THE IMPACT OF EXHIBITIONS

ON THE AUCTION PRICE OF

WORK FOR LIVING ARTISTS

By Paige Wilkinson

Advisors: Professors Sheppard and Nafziger Second Reader: Professor Chao

Thesis prepared as part of the completion of the Williams

College Economics Department Honors Program.

WILLIAMS COLLEGE

Economics Department Honors Program

Wilkinson i

Acknowledgements: I want to thank Professor Sheppard, whom I have more than once this year

called my “spirit guide”, continued to work with me consistently even on his sabbatical, and

without whom this thesis never would have come to fruition. I also want to thank Professor

Nafziger for agreeing to be my adviser “on the ground”, stepping in mid-year and mid-project,

and being a wonderful source of advice as well as line-edits. Finally, I want to thank all of the

amazing friends and family who have talked me off the ledge countless times when I thought I

would not be able to finish this massive undertaking and thus helped me make it here: the final

draft!

Abstract: The exhibition of art work is generally understood as having a positive impact on the

value of the work exhibited as well as the value of other works by the artist. Yet, there is a

surprising absence of careful analysis of the change in the values of art works associated with

exhibitions at galleries and museums. This paper addresses that gap. Using a group of living,

contemporary artists who have established a record of auction sales that I can use for analysis,

data are compiled on more than 47,000 sales of works by 375 artists. Using these data I estimate

the empirical relationship between exhibitions (both contemporaneous and historical) at major

museums and commercial galleries on auction sales prices. I consider the impacts of both solo

exhibitions and group exhibitions that include the artist's work among works of others. I include

other controls that are standard in the literature, including size, age of the work, age of the artist,

and other factors. The findings of this paper suggest that exhibitions are associated with price

increases to varying degrees depending on the exhibition type: solo major museum exhibitions

are associated with the highest increases (7 to 13 percent), then solo major galleries (2-4 percent),

group major museums (1 to 2 percent), and finally group major galleries (0.7 to 2 percent). I also

find that price differentials associated with exhibitions are greater for female artists and

insignificant or negative for artists with MFAs. While issues of endogeneity make it so these price

associations cannot be proven as causal, understanding the magnitudes of these exhibition

associations with price for the first time empirically is still important to our understanding of the

art market. These results help to identify important distinctions in the roles played exhibitions in

the careers of artists, and have serious implications for museum and gallery management as well

as the management of art funds and private collection strategies.

Wilkinson ii

Table of Contents

Section 1. Introduction............................................................................................................... 1

Section 2. Welcome to the Art Market ....................................................................................... 5

2.1 Primary versus the Secondary Market ............................................................................ 6

2.2 Variations in the Art Market ........................................................................................... 7

2.3 Functioning of the Art Market: Supply, Demand, and Marketing .................................... 9

2.4 Exhibitions: Functions and Practices ............................................................................. 11

2.5 Expert Valuation Driving the Art Market....................................................................... 14

2.6 Commercialization of the Art World ............................................................................. 16

2.7 Art as an Asset and Investment .................................................................................... 20

Section 3. Hedonics and the Art Market ................................................................................... 23

3.1 Fine Art and the Housing Market ................................................................................. 23

3.2 Theory of Hedonics ...................................................................................................... 26

3.3 Hedonics in Cultural Economics.................................................................................... 29

Section 4. Exhibitions ............................................................................................................... 37

4.1 Ranking Artists through Exhibition Exposure in Cultural Economics .............................. 37

4.2 Exhibitions as Advertisements for Artist's Works .......................................................... 39

Section 5. Empirical Methodology ............................................................................................ 43

5.1 The Data Set and Descriptive Statistics ......................................................................... 43

5.2 Methodology ............................................................................................................... 50

5.3 Potential Endogeneity .................................................................................................. 53

Section 6. Results ..................................................................................................................... 56

6.1 Initial Results ............................................................................................................... 57

6.2 The Associated Impact of Total Exhibitions................................................................... 61

6.3 The Associated Impact of Major Exhibitions Contemporaneous to the Year of Sale ...... 64

6.4 The Associated Impact of Cumulative Counts of Major Exhibitions on the Year of Sale . 71

6.5 Examining the Differential Impacts of Exhibitions on Price for Female Artists and Artists

with Masters in Fine Arts (MFA) Degrees ..................................................................... 77

6.6 Further Explorations .................................................................................................... 83

Section 7. Conclusion ............................................................................................................... 84

7.1 Museum Exploitation by and Relationships to Commercial Galleries ............................ 85

7.2 Implications for the Art Market .................................................................................... 89

Wilkinson iii

7.3 Implications for Cultural Economic Literature ............................................................... 90

7.4 Suggested Further Research ......................................................................................... 90

Appendix 1. Museums and Galleries in Artist Sample Creation ................................................ 92

Appendix 2. Major Museums and Galleries .............................................................................. 92

Appendix 3. Artists in the Data Set ........................................................................................... 93

Appendix 4. Sample Artist CV ................................................................................................... 96

Appendix 5. Variable Interpretations for Art Hedonic Impacts ................................................ 101

Appendix 6. Artist Coefficients (As Related to Chan Chao) ...................................................... 102

Appendix 7. Country-of-Sale Coefficients (As Related to the Czech Republic) ......................... 109

Appendix 8. Year-of-Sale Coefficients ..................................................................................... 110

Appendix 9. Robustness Check: Extra Hammer Prices............................................................. 111

Appendix 10. No Artist Fixed Effects Models and Why They are Not Used .............................. 112

Appendix 11. Models with Career Exhibition Variables ........................................................... 118

11.1 Career-to-Date Models ............................................................................................ 118

11.2 Career-to-Contemporaneous Models ....................................................................... 120

Appendix 12. Alternative Clustering for Standard Errors ......................................................... 121

12.1 Basic Robust Clustering ............................................................................................ 121

12.2 Country of Sale Clustering ........................................................................................ 122

12.3 Year of Sale Clustering.............................................................................................. 123

12.4 Artist Clustering with Artist Fixed Effects .................................................................. 124

12.5 Artist Clustering without Artist Fixed Effects ............................................................ 125

12.6 Nationality Clustering ............................................................................................... 126

References ............................................................................................................................. 127

References …………………………………………………………………………………………………………………………... 116

Wilkinson 1

1. Introduction

The idea that exhibitions raise the price of a work of art is not a new concept in the art

world. When an artist’s work fetches a larger than expected amount of money, experts often

attribute the outcome to recent or ongoing exhibits of that artist. For example, Robin Porgrebin

of the New York Times Art’s Section writes, “In a period when galleries and auctions strive to land

the highest prices, a museum show can help significantly. […] The average selling price of a Mark

Grotjahn painting at auction, for example, rose from $322,000 in 2010 to $1.2 million in 2015,

according to ArtNet, partly because of exhibitions featuring his work, like ‘The Forever Now:

Contemporary Painting in an Atemporal World,’ which opened at the Museum of Modern Art in

2014.”1 Yet, these conceptions are largely based on anecdotal evidence and do not specify the

magnitude of the impact of exhibitions on prices. Additionally, art world experts believe that

exhibition influences on price differs by show type (i.e. solo or group, major or lesser institutions,

museum or gallery), but conceptions of these variations are based on similar anecdotal evidence.

In contrast, this paper presents an empirical model of the economic associations of exhibitions

with the price of work for living artists.

This study provides a theory-guided empirical analysis of the impact of exhibitions on the

price of work for living artists, specifically taking into account whether these exhibitions were solo

or group and whether they occurred at “major” museums or galleries, or institutions of a

particularly high caliber as related to their attendance and prestige. The analysis employs the

widely-used technique of hedonic price regressions2 using an original dataset of 45,000 auction

results from 1986 to 2015 from 375 living artists as well as their associated exhibition information.

In doing so, I explore new characteristics of art price formation and contribute to a richer

understanding of controversies surrounding the increasing commercialization of art.

This analysis focusing on exhibition price associations for contemporary art is particularly

important in light of recent developments in the fine art market. First, the contemporary art

market has exploded in the last few decades, leading to new power dynamics among art dealers,

works going for record prices, and growing interest in art as an asset class worthy of investment.

With enormous prices and potential gains, many investors and new art buyers are entering the

art sphere – which along with the increasingly international composition of both artists and buyers

1 Porgrebin (2016). 2 Hedonic price regressions are statistical models that decompose the item being researched (in this case the price of art works), into its constituent characteristics (here elements like number of solo exhibitions, place of sale, and date of sale), and obtains estimates of the contributory value of each characteristic.

Wilkinson 2

is changing the configuration of art market demand (see Thompson 2009, Richard Armstrong et

al. 1989, and Crane 2009). Additionally, the present analysis has some bearing on the growing

controversy around the commercialization of museums. Some in the industry have suggested that

museums are leveraging their economic position to gain funding as well as allowing financial

imperatives to influence the art they exhibit. If they allow financial elements to affect their

curatorial practices, some believe they lose sight of their traditional role as educational entities

meant to expose the public to new art (refer to Sheets 2015 and Dobrynski 2013). This argument

is based both in growing evidence of museums’ ties to galleries as well as increasing economic

pressures within the institutions themselves. This paper’s analysis bears significantly on these

issues by exploring the relationship between the prices of living artists’ works and exhibitions both

within businesses – galleries – and within traditionally non-profit, educational organizations –

museums. Understanding this economic linkage contributes to informing both sides of this

complicated issue within the increasingly market-oriented contemporary art world.

In order to model this heterogeneous market for goods which act as both asset and

commodity, economists decompose works into various contributions to its value through hedonic

regression analysis. Using hedonic methods, cultural economic literature on art auctions, art price

indices, and rates of return (see, respectively, Ashenfelter and Graddy 2006; Ginsburgh, Mei, and

Moses 2006; Frey and Eichenberger 1995) has grown enormously in the past two decades.

However, the impact of exhibitions on art prices has only been peripherally explored.

In this study, exhibitions are considered to act as a sort of advertisement for the artist

which conveys information to prospective buyers (via their ability to confer status and

foreshadow price appreciation) and helps the artist develop a reputation or brand over time (as

a signal of expert assessment of artist’s reputation). While these factors act simultaneously for an

exhibition close to the time of sale, the record of an artist’s exhibition history models the

development in their reputation. Thus exhibitions are expected to imbue the same work with

increased value exogenous to the quality of the work itself. Different exhibition types are also

thought to be associated with different impacts from each other – since each is associated with

varying levels of artist exposure and prestige – as well as potentially for the type of artist exhibited

– such as if they are female or have an MFA.

Using the data mentioned earlier, I estimate the associations of exhibitions (both

contemporaneous and historical) at major museums and commercial galleries with auction sales

prices. I consider the impacts of both solo exhibitions and group exhibitions that include the

Wilkinson 3

artist's work among the works of others. The findings of this paper suggest that exhibitions are

associated with price increases to varying degrees depending on the exhibition type. This analysis

is largely based on different exhibitions types lagged by a year to determine the empirical

relationship of exhibitions close to the year of sale to price.3 I also include other controls that are

standard in the literature, including size, age of the work, age of the artist, and other factors which

reinforce the findings of earlier literature within a generally larger data set.

Since exhibition information was collected from individual artist CVs, the reporting

standards for total solo and group exhibitions are not uniform across this collection of artists.

More established artists tend to have “selected exhibitions” from their careers while emerging

artists report everything. However, total exhibition counts reported as contemporaneous or with

a one-year lag of the year of an artwork’s sale are still generally associated with statistically

significant, positive relationships with the price of works. In the cumulative exhibition histories

for the five years prior to these contemporaneous or one-year lagged shows, group exhibitions

are strongly significant while solo shows tend to be more statistically significant for more recent

exhibitions.

Most of this analysis focuses on price associations with major museums, or the top one

hundred museums in the world by attendance,4 and major galleries, established through Don

Thompson’s list of top galleries worldwide.5 These exhibitions constitute such large career

achievements for artists that reporting standards are more uniform for major institutions.

Additionally, because the art world tends to think of these exhibitions as influencing price the

most, understanding their associations with price are of particular interest. For the one-year

lagged major counts, each exhibition type generally maintains positive significance levels.

Different kinds of exhibitions have different levels of association with price: throughout all

models, solo major museum exhibitions are associated with the highest increases (7 to 13

percent), then solo major galleries (2-4 percent), group major museums (1 to 2 percent), and

finally, with the weakest significance, group major galleries (0.7 to 2 percent). The differential

price associations between museums and galleries, with museums having consistently higher

3 These year give the art market time to internalize exhibition information, and it makes sure that exhibitions have at least started by the time of sale due to a data-structuring issue which will be later discussed. 4 “Visitor Figures: 2014 Exhibition & Museum Attendance Survey” (April 2015). 5 Thompson (2009).

Wilkinson 4

associated prices, are particularly interesting since these “mega-galleries”6 are often thought to

act as museums which sell art.

This analysis also examines exhibition history associations with price through the five

years prior to the lagged year and the six to ten years prior to the lagged year. I find that price

relationships are higher per exhibition for the lagged value, and decrease in magnitude through

the years for museum exhibitions. In contrast, gallery exhibitions tend to have the highest price

associations per exhibition in the five year cumulative variables. This could suggest that the

information value from the primary market to the secondary market is sticky, and that museums

tend to have a greater signaling power for both value as well as expert opinion. However, since

cumulative values tend to be considerably larger than the single-year exhibition counts, these

exhibition histories often on average have a larger impact on the artist’s prices than the

exhibitions in the year prior to the sale, even if these coefficients are larger. Thus, while a single

exhibition closer to the year of sale has a greater impact than a single exhibition in the years prior

for museums, the cumulative impact of the exhibitions in the years prior tends to have a larger

association with price.

Finally, the analysis looks at the differential price impacts of artists with Masters of Fine

Arts (MFA) degrees and who are female on the impact of different exhibition types on their prices.

Each of these have implications for an art market generally considered sexist against women7 and

for which whether or not artists need MFAs to become successful is a highly contested issue.8

Female artists, according to this analysis, have an associated additional premium for each major

exhibition type. This indicates that while sexism could explain why women artists are exhibited

less frequently, the information advantage from having a museum exhibition is greater for female

artists. These associations are particularly strong in cumulative models, suggesting that these

positive correlations are particularly impactful over time in the artist’s reputation. For artists with

MFAs, their price differentials are insignificant or negative. This would suggest that for artists with

MFAs the information advantages of exhibitions are lower.

The rest of this paper is organized as follows. The next section provides an overview of

the art market in terms of its functioning, growing commercialization, and the rise of art as an

investment class to provide a baseline of art market information upon which to interpret the

6 This term will be discussed more later, but “mega-galleries” are simply the art market term for galleries of a certain level of prestige and turn-over. 7 Reilly (2015, May 26). 8 Chafin (2011, July 10).

Wilkinson 5

exhibition analysis. I then explore hedonic theory as it pertains to modelling the fine arts market

and review economic literature that models the art market in this way. Next, I explore ideas

surrounding exhibitions, both in the interest surrounding these shows as a measure of artist

acclaim and in how they should be thought about theoretically in their ability to impact

consumers’ willingness to pay. A subsequent section describes the creation and composition of

the data set (the artists, major museums, and major galleries included in this study are listed in

the appendix). I then discuss the methodology involved in this study and estimate hedonic

regressions to measure the impact of varying types of exhibitions on the price of work for living

artists. These results are then discussed in terms of their implications for the art market,

economics, and further research into impacts on art prices.

2. Welcome to the Art Market

“Why should anyone want to buy a Cezanne for $800,000? What’s a little Cezanne house

in the middle of a landscape? Why should it have value? Because it’s a myth. We make

myths about politics, we make myths about everything… My responsibility is mythmaking

and mythmaking material – which handled properly and imaginatively is the job of a

dealer – and I have to go at it completely.”

- Leo Castelli (1907 –1999), top modernist American art dealer9

The value of objects is neither more nor less than what people are willing to pay for them.

This is true for everything from apples to zucchini. For durable objects that can be resold, the

amount a person is willing to pay depends on both the stream of services expected during a period

of ownership and the expected value to be received if and when the object is sold. For durables

like appliances and houses, the value of this stream of services tends to be large relative to the

resale value or capital gains component, as well as being easy to understand. One values a house

because it provides shelter from the elements and privacy in which to live one’s life. What exactly

do people get by way of a stream of services from art, and how can they know what level of capital

gains to expect? Both of these values are very dependent on the social context of the purchaser.

