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The success of art galleries: a dynamic model with competition and information effects

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  • Aloys Prinz
  • Jan Piening
  • Thomas Ehrmann

Abstract

An intrinsic characteristic of cultural goods is the unpredictability of their economic success. Arts goods in particular share characteristics with credence, inspection, and experience goods. Accordingly, art collectors rely on the experience and the reputation of art galleries when investing in artwork. Some qualitative sociological studies have found that only a few very successful galleries represent the bulk of the most visible and most successful artists (e.g., Crane in The transformation of the avant-garde: the New York art world, 1940–1985. The University of Chicago Press, Chicago, 1989 ; Currid in The Warhol economy: How fashion, art and music drive New York City. Princeton University Press, Princeton, 2007 ). This paper investigates the success of art galleries in a dynamic model, which elaborates different statistical processes that allow us to analyze the development of different types of success distributions in the market for art galleries. Instead of applying standard economic analysis only, we employ methods from statistical physics to construct a model of gallery investment and competition. Our model entails information, competition, and innovation effects. Subsequently, art market data are used to test which version of the model fits best. We find that the lognormal distribution provides the best fit and conclude that the data generating process is compatible with the version of the model, which entails an inhomogeneous geometric Brownian motion. Hence, the success of art galleries depends strongly on information and innovation effects, but is hardly affected by competition effects. We argue that the superstar effect in the case of art galleries can be understood as an appropriation of search and entrance costs, which emerge whenever consumption requires special knowledge and social inclusion. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Aloys Prinz & Jan Piening & Thomas Ehrmann, 2015. "The success of art galleries: a dynamic model with competition and information effects," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 39(2), pages 153-176, May.
  • Handle: RePEc:kap:jculte:v:39:y:2015:i:2:p:153-176
    DOI: 10.1007/s10824-014-9217-2
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    3. Luigi Di Gaetano & Isidoro Mazza & Anna Mignosa, 2019. "On the allocation of talents in the contemporary art market," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 43(1), pages 121-143, March.
    4. Giovanni Colavizza, 2022. "Seller-buyer networks in NFT art are driven by preferential ties," Papers 2210.04339, arXiv.org, revised Nov 2022.
    5. Aloys Leo Prinz, 2022. "The concentration of power in the market for contemporary art: an empirical analysis of ArtReview’s “Power 100”," SN Business & Economics, Springer, vol. 2(1), pages 1-32, January.
    6. Kackovic, Monika & Bun, Maurice J.G. & Weinberg, Charles B. & Ebbers, Joris J. & Wijnberg, Nachoem M., 2020. "Third-party signals and sales to expert-agent buyers: Quality indicators in the contemporary visual arts market," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 587-601.
    7. Alicia Mateos-Ronco & Nieves Peiró Torralba, 2019. "Sustainable Management of Contemporary Art Galleries: A Delphi Survey for the Spanish Art Market," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    8. Donatella Depperu & Giacomo Magnani & Lisa Crosato & Caterina Liberati, 2021. "Growth of Firms in a Fragmented Cultural Industry: Italian Commercial Art Galleries’ Competitive Strategies," Sustainability, MDPI, vol. 13(9), pages 1-19, April.
    9. Monika Kackovic & Joop Hartog & Hans van Ophem & Nachoem Wijnberg, 2022. "The promise of potential: A study on the effectiveness of jury selection to a prestigious visual arts program," Kyklos, Wiley Blackwell, vol. 75(3), pages 410-435, August.

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    More about this item

    Keywords

    Success; Reputation building; Art gallery; Dynamic model; Information; Innovation; Competition effects; Lognormal distribution; C13; C46; L15; L82; Z11;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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