Patents statistics and
firm performance
Lionel Nesta
Observatoire Français des Conjonctures Economiques
Department of Research on innovation and competition
The rise of knowledge based activities

Understanding the nature of knowledge activities
 The generation of knowledge
 Publications, patents
 Inventions, innovation,

The diffusion of knowledge
 Technology adoption
 Spillovers: Social rate of return > private rate of return

The exploitation of knowledge
 R&D and productivity
 Knowledge and productivity
Methodological parti pris



Can we say something meaningful about
productivity gains of a techno-industry without
having to attend to the detailed events of the
firm/technology/industry ?
Avoid the story of the technology (but take into
account the history of the technology)
Statistical analysis
 Boost replication
 Gain generality
On Measures of Firm Knowledge

Knowledge
very difficult to grasp / hard to observe
 No authoritative measures/definition
 Use of traces of knowledge


Readily available material
R&D expenses
 Publications
 Patent data

Firm knowledge

Intangible capital



Observable components
Non Observable components
Observable part


Pervasive and systematic properties
Which are they ?
Plan of the talk
The need for “knowledge statistics”
The need for “patent statistics”
Patents and firm knowledge capital (K)
Patents and firm knowledge diversity (D)
Patents and firm knowledge relatedness (R)
The need for “knowledge statistics”
Source: OECD
The need for “knowledge statistics”

Basic propositions going beyond the input-output
relationship



The division of labour within the firm/organisation
reflects knowledge specialisation activities
The division of labour reflects knowledge
specialisation activities between firms/organisations
The division of labour in knowledge production
activities: increasing returns and externalities
The need for “knowledge statistics”
Source: OECD
The need for “knowledge statistics”
Source: OECD
The need for “knowledge statistics”
Source: OECD
The need for “knowledge statistics”
Source: OECD
The need for “knowledge statistics”
Source: Chiara Criscuolo (Not dated) Boosting Innovation and Productivity Growth in Europe:
The hope and the realities of the EU’s ‘Lisbon agenda’
The need for “knowledge statistics”
Source: OECD
Plan of the talk
The need for “knowledge statistics”
The need for “patent statistics”
Patents and firm knowledge capital (K)
Patents and firm knowledge diversity (D)
Patents and firm knowledge relatedness (R)
The need for patent statistics

Why do we need a patent system ?
B
c*
Marginal external benefit
msB
mpB
q1
q*
q
The need for patent statistics

What is a patent?


A patent is a legal instrument, which gives a
temporary monopoly to an inventor in exchange
for detailed and full disclosure of the invention.
Thus it allows the inventor to protect and profit
from the invention and society to gain from wide
dissemination of the knowledge about the
invention.
The need for patent statistics

Basic criteria for compiling patent-based indicators





Reference date
Reference country
PCT applications
Patent families
Classifying patents by additional criteria




Technology fields
Patents by inventors
Patents by patent assignee
Patent citations
The need for patent statistics

Advances in ICT





Reduction in the cost of storage
Reduction in the cost of transmission of information
Reduction in the cost of data treatment
Now all major patent offices provide online access to
their data.
Major online database


European Patent Office (EPO: Esp@ce Acces)
US Patent Office (USPTO: NBER database)
The need for patent statistics


Patent database
 Systematic assessment for the study of technical
change.
 Uniquely detailed source of information on
inventive activity
 The multiple dimensions of the inventive process
(e.g. geographical location, technical and
institutional origin, individuals and networks).
Consistency for comparisons across time and across
countries.
The need for patent statistics

Pros of Patents statistics





Newness: outcome of inventive activities
Commercial application
Costs of patenting
Systematic retrieval of key information
Cons of Patents statistics




Not all inventions are patented
Not all inventions are patentable (software)
Propensity to patent varies across industries
Propensity to patent varies across firms
The need for patent statistics

Scientometrics (Bibliometrics)



A set of techniques base on the quantitative treatment
of patent data, but also of publication data.
Use of all possible information to produce a metric
which may describe the generation, diffusion and
exploitation of S&T knowledge
Examples at the country level



Country performance in given disciplines
National patterns of technology accumulation
And so much more to come…
The need for patent statistics
RTA  1
Losing
momentum
Strengthening
leading position
FGSI  1
FGSI < 1
Lagging
behind
Building up
capacity
RTA < 1
Figure 1. Technology map of countries
Source: Nesta & Patel (2004)
The need for patent statistics
Figure 2. Technology map of countries: Chemical-related (1991-2000)
Source: Nesta & Patel (2004)
The need for patent statistics
Figure 4. Technology map of countries: Mechanical-related (1991-2000)
Source: Nesta & Patel (2004)
The need for patent statistics

STAN database




STructural ANalysis OECD database
Major economic and S&T database by sector
Reports patent statistics at the meso economic level
Examples at the meso-level?


