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Essays on Economics of the Arts View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by IMT E-Theses IMT School for Advanced Studies, Lucca Lucca, Italy Essays on Economics of the Arts PhD Program in Economics XXIX Cycle By Francesco Angelini 2017 The dissertation of Francesco Angelini is approved. Program Coordinator and Supervisor: Prof. Massimo Riccaboni, IMT School for Advanced Studies Lucca Co-Advisor: Prof. Andrea Vindigni, University of Genova Co-Advisor: Prof. Davide Ticchi, Marche Polytechnic Univer- sity The dissertation of Francesco Angelini has been reviewed by: Prof. Guido Candela, University of Bologna Prof. Lorenzo Zirulia, University of Bologna IMT School for Advanced Studies, Lucca 2017 To Beatrice, Ezio, and Paola “Art shouldn’t be fanatical.” Willem de Kooning Contents List of Figures xiv List of Tables xvi Acknowledgements xviii Vita and Publications xx Abstract xxii 1 Introduction 1 2 Understanding the artwork pricing 7 2.1 Introduction . .7 2.2 Contribution to the literature . 11 2.3 The model . 14 2.3.1 Tertiary market . 23 2.3.2 Secondary market . 24 2.3.3 Primary market . 26 2.3.4 The gallery’s and the insider’s choices . 27 2.3.5 The artist’s choice . 30 2.3.6 Comparative statics . 36 2.4 Conclusions . 39 x 3 Value and information in the art market 42 3.1 Introduction . 42 3.2 State of the Art . 44 3.2.1 On value of cultural goods . 44 3.2.2 On information in the art market . 58 3.3 The full-information model . 68 3.4 The “insider-outsider” model . 71 3.4.1 Art as a credence good . 75 3.4.2 Art as an experience good . 77 3.4.3 Art as a search good . 80 3.5 Discussion . 81 3.6 Conclusions . 87 4 A new empirical measure of contemporaneous and mod- ern artists’ talent and fame 91 4.1 Introduction . 91 4.2 Literature review: state of the art and our contri- bution . 95 4.3 Data and empirical models . 100 4.3.1 Data description . 100 4.3.2 Empirical models . 106 4.4 Results . 107 4.4.1 The benchmark model: Hedonic OLS re- gression . 108 4.4.2 The models for art genres on overall data and art-genre sub-samples . 112 4.4.3 The year-by-year models . 118 4.4.4 The price-effect model: Hedonic quantile regression . 121 4.4.5 The year-by-year price-effect models . 125 xi 4.4.6 Analysis of artists’ coefficients dynamics and ranking . 126 4.5 Conclusions . 133 A Proofs of Lemmas and Propositions in Chapter 2 137 A.1 Proof of Lemma 2.1 . 137 A.2 Proof of Lemma 2.2 . 142 A.3 Proof of Lemma 2.3 . 144 A.4 Proof of Lemma 2.4 . 146 A.5 Proof of Lemma 2.5 . 146 A.6 Proof of Lemma 2.6 . 148 A.7 Proof of Proposition 2.1 . 149 A.7.1 Proof of Proposition 2.1.1 . 149 A.7.2 Proof of Proposition 2.1.2 . 150 A.7.3 Proof of Proposition 2.1.3 . 150 A.8 Proof of Proposition 2.2 . 151 A.8.1 Proof of Proposition 2.2.1 . 151 A.8.2 Proof of Proposition 2.2.2 . 151 A.8.3 Proof of Proposition 2.2.3 . 152 A.9 Proof of Proposition 2.3 . 153 A.9.1 Proof of Proposition 2.3.1 . 153 A.9.2 Proof of Proposition 2.3.2 . 153 A.9.3 Proof of Proposition 2.3.3 . 154 A.10 Proof of Proposition 2.4 . 155 A.10.1 Proof of Proposition 2.4.1 . 155 A.10.2 Proof of Proposition 2.4.2 . 155 A.10.3 Proof of Proposition 2.4.3 . 156 B Supplementary material for Chapter 4 157 B.1 Artists’ codes . 157 xii B.2 Results of the OLS estimation of the model in (4.5) in the sub-samples of the art genres . 160 B.3 Results of the OLS estimation of the model in (4.6) in the sub-samples of each of the years . 164 B.4 Results of the quantile regression estimation of the model in (4.7) with q = 0:33 and q = 0:67 .. 173 B.5 Results of the quantile regression estimation of the model in (4.8) with q = 0:50 for each of the six years . 177 References 186 xiii List of Figures 2.1 Full set of potential bargaining . 15 2.2 Reduced set of potential bargaining . 18 3.1 Artwork’s values, artist’s characteristics, and their relationships . 57 3.2 Artwork’s values and characteristics, artist’s char- acteristics, and their relationships . 65 3.3 Potential bargaining in the full-information mo- del from Chapter 2 . 69 3.4 The Nature game . 74 3.5 The ago channel . 86 3.6 The aigo channel . 87 4.1 Distribution of artists’ coefficients from model (4.3)112 4.2 Distribution of artists’ indices from ArtFacts.net data . 