Patents statistics and firm performance Lionel Nesta Observatoire Français des Conjonctures Economiques Department of Research on innovation and competition

Patents statistics and firm performance

Lionel NestaObservatoire 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 observeNo authoritative measures/definition Use of traces of knowledge

Readily available materialR&D expensesPublicationsPatent data

Intangible capital Observable components Non Observable components

Observable part Pervasive and systematic properties Which are they ?

Firm knowledge

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)


Source: OECD


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


Source: OECD


Source: OECD


Source: OECD


Source: OECD


Source: Chiara Criscuolo (Not dated) Boosting Innovation and Productivity Growth in Europe:

The hope and the realities of the EU’s ‘Lisbon agenda’


Source: OECD

Plan of the talk






q

c*

msB

mpB

B

q1 q*

Marginal external benefit

The need for patent statistics

Why do we need a patent system ?


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.


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


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)


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.


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


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…


RTA 1

RTA < 1

FGSI < 1 FGSI 1

Strengthening leading position

Losing momentum

Lagging behind

Building up capacity

RTA 1

RTA < 1

FGSI < 1 FGSI 1

Strengthening leading position

Losing momentum

Lagging behind

Building up capacity

Figure 1. Technology map of countries

Source: Nesta & Patel (2004)


Figure 2. Technology map of countries: Chemical-related (1991-2000)



Figure 4. Technology map of countries: Mechanical-related (1991-2000)



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

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






Reticular nature Structure of correlation Fractal structure (variable and relationships)

Variety of states 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) Knowledge mobilized ⇒ competencies Specialized competencies

Collective nature Interactions between pieces of knowledge Equipment, individuals

Knowledge base Properties of knowledge stock Architectural knowledge Organization of knowledge


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


The economics of R&D

The productivity of R&D relates a set of input with output

With K, the knowledge capital of the firm, being a function of current and past R&D investment R:

The lagged structure of R&D investments


0 1 1 2 2( ) t t t tW B R w R w R w R

( ) ,K G W B R v

, ,Q F X K u


Knowledge stocks (Griliches, 1979)

11it it itK R K

Beware that variables L and M are very rough ones! Taking logs yields the empirical

specification:

it it it it it itq a c l m e u

ituit it it it itQ A C L M K e

ituit it it it itQ A C L M E 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)


Import firm patent data Run ‘DATA_IMPORT.do’ and produce ‘JENA_PAT.dta’

Knowledge capital 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






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


Diversity as a pervasive property of firm KB

D

k k ll k

k e e

K E D

1 1

D D D D D D

k l k kk k l k k k l k

K e e e e E E D


itE D uit it it it itQ A C L M E D e


specification:

where with ,K K K E D

it it it it E it D it itq a c l m e d u



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:

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


k kitit dD

50 t,kikit pP

Another measure used is the coefficient of variation of RTA

First compute :

Then define D :


kit kitk

kitkit kit

i i k

P PRTA

P P

RTAit

RTA

D

Another measure Shannon’s entropic statistics

First compute :

Then define D :


kitkit

kitk

Ps

P

it kitk kit

1D s ln

s

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






Expertise

Diversity

Relatedness


(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


One concept – several definitions Architectural competencies/integrative capabilities Combination of applied to basic knowledge Combination of complementary knowledge

Integrating knowledge is costly 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


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


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


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


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)


The organisation of knowledge measured by means of patent statistics

D

k k l lkl k

k e e

1 1K E D R

D D D

k k lkk k l k

K e e

By substitution we obtain the following empirical model:

itE D I uit it it it it itQ A C L E D R e


itE D I u

it it it it it itQ A C L M E D I e


specification:

where with , , K K K E D R

it it it it E it D it R it itq a c l m e d r u



Step 1. Measuring technological relatedness

i jij ij

O OE O o

N

j2 iij ij

N NN N

T N 1

ij ijNPij

ij

O

Hypergeometric Mutual information

ijNPij

ij

sln

s

ijij

Os

N

jiij i j

OOs s s

N N


Step 2. Measuring Weighted Average Relatedness

kit kll kk

kitl k

PWAR

P

kit kitkit

kitk

P WARR

P


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


use F:\JENA\R.dtacollapse (mean) krel_ap krel_anp krel_p krel_np , by(year)twoway (line krel_ap year) (line krel_p year)