Attempts to link technology with industry classes
Very preliminary and restrictive
The need for patent statistics

Patents can help us answer fundamental, basic and
very concrete questions about S&T activities





Variety of sectors – variety of outcomes
Diversity of knowledge bases within industries
Diversity of processes of knowledge exploitation
Diversity of institutional actors involved
Diversity of knowledge sources (citations)
We will use patents to describe firm knowledge
characteristics and link it with firm performance
Plan of the talk
The need for “knowledge statistics”
The need for “patent statistics”
Patents and firm knowledge capital (K)
Patents and firm knowledge diversity (D)
Patents and firm knowledge relatedness (R)
Patents and Firm Knowledge Capital (E)

Reticular nature



Variety of states




Structure of correlation
Fractal structure (variable and relationships)
Forms: Tacit/Codified
Nature: Basic/Applied (General/Abstract)
Vehicles: Human capital/ Equipment
Cumulative nature



Stock of knowledge
Accumulation
Knowledge tree
Patents and Firm Knowledge Capital (E)

Productive knowledge (S&T)



Collective nature



Knowledge mobilized ⇒ competencies
Specialized competencies
Interactions between pieces of knowledge
Equipment, individuals
Knowledge base



Properties of knowledge stock
Architectural knowledge
Organization of knowledge
Patents and Firm Knowledge Capital (E)

The conceptual origins

Penrosian tension
 Growth of knowledge
 Relative to the growth of management resources

The competence based view of the firm
 Most valuable asset : competencies
 Distinctive, unique, hard to replicate

Economics of science and the dichotomy
 Public good: Basic/Applied = Public/Private
 Semi public good: dichotomy obsolete
Patents and Firm Knowledge Capital (E)

The economics of R&D

The productivity of R&D relates a set of input with
output
Q  F  X , K,u

With K, the knowledge capital of the firm, being a
function of current and past R&D investment R:
K  G W ( B) R, v 

The lagged structure of R&D investments
W ( B) Rt  w0 Rt  w1Rt 1  w2 Rt 2 
Patents and Firm Knowledge Capital (E)

Knowledge stocks (Griliches, 1979)
Kit  Rit  1     Kit 1
Patents and Firm Knowledge Capital (E)
Qit  A  Cit  Lit  M it  Kit  euit
Qit  A  Cit  Lit  M it  Eit  euit
Beware that variables L and M are very rough ones!
Taking logs yields the empirical specification:
qit  a    cit    lit    mit    eit  uit
Patents and Firm Knowledge Capital (E)


156 largest firms: Fortune 500 + USPTO + SIC
(10-37)
More than 3 million USPTO patents (NBER from
1963 to 2000)



All described by a vector of one to several technologies
120 dimensional technological space: >700,000
Datastream (Financial Data)
Patents and Firm Knowledge Capital (E)

Import firm patent data


Knowledge capital


Run ‘DATA_IMPORT.do’ and produce ‘JENA_PAT.dta’
Run ‘KNOW_E.do’ and produce ‘KNOW_E.dta’
Estimate within regression


Merge file with ‘JENA_FIRM_FS.dta’
Run ‘regression.do’
Plan of the talk
The need for “knowledge statistics”
The need for “patent statistics”
Patents and firm knowledge capital (K)
Patents and firm knowledge diversity (D)
Patents and firm knowledge relatedness (R)
Patents and Firm Knowledge Diversity (D)
Expertise
Diversity
Patents and Firm Knowledge Diversity (D)

The drivers of technological diversification





Path dependency, adaptation and the need for diversity
How and why do firms enter into new technology?
Variety in business or variety on technology profiles?
Relationship between business and technological div.
(Business) Diversification discount




Business diversification comes at a cost
A good candidate explanation: technologies !
Learning and the productivity dynamics
Hence we must account for tech. diversification
Patents and Firm Knowledge Diversity (D)