A work of art generally does not satisfy an intrinsic physical or biological need. It can, however,

provide a way of preserving ideas and stories, and serve as a signal to others about ones economic

9 Quoted in Horowitz (2014), p. vii.

Wilkinson 6

success, judgement and values, or appreciation of beauty, and it is not surprising that people are

willing to pay to present these ideas and to send these signals. The future value of artworks, and

hence the expected capital gain associated with ownership, depends in part on the future value

of sending these signals to others. In this sense the commercial value of art might be said to be

based on “collective intentionality” rather than on an intrinsic objective value.10

Like currency, human stipulation and declaration create and sustain the commercial value

of art. The reason the art market seems so foreign to the general public, eliciting continued

surprise or anger at the large sums of money for which art is sold, is because it does not serve a

specific essential activity. Whereas a house which costs millions of dollars contains easily

understood attributes which increase the price – such as location, bedrooms, bathrooms, etc. –

and can at the very least be lived in, most people have no criteria by which to judge the price for

a work of art.11 Especially as art begins to function increasingly as an investment asset as well as

commodity, something which will be returned to later, increasing prices can appear even more

baffling to the lay observer. Yet, before delving into the ways in which we can have objective

standards for attributes of art which influence their value, it is important to comprehend the basic

functioning of the art market as a whole. Understanding how different exhibitions come about,

the differences between auction and gallery prices, how artists are marketed, the increased levels

of investment, and increasing economic pressures for non-profit institutions will inform the

interpretation of empirical relationships between exhibitions and art prices as well as how this

will come to bear on topics within the industry.

2.1 Primary versus the Secondary Art Market

The primary market for art involves the direct payment to the artist for his or her skill and

time reflected in the work as well as the cost of bringing it to market. Over the last 150 years, the

role of the art dealer evolved to provide a venue for the work to be exhibited and brought to the

attention of buyers. The dealer is paid either by buying directly from the artist and selling at a

profit (while the artist is assured a steady income), or by taking the work on consignment from

10 Findlay (2007), p. 13. 11 Findlay (2007), p 125.

Wilkinson 7

the artist and earning a commission when the work is sold. This art dealer, generally the gallery

representation for an artist, will exhibit the work at a listed price12 to interested buyers.

Other than the original purchase of new work either directly from the artist or from the

artist’s dealer, all other market transactions occur in the secondary market. Once an artist reaches

a certain level of acclaim, it is inevitable that their works enter this sector of the market during

the course of their lifetime. These sales of the artwork can take various forms. One is by selling,

again, through art dealers. The artists’ primary dealer, often some kind of gallery representation,

can even directly affect pricing of secondary-market works by artists they represent if they agree

to resell a work. Beyond the primary dealer, the works may also be resold by a dealer largely

separate from the original artist. These dealers may not represent artists directly, but may be very

knowledgeable about the work and therefore strongly contribute to that artist’s market. Finally,

fine art auction houses have become a major sector of the secondary market, particularly in

recent years.

This analysis focuses on prices exclusively confined to the secondary, auction-house

market. However, gallery exhibitions are a product of the primary market and museum exhibitions

(at least claim) to operate outside the market altogether. Thus, by understanding price

correlations for different types of exhibitions and art prices, we can understand how the primary

market affects prices in the secondary market, and non-profit museums affect the overall market.

2.2 Variations in the Art Market

The price of works varies through these sources across space, time, and dealer. Studies of

the art market show that variations in price at galleries tend to be smaller than those observed at

auction. Hutter et al. (2007) shows the level of dealer prices is generally higher than auction prices

and less fluid in their downward movements using comparative data from the artists included in

the Capital Kunstkompass (CKK)13 and their associated auction prices over 30 years. However,

focusing on the market for well-known drawings and prints, Candela and Scorcu (2001) show a

close link between the evolution of prices at public galleries and those at auction. Thus, even if

12 As will be discussed later, these listed prices are not readily available, and not preserved past the work’s sale. Galleries get rid of any potential evidence of the listed price, and there are no guarantees, even if one could access these prices, that this would actually be the price paid for the work. 13 Capital Kunstkompass (CKK) is a yearly ranking of artists by reputation released in Germany every year. This ranking, to be discussed in more depth later, was first created by Willi Bongard in 1970.

Wilkinson 8

galleries have stickier prices than auction houses, a general understanding of the prices within the

art market may be obtained from auction prices, such as in the current study.

This is particularly important since the market for art is not a very transparent one

because the galleries use list prices in a strategic manner. The prices actually paid are not publicly

reported and therefore unknown to a researcher, analyst, or prospective buyer. Once an artwork

has been sold, reputed galleries even conceal the formerly listed price by placing a “red point” or

red sticker right over the original asking price in any price list. Auctions are more transparent, as

the achieved prices are to be published later on publically.14 This is why analyses such as this one

use auction prices to model the art market.

However, in an exhaustive survey of the 2006 global art market, Clare McAndrew

estimated the total art turnover to be about $52 billion with only 48 percent of that total spent

at auction. Thus, an astounding $27 billion of private, individual transactions, representing 52

percent of the market, were essentially invisible. Most analyses of the industry, as well as general

understanding of the prices of works for collectors, come from the auction market representing

less than half of the total industry.

Beyond the fact that public auction sales cannot represent the total art market, auction

prices themselves come with their own elements which can affect the sale of a work. For example,

systematic differences exist across international markets. De la Barre et al. (1994) demonstrates

that the prices fetched for the same type of work (great masters or other painters) are higher in

New York than London, and higher in London than Paris. This, along with findings such as Pesando

and Shum (1996), which show the same item may fetch appreciably different prices depending

on which auction house sells it even if the sale is in the same town at the same time, serve to

emphasize the complexity and possible inefficiencies that exist in the art market. Additionally,

Pesando (1993) found that prices for prints sold at Sotheby’s consistently sold higher than those

sold at Christie’s. Mei and Moses (2002) also found evidence of violation of the “law of one price”

in sectors of the art market such as Old Masters and Impressionists. Thus, not only are prices in

the art market intrinsically subjective, but like other assets, observed prices may not represent

stable equilibrium values.

Additionally, as suggested by auction theory, auction prices are dependent on the number

and quality of bidders. This is part of the reason Sotheby’s and Christie’s, the two top international

14 Bonus and Ronte (1997), 106-107.

Wilkinson 9

auction houses in the world, organize their sales at the same time in the same town – forming a

mutually beneficial relationship by increasing the number and quality of collectors.15 These

sources of variation in auction prices, such as country of sale and auction house, must be

understood and adjusted for within econometric analysis.16 Beyond these factors, understanding

dynamic trends in auction prices is also particularly important due to their increasing importance

in the contemporary fine arts market.

The role of fine art auction houses has risen to unprecedented levels in recent decades.

As recently as the mid-1960s, very few midcareer American artists, even those with major

reputations, appeared at auction, and virtually no younger contemporary artists did. Auction

houses generally avoided selling works by living artists with primary gallery representation.17

Today, the “Post-War and Contemporary” evening sales at Christie’s and Sotheby’s, often largely

featuring living artists, are some of their most prominent and highest-selling sales. Auction sales

rose eightfold between 1998 and 2008. This is in part due to the rise in interest for modernist and

contemporary art in recent decades as well as the increase of art as an investment, both to be

discussed later. Thus, while this represents a smaller portion of the overall market, because of its

increasing importance to the contemporary market, which is the art sector of analysis in this

study, understanding exhibition correlations with auction prices in the contemporary sphere is of

particular interest.

2.3 Functioning of the Art Market: Supply, Demand, and Marketing

As expressed by art dealer Michael Findlay in his book The Value of Art, whether sold in

the primary or secondary market, the price of art (like pretty much any other commodity), is

governed by supply, demand, and marketing. Art market supply is an interesting construct since

generally each work is a monopoly. While there are exceptions to this concept, such as print

multiples18, generally if a work is owned by an individual they are the only ones who may possess

it at a given time. Yet, there are often works by artists which explore similar themes within

15 Sagot-Duvauroux (2011), 46. 16 As will be expressed later, in this analysis, indicator variables for country of sale and error clustering by auction house serve to correct for these sources of variation in auction price. 17 Findlay (2007), 142. 18 A “print multiple” is a type of artwork in which many prints of the same type and state are created to be sold. This particular type of artwork will become more of a topic of conversation later through its unique place in the art market. This is partially because while most objects within the fine arts sphere give their owner a monopoly on the work, print-multiples by definition do not.

Wilkinson 10

particular media that may be considered somewhat substitutable if not identical. In any case, the

concept of rarity within an artist’s oeuvre19 is particularly important to the price of a work of art.

This is something to be careful about, however, since output varies wildly from artist to artist.

While within the realm of living artists technically all of them can still be producing, since artists

go through different stages of their career, previous periods of their output may still be

considered through this lens of “rarity” when determining price. Additionally, all works owned

privately, even gifts promised but not yet given to museums, are potentially available. The only

time a work may be considered entirely absent from the market is those which have been formally

accessioned – or officially acquired – by public museums since it is difficult and rare for these

museums to deaccession works. While such deaccession sales do happen, they are generally

works that are of less than premier quality or condition.20 While artists more established in the

art historical cannon may be extremely difficult to find in the private market – Monets, Picassos,

Pollocks, or Warhols are not just floating around the art market – the works of even very

established living artists are generally still available for collectors.

A combination of marketing by artist, art dealer, art fair, gallery, and auction house may

increase the value of a work dramatically. As art dealer Michael Findlay says, “A century ago most

art dealers were glorified shopkeepers. Now some are themselves celebrity entrepreneurs. For

most of the twentieth century auction houses functioned as wholesalers […] Now the auction

houses make and break the market reputations of midcareer artists whose work they promote

with great fanfare.”21 Galleries began hiring public-relations firms in the 1980s and 90s, and top

galleries keep themselves relevant by holding well-publicized openings and currying favor with

top collectors. This element of increased sensationalism surrounding the art world has also led to

the development of “mega-galleries” – or those international galleries who represent top modern

and contemporary artists with multiple locations, often across the world (e.g. David Zwirner,

Gagosian, Marian Goodman, Pace, etc.). Conveying signals of value close to that of a museum,

these galleries have come to dominate the art world, competing for top artists.

Sotheby’s and Christie’s also began to employ professional marketing agents in the 1980s.

Today, they employ press officers, business managers, public-relations consultants, advertising

departments, and event planners. These events range from private dinners, cocktail parties,

19 The word oeuvre is used in the art historical field to refer to an artist’s entire body of work. 20 Murphy (2016). 21 Findlay (2007), 190.

Wilkinson 11

lectures, to panel discussions all focused on selling a particular item or collection.22 The auction

houses’ primary goal is to sell the auction house itself to future sellers, and in the process they

hope to add value to the items they are selling.

Art fairs have also been growing in importance in the fine arts world.23 Major cities

worldwide vie to host domestic and international fine-arts fairs devoted to various categories;

with cities like Hong Kong, Beijing, Moscow, and Los Angeles attempting to break into the fine

arts circuit with varying success. Many are invitational with strict vetting requirements while

others are tent fairs where anyone with the price of a booth may come sell their wares. Among

the leading vetted annual fairs are TEFAF (the European Fine Art Fair) in Holland, Art Basel and

Art Basel Miami Beach, and the Art Dealers Association of America art fair in New York. Dealers

often save some of their most important works for these five to ten day fairs, and the marketing

of these works through these forums qualitatively appear to be increasing in prominence and

importance.

As will be discussed in further detail later on, these improvements in the dissemination

of information and acclaim for particular artists through dealers, auction houses, and art fairs

informs the market as to the possible value of the works. The increased exposure for specific

artists to an audience of known collectors helps to raise the willingness to pay of the consumer

since more people know about the work and the positive signaling power of these institutions.

This makes both the status of owning the work and the potential resale value higher. However,

perhaps the best marketing for an artist’s work is not through public-relations events,

announcements, or fairs, but through the exhibition of an artists’ works and the signaling power

conveyed through this act of exhibition within an institution.

2.4 Exhibitions: Functions and Practices

Art markets, as points in time where persons with “means and desire” could purchase

works of visual arts, emerged at least by the mid fifteenth century in Florence and Bruges. Yet,

the origins of modern exhibitions come two centuries later, with the emergence of the Paris Salon

22 Findlay (2007), 35. 23 Some artists, especially emerging artists without large numbers of exhibitions to put on their CVs, actually report the art fairs in which they have been a part. In these cases, the reported art fairs are included in the analysis under total counts of group exhibitions, since this is where they are reported. However, these kinds of discrepancies in reporting standards are exactly why this analysis focuses on the major counts, which will be reported by all artists due to their importance in artist exposure.

Wilkinson 12

in 1667 and its eventual opening to the public 70 years later.24 An art exhibition is traditionally

the space in which art objects (in the most general sense) meet an audience. The exhibit is

universally understood to be for some temporary period unless it is stated to be a "permanent

exhibition". For the purposes of this study, the exhibitions of note and in which impact is

determined will be confined to galleries and museums.25 Coming from different commercial roles,

as will be explored more in depth later, as well as cultural positions and styles of exhibition, these

exhibitions create different exposure types and levels for artists’ works. Since these types of

exhibits serve different purposes, I expect to find that exhibition effects will vary depending on

type as related to their functionality.

Today, a gallery, being a place of exhibition for an art dealer, exists within a more

commercial context. All of the works exhibited are for sale. As such, often the visitors to this space

will be in some way connected to buying art. They are smaller spaces than museums, and their

audience is targeted commercial buyers rather than the general public. They may be considered

as signaling taste in the gallery’s choosing of a specific artist, but their first role is as salesperson.26

Museums, whether public or private, are meant to educate the public. They are meant to convey

taste in a way considered outside the commercial sphere – truly devoted to pure quality. Their

marketing is considered a way of increasing attendance rather than for any commercial goal,

although this conception is becoming more contested, as will be later discussed. Thus, museums

have a wider reach but a less targeted audience than that of a gallery.

An important distinction within the realm of galleries and museums, which will be made

in a more empirical sense later in this analysis, is that between “major” museums and galleries

and lesser institutions. This difference is based upon the concept that not all institutions convey

the same level of status or exposure to the exhibited artist. In the same way that an advertisement

during the Super Bowl has more of an impact on demand than one during daytime television, an

exhibition at a major gallery or museum both conveys a greater signal of value, since they are

considered bigger experts on the value of art, and have a wider audience than other institutions.

Either a solo or group exhibition at these institutions will thus have a much larger impact on the

24 In this case, this definition of “exhibition” is separated from the concept of a “public display” of art for educational or inspirational purposes, which goes back to classical civilizations and even pre-history. 25 The exclusion of art fairs as well as auctions comes from the fact that these types of exhibitions are often not reported in artist CVs, which is where the exhibition information for study was collected. This does not mean that they do not have effects within this market, just that this study does not include them. 26 Findlay, p. 125.

Wilkinson 13

exposure and acclaim of the artist which will in turn likely impact their prices much more. Major

galleries may be considered “mega-galleries” as defined above, or high quality contemporary

works with multiple locations. One may think of major museums as large museums of

international acclaim (MoMA, Tate, Pompidou, etc.).27

Beyond these distinctions between venues, another major division is that between solo

and group exhibitions. The distinction is fairly self-explanatory – the first is an exhibition in which

the artist’s work is exhibited alone and the second is one in which their work is exhibited along

with the work of other artists. However, this can create different levels of exposure in each venue

due to differences in exhibition practices – in part stemming from their functionalities. While solo

exhibitions generally have the ubiquitous function of highlighting the artist being shown, group

exhibitions can have quite different impacts depending on the location. Galleries have a primary

objective of selling works being shown, and work to achieve that objective by highlighting the

specific artists they represent. This provides them with an incentive to emphasize each of the

individual artists included in their shows. Thus, most gallery group shows look like the example

from Pace seen below in Figure 1. This exhibition at Pace London, running from November 20,

2015 to January 16, 2016, was called “Hoyland, Caro, and Noland” and showed works by John

Hoyland, Sir Anthony Caro, and Kenneth Noland, exploring the friendship and affinities between

the three artists.28 The artists included in the exhibition are small in number and each is

emphasized as an integral part of the exhibition. Thus, while the acclaim derived from being

included in such an exhibition may not be as high as a solo show, the exposure would be similar.

In contrast, group exhibitions at museums often tend to be more thematic. The aim is

often information on a wide cross-section of a specific movement or theme rather than a focus

27 A list of the major institutions designed for the purposes of this analysis are in Appendix 2. 28 http://www.pacegallery.com/exhibitions/archive

Figure 1 Press Release Blurb for Pace London Exhibition on the Gallery's Website

Wilkinson 14

on the specific artists involved. While there are some exceptions, such as two-artist exhibitions,

most group museum exhibitions tend towards the Solomon R. Guggenheim exhibition called

“ZERO: Countdown to Tomorrow, 1950s-60s” which ran from October 10, 2014 to January 7,

2015. This exhibition was the first large-scale historical survey in the United States dedicated to

the German artists' group Zero (1957–66). While the press release includes a few of the art

movement’s founders, most of the description of the artists exhibited comes from this statement:

“Featuring more than 40 artists from 10 countries, the exhibition explores the experimental

practices developed by this extensive ZERO network of artists, whose work anticipated aspects of

Land art, Minimalism, and Conceptual art.”29 The artists themselves tend to only be minimally

emphasized. The movement or type of art is emphasized. While this can contribute to demand

for an artist’s work as well, it would likely be more through the prestige associated with being

shown than actual artist exposure. Yet, all of these exhibitions at major institutions, possibly

particularly museums, are important for conveying value in their ability to create cultural capital.

