.013
(0.680)
-
(-5.613)
0.040***
(2.628)
0.208
0.000
3399
0.144***
(4.297)
0.033***
(4.454)
-0.214***
(-5.772)
0.047***
(3.223)
0.230
0.000
0.000
0.020
0.000
3399
0.079
-
0.091
0.096
0.046
0.144
0.013
-
0.029
0.030
0.005
0.054
First Difference Estimates (II)-0.257***
Knowledge Spillovers by Group of Firms
Output elasticity to own Knowledge
-0.1
-0.05
0
0.05
Output elasticity to external Knowledge
0.1
0.15
0.2
0.25
Total
Manufacturing
Services
Not significant
HT
MT
LT
Young firms
Old firms
West G.
East G.
Imprecisely estimated
Social Rate of Return in Germany 1991-2005
■ Social rate of return of an additional Euro in R&D: sum of private rate of
return and social excess rate of returns
        i 1  it  
N
i j
Yit
Y
N
 i 1  it
i j
KIit
KEit
ˆ  ˆ Yit   N  1 *ˆ Yit
KIit
KEit
private
rate of
return
Indirect Approach
Scenario 1
0.41
Scenario 2
0.41
Direct Approach
Scenario 1
0.09
Scenario 2
0.09
social
excess
rate of
return
social
rate of
return
social excess
rate of return as
percentage of
private rate of
return
0.52
0.65
0.93
1.06
129%
159%
0.09
0.14
0.18
0.23
106%
163%
Impact of Technology Distance Measure
■ For comparison: Using Jaffe‘s index with patents (instead of research
categories):
● Output elasticity of own knowledge: 0.081 (similar)
● Output elasticiy of external knowledge: 0.331 (nearly 10 times higher!)
■ Most empirical studies using patents for measuring technological
distance comparably find higher effects (e.g. Cincera 2001, Los &
Verspagen 2000)
■ Explanation:
● Only patenting firms, i.e. mainly large firms (selection bias)
● Patenting firms may use patents by other firms more often and more
efficiently
● Firms may be more diversified in terms of research fields than in terms of
patent fields (many firms with only one or a few patents). Jaffe index: more
diversified firms benefit less from spillovers (Jaffe 1988).
Some More Econometric Issues
■ Potential Endogeneity of R&D and capital: shocks (e.g. unobserved
“management skills”) may both lead to higher productivity and to higher
investments and/or R&D expenditure.
■ Panel data econometrics: Traditional way to overcome this problem
● IV or GMM estimator using lagged endogenous variables as instruments
(stata: ivreg or xtabond)
e.g. Aldieri and Cincera (2009)
■ Productivity literature: estimator of Olley and Pakes (1996) or Petrin and
Levinson (2003)
● Yet no application of estimating knowledge spillovers using this estimator
Some More Problems in
Identifying Knowledge Spillovers
■ Productivity effect is a priori unclear due to two countervailing types of
R&D spillovers (Bloom et al. 2007):
● External R&D is presumed to have a positive technological effect:
Firm gets knowledge for free upon which it can build, may reduce its R&D
expenditure and may increase its efficiency
● External R&D may induce negative business stealing effects:
Successful R&D by product market rivals worsen a firm‘s competitive
position and may lead to business stealing
■ Estimated productivity effect is a combination of both effects
■ Identification problem of disentangling both types of spillovers using
existing empirical strategies
Bloom, Schankerman and Van Reenen (2007)
■ Non-tournament model of R&D with technology spillovers and strategic
interaction in the product market (two-stage game).
● Stage 1: firms decide upon their R&D spending  produces knowledge and
potential technological spillovers
● Stage 2: firms compete in some variable, x, conditional on knowledge levels
k (no restriction on the form of this competition except to assume Nash
equilibrium).
■ 3 firms, two only interact in technology space (production of innovations)
but not in the product market; two compete only in the product market.
■ Even in the absence of technology spillovers, product market interaction
create an indirect link between the R&D decisions of firms through the
anticipated impact of R&D induced innovation on product market
competition in the second stage.
■ What matters for the analysis is whether there is strategic substitution or
complementarity of R&D between different firms.
Bloom, Schankerman and Van Reenen (2007)
■ Derive implications of technology and product market spillovers for a
range of firm performance indicators
● market value, patents, productivity and R&D
● Predictions differ across performance indicators, thus providing identification
for the technology and product market spillover effects.
■ Distinct measures for distance in technology (patent classes) and
product market (sales activities across different 4-digit industries).
■ Variation in these two dimensions allow them to distinguish empirically
between technology and product market spillovers.
■ Panel of U.S. firms, period 1981-2001. Results:
● Both technological and product market spillovers are signficant with
technology spillover effects being larger
● (Weak) evidence that R&D by product market rivals is, on average, a
strategic complement for own R&D.
Conclusions
■ This lecture was about the
“Search for R&D Spillovers”
■ In most studies, a positive productivity effect is found which can be
interpreted as a reduced-form evidence of spillovers from external R&D
Challenges
■ However, the mechanisms for such spillovers are not well identified
(Jaffe and Trajtenberg 1998)
■ Not easy to distinguish knowledge spillovers from competitive effects
(Bloom et al. 2007)
■ Not easy to distinguish a spillovers interpretation from the possibility
that positive interactions are just a reflection of spatially correlated
technological opportunities.
● If new research opportunities arise exogenously in a given technological
area, then all firms in that area will do more R&D and may improve their
productivity, an effect which may be erroneously picked up by a spillover
measure (Griliches, 1998).
Challenges
■ No structural model for empirical work to capture all possible effects that
can be associated with knowledge spillovers (Harhoff 2000)
■ Spillovers from the technological frontier (Griffith et al. 2004).
■ Data constraints.
● This explains why we don’t have a standardized measure of spillovers.
• Patent data
• CIS: spillovers usually measured by the use of information sources
● Too much creativity?
■ Zvi Griliches, Scan. J. of Ec. 1992 (“The Search for R&D Spillovers”)
“In spite of (many) difficulties, there have been a significant number of
reasonable well-done studies all pointing in the same direction: R&D
spillovers are present, their magnitude may be quite large and social rates
of return remain significantly above private rates.”
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