Diversity as a pervasive property of firm KB
D
kk  ek   el
l k
D
D
D
D
D
D
k
k
l k
k
k
l k
K   ek   el   ek   ek 1 E  E  D  1
K  ED
Patents and Firm Knowledge Diversity (D)
Qit  A  Cit  Lit  M it  Kit  euit



E
D

Qit  A  Cit  Lit  M   Eit  Dit   euit
Beware that variables L and M are very rough ones!
Taking logs yields the empirical specification:
qit  a    cit    lit    mit   E  eit   D  dit  uit
where
K    K with K  E, D
Patents and Firm Knowledge Diversity (D)




Let pkit be the number of patents applied for by firm i at time
t in technology class k.
To compensate for abrupt changes in firm technological
strategies, define Pkit as the sums of patent applications over
the past five years: Pkit  5 0 p ki ,t 
Let dkit = 1 if the firm has developed competencies in
technology k (Pkit > 0), 0 otherwise.
Knowledge diversity D : number of technology classes
mastered by the firm over the past years
D it  k d kit
Patents and Firm Knowledge Diversity (D)


Another measure used is the coefficient of variation of RTA
First compute :
RTA kit 
Pkit
P
kit
i

Then define D : Dit 
RTA
 RTA
P
 P
kit
k
kit
i
k
Patents and Firm Knowledge Diversity (D)


Another measure Shannon’s entropic statistics
First compute :
s kit 
Pkit
 Pkit
k


 1
Then define D : Dit    s kit  ln 

k 
 s kit

 

Patents and Firm Knowledge Diversity (D)

Knowledge Diversity


Run ‘KNOW_D.do’ and produce ‘KNOW_D.dta’
Estimate within regression


Merge file with ‘JENA_FIRM_FS.dta’ and
‘KNOW_E.dta’
Run ‘regression.do’
Plan of the talk
The need for “knowledge statistics”
The need for “patent statistics”
Patents and firm knowledge capital (K)
Patents and firm knowledge diversity (D)
Patents and firm knowledge relatedness (R)
Patents and firm knowledge relatedness (R)
Expertise
Diversity
Relatedness
Patents and firm knowledge relatedness (R)

(Scientific) Knowledge is dispersed



Heterogeneity of embodiments
Heterogeneity of fields and services
Knowledge leads naturally to the issue of integration



Knowledge correlates variables (Saviotti 1996)
Knowledge correlates knowledge too
Hence knowledge forms a tree (Popper 1972)
 General and abstract knowledge integrates …
 … local and concrete knowledge (Arora & Gambardella 1994)
Knowledge must be integrated
Patents and firm knowledge relatedness (R)

One concept – several definitions




Integrating knowledge is costly



Architectural competencies/integrative capabilities
Combination of applied to basic knowledge
Combination of complementary knowledge
Combining dispersed pieces of knowledge
In a non random way
Robustness checks of previous works



Too much empirical corroboration raises suspicion
Yet another sample
Yet another measure
Patents and firm knowledge relatedness (R)


Methodological challenge

Even harder to grasp and observe

No authoritative definitions and measures
KI is the result of managerial capabilities



It is costly and reveals firm discrete choices (uniqueness)
Knowledge is dispersed and must be integrated in some ways
Revealed integration, not integrative capability
Patents and firm knowledge relatedness (R)

Firms must apply basic knowledge to concrete
production processes
“in order to come up with new products and processes [-],
general and abstract knowledge has to be combined with
concrete information, because one also has attend to the
details that are typically ignored by abstract representation”
(Arora and Gambardella, 1994, p. 524)

Basic – Applied Spectrum of accumulated knowledge
Patents and firm knowledge relatedness (R)

Firms must combine complementary technologies
in order for them to render productive services
which are not reducible to their independent use
“if [Nesta] and [Criscuolo] wish to write a joint paper
together, efficiency is maximized by establishing a mode
of interaction such that [Nesta]’s knowledge is integrated
with [Criscuolo]’s knowledge while minimizing the time
spent transferring knowledge between them” (Grant,1996,
p.114):

Complementary technological competencies
Patents and firm knowledge relatedness (R)

Knowledge integration is the activity of
combining dispersed pieces of knowledge in
a non random way



Human capital
Technical artifacts
Combining technologies is just not obvious!


