Industrial trajectory and regulation of the French TV-market Victor Lavialle To cite this version: Victor Lavialle. Industrial trajectory and regulation of the French TV-market. Economics and Fi- nance. Université Paris sciences et lettres, 2019. English. NNT : 2019PSLEM049. tel-02439079 HAL Id: tel-02439079 https://pastel.archives-ouvertes.fr/tel-02439079 Submitted on 14 Jan 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Prepar´ ee´ a` MINES ParisTech Trajectoire Industrielle et Reglementation´ de l’Audiovisuel en France Industrial Trajectory and Regulation of the French TV-market Soutenue par Composition du jury : Victor Lavialle Franc¸oise Benhamou Le 17 decembre´ 2019 Professeure d’Economie,´ Universite´ Presidente´ Paris 13 Marc BOURREAU Professeur d’Economie,´ Tel´ ecom´ Paris- Rapporteur ´ o Tech Ecole doctorale n 396 Paul BELLEFLAMME Economie,´ Organisation, Professeur, UC Louvain Rapporteur Societ´ e´ Thomas PARIS Professeur, HEC Paris Examinateur Elisabeth´ FLURY-HERARD¨ Vice-presidente´ de l’Autorite´ de la Con- Examinatrice Specialit´ e´ currence Olivier BOMSEL Economie´ et Finance Professeur d’Economie,´ Mines Paris- Directeur de these` Tech Acknowledgements First and foremost, I would like to express my gratitude to Olivier Bomsel, for trusting me with this challenging research topic, and for providing me with a dedicated and pedagogical supervision. My sincere thanks to Fran¸coiseBenhamou, Paul Belleflamme, Marc Bour- reau, Elisabeth´ Fl¨ury-H´erardand Thomas Paris, who have accepted to serve as my referees. I want to thank the CNC, CANAL+, M´ediam´etrieand France T´el´evisions for letting me access and work with their data at different times during my PhD. I thank my colleagues from CERNA in for the stimulating discussions and for all the fun we have had in the last three years: Guillaume, Con- nie, Anne-Sophie, Simon, Damien, Laurie, Dandan, Paul-Herv´e,Maddalena, Sabrine, Btissam, Julie, Jiekai. I would like to give special thanks to Maxime, Matthieu, Ivo, and Thibault for their tremendous help and collaboration dur- ing this work. I am profoundly grateful to Sesaria, whose conversation never failed to make my day better, and Barbara for helping me with administra- tive work. I am thankful to my closest friends, for their support during these years. Last but not the least, I would like to thank Jeanne and my family for their love and encouragement throughout writing this thesis. i Contents Introduction 1 1 Definitions and perimeter of the thesis 6 1.1 Media and network effects . .6 1.2 Institutions and institutional change . .8 1.3 Ecosystems . 12 1.4 From State monopoly to media platforms: institutional tra- jectory of the French audiovisual ecosystem . 13 1.4.1 Initial conditions: the State monopoly . 13 1.4.2 1982: the “concession-obligations” system . 15 1.4.3 Digitization and the fall of barriers to entry . 16 1.4.4 Platform entry . 17 2 A Multivariate Analysis of Regulated Funding of the French Cinema by Broadcasters 19 2.1 Introduction . 21 2.2 Institutional framework . 22 2.2.1 Historical context . 22 2.2.2 Details on the French regulation . 24 2.3 Descriptive statistics . 25 2.4 Determinants of broadcasters’ investment choices . 29 2.4.1 Separated analysis . 31 2.4.2 Joint analysis . 32 2.5 Conclusion . 35 2.6 Appendix . 36 2.6.1 Regression Tables . 37 2.6.2 Details of the classification protocol . 41 3 Rational Addiction to Audiovisual Narratives: an Analysis of Broadcasting and Consumption of Fiction in France 46 3.1 Introduction . 47 ii 3.2 Economic effects of narrative: habits and addiction . 48 3.2.1 TV series viewing in social sciences’ literature . 48 3.2.2 Preferences and cultural goods: a theoretical framework 51 3.3 Competition for broadcasting of fiction on the free-to-air tele- vision market . 54 3.3.1 Demand for TV programs: the audience scatters . 54 3.3.2 Strategies and competition for the broadcasting of fiction 57 3.4 The effect of narrative format on the demand for TV series . 62 3.4.1 Serials induce non-linear consumption . 62 3.4.2 Model of rational addiction . 64 3.4.3 Case study 1: Plus Belle la Vie ............. 67 3.4.4 Case study 2: Desperate Housewives .......... 71 3.5 Conclusion . 74 3.6 Appendix . 75 3.6.1 Rational addiction model . 76 3.6.2 ARIMA Models . 77 4 Regulation game and copyright in digital media industries: the destructive-creation of the French audiovisual ecosystem 80 4.1 Introduction . 81 4.2 The System of obligatory funding and its implications . 83 4.2.1 The French system of obligatory funding . 83 4.2.2 Data and empirical methodology . 85 4.2.3 Industrial organization and regulation capture . 