Patents statistics and firm performance

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

2. 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

3. 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 Methodological parti pris

4. 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

5. Intangible capital Observable components Non Observable components Observable part Pervasive and systematic properties Which are they ? Firm knowledge

6. 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)

7. The need for “knowledge statistics” Source: OECD

8. 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

9. The need for “knowledge statistics” Source: OECD

10. The need for “knowledge statistics” Source: OECD

11. The need for “knowledge statistics” Source: OECD

12. The need for “knowledge statistics” Source: OECD

13. 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’

14. The need for “knowledge statistics” Source: OECD

15. 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)

16. Marginal external benefit msB mpB q1 q* The need for patent statistics • Why do we need a patent system ? B c* q

17. 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.

18. 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

19. 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)

20. 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.

21. 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

22. 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…

23. The need for patent statistics Figure 1. Technology map of countries Source: Nesta & Patel (2004)

24. The need for patent statistics Figure 2. Technology map of countries: Chemical-related (1991-2000) Source: Nesta & Patel (2004)

25. The need for patent statistics Figure 4. Technology map of countries: Mechanical-related (1991-2000) Source: Nesta & Patel (2004)

26. 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

27. The need for patent statistics We will use patents to describe firm knowledge characteristics and link it with firm performance • 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)

28. 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)

29. 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)

30. 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 Patents and Firm Knowledge Capital (E)

31. 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)

32. 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 Patents and Firm Knowledge Capital (E)

33. Patents and Firm Knowledge Capital (E) Knowledge stocks (Griliches, 1979)

34. Patents and Firm Knowledge Capital (E) Beware that variables L and M are very rough ones! Taking logs yields the empirical specification:

35. 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)

36. 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’ Patents and Firm Knowledge Capital (E)

37. 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)

38. Patents and Firm Knowledge Diversity (D) Expertise Diversity

39. 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)

40. Patents and Firm Knowledge Diversity (D) Diversity as a pervasive property of firm KB

41. Patents and Firm Knowledge Diversity (D) Beware that variables L and M are very rough ones! Taking logs yields the empirical specification: where

42. Let pkitbe 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 Patents and Firm Knowledge Diversity (D)

43. Another measure used is the coefficient of variation of RTA First compute : Then define D : Patents and Firm Knowledge Diversity (D)

44. Another measure Shannon’s entropic statistics First compute : Then define D : Patents and Firm Knowledge Diversity (D)

45. 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’ Patents and Firm Knowledge Diversity (D)

46. 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)

47. Patents and firm knowledge relatedness (R) Expertise Diversity Relatedness

48. (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)

49. 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 Patents and firm knowledge relatedness (R)

50. 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)