113 4.3 Distribution of artists’ coefficients for public and private prices in the Italian market . 113 4.4 Distribution of artists’ coefficients, using data from other studies . 114 4.5 Distribution of artists’ coefficients from model (4.4)117 xiv 4.6 Genre-by-genre distribution of artists’ coefficients from model (4.5) . 119 4.7 Year-by-year distribution of artists’ coefficients from model (4.6) . 120 4.8 Distribution of artists’ coefficients from model (4.7) for q = 0:50 ................... 124 4.9 Distribution of artists’ coefficients from model (4.7) for q = 0:33; 0:67 ................ 124 4.10 Year-by-year distribution of artists’ coefficients from model (4.8) for q = 0:50 ........... 127 4.11 Year-by-year regressions and benchmark model coefficients . 128 4.12 Quantile regressions and benchmark model co- efficients . 130 xv List of Tables 1 Top 10 Lots by Living Artists, 2012-16 (from Kin- sella (2016a)).....................2 2.1 Nomenclature (prices) . 21 2.2 Nomenclature (bargaining powers) . 22 2.3 Conditions for the emergence of each path . 35 3.1 Comparison among the benchmark decomposi- tion of cultural value and alternative views . 49 3.2 Bargaining prices . 72 3.3 Conditions from the model in Chapter 2 for the paths aigo and ago ................. 72 4.1 Transaction-level descriptive statistics . 102 4.2 Artist-specific descriptive statistics . 103 4.3 Year-by-year covariates’ and artist-specific vari- ables’ averages . 104 4.4 Year-by-year descriptive statistics for price ... 105 4.5 Quartile-by-quartile descriptive statistics for price 105 4.6 Benchmark model: Hedonic regression on over- all data . 110 xvi 4.7 The art genres’ model on overall data . 115 4.8 Hedonic quantile model on overall data for q = 0:5122 4.9 Ranking of the first 25 artists in the benchmark model in the various models . 132 B.1.1 Artists’ names and codes used in the models . 157 B.2.1 Hedonic regression on paintings sub-sample . 160 B.2.2 Hedonic regression on drawings sub-sample . 161 B.2.3 Hedonic regression on sculptures sub-samples . 162 B.2.4 Hedonic regression on graphics sub-sample . 163 B.3.1 Hedonic regression for year 1 . 164 B.3.2 Hedonic regression for year 2 . 165 B.3.3 Hedonic regression for year 3 . 167 B.3.4 Hedonic regression for year 4 . 168 B.3.5 Hedonic regression for year 5 . 170 B.3.6 Hedonic regression for year 6 . 171 B.4.1 Hedonic quantile model for q = 0:33 on overall data . 173 B.4.2 Hedonic quantile model for q = 0:67 on overall data . 175 B.5.1 Hedonic quantile model for q = 0:5 for year 1 . 177 B.5.2 Hedonic quantile model for q = 0:5 for year 2 . 178 B.5.3 Hedonic quantile model for q = 0:5 for year 3 . 179 B.5.4 Hedonic quantile model for q = 0:5 for year 4 . 180 B.5.5 Hedonic quantile model for q = 0:5 for year 5 . 182 B.5.6 Hedonic quantile model for q = 0:5 for year 6 . 184 xvii Acknowledgements First and foremost, I would like to sincerely thank my advisor Prof. Massimo Riccaboni and my co- advisors Prof. Andrea Vindigni and Prof. Davide Ticchi for their support during my Ph.D. and for the possibility they gave me to meet some of the most important economists alive during my period at IMT, as Oded Stark, Gilles Saint-Paul, Gerard´ Roland, James A. Robinson, Laurence J. Kotlikoff, Kiminori Matsuyama, and to attend their lessons. I also want to express my gratitude to them for the interesting discussions we had on topic related to my research, and for sharing their points of view and knowledge. Besides my advisors, I also want to thank Prof. Massimiliano Castellani at University of Bologna, co-author of Chapters 2, 3, and 4, for his support and for the pleasure I had in working with him. I would like to thank Prof. Guido Candela and Prof. Lorenzo Zirulia, for their helpful comments and suggestions on the first version of my thesis. I want to express my sincere gratitude to Professor Gilles Saint-Paul, to Dr. Matteo Squadrani, and xviii to Prof. Pier Paolo Pattitoni, for their insightful comments on previous versions of the papers this dissertation consists of, as well as the participants of the Workshop on Economics topics organized by Prof. Vindigni on December 6, 2016 at IMT School for Advanced Studies Lucca for their suggestions.
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