24

68

10

1970 1980 1990 2000 2010year

(mean) krel_ap (mean) krel_p


Run REGRESSION.doscatter lny lnkcap85

24

68

10

12

lny

0 2 4 6 8 10lnkcap85


Run REGRESSION.doscatter lny lnNT

24

68

10

12

lny

0 1 2 3 4 5lnNT


Run REGRESSION.doscatter lny lnkrel_ap

24

68

10

12

lny

0 1 2 3 4 5lnkrel_ap


Run REGRESSION.doscatter lnkcap85 lnNT

02

46

81

0ln

kca

p85

0 1 2 3 4 5lnNT


Run REGRESSION.doscatter lnkcap85 lnkrel_ap

02

46

81

0ln

kca

p85



Run REGRESSION.doscatter lnNT lnkrel_ap

01

23

45

lnN

T




Run REGRESSION.docorr lnkcap* lnNT lnH lninvspe lnkrel*

lnkrel_anp -0.2058 -0.1829 -0.1629 -0.4007 -0.6071 0.8954 1.0000 lnkrel_ap -0.0523 -0.0550 -0.0535 -0.3412 -0.6040 1.0000 lnH 0.3953 0.4005 0.3960 0.7439 1.0000 lnNT 0.8398 0.8635 0.8665 1.0000 lnkcap75 0.9694 0.9951 1.0000 lnkcap85 0.9884 1.0000 lnkcap95 1.0000 lnkcap95 lnkcap85 lnkcap75 lnNT lnH lnkre~ap lnkr~anp

(obs=3353). corr lnkcap* lnNT lnH lnkrel_ap lnkrel_anp

Run ‘REGRESSION.do’ on the production function


DEPVAR : lny -1 -2 -3 -4 -5lnl 0.6 0.604 0.603 0.603 0.603

(35.78)** (36.16)** (36.17)** (36.17)** (36.17)**lnk 0.276 0.273 0.272 0.272 0.272

(16.78)** (16.69)** (16.56)** (16.56)** (16.56)**lnkcap85 0.143 0.201 0.191 0.155 0.158

(14.53)** (12.73)** (11.57)** (15.17)** (15.34)**lnNT -0.127 -0.088

(4.47)** (2.81)**lnkrel_ap 0.052 0.052 0.093

(2.85)** (2.85)** (4.87)**NT_sur1 -0.088

(2.81)**NT_sur2 -0.088

(2.81)**Constant 3.311 3.44 3.23 3.555 3.421

(41.35)** (39.86)** (30.22)** (32.76)** (29.64)**Observations 2345 2344 2337 2337 2337Number of firm_id 103 103 103 103 103R-squared 0.92 0.92 0.92 0.92 0.92Absolute value of t statistics in parentheses* significant at 5%; ** significant at 1%

Run ‘REGRESSION.do’ on the knowledge production function (Negative binomial regressions)


DEPVAR: FPAT -1 -2 -3 -4 -5

lnrd 0.107 0.113 0.115 0.115 0.115

(6.18)** (6.53)** (6.61)** (6.61)** (6.61)**

lnkcap85 0.737 0.627 0.638 0.738 0.73

(28.98)** (16.72)** (16.95)** (28.17)** (27.96)**

lnNT 0.29 0.248

(4.18)** (3.41)**

lnkrel_ap -0.1 -0.1 -0.214

(2.21)* (2.21)* (4.45)**

NT_sur1 0.248

(3.41)**

NT_sur2 0.248

(3.41)**

Constant -2.754 -3.313 -2.947 -2.696 -2.321

(20.02)** (18.70)** (11.88)** (12.74)** (11.62)**

Observations 2023 2022 2016 2016 2016

Number of firm_id 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

The search for complementary technologies is costly Costs may be decreased with the familiarity of the new

tech. Integration combines similarity and complementarity

Division of labour between companies 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

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 Publishers.

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 Statistics”, in Kluwer Handbook of Quantitative S&T Research, Schmock, U. (Ed), Book Chapter.

o Scherer, F. M., 1982, Using Linked Patent and R&D Data to Measure Interindustry Technology Flows, Review of Economics and Statistics, 64, 627-634.

o Theil, H., 1972, Statistical Decomposition Analysis, North-Holland Publishing Compnay, Amsterdam, London.

