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ÉCOLE CENTRALE DES ARTS

ET MANUFACTURES

" ÉCOLE CENTRALE PARIS »

THÈSE

présentée et soutenue par :

Hugo PILLU

Le 18/12/2009

pour l"obtention du grade de

DOCTEUR

Spécialité : Sciences Economiques Laboratoire d"accueil : ERASME

TITRE :

Knowledge Flows through Patent Citation Data

Directeur de Thèse :

Paul ZAGAME, Professeur à l"Université Paris 1 - Panthéon Sorbonne

Membre du Jury :

Dominique FORAY, Professeur à l"Ecole Polytechnique Fédérale de Lausanne, rapporteur Jean-Luc GAFFARD, Professeur à l"Université de Nice - Sophia Antipolis, rapporteur Gilles KOLEDA, Directeur des Etudes à Coe-Rexecode Bruno van POTTELSBERGHE, Professeur à l"Université Libre de Bruxelles, président

Numéro d"ordre : 2009ECAP0048

Knowledge

ows through patent citation data

Hugo Pillu

ERASME Ecole Centrale Paris, Grande voie des vignes, 92295 Ch^atenay Malabry cedex, FRANCE Phone: +0033 (0)1 41 13 11 79 Fax: +0033 (0)1 41 13 16 67 Email: hugo.pillu@ecp.fr2

Acknowledgement

First I would like to express my deep and sincere gratitude to my supervisor, Professor Paul Zagame, Head of ERASME Laboratory and professor at the University Paris 1 - Pantheon Sorbonne, for his supervision, advises, and guidance from the early stages of this research. His vast knowledge and his logical way of thinking have been of great value to me. Above all and most needed, he gave me unwavering encouragement and support in various ways. His scientist intuitions made him a constant oasis of ideas and passions in science, which exceptionally inspired me and enriched my improvement as the student, the researcher and the scientist I want to be. I am indebted to him more than he knows. A special thank goes to Gilles Koleda, who is most responsible for the completion of my rst paper and the challenging research that lies behind it. He taught me how to write academic papers, helped me become a researcher, and highlighted my most signicant ideas. Without his encouragement and constant guidance in the rst period of my Ph.D., I could not have nished this dissertation. His involvement and his originality triggered and nourished my intellectual maturity. A maturity that I will enjoy for a long time to come. I gratefully thank Professor Dominique Foray and Professor Jean-Luc Gaard for agreeing to be

the ocial referees of this thesis and for their detailed review. I also wish to thank Professor Bruno

van Pottelsberghe, I am honored he has agreed to participate in the jury of this thesis. During this work in the ERASME Laboratory at Ecole Centrale Paris, I collaborated with many colleagues for whom I have great regard, and I wish to extend my warmest thanks to everyone who helped me in my work. I thank Pascal Da Costa and Florent Pratlong for their intelligent comments on my work, I also thank Arnaud Fougeyrollas and Pierre Le Mouel for their useful comments and the fact they introduced me to one of the most challenging science that is modeling ; and Danielle Schirmann Duclos for her energy and for her help during these years. Many thanks also go to Baptiste Boitier, Gabriel Galland, Oualid Gharbi, and Boris Le Hir for giving me a good time when working with them. A special thanks goes to Boris, who taught me how to y a helicopter. The research of this thesis was sponsored by the Ecole Centrale Paris for three years (2006 to

2009). I am also grateful to the Research D.G. of the European Commission for their additional

nancial support of my thesis and of several conferences. It is also a pleasure to pay tribute to Marion Senant, Marine Rabreau and Rudy Bezir for reading over my manuscript and for their comments on the latter, it was a great help for me.3 I thank my family: my mother Nathalie Leconte, my father Pascal Pillu and my stepfather Francois Coutin, who taught me the joy of intellectual pursuit, provided me moral advises and supported me ever since I was a child. I have been fortunate to come across many funny and good friends, without whom life would be bleak. It would be dicult to list them all because it would be dangerous to forget some. Last but not least, I wish to thank Charlotte for accompanying me during these years, for putting up with me and just be present.4