Firms achieve different levels of KR
Related diversification (activities, products)
performs better than aggressive diversification
(in terms of productivity)
Patents and firm knowledge relatedness (R)

The organisation of knowledge measured by means
of patent statistics
D
kk  ek   el  lk
l k
D
D
D
k
k
l k
K   ek   ek  lk
K  E  1   D  1  R 
By substitution we obtain the
following empirical model:

Qit  A  Cit  Lit   Eit E  Dit D  Rit I   euit
Patents and firm knowledge relatedness (R)
Qit  A  Cit  Lit  M it  Kit  euit



E
D
I

Qit  A  Cit  Lit  M   Eit  Dit  I it   euit
Beware that variables L and M are very rough ones!
Taking logs yields the empirical specification:
qit  a    cit    lit    mit   E  eit   D  dit  R  rit  uit
where K    K with K  E, D, R
Patents and firm knowledge relatedness (R)

Step 1. Measuring technological relatedness
Hypergeometric
ij  E  Oij  o  

2
ij

NP
ij
 N  Ni
 ij 
 T

Oij  ij
ij
Oi O j
N
 N  Nj 


N

1


Mutual information
s ij 
Oij
N
Oi O j
sij  si  s j 

N N

NP
ij
 sij 
 ln  
 sij 
Patents and firm knowledge relatedness (R)

Step 2. Measuring Weighted Average Relatedness
WAR k
P 


 P
l k
kit
l k
R it 

k
kl
kit
Pkit  WAR kit

k
Pkit
Patents and firm knowledge relatedness (R)

Knowledge relatedness: 2 Steps (2 choices)

Step 1. Measuring technological relatedness




Run ‘TAU.do’ and produce ‘tau.dta’
Parametric measures
Non parametric measures
Step 2. Measuring Weighted Average Relatedness



Run ‘KNOW_R.do’ and produce ‘KNOW_R.dta’
Fully connected graph
Maximum Spanning Tree
2
4
6
8
10
Patents and firm knowledge relatedness (R)
1970
1980
1990
year
(mean) krel_ap
2000
2010
(mean) krel_p
use F:\JENA\R.dta
collapse (mean) krel_ap krel_anp krel_p krel_np , by(year)
twoway (line krel_ap year) (line krel_p year)
2
4
6
lny
8
10
12
Patents and firm knowledge relatedness (R)
0
2
4
lnkcap85
6
Run REGRESSION.do
scatter lny lnkcap85
8
10
2
4
6
lny
8
10
12
Patents and firm knowledge relatedness (R)
0
1
2
3
lnNT
Run REGRESSION.do
scatter lny lnNT
4
5
2
4
6
lny
8
10
12
Patents and firm knowledge relatedness (R)
0
1
2
3
4
lnkrel_ap
Run REGRESSION.do
scatter lny lnkrel_ap
5
4
2
0
lnkcap85
6
8
10
Patents and firm knowledge relatedness (R)
0
1
2
3
4
lnNT
Run REGRESSION.do
scatter lnkcap85 lnNT
5
4
2
0
lnkcap85
6
8
10
Patents and firm knowledge relatedness (R)
0
1
2
3
4
lnkrel_ap
Run REGRESSION.do
scatter lnkcap85 lnkrel_ap
5
0
1
2
lnNT
3
4
5
Patents and firm knowledge relatedness (R)
0
1
2
3
4
lnkrel_ap
Run REGRESSION.do
scatter lnNT lnkrel_ap
5
Patents and firm knowledge relatedness (R)
. corr lnkcap* lnNT lnH
(obs=3353)
lnkrel_ap lnkrel_anp
lnkcap95 lnkcap85 lnkcap75
lnkcap95
lnkcap85
lnkcap75
lnNT
lnH
lnkrel_ap
lnkrel_anp
1.0000
0.9884
0.9694
0.8398
0.3953
-0.0523
-0.2058
1.0000
0.9951
0.8635
0.4005
-0.0550
-0.1829
1.0000
0.8665
0.3960
-0.0535
-0.1629
lnNT
1.0000
0.7439
-0.3412
-0.4007
lnH lnkre~ap lnkr~anp
1.0000
-0.6040
-0.6071
Run REGRESSION.do
corr lnkcap* lnNT lnH lninvspe lnkrel*
1.0000
0.8954
1.0000
Patents statistics and firm performance