Patent statistics

Reference list

o Griliches, Z., 1986, Productivity, R&D, and Basic Research at the Firm Level in the 1970s, American Economic Review, 76(1), 141-154.

o Griliches, Z., 1990, Patents Statistics as Economic Indicators: A Survey, Journal of Economic Literature, 28, 1661-707.

o Griliches, Z. and K. Clark, 1984, Productivity Growth and R&D at the Business Level: Results From the PIMS Data Base, in Z. Griliches (Ed.) R&D, Patents and Productivity (Chicago, University of Chicago Press).

o Griliches, Z. and J. Mairesse, 1983, Comparing Productivity Growth: An exploration of the French and U.S. Industrial and Firm Data, European Economic Review, 21, 89-119.

o Griliches, Z. and J. Mairesse, 1984, Productivity and R&D at the Firm Level, in Z. Griliches (Ed.) R&D, Patents and Productivity (Chicago, University of Chicago Press).

o Griliches, Z. R&D, Patents and Productivity (Chicago, University of Chicago Press).

o Hall, B.H., A.B. Jaffe and M. Trajtenberg, 2001, The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools, NBER Working Paper 8498.

o Jaffe, A. D., 1986, Technological Opportunity and Spillovers of R&D: Evidence From Firms Patents, Profits and Market Values, American Economic Review, 76, 984-1001

Knowledge Expertise – E -

Reference list

o Fai, F., 2003, Corporate Technological Competence and the Evolution of Technological Diversification. Cheltenham, UK and Northampton, Massachusetts, USA: Edward Elgar.

o Montgomery, C.A. and S. Hariharan, 1991, Diversified Expansion by Large Established Firms, Journal of Economic Behavior and Organization, 15(1), 71-89.

o Montgomery, C.A., 1982, Diversification, Market Structure, and Firm Performance: An Extension of Rumelt's Work, Academy of Management Journal, 25, 299-307.

o Palepu, K., 1985, Diversification Strategy, Profit Performance and the Entropy Measure, Strategic Management Journal, 6, 239-255.

o Patel, P. and K. Pavitt, 1997, The Technological Competencies of the World’s Largest Firms: Complex and Path-Dependent, But Not Much Variety, Research Policy, 36, 141-156.

o Rajan, R., H. Servaes and L. Zingales, 2000, The Cost of Diversity: The Diversification Discount and Inefficient Investment, Journal of Finance, 55, 35-80.

o Ramanujam, V. and P. Varadarajan, P., 1989, Research on Corporate Diversification: a Synthesis, Strategic Management Journal, 10, 523-551.

o Rumelt, R. P., 1974, Strategy, Structure, and Economic Performance, Harvard: Harvard Business School Press.

o Schoar, A., 2002, Effects of Corporate Diversification on Productivity, Journal of Finance, 57, 2379-2403.

Knowledge Diversity – D –

Reference list

o Breschi, S., F. Lissoni and F. Malerba, 2003, Knowledge-Relatedness in Firm Technological Diversification, Research Policy, 32, 69-87.

o Fai, F., 2003, Corporate Technological Competence and the Evolution of Technological Diversification. Cheltenham, UK and Northampton, Massachusetts, USA: Edward Elgar.

o Henderson, R.M. and I. Cockburn, 1996, Scale, Scope and Spillovers: the Determinants of Research Productivity in Drug Discovery, Rand Journal of Economics, 27(1), 32-59.

o Nesta, L. (2008), Knowledge and Productivity in the Worlds Largest Manufacturing Corporations, 2008, Journal of Economic Behavior and Organization 67(3): 886-902.

o Nesta, L., Saviotti, P.P. (2005), The Coherence of the Knowledge Base and the Firms’ Innovative Performance. Evidence from the Bio-Pharmaceutical Industry, Journal of Industrial Economics 53(1): 123-142.

o Scott, J. T., 1993, Purposive Diversification and Economic Performance (Cambridge University Press, Cambridge, New York and Melbourne).

o Scott, J. T. and G. Pascoe, 1987, Purposive diversification of R&D in Manufacturing, Journal of Industrial Economics, 36, 193-205.

o Teece, D. J., R. P. Rumelt, G. Dosi and S. Winter, 1994, Understanding Corporate Coherence: Theory and Evidence, Journal of Economic Behavior and Organisation, 22, 1-30.

Knowledge Relatedness – R –

Reference list

Documents

Patents statistics and firm performance Lionel Nesta Observatoire Français des Conjonctures Economiques Department of Research on innovation and competition