2.5 Expert Valuation Driving the Art Market

Art does not have the same kind of objective value that may be found in other

commodities. A man who walks up to a Jackson Pollock action painting with no knowledge of art

history may think the picture is, at most, worth a few hundred rather than a few million. The

quality of artwork is not open to immediate experience, but instead, as Bonus and Ronte (1997)

argue, the recognition of art has the nature of a social convention. In an expression of this, they

point to the painting “Man with Golden Helmet”. Originally thought to be a Rembrandt, the

painting was a topic of much critical discourse and attracted large crowds. However, once it

became known that the painting was not in fact created by the Dutch Baroque master, the picture

lost virtually all its economic value and no longer drew crowds. While the art retained its value as

an interesting depiction, it lost its ability to serve as a signal of taste or status. The painting did

not change at all; the perception of its value did.30

29 http://www.guggenheim.org/exhibition/zero-countdown-to-tomorrow-1950s60s-2 30 In an entertaining anecdote which proves this exact point, as reported in the L.A. Times by Jori Finkel, in 2013 the J. Paul Getty Museum bought a tiny oil painting portrait 'Rembrandt Laughing' for about $5.2 million. The auctioneers estimated its value at around $3,000 with no knowledge that this work was indeed a Rembrandt. It has since been authenticated, but the discrepancy in price for the same work is exactly the kind of difference that can only be created through cultural construction.

Wilkinson 15

Whereas other investments, such as shares on a stock exchange, can similarly experience

drastic changes in valuations extremely quickly, these changes are generally based on some

objective evidence such as cash flow, profits, losses, etc. For fine art, these measures for

establishing the quality of a certain picture or oeuvre are not as overt. Because measures of value

in art are more open to interpretation, economic value for a work will emerge on the market for

visual arts only if potential buyers trust an artwork whose quality they cannot verify directly. As

Bonus and Ronte explain, “The evaluation of the visual arts thus involves a process of generating

credibility, a process in which experts from the art scene have a key role.”31

The reputation of artists and the creation of value for a work is thus determined by the

ability of potential consumers to both understand the artist and the work in question. To acquire

this understanding, consumers must discuss the artist with others who are knowledgeable about

the same artist. The market for visual arts is especially hard to find knowledgeable discussion

partners when new artists are to be evaluated since knowledge concerning emerging artists is not

common to the entire art world, but only to a few early-adopters. These insiders determine how

the artist is to be perceived, generating public credibility for these artists and translating their

determinations into market value. Bonus and Ronte (1997) argue that the role of these “insiders”

is filled by experts in the field trusted by fine art consumers to convey standards of value. In order

to make new determinations, they consult each other until they come to a consensus in their

valuations.

While Bonus and Ronte claim to be looking into the dynamics of how artist reputations

are established, my analysis will investigate the empirical relationship of exhibitions to price as

concrete representations of the building of artist acclaim, both through the information directly

surrounding the year of sale and the development of reputation through exhibition histories over

time. Museums and galleries are institutions that represent conglomerations of the experts that

Bonus and Ronte discuss. Understanding the role of exhibitions, as a proxy for expert indication

of value among other indicators of a particular artists’ acclaim, is thus particularly important for

study since it indicates how value is built in the contemporary art market. The impact of artist

31 Bonus and Ronte (1997), p. 104. ; In this sense, the art market can actually be seen as relatively similar to other investments. For example, the market price of a stock may fall because some financial analyst changes views about the prospects of the company. The buyers of the stock often lack a complete basis for understanding why the expert changed her or his mind. They just trust the expert. Similarly, in the art market, because value is difficult to determine, art buyers rely on the judgment of curators in the same way as investors rely on the judgment of financial analysts.

Wilkinson 16

acclaim through exhibitions is also especially significant right now due to developments in the art

market generally – namely in its commercialization, as market forces control which art gets

exhibited more and more, and the growth of art as an asset class – which makes studying elements

which contribute to potential shifts in the value of art particularly important to the art market

today.

2.6 Commercialization of the Art World

Capitalism has overtaken contemporary art, quantifying and reducing it to the status of a

commodity. Ours is a system adrift in mortgaged goods and obsessed with accumulation.

– Richard Armstrong et al., 1989 Whitney Biennial Exhibition Catalog32

While the bleakness and negativity of this quote is not mirrored in all sentiments

regarding the commercialization of art, the concept that the level of market involvement in the

art world has reached unprecedented levels is fairly ubiquitous. Due to the increased wealth of

developing countries and its expansion to a more international field, the art market as a whole is

growing. Within this expanding market, the post-war and contemporary sector represents almost

half of revenue and 40 percent of volume of sales as of 2014, seen in Figure 2. Also represented

on the next page in Figure 3, this sector has grown enormously over the past decade, with an

almost 80 percent increase in mean price, an increase even larger when compared to the decline

in 19th century and Old Masters’ prices.

Within this sector, individual prices for living artists have increased to extraordinary

levels. There are many available examples of artworks such as Jeff Koons’s Balloon Dog (Orange)

(1994-2000), which sold in 2013 for $58.4 million33, or Edward Rushca’s text painting Smash

(1963), which sold in 2014 for $30 million.34 Beyond individual works, artists like Christopher

Wool, Cindy Sherman, Anish Kapoor, or Gerhard Richter’s routinely sell for well over seven

figures.35 As Lucia van der Post wrote in the Financial Times, “Today art and artists are attracting

the fans, the adulation, the attention, and the bank balances that were once the domain of rock

stars.”36

32 Armstrong, Marshall, & Philips (1989), p. 911. 33 Waxman (2013, November 14). 34 Palmer (2015, August 13). 35 Velthuis (2012), p. 35. 36 Van der Post (2010, February 27), p. 33.

Wilkinson 17

Figure 2: Market Sector by Share of the Fine Art Market

Figure 3: Changes in the Relative Price of Art by Sector over the Last Decade

*Source: Graph taken from Artprice.com’s 2014 Art Market Report.

Seen in Figure 4, four of the top five fine art auctions by auction turnover were

contemporary auctions, with top hammer prices ranging from $90 to $26.8 million dollars. With

so much money in the contemporary art sphere, it is no surprise that many claim that the motives

of artists, collectors, and intermediaries have become more profit-oriented and less dedicated to

creative or artistic goals.

Wilkinson 18

Figure 4: Top 5 Fine Arts Auctions in 2014 Worldwide

*Source: Data and figure taken from Artprice.com’s 2014 Art Market Report.

Within this sector, individual prices for living artists have increased to extraordinary

levels. There are many available examples of artworks such as Jeff Koons’s Balloon Dog (Orange)

(1994-2000), which sold in 2013 for $58.4 million37, or Edward Rushca’s text painting Smash

(1963), which sold in 2014 for $30 million.38 Beyond individual works, artists like Christopher

Wool, Cindy Sherman, Anish Kapoor, or Gerhard Richter’s routinely sell for well over seven

figures.39 As Lucia van der Post wrote in the Financial Times, “Today art and artists are attracting

the fans, the adulation, the attention, and the bank balances that were once the domain of rock

stars.”40 Seen in Figure 4, four of the top five fine art auctions by auction turnover were

contemporary auctions, with top hammer prices ranging from $90 to $26.8 million dollars.

With so much money in the contemporary art sphere, some critics argue that artists have

abandoned artistic autonomy and focused squarely on profits; eroding the traditional taboo of

the previous decades on catering to pre-existing demand.41 This concept of branding artwork for

the sake of profit – making easily recognizable and digestible, iconic or provocative images, often

borrowed from popular culture – is hard to refute when confronted with the aforementioned

Rushca pop culture text image or Jeff Koons balloon dog, which fetch some of the highest prices

in the art market.42 However, this same critical argument dates back to at least Pop Art of the

1960s, and similar arguments of easily-digestible images could be applied to art going back to

37 Waxman (2013, November 14). 38 Palmer (2015, August 13). 39 Velthuis (2012), p. 35. 40 Van der Post (2010, February 27), p. 33. 41 American cultural sociologist Diana Crane (2009) argues, “[Until the 1990s], artists were motivated less by financial gain than by their aesthetic goals and assessments of their works by their peers.” Although this concept of the “art for art’s sake” artist is seen as “traditional” at this point by some critics, it is really a relatively recent conception within the art market, really only dating from around the late nineteenth century. The rest of art history is founded on the idea of “catering to pre-existing demand” – everything from Michelangelo’s sculptures for the Medicis to Rembrandt’s large commission for “The Night’s Watch.” 42 Velthuis (2012), p. 19.

Wilkinson 19

eighteenth century Rococo if not further. Thus, this critique of the creative impact derived from

commercialization is more related to individual aesthetic preference than an irrefutable quality

assessment. In contrast, the dispute that museums are becoming overly commercialized is

arguably in contrast to the service these institutions are meant to provide.

Museums have long been held to provide public services – exposing the public to fabulous

works of art while acting as nonprofit institutions of learning, and fostering diversity of

independent thought. Yet, today, the wall separating museums from businesses like galleries is

becoming thinner, and some, such as Christopher Knight at the Los Angeles Times, argue this

relentless commercialization is to the public’s detriment and symptomatic of the declining

morality of the museum as an institution. Knight claims that, with for-profit art dealers organizing

shows for non-profit museums and museum professionals organizing shows for commercial art

fairs and galleries, “museum collections are being monetized, rented out for profit to other

museums and private corporations.”43 While growing external commercial forces play a part in

museums commercial endeavors, internal economic pressures have also forced museums into

this position. For example, soaring prices of art increase the need for museums to obtain more

donations to afford collection development, placing them in a position where they bow to these

economic pressures or potentially close their doors.

Whatever the reason for this growing commercialization of museums, the recent

discovery by Julia Halperin at the Art Newspaper that almost one third of solo shows in U.S.

Museums go to artists represented by five top galleries – Pace, Gagosian, Marian Goodman, David

Zwirner and Hauser & Wirth – further emphasizes museum commercial overlap.44 When the U.S.

art dealers and galleries industry includes about 5,000 establishments (single-location companies

or units of multi-location companies)45, having five galleries which only represent contemporary

artists represent 30 percent of the total market is amazing. While museums hold that these artists

are simply the ones developed enough in their careers to be exhibited, this continued cross-over

serves to underscore museums’ commercial ties. The analyses of this paper contribute to this

debate by analyzing the associated price impacts of these museum exhibitions so that the art

world may further understand the links between the two. If museum exhibitions consistently and

concretely impact art prices, they cannot claim to be entirely separate from the art market. They

43 Knight (July 17, 2015). 44 Halperin (April 2, 2015). 45 First Research (2015).

Wilkinson 20

must either recognize their role as a commercial actor or consciously avoid the moral corruption

which is associated with the ability to raise the value of exhibited art; both pertinent to this

controversy as well as to the rise of art as an asset class – a recent phenomenon which contributes

to the overall composition of art world consumers, increasing demand, and the growing influence

of experts in the market.

2.7 Art as an Asset and Investment

André Level’s art investment fund, managed and operated from 1904 until its sale in 1914,

serves as the oldest recorded example of a successful organization investing in art specifically to

turn a profit. Based on works from artists such as Picasso, Matisse, Paul Sérusier, Guys, Vuillard,

and Gauguin, Level made four times the original outlay of his investors within those ten years.46

Yet, while the idea of art as an investment has been around at least since André Level, the concept

of art as an asset has its roots in the late 1950s and has increasingly come to the forefront of the

art sphere particularly in the last decade. As such, art investors are increasingly looking for signals

of the increase in the value of a work over time to develop returns on their investments. Thus,

this growth in level of investors may increase the impact of an exhibition on the price of works.

As cultural economist Clare McAndrew wrote, “The growth of art funds and other

professional art investment vehicles bears out the fact that both individuals and institutions have

fully embraced the notion of art as an asset class for investment.”47 However, this concept may

not be as new as some are led to believe. As early as the late 1950s, popular newspapers and

magazines such as Fortune and The New York Times discussed art in investment terms. In the

boom years of the 1960s a few art investment firms were established.48 Perhaps more

influentially, in 1967, then chairman of the board of Sotheby’s Peter Wilson initiated the Times-

Sotheby Index, akin to the Dow Jones index, which made art explicitly comparable to stocks. As

journalist Philip Hensher would later observe:

By demonstrating that pictures could be thought of in this way, the index guaranteed that

they would be. It presided over a vertiginous rise in the value of art, as moneyed individuals,

corporations, even pension funds found that they could justify the acquisition of a painting

in exactly the same way that they could a block of shares.49

46 Finkel (2014, July 31). 47 McAndrew (2009). 48 Velthuis (2012), p. 41-42. 49 Hensher (2006, February 13).

Wilkinson 21

However, this boom period was followed by a slump in the art market in the 1970s until another

upturn in the 1980s. Art historian Olav Velthuis (2012) argues that booms of investment in art

may be cyclical, albeit not along the exact same path as the stock market – correlating the boom

of the 1960s to the pre-crisis 2000s.50

Indeed, beginning with the boom of the 1980s, art has developed into a recognizable

asset category utilized today in a wide array of financial transactions.51 Beyond the wide

proliferation of art funds during this decade, works of art are now also used in transactions such

as collateral for multi-million dollar bank loans and investments for pension fund portfolios.52 This

most recent period has been one of unprecedented increases in the price and volume of works,

as discussed in the previous section, which is correlated with the unprecedented increase in its

use as an asset class. A product of the compositional change in the art market through

commercialization and globalization rather than just a normal cyclical transition in the art market,

in 2010 to 2011 the art market continued its boom despite severe economic and financial turmoil

in the United States and Europe. This sustained increase has led to new collectors using art largely

as an investment as well as status symbol. In 2014, more than three-quarters of art buyers and

collectors said they were looking to buy art for investment purposes — up almost a full quarter

from the year before.53 The rise of these new art buyers with an investment focus, who largely

buy at auction houses like Christie’s, Sotheby’s, and Phillips, has led to increased market share

now going to auction houses, as opposed to galleries, as well as new venues such as art fairs and

the internet.54

50 Velthuis (2012), p. 43. 51 This cultural art boom of the 1980s spawned some of the earliest literature dealing with art as an asset class, including Baumol (1986) - widely considered one of the founding articles of cultural economic literature – or the recent sector of economics analyzing the art market which will be later discussed. However, while this much earlier literature recognizes art as a potential asset, it also calls this concept into question as one generally unworthy of consideration for investment by anyone not also interested in the aesthetic pleasure (returns) to be added from ownership. The major difference in public sentiment in the last decade is the rise of art being considered solely an asset class outside of its aesthetic value. The rise of art funds during this period, more than ever before, there has also been an increase in art being warehoused as it accumulates value, rather than displayed. 52 Velthuis (2012), p. 26. 53 Deloitte. (2015). 54 Despite hostile attitudes from many galleries the old divide between primary and secondary markets, between galleries and auction houses, is narrowing. For example, in part due to increasing sales generally, but also due to this narrowing divide, auction sales rose eightfold between 1998 and 2008.

Wilkinson 22

With increased avenues for procuring art and more potential profits to be made than ever

before, it is no wonder that the use of art as an investment has taken off since 2000. As Noah

Horowitz explains in Art of the Deal:

The premise of art investing is fairly intuitive: to buy cheap and resell at a premium while

minimizing the onerous transaction, shipping, storage, and insurance costs that make art an

expensive asset to hold. Two widely held assumptions undergird this basic objective: first

that art as an asset class is weakly or non-correlated to the international equities markets,

and second, that the art market is highly inefficient, riddled with opaque prices, low levels of

liquidity, and large informational asymmetries.55

While Horowitz somewhat contests the first assumption and argues for exploitation of the

second, along these lines both private investors and art funds have proliferated. While many art

funds have not found much success, some like The Fine Art Fund, Tiroche Deleon Collection, and

Day Star Art Fund report an average annual return of 15 to 30 percent.56 These phenomenal

returns, however, are quite anomalous when compared to general art investment fund returns.

Despite significant differences between art and traditional investments or even other commodity

markets making studying this relationship somewhat difficult, the attempt to understand the

price of art and particularly the potential returns of art has spawned a generation of cultural

economic literature.