Run ‘REGRESSION.do’ on the production function
DEPVAR : lny
lnl
lnk
lnkcap85
-1
0.6
(35.78)**
0.276
(16.78)**
0.143
(14.53)**
lnNT
-2
0.604
(36.16)**
0.273
(16.69)**
0.201
(12.73)**
-0.127
(4.47)**
lnkrel_ap
-3
0.603
(36.17)**
0.272
(16.56)**
0.191
(11.57)**
-0.088
(2.81)**
0.052
(2.85)**
NT_sur1
-4
0.603
(36.17)**
0.272
(16.56)**
0.155
(15.17)**
-5
0.603
(36.17)**
0.272
(16.56)**
0.158
(15.34)**
0.052
(2.85)**
-0.088
(2.81)**
0.093
(4.87)**
NT_sur2
Constant
3.311
(41.35)**
Observations
2345
Number of firm_id
103
R-squared
0.92
Absolute value of t statistics in parentheses
* significant at 5%; ** significant at 1%
3.44
(39.86)**
2344
103
0.92
3.23
(30.22)**
2337
103
0.92
3.555
(32.76)**
2337
103
0.92
-0.088
(2.81)**
3.421
(29.64)**
2337
103
0.92
Patents statistics and firm performance

Run ‘REGRESSION.do’ on the knowledge production
function (Negative binomial regressions)
DEPVAR: FPAT
lnrd
lnkcap85
-1
-2
-3
-4
-5
0.107
0.113
0.115
0.115
0.115
(6.18)**
(6.53)**
(6.61)**
(6.61)**
(6.61)**
0.737
0.627
0.638
0.738
0.73
(28.98)**
(16.72)**
(16.95)**
(28.17)**
(27.96)**
0.29
0.248
(4.18)**
(3.41)**
-0.1
-0.1
-0.214
(2.21)*
(2.21)*
(4.45)**
lnNT
lnkrel_ap
NT_sur1
0.248
(3.41)**
NT_sur2
0.248
(3.41)**
Constant
Observations
Number of firm_id
-2.754
-3.313
-2.947
-2.696
-2.321
(20.02)**
(18.70)**
(11.88)**
(12.74)**
(11.62)**
2023
2022
2016
2016
2016
94
94
94
94
94
Absolute value of z statistics in parentheses
* significant at 5%; ** significant at 1%
Patent statistics and Firm Performance
The Road Ahead

Knowledge integration




Division of labour between companies



The search for complementary technologies is costly
Costs may be decreased with the familiarity of the new tech.
Integration combines similarity and complementarity
Test of Richardson’s ideas
Firms relate with complementary organizations
Imagine new statistics



See NBER database!
Have we exhausted patent data?
National census/CIS/R&D survey to account for productivity
gains
Reference list
Patent statistics
o
Archibugi, D. Patenting as an Indicator of Technological Innovation: A Review, Science
and Public Policy, 1992, 19(6), 357-68.
o
Pavitt, K., 1988, Uses and Abuses of Patent Statistics, in A. F. J. van Raan, (Ed.) Handbook
of Quantitative Studies of Science and Technologies, Amsterdam: Elsevier Science
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o
Dibiaggio, L. Nesta, L. (2005) “Patent Statistics, Knowledge Specialisation and the
Organisation of Competencies”, Revue d’Economie Industrielle, 110, 106-126.
o
Nesta, L., P. Patel (2004) “National Patterns of Technology Accumulation: Use of Patent
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o
Theil, H., 1972, Statistical Decomposition Analysis, North-Holland Publishing Compnay,
Amsterdam, London.
Reference list
Knowledge Expertise – E o
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American Economic Review, 76(1), 141-154.
o
Griliches, Z., 1990, Patents Statistics as Economic Indicators: A Survey, Journal of
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o
Griliches, Z. and K. Clark, 1984, Productivity Growth and R&D at the Business Level:
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o
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Reference list
Knowledge Diversity – D –
o
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o
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o
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Extension of Rumelt's Work, Academy of Management Journal, 25, 299-307.
o
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o
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o
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o
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o
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o
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Reference list
Knowledge Relatedness – R –
o
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Technological Diversification, Research Policy, 32, 69-87.
o
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o
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o
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o
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Innovative Performance. Evidence from the Bio-Pharmaceutical Industry, Journal of
Industrial Economics 53(1): 123-142.
o
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University Press, Cambridge, New York and Melbourne).
o
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o
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