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Published in: Empirical Economics 1/2016

01-08-2016

On Rosen’s and Adler’s hypotheses in the modern and contemporary visual art market

Authors: Guido Candela, Massimiliano Castellani, Pierpaolo Pattitoni, F. Marta L. Di Lascio

Published in: Empirical Economics | Issue 1/2016

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Abstract

Using a unique hand-collected dataset, comprising all artwork sales in Italy between 2006 and 2010, we test Rosen’s and Adler’s hypotheses in the modern and contemporary visual art market. We extract our measures of artist talent and fame from a set of observable artist-specific variables by means of a factor analysis and estimate the elasticities of income with respect to talent and fame. Consistent with Rosen’s and Adler’s hypotheses, our results suggest a convex relationship between income and talent and a linear relationship between income and fame. Using SUR models to evaluate the effects of artist talent and fame on the average trade prices and number of sales, we find that the number of artwork sales is the main ‘channel’ through which talent and fame influence income. Copula models provide additional insights on the nature of the conditional dependence relationship between average prices and number of sales. Poolability tests suggest a single model of artist income applies to all artists in our dataset whether their works are generally traded in auction houses or galleries, so it is not necessary to specify different models. Finally, quantile regressions reveal that artists in low-income quantiles are not superstars, Rosen’s hypothesis holds only above the median income, and fame plays no role for low-income quantiles.

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Footnotes
1
By the expressions ‘Rosen’s hypothesis’ and ‘Adler’s hypothesis,’ we do not mean the theoretical assumptions underlying ‘Rosen’s theory’ and ‘Adler’s theory,’ but their implications to be tested empirically. As Filimon et al. (2011) note, the main problem in testing Rosen’s and Adler’s hypotheses is due to the difficulty to measuring artist talent and fame.
 
2
Ehrmann et al. (2009) provide an interesting analysis of superstar effects in the ‘deep-pocket’ market of gastronomy (deluxe cuisine) in German quality restaurants. The modern and contemporary visual art market also belongs to this kind of market where a small number of consumers are willing to pay an extra premium to the stars. The majority of existing research presents evidence for mass markets (e.g., entertainment industry) rather than these ‘deep-pocket’ markets.
 
3
Collectors often refer to pieces of art using the names of the artists, and collections are often remembered for the number of pieces by a specific artist, e.g., the Agnelli collection is remembered because it includes Picasso, Renoir, Canaletto, Matisse, and Canova. Cultural tourists are known to select museums that host exhibits by a particular artist rather than simply because they exhibit a specific piece of art (notable masterpiece exceptions aside). For this reason, tourist guides often highlight and promote museums solely through the names of the artists on exhibit.
 
4
Thompson (2010) provides a striking example to support our assumption. In February 2007, Adrian Anthony Gill, a well-known journalist for the London Sunday Times, offered Christie an old portrait of Stalin, by an anonymous artist, which he had purchased for £200. Christie rejected the portrait, since they did not deal in dictator portraits. Gill asked Hirst to paint a red nose on his Stalin portrait and Hirst signed the portrait after painting it. Christie’s accepted the modified portrait with Hirst’s signature, and it sold for £140,000. Artist’s shit by Manzoni is another good example: Buyers are not really interested in Manzoni’s feces but in his signature. The same reasoning applies to Duchamp’s Fountain. Another example is the Andy Warhol flyers mailed to collectors for the release of his ‘Mao’ portfolio of ten screen prints. Warhol signed some of these flyers during his first showing and today, these flyers are traded in auction sales.
 
5
According to the Tefaf report 2010 (the most relevant considering the time window of our study), on a worldwide scale, Italy is fifth (third in Europe) in terms of art auction turnover and experienced an increase in the art market turnover of about 60.2 % in the period 1998–2008 (the highest worldwide percentage).
 
6
Deceased artists are 41.28 % of our sample. Moreover, old artists are the largest part of our sample (only 10 % of the living artists are less than 40 years old). We can reasonably assume that old artists are inactive in the primary market or, at least, less active than young artists. In addition, most of the artists in our sample are highly traded artists (more than 10 trades). Only 2.33 % of artists are simultaneously living, young, and nonhighly traded. Only for these artists, our assumption is less likely to be invalid. However, our findings are not altered if we exclude these artists from our sample. Results are not reported to save space, but are available from the authors on request.
 
7
Choosing the year of birth or any other year as a reference point (e.g., 30 years) has no effect on the results since this variable is used in a factor analysis and factor analyses are based on correlation matrices that, by definition, are invariant to linear transformation.
 
8
At this stage, inference is not our main concern. We are only performing an explorative analysis of the correlation among variables in this specific sample of data.
 
9
We choose the promax rotation because it allows the factors to be correlated: In our application, the correlation between the two factors is 0.167. The average value of the raw residuals of correlations (observed correlations—fitted correlations) is \(-\)0.090, evidencing a good fit of the estimated model.
 
10
We are using a backward stepwise selection where we choose 0.2 as the significance level for removing a control variable from the model. The removed control variables are male, sculpture, and a couple of artistic period dummies.
 
11
Note, however, as the standard errors indicate, the estimates of the second model are slightly more precise than the first model. Since some of the control variables in the first model are nonsignificant, omitting them in the second model increases the precision of the estimates.
 
12
The theorem by Sklar (1959) provides the theoretical foundation for using copulas. The probabilistic interpretation of this theorem allows us to write any multivariate cumulative distribution function in terms of two marginal distribution functions and a copula, which describes the dependence relationship between the variables independently from the margins.
 
13
We model marginal probability densities, without making any assumptions on their parametric form by using the empirical cumulative distribution function computed from the residuals under investigation, and the copula parameter \(\theta \) through the maximum likelihood function of the copula.
 
14
We choose the Clayton copula based on the visual inspection of the scatter plot of probability integral transform of estimated \(\varepsilon _{pi} \) and \(\varepsilon _{ni} \), the Akaike information criterion, and the Cramèr-von Mises test (Genest et al. 2009). All analyses performed are available from the authors on request.
 
15
The Gini indexes of the two subsamples are quite similar: The Gini index for auction houses is equal to 0.808; the Gini index for galleries is equal to 0.775. We estimated two SUR models on the two subsamples. Results of these two models are not informative and are not reported.
 
16
This variable is based on the number of Google hits that resulted by including in the search: ‘name of the artist’ AND ‘art.’ See Garcia-del-Barrio and Pujol 2007 for additional details.
 
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Metadata
Title
On Rosen’s and Adler’s hypotheses in the modern and contemporary visual art market
Authors
Guido Candela
Massimiliano Castellani
Pierpaolo Pattitoni
F. Marta L. Di Lascio
Publication date
01-08-2016
Publisher
Springer Berlin Heidelberg
Published in
Empirical Economics / Issue 1/2016
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-015-1002-3

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