Additionally, this increased interest in art as an investment class instead of a status

symbol or commodity has brought more “lay” people into the market, or those without extensive

experience in the art market or how to value works of art. As such, there is a greater tendency to

rely on expert opinions of value than previously. As Sagot-Duvauroux discusses in the book Le

Marche de l’Art Contemporain¸ in the face of uncertainty regarding the quality of contemporary

artists, poorly informed collectors may be rationally led to adopt copy-cat forms of behavior that

consist in following the opinions of a few agents who are believed to know the values concerning

the artworks.57 Additionally, returning to the theories of Bonus and Ronte (1997), an artist must

be credible to the public in order to generate economic value, and this credibility is created by the

interaction of various insider experts who are in command of cultural knowledge. These expert

opinions allow the bearer to ascertain cultural quality since to command a market price the work

55 Horowitz, N. (2014), p. 147. 56 Hawkins, R. (2015, April 12). 57 Moueau, N. and D. Sagot-Duvauroux (2003, 2010).

Wilkinson 23

must be recognizable to the public.58 This psychological construct developing in the art market

could have particular bearing on understanding the impact of museum and gallery exhibitions on

the price of art. Since galleries and especially museums are considered experts in the art field,

exhibitions of an artist create a significant signaling quality to the art market that the work should

be worth more.

This increase in investment activity makes this analysis of price changes associated with

exhibitions particularly important now as the premium on information regarding potential

investment return has increased. With even traditional art buyers viewing any fine art purchase

as an asset as well as a commodity good, any potential understanding to return on investment is

of particular interest to the art market. As Kevin Murphy, ex-art dealer and current Williams

College Museum of Art Eugenie Prendergast Curator of American Art, described:

“When I used to show potential clients art works from various artists, they would ask me

which one is better – which one should they choose. To which I would respond, ‘Well

which one do you like better?’ They would always look at me with some suspicion, like I

knew better, but just refused to tell them which one would be better for their collection

over the long term.”59

3. Hedonics and the Art Market

3.1 Fine Art and the Housing Market

Looking at the world of fine arts from the outside may make the entire enterprise seem

like an enigma. Why should a scribble “a child could do” go for millions of dollars? Why does a

Damien Hirst decaying sculpture cost millions of dollars and a David Hockney print multiple cost

a couple hundred thousand? While the influence of experts in this market in conveying value has

already been discussed, the art market is not quite as foreign as one might believe even at this

point. Some of these elements of “other” which plague one’s thinking about this market may be

somewhat dispelled by understanding this market’s similarities with the housing market.

Although objective standards of value for a lay person do not necessarily exist, there are ways of

58 Bonus and Ronte (1997) also lay out an argument that this can lead to so-called “mistakes” in the fine arts market, where the process by which experts generate public credibility for a given oeuvre is path dependent and therefore end up at inferior solutions over time. In contrast, this study lays out a more fluid conception of the art market which allows for shifting tastes to control the market which allows for shifts in expert perceptions as well. 59 Murphy (2016, January).

Wilkinson 24

considering the fine art market which allow economists to break them down into their composite

pieces in relatively accurate empirical models.

Consider fine art in the way one thinks of the components of a house through hedonic

modeling. When determining the willingness of a consumer to pay for a house, many different

factors come into consideration – how many bathrooms and bedrooms; what the neighborhood

is like; where is it; does it have an ocean view, etc.

Similarly, when a fine art buyer looks to acquire a

work of art, they too look at its various components

to determine what exactly they would pay for it.

Instead of bedrooms and bathrooms, they consider

the acclaim of the artist, the size of the work, when

in the artist’s career it was produced, and the type of

work – to name a few of the considerations. For

example, when looking to potentially acquire the

Christopher Wool painting, such as the one in Figure

5, a collector may consider the international acclaim

of Christopher Wool, the fact that just last year he

had a solo exhibition at MoMA PS1, the size of the

work at 88.9 cm x 61 cm (which could comfortably fit

in a residential space), and the fact that this type of

word painting is the best known type within the

artist’s oeuvre. Yet, while all of these different aspects add to both the aesthetic qualities and the

status of the artwork, each contributing to the price of the work, the only price known is the total

sale of the work – which for the work pictured here was $2 million. Because of this single price

which represents the total of the willingness to pay of the composite parts of a work of art in the

same way that the housing market only contains the single price of the house itself, both the

functioning and problems associated with the fine arts market are strikingly similar to the housing

market.

Looking first to the functioning of these markets, both housing and art represent objects

which are both commodities and assets. While it is true that houses also have the utilitarian

function of allowing someone to live inside them, much of the status and investment potential

associated with fine art could also be associated with the housing market. In the same way that

Figure 5: Christopher Wool, Untitled, 88.9 cm x 61 cm

Wilkinson 25

fine art may be bought as a store of value for the super-rich, these same consumers buy multiple

homes in various areas around the globe, some of in which they never live. The status of having

an apartment on Park Avenue between 75th and 76th in New York is a similar concept to owning a

Damien Hirst sculpture. Additionally the resale-value can definitely influence demand for both art

and housing in terms of return on investment. Someone may buy an apartment on the Lower East

Side of Manhattan since the neighborhood is gentrifying and thus the resale value may skyrocket

in just a few years. Similarly, an art collector may buy the work of a mid-career artist since they

think their career is taking off with a few big exhibitions coming up, with the idea that the

exhibitions will lead to an increased demand for the works of the artist.

Additionally, both markets are characterized by infrequent sales of the same object.

Someone may “flip” a house in a year or two to attempt to make a profit after fixing it up in some

way or just due to a boom in the art market. Recently, especially with the rise of art as an

investment, collectors too will profit by bouncing recent gallery purchases in auction after barely

months of ownership. Yet, in other cases, a family will live in a home for thirty years, or a die-hard

collector of American photographer Thomas Ruff will decide they never want to part with their

acquisition. Also, the status of owning each commodity may offset investment potential in both

cases: living in Chelsea right under the Highline near the new Whitney or owning a work by Iranian,

female artist Monir Farmanfarmaian right after her solo show at the Solomon R. Guggenheim

Museum might each be worth more than the money one would make from the sale. Since each

market acts as commodity, status symbol, and asset, neither market offers continuous or even

consistent sales on which to base analysis.

Each of these products – both housing and fine art – represent a group of characteristics

within heterogeneous markets of infrequent sales and roles as both commodity and investment

asset. In the same way that a tiny apartment in Manhattan can cost far more than a house in

Wisconsin, a small print-multiple by Japanese contemporary artist Yayoi Kusama can cost far more

than a large-scale painting by emerging American artist Ruby Sterling. Rather than being arbitrary

or outlandish in the way that many outsiders view the soaring prices of art, arguably the price of

each work is a function of its various components in a similar way that houses are. By codifying

the various visual indicators of value as well as exhibition of the works, along with the

development of artist acclaim over time, one can fairly accurately model a piece’s price on the art

market. Thus, as with housing, the best way to understand the pricing or willingness to pay of

consumers for specific pieces is to decompose them into their various characteristics.

Wilkinson 26

3.2 Theory of Hedonics

To frame the subsequent hedonic methodology, it is important to understand the

distinction between implicit and explicit markets in heterogeneous markets such as housing or

fine art. The concept of implicit markets denotes the process of production, exchange, and

consumption of commodities that are primarily (perhaps exclusively) traded in “bundles”. The

explicit market, with observable prices and transactions, is for the bundles themselves. One can

consider these markets as aggregating several implicit markets for the components of the bundles

themselves.60 This is particularly important when bundles of goods are heterogeneous, or

containing varying amounts of their different components – examples being automobiles,

workers, houses, and of course, fine art. These markets are not characterized or even

approximated by a single price, but rather a range of prices that depend on the quality of the

commodity or the characteristics it contains. For example, looking to the data set utilized in this

study, the minimum price included in the sample is $10, which applies to a few print multiples by

various lesser-known artists, and the maximum price is $26.5 million for a painting by Christopher

Wool. While these objects all technically constitute the fine art market, the difference in price

based on various characteristics is staggering.

The hedonic method addresses the difficulty posed by this level of heterogeneity by

asserting that these goods are composed of aggregates of more or less homogeneous parts. While

the aggregate bundle of the work itself may not have a common price, the component attributes

either have a common price or at least a common price structure. By estimating the hedonic price

function, this approach provides a methodology for identifying the structure of prices of the

component attributes. One can then use these prices to analyze consumer demand or willingness

to pay by treating attributes as goods. This involves an implicit assumption that consumers,

constrained only by their incomes and the price of the resulting bundle, can choose any bundle of

attributes they wish from a vast variety available.

How can we understand the system of individual choices that provides the foundation for

the hedonic price function that is to be estimated? In a hedonic discussion based largely on the

general case considered in Sheppard (1999), consider a collector who is assumed to have a utility

function:

𝑈𝑈 = 𝑈𝑈(𝑍𝑍,𝑌𝑌,𝛼𝛼) (1)

60 Sheppard (1999).

Wilkinson 27

where 𝑍𝑍 is a vector of characteristics of the artworks in the collection. These would include size,

style, and age of each work, the name of the artist who created each work, etc. The variable 𝑌𝑌

represents a composite commodity of expenditures on all other goods (not part of the art

collection), and 𝛼𝛼 is a vector of parameters that characterize the utility function. The

household/collector has income 𝑀𝑀 to allocate between adding to the art collection and spending

on other goods. We assume that the utility function 𝑈𝑈(𝑍𝑍,𝑌𝑌,𝛼𝛼) is strictly quasi-concave so that

there is a well-defined solution to the problem of allocating income 𝑀𝑀 between the artworks and

other goods.

What is the maximum amount that the collector with income 𝑀𝑀 would be willing to bid

for a collection providing art characteristics 𝑍𝑍? We can define the bid function 𝛽𝛽(𝑍𝑍,𝑀𝑀,𝑢𝑢,𝛼𝛼)

implicitly by:

𝑢𝑢 = 𝑈𝑈(𝑍𝑍,𝑀𝑀− 𝛽𝛽,𝛼𝛼) (2)

That is, the bid function is the bid amount that would leave just enough remaining income to be

spent on the composite good that when combined with the art characterized by the vector 𝑍𝑍

would suffice to allow the collector to realize utility level 𝑢𝑢. The derivative 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

of the function 𝛽𝛽

with respect to an individual component of the vector 𝑍𝑍 would measure the rate at which the

collector would be willing to increase the bid price as the characteristic 𝑧𝑧 increases.

Suppose that the collector acquires the collection in a market that offers artworks for a

price 𝑃𝑃(𝑍𝑍) that depends on the characteristics of the art. A utility-maximizing collector would

build a collection 𝑍𝑍 by choosing 𝑍𝑍 and 𝑌𝑌 to solve:

𝑀𝑀𝑀𝑀𝑀𝑀 𝑈𝑈(𝑍𝑍,𝑌𝑌,𝛼𝛼) 𝑠𝑠𝑢𝑢𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑠𝑠𝑡𝑡 𝑀𝑀 ≥ 𝑃𝑃(𝑍𝑍) + 𝑌𝑌 (3)

Given that 𝑈𝑈 is quasi-concave, first order conditions to solve this will require that:

𝜕𝜕𝜕𝜕𝜕𝜕𝑍𝑍𝑖𝑖

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑍𝑍𝑖𝑖

𝑓𝑓𝑡𝑡𝑓𝑓 𝑀𝑀𝑎𝑎𝑎𝑎 𝑖𝑖 (4)

The derivative 𝜕𝜕𝜕𝜕𝜕𝜕𝑍𝑍𝑖𝑖

is the hedonic price of characteristic i and the function 𝑃𝑃(𝑍𝑍) is the hedonic price

function for artworks in that it reflects the valuations placed on characteristics.

The artworks that the collector hopes to acquire at auction are made available by a profit-

maximizing dealer or seller. The dealer offers 𝑁𝑁 works for sale having characteristics 𝑍𝑍 and from

this expects to realize revenues 𝑃𝑃(𝑍𝑍) × 𝑁𝑁. These artworks are acquired from artists who must be

supported, and this generates costs 𝐶𝐶(𝑍𝑍,𝑁𝑁, 𝛾𝛾) that depend on the characteristics 𝑍𝑍 of the

artworks offered, the number 𝑁𝑁 of works offered, and exogenously determined parameters 𝛾𝛾.

This cost function 𝐶𝐶 is assumed to be convex. The seller of artworks chooses 𝑍𝑍 and 𝑁𝑁 to solve:

Wilkinson 28

𝑀𝑀𝑀𝑀𝑀𝑀 𝑃𝑃(𝑍𝑍) × 𝑁𝑁 − 𝐶𝐶(𝑍𝑍,𝑁𝑁, 𝛾𝛾) (5)

The first order conditions to solve this choice problem are:

𝜕𝜕𝜕𝜕𝜕𝜕𝑍𝑍𝑖𝑖

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑍𝑍𝑖𝑖

∀ 𝑖𝑖 𝑀𝑀𝑎𝑎𝑎𝑎 𝑃𝑃(𝑍𝑍) = 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

(6)

This requires that the hedonic price of each artistic attribute be equal to the marginal cost of

making it available, and that the overall price of an individual artwork of type 𝑍𝑍 is equal to the

cost of making such an artwork available.

There will be a large number and variety of sellers of art works. Many of them will be

individual collectors or estates with only a single work to offer (so that 𝑁𝑁 = 1), others will be

estates with an entire collection of works to offer or dealers with several works of varying types

available for purchase.

The equilibrium determination of the hedonic price function is illustrated in Figure 6

above, which shows the art acquisition choices of two collectors with regards to a single art

characteristic 𝑍𝑍𝑖𝑖. Collector I has a willingness to bid function given by b' and collector II has a

willingness to bid function b''. Collector I purchases from seller I whose cost function (as artistic

Figure 6: Generalized Art Acquisition Willingness to Bid Functions

Wilkinson 29

attribute 𝑍𝑍𝑖𝑖 varies) is given by c'. Similarly, collector II purchases from seller II with cost function

c''. Collector I purchases from seller I an artwork with amount z' of the artistic characteristic, and

collector II purchases from seller II an artwork with the amount z'' of the characteristic.

For the artwork purchased by collector I, the price of art is increasing as 𝑍𝑍𝑖𝑖 increases at a

rate equal to the slope of the line p'. This is the hedonic price of 𝑍𝑍𝑖𝑖 evaluated at z' which as noted

above is the slope of the hedonic price function with respect to the characteristic 𝑍𝑍𝑖𝑖. Similarly, the

hedonic price of 𝑍𝑍𝑖𝑖 evaluated at z'' is the slope of the line p''. In the example illustrated, collector

II pays a smaller marginal price for characteristic 𝑍𝑍𝑖𝑖 and (naturally enough) purchases artwork

with a larger amount of this characteristic than collector I.

Estimating the hedonic price function is the process of using data on a large number of

observed transactions, with artworks that vary in many different dimensions, to make inferences

about the slopes of the hedonic price function and in this way to obtain an estimate of the

function 𝑃𝑃(𝑍𝑍) that can help us to better understand the role of the various artistic attributes in

determining the price of artworks. This valuation depends on the distribution of buyers and sellers

in the market, since this hedonic function is constructed from individual transactions and requires

that we make use of the most complete set of artistic attributes available to avoid omitted

variable bias. Because the enthusiasm of collectors for particular types of art (or art in general)

and the cost of making particular types of artwork available can vary over time, we include general

indicators of time (at least the year of sale) that can capture shifts in the overall market.

We should also include measurements of those attributes that may not be physical

characteristics of the artwork itself, but are important components of the benefit to owning

artworks, and hence important determinants of the bid function 𝛽𝛽. These non-physical attributes

are aspects such as artist acclaim, built through information conveyed as well as the development

of artist reputation over time, which in this study is measured through the exhibition history and

exhibitions contemporaneous to the sale of work.

3.3 Hedonics in Cultural Economics

Attempts to model the prices of these heterogeneous, relatively illiquid, real assets which

compose the art market, where only a fraction of the stock is on sale during one run of the market,

are derived from other commodity markets such as housing and automobiles which sought to

solve similar problems facing the art market. The first hedonic price indices were initiated by Court

Wilkinson 30

(1939), extended and used among others by Griliches (1971) for car prices and Ridker and Henning

(1967) for housing.61

Stemming from this lineage, a small but growing literature has emerged over the last

twenty years concerned with developing hedonic models of the prices of artwork. Increased

sources of data for analysis as well as the global cultural phenomenon of the boom in art prices,

and its resulting increase in art’s consideration as an asset class, has encouraged this field to

expand. Some important works that employ hedonics deal with the economics of museums,

artistic competitions, variance in art auctions, investing in the arts, and aspects of the creative

process. In using hedonic regressions, researchers are able to construct a price index for an

average work for a particular region over a certain period of time by controlling for varying

characteristics within the price indexw.