Contents

1 Introduction17

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17

1.2 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20

1.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24

2 Knowledge Spillovers: a literature review 29

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

29

2.2 An overview of knowledge externalities . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31

2.2.1 Knowledge as an economic good . . . . . . . . . . . . . . . . . . . . . . . . . . .

31

2.2.2 Knowledge transfers mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . .

32

2.2.3 The genesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

33

2.3 Two kinds of spillovers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34

2.3.1 Dierence between private and social return created by knowledge spillovers . . .

36

2.3.2 Dierence between private and social return created by a rent spillover . . . . . .

38

2.4 Dierent channels used in the measure of knowledge externalities . . . . . . . . . . . . .

40

2.4.1 Economic transaction

ows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.4.1.1 I-O matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

42

2.4.1.2 Import

ows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.4.1.3 Which spillovers aspects are measured through economic good

ows? . 44

2.4.2 Foreign direct investment: FDI . . . . . . . . . . . . . . . . . . . . . . . . . . . .

44

2.4.3 Technological

ow matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2.4.4 Immaterial transmissions channels . . . . . . . . . . . . . . . . . . . . . . . . . .

48

2.4.4.1 Technological proximity matrices . . . . . . . . . . . . . . . . . . . . . .

48
5

CONTENTS

2.4.4.2 matrices based on technological classication of patents . . . . . . . . .

50

2.4.5 Patent citations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

50

2.4.5.1 The mechanism of patent citations . . . . . . . . . . . . . . . . . . . . .

50

2.4.5.2 Are Citations a good knowledge

ows measure? . . . . . . . . . . . . . 51

2.4.5.3 The use of patent citation data to measure knowledge

ows . . . . . . . 53

2.5 Geographical distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55

2.5.1 The dierent measures used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55

2.5.2 What is the geographical component of knowledge spillover? . . . . . . . . . . .

56

2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58

2.7 Annexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59

2.7.1 Note on the adoption capacity of technology . . . . . . . . . . . . . . . . . . . .

59

2.7.2 Production Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

60

3 Knowledge Spillovers: A Meta-Analysis 63

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

63

3.2 On the measurement of knowledge spillovers eects . . . . . . . . . . . . . . . . . . . . .

65

3.2.1 Specications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65

3.2.2 Aggregation level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69

3.2.3 Spillovers channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69

3.2.4 Others explanatory variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71

3.3 Meta-Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71

3.3.1 Sample selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

72

3.3.2 Dealing with multiple results per study . . . . . . . . . . . . . . . . . . . . . . .

73

3.3.3 Publication Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

76

3.3.4 Meta Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

78

3.4 Econometric results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79

3.4.1 FTA and PET tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79

3.4.2 MRA econometrics results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

81

3.4.2.1 Denition of the moderators variables . . . . . . . . . . . . . . . . . . .

81

3.4.2.2 MRA of Spillovers at a rm level within a same sector . . . . . . . . . .

82

3.4.2.3 MRA of intersectoral spillovers within a same country . . . . . . . . . .

82

3.4.2.4 MRA of Spillovers between countries . . . . . . . . . . . . . . . . . . .

82 6

CONTENTS

3.4.2.5 MRA of Spillovers within sectors and between countries . . . . . . . . .

83

3.4.2.6 MRA of Spillovers between sectors and countries . . . . . . . . . . . . .

84

3.4.2.7 General remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

85

3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89

3.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89

3.6.1 Methodological issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89

3.6.2 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

92

4 Patent Citation Data and Knowledge Flows 101

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

101

4.2 Patent and Patent Citation as indicator of knowledge and knowledge

ows . . . . . . . . 103

4.2.1 What is a patent ? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

104

4.2.2 Patent Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

107

4.2.2.1 EPO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

109

4.2.2.2 JPO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

111

4.2.2.3 USPTO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

112

4.2.2.4 National European patent oces . . . . . . . . . . . . . . . . . . . . . .

114

4.2.2.5 Cost for obtaining a patent . . . . . . . . . . . . . . . . . . . . . . . . .

114

4.2.3 Patent as an indicator of innovative activities . . . . . . . . . . . . . . . . . . . .

115

4.2.3.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

116

4.2.3.2 Drawbacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

116

4.2.4 Citations and knowledge

ows . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

4.2.4.1 Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

119

4.2.4.2 Practical dierences between oces . . . . . . . . . . . . . . . . . . . .

120

4.2.4.3 Patent citations and knowledge

ows . . . . . . . . . . . . . . . . . . . 121

4.3 The citation function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

124

4.3.1 The origin of the patent citation function . . . . . . . . . . . . . . . . . . . . . .

124

4.3.2 Rationalization of the patent citation function. . . . . . . . . . . . . . . . . . . .

128
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