86 4.2.4 How works the regulation game . 89 4.3 Entry of platforms and transition to a negative-sum game (2015-2018) . 91 4.3.1 Digitization and the fall of barriers to entry . 91 4.3.2 Consequences on the competition game . 93 4.3.3 The destructive creation of the French audiovisual ecosys- tem............................. 95 4.4 Conclusion . 97 4.5 Appendix . 99 4.5.1 Additional Tables . 99 4.5.2 Hierarchical upward classification . 100 Conclusion 107 Conclusion et R´esum´een Fran¸cais 111 References 115 iii List of Figures 2.1 Cinema: evolution of producer and broadcaster contributions . 27 2.2 Dendogram of the classification algorithm . 42 2.3 Criteria for the choice of the number of classes . 43 3.1 Average audience for fiction programs (thousands) . 55 3.2 Average audience for fiction programs for each channel (thou- sands) . 55 3.3 Audience for TV series (mean) . 56 3.4 Evolution of TV series broadcasting . 57 3.5 Total hours of fiction aired on free-TV . 58 3.6 Program choice for TF1 . 59 3.7 Program choice for M6 . 59 3.8 Live audience and share of serials in broadcasting (TF1) . 60 3.9 Catch-up audience (thousands) . 61 3.10 Channel differentiation by program type (2016) . 62 3.11 Testing for rupture in the estimation . 72 3.12 Mean of monthly audience . 78 3.13 Monthly live audience for Plus Belle la Vie (2011-2015) . 78 4.1 Window time-frame, Chronologie des M´edias .......... 85 4.2 Audiovisual Fiction and cinema Funding . 88 4.3 International funding strategies . 89 4.4 TV and Cinema exports . 89 4.5 TV audience . 93 4.6 Rise in capital-intensity of audiovisual programs . 94 4.7 Concentration among the fiction producers . 95 4.8 Dendogram for cluster analysis . 103 4.9 Dendogram for cluster analysis (cinema) . 105 iv List of Tables 1.1 Audiovisual Ecosystem Timeline (Acrimed.com, viepublique.fr) ................................... 18 2.1 Simplified representation of obligatory investments . 25 2.2 Database description . 26 2.3 Financing of French movies and audiovisual works (2007-2015) 26 2.4 Frequency of investment for broadcasters . 27 2.5 Investment correlation matrix between groups of broadcasters 28 2.6 Correlation with movie commercial success . 30 2.7 Ecosystem of audiovisual production (2007-2015) . 31 2.8 Ecosystem of cinema production (2007-2015) . 31 2.9 Variation of probability of investment: probit model . 33 2.11 Additional Regression (1) . 37 2.12 Additional Regression (3) . 38 2.13 Additional Regression (4) . 39 2.14 Additional Regression (5) . 40 2.15 Additional Regression (6) . 41 2.16 Principal Components Analysis . 42 2.17 PCA: Principal Components . 42 2.18 Calinski index . 44 2.19 Duda/Hart criterium . 44 2.10 Multivariate probit model (separated analysis) . 45 3.1 Estimation results with heteroskedasticity-consistent standard errors . 64 3.2 Estimation results: rational addiction model . 66 3.3 Live audience for Plus Belle la Vie and first-difference series . 67 3.4 Estimation of live audience as a seasonal autoregressive pro- cess . 69 3.5 Estimation of catch-up audience as a seasonal autoregressive process . 70 3.6 Augmented Dickey-Fuller test . 71 v 3.7 ARIMA model for Desperate Housewives ............ 71 3.8 Description of the database . 75 3.9 Definition of narrative structures . 76 3.10 Rational addiction model on catch-up audience . 76 3.11 Rational addiction model: Independent-episodes series . 77 3.12 Autocorrelations for live audience (Plus Belle la Vie)..... 77 3.13 Autocorrelations for monthly live audience (Plus Belle la Vie) 79 3.14 Estimation results : arima . 79 4.1 Database Description . 86 4.2 The Audiovisual production industry - Clustering . 87 4.3 The Cinema production industry - Clustering . 87 4.4 Evolution of the individual watching time: live TV+replay (M´ediam´etrie). 92 4.5 Simplified representation of obligatory investments . 99 4.6 Fiction Funding (2007-2018), M¤ ................ 99 4.7 Cinema Funding (2007-2018), M¤ ................ 99 4.8 Number of fiction hours . 100 4.9 Gini indexes . 100 4.10 Principal components analysis . 102 4.11 Principal components analysis: variable projections . 103 4.12 Duda and Calinski indicators . 104 4.13 Principal components: cinema . 105 4.14 Principal components: cinema, projection . 105 4.15 Duda and Calinski criterion: cinema . 106 vi Introduction Broadcast television began in the mid-1930s, as an evolution of radio-diffusion technologies. It then developed as a mass media in the second half of the twentieth century. Economic and institutional aspects were central to this development process, and explain the existence of radically different trajec- tories of evolution.