David Galenson used the hedonic method and auction prices to investigate creativity

patterns among French and American painters from the nineteenth and twentieth centuries in a

series of works (see Galenson 1997, 1999, 2001, 2002; also Galenson and Weinberg 1999;

Galenson and Jensen 2002). In Galenson (2002), for example, he argues that there are two major

“life cycles” for great modern artists – either they make their major contributions early in their

careers or they produce their best work later in their lives. The “great modern artists” on which

the study is based are ten artists considered “great innovators” in the Abstract Expressionist and

Pop Art periods: including Jackson Pollock, Mark Rothko, Andy Warhol, and Jasper Johns. The data

considered in this econometric analysis are the just over 1,600 paintings and watercolors by the

ten artists considered in the study sold at auction during 1970-97. In order to determine the

“greatest contribution” of each artist to the art historical cannon, Galenson uses hedonic

regression analysis to estimate the relationship between the auction value of his work and his age

at the date of the work’s execution. Each painter’s implied age at this peak value was then

estimated. In this sense, the highest value for the work of art means the best contribution to the

art market for Galenson. The estimated ages at peak value of the Abstract Expressionists, which

range from 38 to 54, are all greater than all of the peak ages of the painters of the next generation,

which range from 24 to 35. Thus, Galenson concludes that Abstract Expressionists made their

greatest contributions later in their career than the subsequent generation.

61 Chanel, O., Gérard-Varet, L. A., & Ginsburgh, V. (1996). The relevance of hedonic price indices. Journal of Cultural Economics, 20(1), 1-24.

Wilkinson 31

Similarly, in Galenson and Weinberg (1999), they use auction records from 1980-96 from

annual editions of Le Guide Mayer62 to estimate the relationship between artists' ages and the

value of their paintings for two successive cohorts of modern American painters to determine

whether this peak age changes over time. This study collected the records of all sales of paintings

and watercolors by the 51 global modern artists included in this study, yielding a total of 4,395

sales of individual works. In order to compare across their two cohort types (artists born during

1900-20 and those born during 1921-40), they estimate a regression in which the price of a

painting is expressed as a polynomial in the age of the artist, interacted with whether the artist

was born after 1920. Other variables included in the regression are fixed effects for individual

artists due to the large differences in the value of works by different artists, binary variables for

year of sale to account for fluctuations in the art market, an indicator variable for whether the

work was done on paper or canvas, and the size of the work. Looking at the age-price profiles for

each cohort, for artists born 1900-1920, they find prices increase from age 20 to the early 50s

before declining; the implied peak is at 50.6 years. For those born after 1920, the maximum is at

28.8 years, considerably earlier than the first cohort. They use this information to argue that a

shift in the demand for modern art caused artists in the later cohort to product their most

significant contributions later in their careers.

While many control variables of this regression analysis have become standard in cultural

economic models, Galenson’s main contention that the peak of an artist’s career may be

empirically measured as the period of his career which fetches the highest art prices at auction is

somewhat controversial. Although this may make sense in the context of the labor market, that

the worker whose skill is worth the most makes the most money, the art market is not as clear-

cut. The view that, on average, prices reflect the inherent value and quality of work of art is

extremely controversial among the public and art historians in particular. To show that his quality

assessment obtains similar results to an exhaustive reading of art historians and critics, Galenson

analyzes a large number of works published in the United States and Europe to identify which

works are most reproduced and discussed by experts - a method called historiometry. He argues

this allows him to see which works scholars think are most important to an artist’s career, and

finds that they are close to his regression results.

62 Le Guide Mayer complies the results of fine art auctions held all over the world. Mayer classifies the works sold into five groups: prints, drawings, watercolors, paintings, and sculptures.

Wilkinson 32

There are many reasons why Galenson’s methodology remains controversial for

economists as well as art historians. First of all, aspects external to the work itself may affect the

price, making the price paid at auction an unreliable measure of inherent, unchanging value.

Tastes in the art market change over time. Certain periods of art may be largely popular with

collectors and then taper off in terms of collecting base or certain periods may become suddenly

popular. For example, say works by the Nabis – a group of artists such as Emile Bonnard, Pierre

Vuillard, and Maurice Dennis during 1890 to 1896 – became more popular among collectors. Emile

Bonnard had a successful career after his tenure within the Nabis movement, but due to an

upsurge in demand for his works from earlier in his career as a Nabis, his highest selling work

shifts to being from this period. Does this mean that his greatest contribution to the art market

shifts to his time as a Nabis? Does this represent a change in quality? While indicator variables for

years in Galenson’s standard hedonic regressions may control for trends in the overall art market,

they cannot control for shifts in taste during those same periods. Thus, an artist’s age of greatest

contribution may be 33 in one sample of auction prices and 45 in another decade of reported

auction prices.

The current study concerning the impact of museum exhibitions on the price of works for

living artists also speaks to this controversy. If exposure through art exhibitions both through the

advertising and information signaling effect, exogenous to the quality of the art itself, adds value

to the works of art, then these prices cannot be considered constant standards of value. Similar

to the previous argument, it means that studying prices over a five year period where an artist

has many exhibitions and then a five year period where they have very few exhibitions could yield

very different prices for a similar art object. Thus, the variation due to these elements outside the

art itself makes price an inaccurate standard of inherent value.

Many characteristics of Galenson's hedonic framework have been adopted in subsequent

studies to explore other areas of the art market. For example, Edwards (2004) explores the career

trajectories of Latin American artists through an almost identical methodology. In this study,

Edwards uses 12,690 observed auction sales from 115 artists from seventeen Latin American

countries for the period 1977 to 2001.63 One significant difference from Galenson as well as most

other studies on the economics of art is that rather than relying on subjective criteria for including

certain artists, Edwards just set the parameter that the artist must have sold at least thirty-five

63 Edwards’s auction data is collected from two main sources: for 1977 through 1986, he uses Leonard's Price Index of Latin American Art at Auction; for 1987-2001 he uses the Artprice CD-ROM.

Wilkinson 33

works at international auctions during the period under study. In his empirical model, Edwards

too uses an age polynomial, natural log measures of the area of the work, an indicator for whether

it is a work on paper, whether it was signed, and fixed effects for year of sale, artist, and decade.

The analysis indicates that Latin artists born after 1920 had their highest-priced works at a later

age than their colleagues born before 1920, exactly the opposite of those in North America. He

explains this phenomenon as a difference in the development of the art market in the two regions.

Other than these studies, hedonic models have also been utilized to examine various

phenomenons within the global art market. For example, Maddison and Jul-Pedersen (2008) and

Urspring and Wiermann (2010) explore the “death effect”, or the impact on prices from an artist’s

death. In Urspring and Wiermann (2010), they control for time-invariant idiosyncratic

characteristics [size, medium, etc.], time-varying characteristics [the flow-supply of the artist’s

work in a particular year, the artist’s state of being alive or dead etc.], artist fixed effects capturing

artists’ abilities and reputation, indicator variables for auction house64, and time indicators to

estimate the influence of the overall art price movement on the price of a specific work of art.

Besides these standard hedonic variables, to capture the death effect, they include an indicator

variable for whether the artist died either in the year of sale or in the two previous years, as well

as a Time Since Death variable to capture changes after this three year period (which is zero up to

T+2). Beyond finding a death-induced price increase, they also demonstrated that the death effect

is negative in the case of an untimely death. They explain that in the event of an artist dying before

reaching their potential, the early collectors’ conceptions of value are frustrated. Thus, this study

is based around explaining death-induced changes in art prices by acknowledging that demand

for works of art is to a large extent driven by the respective artist’s reputation. This is precisely

what my analysis does, but from a different angle. It recognizes that the price of a work of art is

driven by the artist’s status and means to understand the associated evolution of that status.

Beyond this kind of shock to the market for the works of a specific artist, hedonic

regressions have been employed to explore returns to art in specific sectors, like modern and

contemporary Australian artists in Worthington and Higgs (2006) or Polish art in Witkowska

(2014). Determinants of prices for specific artists over time have also been explored, like Cezanne

in Bakhouche and Thebault (2011). All of these examples employ standard controls such as the

64 As will be discussed in a subsequent section, Sotheby’s and Christie’s hold a virtual duopoly in the fine art auction market. Thus, since many of the auctions of top artists happen here, they tend to fetch higher prices than other auction houses, and therefore are worthy of separation from the general sample.

Wilkinson 34

natural log of the size of the artwork, artist fixed effects, and year of sale fixed effects in exploring

these various subsections of the art market.

Other than the standard hedonic sales approach, a fair amount of cultural economic

literature has also been devoted to the repeated sales technique. This method, too, is derived

from the housing market. Following Bailey, Muth and Nourse (1963), in order to avoid omitted

variable bias in choosing various physical components of price decomposition, economists have

confined their samples to commodities which have been sold more than once and estimate the

change in the (logarithm of the) price of each commodity by regressing it on a set of indicator

variables (one for each time period during which the commodity is held). This repeat-sales

regression technique has been used to compute indices for property values or family houses by

Palmquist (1980), Mark and Goldberg (1984), Case (1986), Case and Shiller (1987, 1989), and

Goetzmann (1990).

By comparing prices of objects that have been sold more than once, the analyst ensures

that the

characteristics of

the asset – in this

case, the work of art

– are exactly the

same at different

points in time.65

Baumol (1986)

pioneered the

repeat-sales

regression in

cultural economics

by using this

methodology to analyze the rate of return for art generally; finding an extremely weak return in

65 While hypothetically looking to the same works of art being sold should keep their characteristics constant, there may be other changes in the work over time that may make this untrue. For example, especially if a work is sold multiple times over the span of a few years, this means that it may be moved around more, making it more likely to be damaged in the process. It also might be cut down over time or other such modifications. Thus, it is possible that the repeat-sales method does not actually control for fixed characteristics as well as it claims.

Figure 7: Mei Moses Relationship between Stock and Art Investment Performance

Wilkinson 35

comparison to other investment assets of 0.55 percent per year over the centuries from 1650 to

1960. Other cultural economists have explored the returns to art in different sectors with repeat-

sales regression methods: notably including the Pesando (1993) calculation of the rate of return

on modern prints and Goetzmann (1993) on paintings.

More recently, Mei and Moses (2002) use the repeated sales technique to estimate rates

of return on American paintings, Old Masters, and Impressionists. In this study, they construct a

dataset with auction information spanning from 1875 to 2000 in order to determine the average

rate of return in these three common collecting categories. They find that art underperforms

stock, but has less volatility and lower correlation with other assets than found in previous

studies66. Other than masterpieces, which tend to underperform, they recommend that a

diversified portfolio of art may be useful; a conclusion which was later reinforced through a similar

methodology but different dataset in Korteweg, Kraussi and Verwijmeren (2015).67

Utilizing the same repeat-sales regression methodology, Mei and Moses construct art

indices from auction art price data collected over the last twenty years as an investment service.

Mei and Moses indices consider over 30,000 purchase and sale price pairs for objects that have

sold at public auction more than once. To measure relative performance, they compare these

indices to equities, government bonds gold, cash, real estate, etc. Return, risk, and correlation

among the assets over many time periods and holding periods are analyzed in detail. In Figure 7,

their All Art Index is compared to the S&P 500 – displaying the general trends in returns from art

overall as compared to this index of returns of the overall stock market.

While the Mei Moses® family of fine art indexes is perhaps the most cited indicators of

the changing price of art, and many similar studies have been created along the same vein, there

are multiple reasons why the current analysis employs a standard hedonic regression rather than

a repeat-sales regression. Using all of the collected sales observations provides many more

observations and also avoids the difficult work of searching for paintings sold at least twice.

Additionally, many aspects of the art market make this process of matching even more

complicated: the title is often translated into the language of the country where it is sold; many

works bear titles which make them indistinguishable (such as Reclining Nude, Still Life, or

Untitled); and dimensions are not always accurately reported or measured.

66 For these previous studies which find more volatility and higher correlation with other assets, see Goetzmann (1993) and Pesando (1993). 67 This latter article, however, also warns against investing in a broad portfolio of paintings, but recommends targeting specific styles or top selling artists to add value to one’s portfolio.

Wilkinson 36

Problems arise due to the fact that most hedonic analyses, including this one, are based

on the results of public auctions. Guerzoni (1994) observes that unrecorded private sales through

galleries or other intermediaries may take place between sales at auction. These sales, excluded

from the repeat-sales regressions, might change the results if they were included. Also, selection

biases are often involved when using resales only since it may be the case that only “good” works,

with high resale values68, or “lemons”69, who have lower resale values, appear often on the

market. Finally, and perhaps the most important reason not to use repeat-sales regressions in my

analysis, since my sample deals with living, contemporary artists, their level of repeat-sales are

probably very low. By cutting out the majority of the collected data in an attempt to adopt this

common methodology, the sample would not be representative of the sector which this study

aims to understand. Thus, my analysis will utilize a standard hedonic model to avoid selection bias

and come to more robust results through a larger sample.

Although my analysis makes use of a standard hedonic methodology which involves the

entire sample collected rather than the repeat-sales technique, it also attempts to provide

information that has never been dealt with in the field of cultural economics or otherwise. Most

cultural economic papers only consider a frustratingly limited number of characteristics as

determining the prices of art pieces. Those which include standard hedonic regressions, such as

Galenson’s papers, Edwards (2004), Urspring and Wiermann (2010) and Bakhouche and Thebault

(2011), only include the era of the work, its sell date, artist age, auction house, and simple

characteristics of the work itself (such as size and medium) even in the most extensive examples.

In those which employ repeat-sales regressions, they use largely the same price determinants,

but without the work characteristics since these aspects are controlled for through the pairing of

works.

In this analysis, I hope to expand cultural economists’ considerations of what contributes

to price by using the impact of museum exhibitions as conveying both information as well as

quality signaling for an artist surrounding the sale of their work and over time (particularly in the

case of “major” museum and gallery exhibitions). By understanding the impact that this kind of

exposure gives to an artist, this can show that there are other measurable factors impacting the

68 Goetzmann (1993) first argued that the decision of an owner to sell a work of art could be conditional upon whether or not the value has increased. 69 The concept of a “lemon” artwork is especially significant in the art world where if an artwork is sold too often for the marketplace, collectors and curators refer to the artwork as “burned” and refuse to buy it so it tends to sell for much lower values.

Wilkinson 37

price of art than those which have been explored (such as size, artist, signature, medium). It also

may call into question existing cultural economic literature, such as Galenson, which uses price as

a standard of creative value. These contributions should have interesting consequences moving

forward for hedonic-based cultural economic literature. However, while the impact of exhibitions

is surprisingly absent from the literature associated with hedonic modeling of art prices,

exhibitions as proxies for exposure have been utilized in the ranking of top artists within the

contemporary sphere.

4. Exhibitions

4.1 Ranking Artists through Exhibition Exposure in Cultural Economics

Explorations of price determinants in the art market have not considered the impact of

exhibition. There has been some research into the impact of such exposure on the standing of an

artist in the art world by collecting data regarding the recognition an artist has received. The first

and perhaps most well-known example of this is the work of the late Willi Bongard of Cologne.70

In his artist rankings, continued into today and now referred to as Capital Kunstkompass (CKK),

Bongard registered qualities such as how many works of the artist were integrated into the

permanent collections of museums or major private collectors, the number of solo or group

exhibitions in which the artist was involved, and the notice given to him or her by periodicals,

books and television, and similar marks of esteem. Bongard then weighted the data by

subjectively assigning numerical values to them. For instance, 300 points were awarded by

Bongard’s measure for each work present in the Metropolitan Museum of Art in New York, and

200 points for each in the Art Institute in Chicago.71 If the artist was mentioned in Art Actual he

received 50 points and in Conaissance des Artes 10 points.72

This extensive collection of aspects which impact the acclaim of an artist is impressive,

but the weights were arbitrarily defined. As Bonus and Ronte (1997) outline in their discussion on

Bongard’s methodology, some of the rankings are unnecessary or useless – such as the

Metropolitan. The Metropolitan Museum of Art is widely regarded as the most important of all

American art museums, but at the time of Bongard’s analysis, it was not important at all to

70 Bongard (1974), p. 250–264. 71 Bonus and Ronte (1997), p. 105. 72 Grampp (1989), p. 33.

Wilkinson 38

contemporary artists since it mainly displayed Old Masters.73 None of the top-ranking artists in

Bongard’s list was ever shown in the Metropolitan. Additionally, the rank of one given museum

or exhibition to the art scene is not a constant, but shifts over time. Museums which were

relocated into new and prestigious buildings (as those in Atlanta and San Francisco) get more

influential to an artist’s reputation. Furthermore, these rankings can be quite subjective in terms

of which weights should be applied to which institutions.

Bongard’s data has an intuitive appeal that can be useful in identifying some of the main

influences such as exhibitions regarding artist acclaim effective on the market for visual arts. In

support of this idea, in his book on the economics of paintings and painters, Grampp (1989) used

regression analysis on Bongard’s data to compare the price of a painter’s work and the number

assigned to them. While the correlation was not perfect (having an R2 = 0.25), the results did show

that an increase in 10 percent for the number of points led to a statistically significant increase of

8 percent for the price of representative work.74 Yet, at the very least, this shows that the acclaim

of the artist, in this case measured at least in part through exhibitions, has significant correlations

to price.

Stemming from an earlier article by Schneider and Pommerehne (1983), Frey and

Pommerehne (1989) utilize Bongard’s measures to obtain econometric estimates of an “aesthetic

value function.” They argue that the aesthetic evaluation of an artist’s oeuvre is influenced by the

artistic capital stock they accumulate over time. Using the number of exhibitions and prices

awarded to an artist’s oeuvre, the years that have passed since the artist’s first exhibition, the

number of mediums in the visual arts (i.e., sculpture, painting and graphics) in which an artist

works, and selling prices realized in the past as proxies for artist capital stock, they attempt to

explain the artists’ standing as determined by Bongard. Frey and Pommerehne use standard

multiple regression analysis to estimate the relationship between this aesthetic evaluation

variable and characteristics such as the type of art, number of one-man exhibitions, number of

group exhibitions, awarded art prizes, years since first solo and group exhibitions, technical

variety of the artist, and past prices.

73 At the time of writing, the Metropolitan has been making more of a conscious effort to incorporate contemporary artists into various installations and exhibitions in comparison to Old Masters. In the sample of 375 artists included in this analysis with exhibitions going back to 1947, there were 130 exhibitions at the New York Metropolitan Museum reported (not all from different artists). This may be in part as a result of a shift in the market towards contemporary and thus the attempt of the museum to stay relevant as well as just well known. 74 Grampp (1989), 33.

Wilkinson 39

The authors find that the standing of an artist predicted by their aesthetic valuation

function corresponds “extremely well” to his or her actual standing.75 Yet, their dependent

variable, Bongard’s measure, is not comprised of actual observations to be explained by

explanatory variables, but was calculated by Bongard, who drew on similar observations as those

used as explanatory variables by Frey and Pommerehne, such as number of exhibitions.

Considering the circular reasoning of this exercise, their strong correlation is hardly surprising.

In addition, Frey and Pommerehne evaluate the same characteristics76 on a model using

“a price of a representative work,” based on expert appraisals and sales records. However, in each

of these regressions, each observation is a single work by an artist, and thus, the choosing of such

a “representative work” is highly subjective. For example, in the second regression model, based

on auction sales records, the sales chosen were from only “international and important national

auctions between 1971 and 1980” to the point that 15 artists, of their original 1100, were

excluded since they were not included in these auctions. Furthermore, controls for the nation in

which the sale took place or even year of sale for these exhibitions between 1971 and 1980 are

not included.77 These singular prices have high correlations to the data (R2=0.74 for expert

evaluation and R2=0.61 for auction prices respectively). However, the subjective nature of the

selection of “representative works” and small sample size make this relationship suspect. Yet, the

high significance and impact of the aesthetic evaluation variable (significant at the 5 percent level,

coefficient of 0.34 for the logarithmic value), which captures the exhibition counts and general

exposure of the artist, does suggest a strong value creation due to acclaim-building factors like

exhibitions on the price of art.

4.2 Exhibitions as Advertisements for Artist’s Works

Consider the oeuvre of a post-war, contemporary artist. The price of his or her artwork

varies positively with the quality as perceived by collectors. Following the discussion of hedonic

valuation presented above, it is natural to regard this characteristic of quality as represented by

one of the dimensions of the characteristics vector 𝑍𝑍 that determines the buyer’s willingness to

pay for art. There may in fact by several related dimensions that would include the difficult-to-

measure notions of quality of the work, reputation of the artist, ability of the painting to confer

75 Frey & Pommerehne (1989), p. 84. 76 The regression uses independent variables like type of artist, size, income, past prices, inflation, years since death, and whether they are an American citizen as well as a logarithm for aesthetic valuation values. 77 Frey & Pommerehne (1989), p. 98-99.

Wilkinson 40

status on its owner and the likelihood of appreciation in value. To gain a reputation in the global

art scene, an artist’s work needs to be well known to a large audience, with reputation increasing

with both the number of times and the status of the institutions in which the work of an artist is

shown. A similar observation might be reasonably made concerning the potential of the work to

increase in value or to confer status upon the owner.

Exhibitions may therefore be considered complements to the work of the artist exhibited

since the exposure through increased information and status conveyed by exhibitions serves to

increase the value of the work exhibited as well as the value of other works by the artist. As such,

exhibitions give favorable notice to these artist’s works and raise the demand for these works.

Beyond raising market awareness of the artist and potentially increasing the status of the owner,

the selection of an artist’s work for inclusion in an exhibition provides a signal of expert curatorial

opinion that communicates to the potential buyer an assessment of the quality of the artist’s work

that may be difficult to observe by buyers with less experience and training in evaluation and

analysis of art.

It therefore makes sense to regard exhibitions as a separate component that contributes

to a buyer’s willingness to pay for artwork both directly, via its ability to confer status and

foreshadow price appreciation, and indirectly, as a signal of expert assessment of artist’s

reputation. With this in mind, consider a modified version of the utility function presented in

equation 1.

Consider a single-period utility function that depends as before on 𝑍𝑍, a vector of

characteristics of the artwork that includes physical characteristics as well as more difficult to

measure characteristics such as quality of the artist’s work. Utility also depends on 𝑌𝑌, the

composite commodity of expenditures on all other goods (not part of the art collection), and 𝛼𝛼, a

vector of parameters that characterize the utility function. Finally we consider the role of 𝐸𝐸, a

vector of counts of different types of exhibitions of the artist’s work.

𝑈𝑈 = 𝑈𝑈(𝑍𝑍,𝑌𝑌,𝛼𝛼,𝐸𝐸) (7)

By definition, an exhibition gives favorable notice to the work exhibited as well as other works by

the artist, so that an increase in 𝐸𝐸 raises the willingness to pay for the art work similar to the

components of 𝑍𝑍.

The production of exhibitions is separated into at least two distinct types: those that take

place at a gallery and those that take place at a museum. Thus we can think of 𝐸𝐸 = (𝑠𝑠𝑔𝑔 ,𝑠𝑠𝑚𝑚). For

galleries, the exhibition is given away to consumers – as a commercial institution they display the

Wilkinson 41

work in order to sell it. Thus, the exhibition becomes like an open house would be in the housing

market – putting the products on display for potential customers. For museums, the exhibition is

sold as a cultural experience, generally thought to be produced separately to the economic

sphere, though impacting it.

The these exhibitions may be seen as “advertisements” for various contemporary artists

in a concept similar to the theory put forward by Becker and Murphy (1993). The focus of Becker

and Murphy’s analysis is to reconcile the idea that some types of advertisements are “bad” and

others are “good”, delineating between a “good” as something consumers are willing to pay for

and a “bad” as something consumers pay to have removed or must be compensated to accept.

Yet, the essential component of interest to us is the notion that there are different types of

advertising that are valued differently by prospective consumers. Under Becker and Murphy’s

binary, both museum and gallery exhibitions are “goods” in a utility function since people are

willing to pay for them; even though they need not actually pay in equilibrium, as they don’t for

gallery exhibitions. Furthermore, the amounts consumers are willing to pay for advertising/

exhibitions will differ between the two types according to the ability of each type to enhance the

utility associated with owning the work of art. Although those “supplying” exhibitions may or may

not be the same as suppliers of art, each of the exhibition types will still affect consumer

willingness to pay.

While an advertisement “good” may seem somewhat rare in the general consumer

market, there are many examples of the payment for advertising, even if it is largely more

surreptitious than within the art market. An example of this phenomenon outside of the art world

is a sports column. Sports columns in newspapers provide plenty of notice about local professional

teams, even though sports sections are not free to readers, and team owners do not pay for the

columns. An example expressed in Becker and Murphy (1993) is the strike of Pittsburgh’s two

newspapers in mid-May 1992, which was said to have reduced sales to games of the Pittsburgh

Pirates baseball team by 3000 to 4000 tickets a game. Similarly, someone interested in the art

market would pay to experience a museum exhibition, which conveys both status and information

on the artist involved in a similar way that this sports column would. While museums do not have

the counterfactual displayed for the sports column in the above example, one would expect the

increase in prices to be a reflection of the increase in demand for an artist’s works based on

museum exhibitions.

Wilkinson 42

Given the power of advertising/exhibitions to affect willingness to pay, it is natural to

expect an equilibrium hedonic price function 𝑃𝑃(𝑍𝑍,𝐸𝐸) that depends on the counts of exhibition.

The expense of producing exhibitions will naturally be reflected in the cost function

𝐶𝐶(𝑍𝑍,𝐸𝐸,𝑁𝑁, 𝛾𝛾), and the same conditions of individual rationality of sellers and buyers will imply that

sellers seek to maximize:

𝑀𝑀𝑀𝑀𝑀𝑀 𝑃𝑃(𝑍𝑍,𝐸𝐸) × 𝑁𝑁 − 𝐶𝐶(𝑍𝑍,𝐸𝐸,𝑁𝑁, 𝛾𝛾) (8)

Solution to this will require that:

𝜕𝜕𝜕𝜕𝜕𝜕𝑍𝑍𝑖𝑖

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑍𝑍𝑖𝑖

∀ 𝑖𝑖 , 𝜕𝜕𝜕𝜕𝜕𝜕𝑒𝑒𝑚𝑚

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑒𝑒𝑚𝑚

, 𝜕𝜕𝜕𝜕𝜕𝜕𝑒𝑒𝑔𝑔

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑒𝑒𝑔𝑔

𝑀𝑀𝑎𝑎𝑎𝑎 𝑃𝑃(𝑍𝑍) = 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

(9)

Combining the optimal choice conditions for consumers of art with the optimal provision and

marketing of art by sellers, we note that equilibrium in the art market will be characterized by:

𝜕𝜕𝜕𝜕𝜕𝜕𝑍𝑍𝑖𝑖

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑍𝑍𝑖𝑖

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑍𝑍𝑖𝑖

∀ 𝑖𝑖 , (10)

𝜕𝜕𝜕𝜕𝜕𝜕𝑒𝑒𝑚𝑚

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑒𝑒𝑚𝑚

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑒𝑒𝑚𝑚

, 𝜕𝜕𝜕𝜕𝜕𝜕𝑒𝑒𝑔𝑔

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑒𝑒𝑔𝑔

= 𝜕𝜕𝜕𝜕𝜕𝜕𝑒𝑒𝑔𝑔

(11)

𝑃𝑃(𝑍𝑍) = 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

(12)

In particular, equations (10)-(12) reveal that the equilibrium hedonic price of exhibitions of

artwork need not be equal between museum exhibitions and gallery exhibitions. These

differences can arise, for example, if the two types of exhibition affect utility differently (as would

be expected given the different signals of artist quality conveyed by curatorial judgment of a

museum versus marketing enthusiasm of a gallery).78

Differences might also be expected if the costs of mounting and presenting exhibitions at

galleries differ substantially from similar endeavors at large commercial galleries. As suggested

above, this is very likely to be the case. Even if sellers wish to be proactive about the procurement

of exhibitions for their represented artists, exhibitions at museums may not be purchasable at all

in the usual sense, or may require substantial contributions to museums to obtain a seat on the

museum board, or may require general willingness to underwrite all or most of the costs of an

exhibition as suggested from recent experience reported in the press. 79

Additionally, not only do museums and galleries potentially have different impacts on

consumer willingness to pay from each other, but they can also differentially impact various types

of artists. If exhibitions affect utility for artists differently due to specific characteristics, the

78 This hedonic discussion is based on concepts put forth in Sheppard (1999). 79 Pogrebin (2016).

Wilkinson 43

equilibrium hedonic price of exhibitions of artwork need not be equal between female artists and

male artists, for example (an idea which will later be empirically explored).

Finally, we might also expect to see differences arise in terms of when an exhibition occurs

versus the year of sale. On the one hand, the associated effects might lessen over time. However,

because exhibition histories may become associated with the artist’s reputation, they may

become be a significant indication of price but differently from those exhibitions closer to the year

of sale.

I next turn to description of data collection and methodology used in evaluating the

impact of exhibitions and other characteristics on the prices of contemporary art.

5. Empirical Methodology

This analysis represents a combination of the hedonics approach to pricing structure and

the cultural economic literature which relates a weighted points system of artist acclaim to prices.

Rather than simply creating a comparison between the somewhat arbitrary points system and the

average price or price of a “representative work”, this analysis will examine the specific impact of

a single exhibition, as a unitary measure of specific artist acclaim, on prices, controlling for the

artists’ observable and unobservable characteristics, as well as other standard hedonic controls.

5.1 The Data Set and Descriptive Statistics

“Get this into your head, no one really knows anything about it. There’s only one indicator for

telling the value of paintings, and that is the sale room.”

- Pierre-Auguste Renoir, Impressionist painter80

As discussed, prices from galleries or directly from artists tend not to be reliable or easily

obtained. Individual works of art have yet to be securitized nor are there publicly traded art

investment funds so studying the value of works of art from financial sources are difficult or

impossible. Auction prices, however, are reliable, publicly available, and relatively easily obtained.

Thus, these prices are used as the basis for analysis of the marginal contribution of an exhibition

on the price of a living artist’s work.

80 Quoted by Duret-Robert (1977).

Wilkinson 44

For this study, I created an original database of the auction prices for a sample of living

artists. These auction prices were retrieved from the ArtPrice website, which contains some of

the best available auction data on works by more than a half-million artists sold through more

than 4,500 auction houses and dealers, worldwide, since the 1960s.81 The artists in my sample

were all living at the time of research and had at least five recorded auction sales on ArtPrice as

well as an available CV. With these requirements, my sample is confined to successful

contemporary artists since they needed to have this level of secondary market results, but within

this sphere, I attempted to create a broadly representative sample.

In generating my sample of artists, I started with a selection of museums whose budget

expenditures were among the 25 largest in the United States, based on information from the

National Center for Charitable Statistics.82 I then picked artists from the eleven museums within

this sample that routinely exhibit contemporary art.83 For these selected museums, I gathered the

living artists listed on museum websites from both solo and group exhibitions spanning the last

fifteen years (from 2000 to 2015), who had also generated a minimum of five recorded auction

sales in the ArtPrice data. Then, to get a more representative sample of contemporary artists, I

included living artists exhibited in the top five major galleries in the U.S. based on size (number of

locations, artists and staff members) as well as published estimates of annual turnover: Gagosian,

Pace, Marian Goodman, David Zwirner and Hauser & Wirth. The result is a sample of 375 living

artists.84

While the sample creation focused on institutions located in the United States, it actually

contains a very international selection of artists. Globalization has increased the flow of art

between countries around the world and increasing wealth in developing countries (especially

Brazil, Russia, India, and China) has made the share of high-net-worth individuals from Latin

America and Asia buying art comparable to their American or European counterparts.85 However,

81 http://www.artprice.com/client/subscriptions/ 82 National Center for Charitable Statistics (U.S.), & Center on Nonprofits and Philanthropy (Urban Institute). (1999). 83 This list of museums is included in the Appendix. 84 The sample was created from the United States only because, especially concerning the opaque nature of global museums’ expenditures, the year range of study, and the language barriers that would have been involved, the extraction costs of finding artists through global museums would have been much higher. While perhaps involving a sample of artists gathered from global museums might be an interesting topic of further study, as will be discussed, because of the international acclaim of these museums, a global method of collection likely would not have substantially changed the composition of the sample. 85 Lind (2013), 9.

Wilkinson 45

as curator Maria Lind argues, “Despite the vigorous rhetoric around geography not being

important anymore, […] the most influential geographical zones for the art economy remain the

United States and Western Europe.”86 Thus, while more international artists and buyers beyond

the Western world are increasing substantially, the areas of highest exposure are largely in the

West. Additionally, because of the ease of a single artist exhibiting all over the world, artists with

exposure at the museums and represented by galleries which served as the basis of my sample

tend to be from, exhibit in, and have work sold all over the world. This is seen above in the number

of nationalities and countries in which works were auctioned in my sample. My sample includes

an international array of successful, living artists coming from over 50 nationalities, with dates of

birth ranging from 1923 to 1986, earliest reported exhibitions from 1948 to 2002. One fourth are

women.87

Figure 8: Living Artist Sample Generation for Data Set

After assembling these 375 artists, listed in Appendix 2, I gathered all of their reported

auction prices from ArtPrice which included each auction record’s name, date of sale, estimate

price, hammer price, date of creation, and location of sale. If the art piece was sold overseas, I

converted the sale price into U.S. dollars based on the ArtPrice foreign exchange rate conversion.

86 Ibid. 87 The artists compiled in this sample includes about 75 percent of the top 100 Capital Kunstkompass artists, or a renowned ranking of the top 100 living artists worldwide. The reason there isn’t a full overlap is potentially because, while my list is more extensive, it is also based on current popularity since it was compiled using exhibitions which happened recently – over the last 15 years. Thus, if the artist was included on the list for past popularity or a medium generally outside what would be exhibited, such as Claes Oldenburg, they would not be included in my list. Additionally, some of the artists that may have been in my list are excluded since their CVs could not be obtained.

Top 11 Museums by expenditure with the most contemporary

exhibitions in the U.S.

Living artists in group and solo exhibitions listed on museum

websites (2000-2015)

Artists with a minimum of 5

auction records on ArtPrice and an

available CV

375 Living Artist Sample

Artists with a minimum of 5

auction records on ArtPrice and an

available CV

Living artists represented by the top 5 U.S. galleries based on size and annual turnover

Wilkinson 46

Additionally, since the prices are from the year in which the works were sold, all prices are in their

nominal dollars. Summary statistics for artists in the sample are reported in Table 1.

In fine arts auctions, sellers have a reserve price for the lowest price they will accept for

the work. In order to help protect this price, as well as to indicate to potential bidders what the

auction house considers to be the value of the work based on their knowledge of the market, the

auction house will include published estimates of ranges for the work to be sold. Auction houses

will often listen to what people are saying about their estimates, and this may cause them to

adjust their sights as the sale draws near. Typical fine art auctions are English auctions, where the

bidding begins low and edge upward as bidders escalate their bids. When the bidding stops, the

item for sale is said to be knocked down or hammered down. During the auction, the auctioneer

generally starts the bidding below the reserve price and moves the price up in regular increments,

awarding the purchase to the highest bidder. If the reserve price is not met during the auction,

the work is “bought-in” or not sold at the auction.88 An item that has been bought-in may be put

up for sale at a later auction, sold elsewhere, or taken off the market. This is common in fine art

auctions. In auctions of Impressionist paintings, about one-third of the paintings put up for sale

will not find buyers during the auction. 89 For the purposes of this study, most of the analysis will

focus on works actually sold at auction.

88 This terminology is somewhat misleading since the auction house rarely buys unsold items. 89 Ashenfelter & Graddy (2006), p. 910.

Wilkinson 47

Additionally, there are three prices involved in every auction sale: the hammer price, the

buyer price, and the seller price. The hammer price is the winning bid during the auction; the

buyer price is the hammer price plus the buyer’s premium, or the commission or profit paid to

the auction house, which tends to be between 15 and 25 percent for the work depending on the

sale price; and the seller’s price is the price charged to the seller by the auction house for aspects

like shipping, photography, and insurance. For the purposes of this study, the price focused on

will be the hammer price as the independent variable. Focusing on these values may entail some

selection bias since the sold works may tend to be those of greater value. A robustness check for

this selection bias for the analysis in the paper is included in Appendix 7. However, even in the

case of the sold works, the resulting information as to the impact of exhibitions on these auction

prices should be regarded as an approximation to the broader impact in the art market as a whole.

To match to the hammer prices and the associated auction information of individual

pieces, I gathered the current curriculum vitaes (CVs) of the sample artists from their artist pages,

gallery representations, or the artists themselves. 90 Using this CV information, I derived counts of

each artists’ reported solo exhibitions and group exhibitions for each year they were active (an

artist CV example as well as notes on how these counts and biographical information were derived

are included in Appendix 3). I also used CV information to determine counts of “major” gallery

and museum exhibitions, again divided into solo and group exhibitions for each category. This

“major” designation was derived differently for each category. Major museums are defined as the

top 100 museums by attendance internationally taken from the Art News 2014 Rankings with two

additions: The Whitney Museum of American Art and the Venice Biennial.91 Both this museum

and the art event are widely renowned and respected by the artistic community in ways that

warrant their inclusion. Major galleries in this study are based largely on “mega-galleries” referred

to in the book The $12 Million Stuffed Shark by Don Thompson (2009), in large part because

statistical information on attendance, sales, and expenditure is largely unavailable for all galleries

(major museums and galleries included in this study are listed in Appendix 1).

Including separate counts for major museums and galleries serves two important

functions. Firstly, the impact of these specific institutions is of particular interest to the art world.

If one thinks about exhibitions as a kind of advertising, described earlier in this analysis, then an

90 This means that there are as many sources as there are CVs in my sample. No standardized database of artist exhibitions exists, so it had to be created for this sample, all of which were collected in August 2015. 91 “Visitor Figures: 2014 Exhibition & Museum Attendance Survey” (April 2015).

Wilkinson 48

exhibition at a major institution has very different implications than at a smaller museum or

gallery. Secondly, it helps to alleviate the discrepancy in reporting standards. All of the exhibition

information included in the sample is derived from individual artist CVs. Beyond the differences

in reporting standards between CVs, artists at different stages of their careers are also likely to

report differently. An artist relatively early in their career is likely to report every show in which

they have been included. Some artists include art fairs or small gallery shows from the beginning

of their careers - such as multimedia artists Marcel Dzama who lists the World Chess Hall of Fame

in St. Louis, Missouri as one of his exhibitions for 2015. An artist more developed in their career

is likely to leave off quite a few small exhibitions or those they deem unworthy of incorporation.92

In some more extreme cases, some highly acclaimed artists, such as Jenny Holzer or Lee Bontecou,

do not include any group information, but only list selected solo exhibitions.

In this environment, the idea of counting major museums and galleries serves to equalize

this large range of reporting standards. This is because every artist, no matter how acclaimed, will

report their exhibitions at major museums and galleries. The exposure and acclaim gained

through being associated with these institutions is too great to not include them in one’s CV.93

Thus an entirely original database built from the merging of auction price information and

each artist’s exhibition information serves as the source of the analysis in this paper. It is

constructed so that an observation is the price of a work matched to all the information associated

with the sale, the artist of the sale’s biographical information, and the exhibition history of the

artist up to and including the year of sale.

Table 2 below summarizes the range of data employed in the majority of the analysis.

Each of the variables are summarized for those auction records which have auction prices,

excluding observations which are “bought-in” or without necessary control variable

92 This is often even noted in their CVs through titles of “Selected Solo Shows” and “Selected Group Shows” to display that not everything they have been exhibited in is incorporated into the document. 93 This is similar, for example, to the CV of an economics professor. Early in one’s career, a professor may list all of their publications as well as every conference at which they have ever presented. However, further down the line, especially after many publications as well as many conferences, a tenured professor will likely leave off a number of conferences and even minor publications. Yet, they will always include publications in highly acclaimed journals such as the American Economic Review. Similar to how counting these publications would equalize reporting, so do major museums and galleries in the fine arts world.

Wilkinson 49

information.94 The counts of shows recorded in Table 2 are the exhibitions which happen in the

year preceding the year of sale since this is the focus of the majority of the analysis.95

All exhibitions are counted for the year in which they started rather than ended and are

not double counted for multiple years. 96 Traveling exhibitions are counted as separate exhibitions

for each new institution. The discrepancy between the number of observations for solo exhibition

variables and group exhibition variables is due to differences in reporting standards. As discussed

previously, some artists entirely exclude group exhibitions from their CVs. While the number of

artists that do this are low, they tend to be more successful artists – with higher numbers of

auction records. This leads to the not insubstantial differences between solo and group

observations. The low average values of the exhibition types display the relative rarity of a major

institution exhibition within a single year. Particularly for the counts of major exhibitions, there

are many observations with zero exhibitions. All artists included in the sample include at least

some observations with zero Lag1 exhibitions.

The five auction sale minimum restriction was imposed to have enough observations to

obtain estimates for each artist but has the effect of restricting the sample to artists that are

94 All of the observations included in Table 2 are included in the regression analysis for the results section of this paper. However, there is a robustness check in Appendix 9 which includes some of the bought-in observations by using 75 percent of the low auction estimate as their price for the regression analysis. The additional 15,000 auction prices in this analysis are not included in this summary statistics table. 95 It is worth noting that the summary statistics for exhibitions contemporaneous to the year of sale (i.e. not lagged by a year) do not have statistically different values to the ones displayed in the table above. 96 This is part of the reason that a large part of the analysis is based on the exhibition variables lagged by a year. Since time in this analysis is only measured in years, and exhibitions are reported from their start date, if a sale is reported in February 2008, an exhibition starting in September 2008 will technically be reported as contemporaneous even though it starts after the sale. Thus, the lagged variables make sure all reported exhibitions have at least started at the time of sale.

Wilkinson 50

relatively successful. This is emphasized by the sample’s mean hammer price of over 81,000 U.S.

dollars, although the median is only 8,400 dollars, displaying the significant left skew to the data.

The data collected span early stages of the careers of these artists, however, prior to the critical

acclaim that some of them eventually receive as well as their eventual success. Therefore, the

analysis should be able to discern the correlation between exhibitions and the price of the work

for this talented artist sample, and not simply the fact that they are all successful to some degree.

With auction prices as early as 198697, the sample exists within the context of the

increasing value of marketing in the industry, the rise of art as an investment class, and the rise

of auction houses as a growing sector of the art market. Additionally, by focusing on the

contemporary sector, it examines the most contested and expanding sector in the art market at

the moment. Perhaps most exciting, it represents a completely original dataset with information

which has never been conglomerated in this way previously. Hedonic models look only at work

characteristics. Artist rankings look at single “representative” works for artists in relationship to

their exposure information, rather than a range of works sold at auction. This data set makes it

possible to combine the two frameworks by incorporating a range of works sold by each artist at

auction, including both physical characteristics and artist acclaim information.

5.2 Methodology

To identify the impact of exhibitions on the price of work for my sample of artists, I

estimate hedonic regression functions that relate the natural log of the hammer price of a

particular work to a number of characteristics of the work in question, including measures of both

counts of exhibitions which happen contemporaneous to the sale of the work as well as five-year

and ten-year cumulative counts of exhibitions for the artist up to the year of sale. These equations

are estimated for varying combinations of exhibition types, including models which incorporate

contemporaneous counts and the career-to-date counts separately as well as together. I

estimated equations of the following type with these various specifications:

97 The sample starts in 1986 because, although there are exhibition histories as far back as 1947, none of the artists in the sample have auction records on ArtPrice before 1986. Especially considering that ArtPrice has records going back as far as 1960, it is quite possible that these works just were not sold at auction (for this specific artist sample) before 1986. This is because selling art for living and contemporary artists, as mentioned in a previous section, is a relatively new phenomenon beginning in the 1980s.

Wilkinson 51

𝑎𝑎𝑎𝑎 𝑃𝑃𝑓𝑓𝑖𝑖𝑠𝑠𝑠𝑠𝑗𝑗𝑗𝑗 = 𝛼𝛼1𝐸𝐸𝑀𝑀ℎ𝑖𝑖𝑠𝑠𝑖𝑖𝑠𝑠𝑗𝑗−1+𝛼𝛼2𝑊𝑊𝑡𝑡𝑓𝑓𝑊𝑊𝑊𝑊𝑊𝑊𝑠𝑠𝑗𝑗 + 𝛼𝛼3 𝑎𝑎𝑎𝑎 𝑀𝑀𝑓𝑓𝑠𝑠𝑀𝑀𝑗𝑗 + 𝛼𝛼4𝑠𝑠ℎ𝑓𝑓𝑠𝑠𝑠𝑠𝑟𝑟𝑗𝑗 +

∑𝜇𝜇𝑖𝑖𝑀𝑀𝑠𝑠𝑎𝑎𝑖𝑖𝑢𝑢𝑀𝑀𝑗𝑗 + ∑𝛽𝛽𝑖𝑖 𝑌𝑌𝑠𝑠𝑀𝑀𝑓𝑓𝑡𝑡𝑓𝑓𝑌𝑌𝑀𝑀𝑎𝑎𝑠𝑠 +∑𝛾𝛾𝑖𝑖𝑊𝑊𝑓𝑓𝑠𝑠𝑖𝑖𝑠𝑠𝑠𝑠 +

∑𝜎𝜎𝑖𝑖𝐶𝐶𝑡𝑡𝑢𝑢𝑎𝑎𝑠𝑠𝑓𝑓𝐶𝐶𝑡𝑡𝑓𝑓𝑌𝑌𝑀𝑀𝑎𝑎𝑠𝑠 + 𝜖𝜖 ( 13)

where subscripts jt refer to artwork j sold in period t. Pricejt is the hammer price of the artwork

expressed in United States dollars as reported by ArtPrice. WorkAge attempts to control for artist

development within their careers as a function of the creation date of the work relative to the

artist’s age and the time of sale. Limited biographical information is included through gender and

MFA indicator variables expressing whether the artist is female and whether they have received

a Masters of Fine Arts.98 Area is the product of the width and height of the artwork, and threeD is

an indicator variable which expresses whether or not the artwork is three-dimensional. This

model also includes indicator variables for the medium of the work (Medium), artist indicator

variables (Artist) [Christopher Wool, Damien Hirst, Yayoi Kusama, etc.] to capture the fixed and

unobserved artists’ abilities and reputations, time indicators (YearofSale), which allow for

influence of overall art price movement on the price of a specific work of art, and country of sale

indicators (CountryofSale), which account for variation in price across different global markets.

Finally, ɛ is the error term.99

In this equation, the variable Exhibitt-1 represents a generic stand-in for several different

exhibition measures which are the reported totals for each exhibition type within a specific year.

This variable is always time-dependent and a function of a particular exhibition count associated

with the year of sale for the art work. In equation 13, the subscript t-1 indicates that the exhibition

count is in the year prior to the year of the auction sale observation because, while the results

section reports both contemporaneous and lagged variables, exhibitions lagged by one year are

the focus of this analysis. This is to both allow the market time to internalize the price changes

and because exhibitions and auction sales are only indicated by year of sale. Since exhibition years

of sale are reported in the year they begin, an auction record could actually be from before the

start of an exhibition (such as if the sale is in February 2014 and the exhibition begins in September

2014 since they would both be reported as 2014). Thus, estimates for exhibition counts

98 The choice of these specific attributes will be discussed more in depth later, but they were chosen largely because of their specific relevance to the art market. The art market tends to be sexist in that female artists represent 51 percent of the visual artists in the art market and only 28 percent of contemporary shows (). MFAs are controversial as to whether or not they are worth the expense for an emerging artist’s career. 99 Auction house fixed effects are not included in this model because standard errors are clustered by auction house, as will be later discussed.

Wilkinson 52

contemporaneous to the year of sale are both less precise and less accurate. The exhibition counts

lagged by a single year make sure that all of the included exhibitions have at least begun by the

sale observation.

There are six categories of exhibition types indicated by Exhibitt-1 which will be combined

in various ways in the regression analysis. Looking first to the contemporaneous variables in this

analysis (and thus more accurately denoted as Exhibitt), TotalSolo and TotalGroup, represent the

contemporaneous counts of the total reported solo shows and group shows, respectively,

reported in the artist’s CV within the year of the sale of the artwork. As discussed previously, due

to differences in reporting standards, these variables are somewhat more suspect as indicators of

exhibition impact on art prices. The four variables associated with the main focus of study, which

creates a more uniform count for reporting standards, are based on “major” museums and

galleries as defined in the data section. These categories of majors are broken down further by

solo and group to create SoloMM, SoloMG, GroupMM, and GroupMG – where MM stands for

“major museum” and MG stands for “major gallery” – which again are the number of

contemporaneous exhibitions of each type within the year of sale. Beyond than these

contemporary counts, other variable types are based on these same exhibition distinctions, but

with additions which display their variation in type. The main focus of this analysis will be these

variable types with the addition of Lag1, representing that they are lagged by one year from the

year of sale. Exhibition histories are explored through 5Before (a cumulative count of the total

exhibitions of a specific type in the five years before the contemporaneous counts), 5BeforeL (a

cumulative count in the five years before the lagged year), and 610BeforeL (a cumulative count of

the six to ten years prior to the lagged year).100 These variables attempt to determine the price

correlations of each exhibition type over time.

All of the regressions included in this analysis have standard errors clustered by the

auction house in which they were sold. While other methods for clustering standard errors were

experimented with and are presented in Appendix 12, auction houses were chosen as the

preferred method for a few reasons. If one thinks about the process that generates fluctuations

in auction outcomes, it seems likely to be the circumstances of the auction. Clustering by auction

house therefore corrects for some of these auction circumstances, particularly since I delineate

by location rather than the umbrella auction house. For example, if a painting was sold in Christie’s

100 Regressions which estimate career-to-date counts which are the total number of exhibitions from the beginning of the artist’s career to the year of sale are also included in Appendix 11.

Wilkinson 53

Hong Kong versus Christie’s New York, these would come up as two separate auction houses

rather than just Christie’s.

Before I present my results, it is important to discuss some of the limitations of auction

prices for this type of analysis. The most significant limitation is that the data entails a selection

bias. By definition, the analysis includes only those works that have been sold and excludes those

that are bought-in. It also cannot include some of the highest priced work sold to institutions like

museums through galleries or any other private sales. The data set thus tends to exclude prices

that are on the upper and lower ends of the distribution. Also, if the auction results are measured

with error, there could be an issue where the auction house understands that a painting is likely

to sell for a premium or a discount, and exerts more or less care in recording the characteristics.

This may be seen by the fact that estimates, dimensions, and even occasionally hammer prices

are excluded from some of the auction data entries for the selected artists on ArtPrice. In these

cases, these works are also excluded from the overall analysis. If this care, or lack-thereof, is

correlated with the expected error term, there could be bias in estimating the associated

coefficient of the corresponding parameter.

The data set is also subject to an omitted variables problem in that it does not include

some characteristics of the pieces sold such as their specific style, color, or condition. Moreover,

auction prices exclude transaction costs, both the buyer's premium and the seller's commission.

The data set has no direct information on the supply of works of art, such as how many works

each artist produced per year or the artist's overall production.

Additionally, the data set does not include observations on some ephemeral "quality" or

"originality" of the work. There are a few ways in which this analysis attempts to correct for this.

By including the WorkAge variable, the regression equation attempts to control for each point in

the artist’s career to separate out how different stages of their work effect their prices. Artist

indicator variables can also control for stylistic variation among artists. Some of this is also

captured in the exhibition variable since ostensibly these institutions are choosing artists with

some sort of creative value to exhibit.

5.3 Potential Endogeneity

One possibly significant difficulty in estimating demand for fine art attributes, here

defined as both physical attributes (such as size and material) as well as level of exposure (the

acclaim afforded the works through exhibitions), is the identification problem that arises because

Wilkinson 54

of the endogeneity of these attributes. This same issue of endogenous attribute prices plagues

understanding of housing hedonics as well. Although this problem is one of identification, it is

useful to separate this concept from econometric difficulties faced when attempting to estimate

the parameters of demand and supply simultaneously. First noted as problematic by Freedman

(1979) and alluded to in Rosen (1974), such issues of “conventional” simultaneity may arise for

studies based on cross-sectional data with units large enough to influence market prices.

However, most hedonic studies, including this paper, are based on datasets with observations of

individual decisions. One could argue that the observations within the dataset of this paper could

be linked through serial correlation since particularly high auction market prices may influence

the auction bids for the same artist in the future. However, other than obscenely high prices (such

as the Modigliani nude that went for a $170 million hammer price101) these auction results are

not widely publicized and that there is a wide heterogeneity of goods even within an artist’s

oeuvre. Thus, each observation may be viewed as a relatively individual decision.

For estimates based on individual data, individual demands are determined by taking the

hedonic prices as exogenous. Implicitly lying at the foundation of a very large literature of hedonic

modeling, this perspective holds that as long as there is linearly independent variation in income

and hedonic prices, one has observations of individual behavior, and there is a common

preference ordering determinants of individual art-buying decisions, one can estimate the

structural equations of consumer willingness to pay. Thus, if one considers number of exhibitions

as a consistent preference ordering determinant, these exhibitions may simply be incorporated

into these functions. 102

This cross-sectional endogeneity is only a significant problem if this correlation between

the error term and the hedonic prices exists since the correlation between estimated hedonic

prices and errors in measured demand behavior leads to inconsistent estimates of the structure

of demand. In this specific study, this would mean an unexpected high or low value of an auction

sale price (the residual) was correlated with the number of exhibitions, the area, the age of the

work, etc. In this sense, the main concerns with potential endogeneity in between prices and

exhibition types in this analysis come from two main sources: 1) reverse causality between prices

101 Childs, M. (Nov. 10 2015). 102 Thus discussion of the common hedonic concern of cross-sectional endogeneity is derived from Sheppard (1999).

Wilkinson 55

and exhibitions, and 2) omitted variable bias of a cofounding variable that exhibitions become a

proxy for in the correlations.

Turning to the first potential source of endogeneity, because there is a large amount of

individual transactions data, it is difficult to come up with a mechanism for increasing (or

decreasing) the size or number of exhibitions in response to a single auction being “hot” or “cold”.

The number of exhibitions at the time of sale is what is on the auction date. The number of

exhibitions may affect the willingness to pay of bidders, but the enthusiasm of or competition

between bidders cannot affect the number of exhibitions as of that date. Additionally, the fact

that this analysis largely uses values of exhibition counts lagged by at least a year weakens the

expressed concern due to the timing of exhibitions. Curating and arranging for an exhibition is a

process that generally requires more than a year – with the average major gallery exhibition taking

one to two years to curate and the average major museum exhibition taking three to five years.

Thus, prices would not be able to influence exhibition creation at least within the year (or year

prior) to that of the sale. Additionally, looking specifically to museums, the training and academic

reputation of museum curators does not derive from mounting shows of works that have recently

sold for high prices at auction. Rather, it develops through featuring the novel and interesting

artist, or drawing attention to particular aspects of the work that had been previously

unrecognized (much in the manner of other academic researchers).103 As we generally consider

them to be relatively outside this part of the art market since they do not stand to gain financially

from any subsequent success of an exhibited artist, these museum exhibitions – which as the

results will show, have stronger price correlations than gallery exhibitions – should not consider

auction prices in the development of their exhibitions.

For the second endogeneity concern of omitted variable bias, exhibitions would be a

proxy for another potentially unmeasurable factor, such as artist “buzz” or reputation over time,

rather than influencing price themselves. In an ideal world, to rule out this kind of bias, we would

want to randomly assign different exhibition types to artists and see how this effected their prices.

Since this is not possible, the idea that exhibitions are proxying for some other element of artist

buzz or reputation cannot be ruled out. Yet, certain aspects of the art market suggest that

103 Museum curators, in a sense, may be thought of along a similar vein as academic researchers. If an economics professor is researching the influence of Apple in the stock market, the fact that the company’s stock just massively decreased does not mean that the economist will abandon the project. It would actually likely serve to assist them in their project. The hypothetical economist’s interest in the matter is driven through interest, not prospects of financial gain. This idea is similar for the museum curator.

Wilkinson 56

exhibitions are the factor responsible for artist buzz and building of reputation within the art

market rather than proxying for it. In a sense, exhibitions for artists may be thought of in the same

way that publications are for academics. The reputation of the academic in the field is generally

dependent on how much and the quality of the journals in which they are published. For artists,

exhibitions are considered a similar record of accomplishment – which is why their CVs are largely

a list of their exhibition histories.

Although aspects of the art market suggest that exhibitions are the factors responsible

for increasing art prices, these endogeneity concerns cannot be definitively disproven. Because

this is real-world data rather than a theoretical model, there are simply elements for which this

analysis cannot control. Thus, the results of my findings in the subsequent section should be

considered correlational rather than causational. Yet, since this phenomenon has not been

examined previously, it is still important to be able to understand the size of associations between

price and different types of exhibitions.

6. Results

Before exploring the results of this analysis, it is worth re-emphasizing that even though

the regression analysis incorporates exhibitions put on by galleries, both within the total count

variables and the major gallery variables, prices for this analysis do not include gallery prices. The

estimated models document the relationship of exhibition exposure to the auction market, since

this is the market with available data. Thus, while these shows may have greater or simply

different impacts for gallery sales than those reported in this analysis, they are beyond the scope

of this paper. It is also worth mentioning that for all of the sales mentioned in relationship to the

contemporaneous exhibitions, none of them are actually included in the reported exhibitions

(since then they would be unable to be sold at auction within the same year). Thus, rather than

being a measure of the impact of the work being exhibited on its price, this analysis measures the

impact of the artist being exhibited (or the growth of the specific artist’s acclaim through

exhibition) on the price of their other, available works.104

This section will first explore the controls employed in the regression equation based on

the methodology section. Then, it will examine the various impacts of different types of

exhibitions as determined in the hedonic estimations of price based on various models

104 In this sense, it may be thought of as the effect of having an open house which underscored a particular type of kitchen on the price of every house with the same type of kitchen rather than that specific house.

Wilkinson 57

incorporating both contemporaneous exhibition counts and five-year and ten-year cumulative

counts. This paper will then look at interactions between different exhibition types and whether

the artist is female as well as whether they have earned an MFA.

6.1 Initial Results

Before exploring estimated exhibition effects on the price of work for living artists, I

discuss here the estimates of the other coefficients incorporated in the models. The controls

discussed here are used in all of the estimated regression models throughout this paper. As such,

the coefficients related to these controls from a selection of models included in this paper

incorporating various combinations of exhibition types are included as in Table 3 (excluding the

artist, country of sale, and year of sale indicator variables included in Appendixes 3-5).105 These

control coefficients are largely consistent with existing cultural economic literature. They also

tend to have consistent signs, coefficient values, and significance over all of the included models.

It is common knowledge in the art world that an increase in size tends to lead to an

increase in market value. In the case of emerging artists, often size is the defining characteristic

for determining price.106 Additionally, in other similar econometric analyses, such as Galenson and

Weinburg (1999), Edwards (2004), and Urspring and Weiman (2010) the size effect has always

proven to have a fairly large, positively significant impact on price. My model again proves this

concept. Across the various specifications, a doubling of the area of the work leads to an

approximately 50 percent increase in price.107

105 The regression to which each of the control models refers to is referenced in two ways. The first is the descriptive title at the top. This expresses the exhibition type run with the model number. For example, Model 2 is described by “Major 1Yr Lags.” This means that the exhibition types run in this model are major exhibitions lagged by one year from the year of sale. The second reference, in parentheses, displays the specific table and model which these controls come from. Looking again to Model 2, it has “(T7M5).” This means that the controls are specifically from the regression specified in Table 7, Model 5. 106 Findlay (2012), p. 153. 107 All of the independent variables included in this regression analysis are compared to the natural log of price, as is the standard in hedonic literature. The interpretation of variable coefficients such as ln(area), comparing a two log variables, means that a doubling in the dependent variable, in this case ln(price), results in close to the coefficient times 100 to get the percentage change. When there is a log dependent variable with an independent variable not in log form, this just means a one unit change in the independent variable results in about the coefficient times 100 to get the percentage change. If this is a dummy variable, it means that if the value indicated is true, such as if the object is a drawing or watercolor, this is associated with a 25 percent decrease in price as related to the object category. If this is a numerical variable, such as the exhibition counts included later in the results section, one addition exhibition leads to about a percentage change related to the coefficient times 100. A longer discussion of why this is the case is included in Appendix 5.

Wilkinson 58

If the work is three-dimensional, i.e. a sculptural form, it tends to be 78 to 83 percent less

in value than a two-dimensional work. This is consistent with the general art market since two

dimensional works, particularly paintings, tend to be priced the highest, and sculptures tend to

Wilkinson 59

be less prized on the fine arts market.108 Additionally, it is well known that different types of

artwork fetch different prices. My results confirm this. The highest prices are yielded by paintings

(71 to 77 percent higher than the general “object” auction category109). The lowest prices are

print multiples at over 218 percent less than the object category. This means that a similar object,

with the other controls, would sell for considerably more. For example, a $5000 print might sell

for $15000 if it were an original unique object. This is to be expected since print multiples are one

of the few art market objects which are not unique and thus tend to sell for much less. In fact,

they are often made for this specific purpose.110 Also with negative values compared to general

objects are photography (78 percent less), lighting111 (150 percent less), furniture112 (155 percent

less), drawings/watercolors (25 percent less), and tapestries (150 percent less).

Looking to the WorkAge variable, this measure of when the work was produced in relation

to the artist’s career generally has a relatively small (about 1 percent increase in the price of the

work), but consistently significant impact across all models. Additionally, as this is a measure that

varies yearly, it could be quite significant in the overall price of the work, particularly for those

works produced earlier in the artists’ career or those works which have been around long enough

to gain some acclaim both through exhibition or sales on the primary market.

The influence of Country of Sale on the price of a work of art is summarized in Figure 9 on

the next page (actual coefficients are reported in Appendix 4). All percentage changes reported

in Figure 9 are with respect to a work sold in the Czech Republic113 versus the other countries in

the sample. These price relationships associated with countries come to some interesting results

which may be worth further exploration. Being sold in certain developing countries is associated

with higher prices than works sold in the West – a result which one might expect with the

increasing globalization of art as well as the increasing super rich in these areas. Places like Hong

108 Ginsburgh & Throsby (2006). 109 The “object” category designation at auction is generally reserved for small sculptures or other kinds of artist-created three-dimensional forms. They tend to be lesser works since otherwise they would be placed in the “sculpture” category for auction. 110 Print multiples are often created by artists for the specific purpose of being more accessible, through their lower price, and therefore more widely disseminated. 111 This “lighting” category is often associated with installation pieces by contemporary artists which include aspects of lighting. 112 Furniture sold at fine art auctions in these sales tends to be those objects created by famous designers and architects since their buildings would not be sold at auction. 113 This country was chosen as the comparison as had the lowest negative correlation. Thus, through its exclusion, one might more easily see the relationships of other countries of sale since all but one of the countries left in the sample have positive values for comparison.

Wilkinson 60

Kong (+155%), China (+128%), India (+123%), United Arab Emirates (+170%), Qatar (+196%),

Morocco (+144%), Lebanon (+277%), and Brazil (+103%) have extremely strong positive influences

on prices. Places which usually appear to dominate in prices, such as the United States (+107%)

and United Kingdom (+105%) are significantly lower than many of these developing countries.

Although part of this is a function of the auction house being used in each location, controlled for

through error clustering, these large price differentials for sales in developing countries are

interesting and worthy of further study in the future.

Figure 8: Country of Sale Effect on Price

*Source: This index was constructed from the created database through ArtPrice auction records.

The hedonic price index which results from the estimated coefficients of the year-

indicator variables is depicted below in Figure 10. While concepts surrounding this index will be

explored later, it is worth noting here that the findings of this graph are somewhat in line with

previous findings (see, e.g., Ashenfelter and Graddy 2006 and Ursprung and Wiermann 2010).

During the late 1980s, there was a boom in art prices. Then, in the 1990s prices were rather low

and constant. In the late 2000s was another spike in art prices which reached levels associated

with the previous boom, which has since leveled off in the early 2000s. Worthy of note, they have

not dipped as low as in the decline period of the 1990s. Thus, while the associated price changes

may be slightly lower or higher than art prices in other sectors, the general trends seen in this

graph are consistent with other similar studies.

Wilkinson 61

Figure 10: Hedonic Price Index for Living Artist Prices, 1989 to 2015

All things considered, these results both strongly confirm received wisdom as well as

contribute some findings worthy of future research using a larger, genre-specific, more broadly-

based dataset than most utilized in cultural economic literature.114

6.2 The Associated Impact of Total Exhibitions

I begin with OLS regressions, with standard errors clustered by the auction house of sale,

using total contemporaneous exhibition counts reported on the sample artists’ CVs as regressors

(Equation 7). These initial estimates, while not the main focus of the analysis, are indicative of the

overall market as well as the overall impact of exhibitions on the price of work for living artists.

Since the auction records cover the period from 1986-2015, the estimated impacts of exhibitions

are based on exhibition counts within this period. All of the estimated regressions throughout the

rest of this thesis include the control variables discussed in the previous section.

Table 4 includes many of the variable types that will be the focus of study for the major

exhibitions throughout the rest of this section. Models 1 and 2 run the total exhibitions

114 More elements of how biographical elements and being sold at major auction houses impact price are included in Appendix 9, where the models are run without artist fixed effects. While these elements are outside the main scope of this paper, whose focus is the impact of exhibitions, they are interesting for understanding how the art market functions generally.

Wilkinson 62

contemporaneous to the year of sale, TotalSolo and TotalGroup, separately. When included in the

regression analysis alone, they are both statistically significant, with a indicating that doubling the

number of exhibitions is associated with of about 2.9 percent for solo exhibitions and 0.6 for group

exhibitions with price. While these numbers seem relatively small, the fact that major artists can

have many exhibitions of each type within a single year means that these numbers could actually

correspond to high price returns.

Table 5 reports the statistics for these exhibition counts, with an average of about four

solo exhibitions in a year and eight group exhibitions. Based on this analysis, an average artist in

this sample has about a 17 percent corresponding increase in price due to their exhibitions in a

single year (with both average group and solo means together with their coefficients in Model 1

Wilkinson 63

and 2). However, although it is interesting that these contemporaneous counts are significant,

when they are run with the cumulative totals of exhibitions in the five years preceding the year

of sale in Model 3, they become insignificant. This would suggest that previous exhibition history

is correlated more strongly with price than exhibitions within the year of sale.

However, this narrative changes somewhat with Models 4and 5, which run these total

values lagged by a year, i.e. the counts of total reported solo and group exhibitions in the year

prior to that of the sale. In this case, they again become strongly significant at the 1 percent level

with similar price associations. Yet, with the inclusion of cumulative variables for five years prior

to the lagged year in Model 6, only the total solo exhibitions prior to the year of sale retain their

significance. While total group exhibitions prior to the year of sale become insignificant, their five

year cumulative value is significant. This would seem to suggest that solo exhibitions are

associated with greater price impacts closer to the time of sale while group shows containing the

artist have greater impacts over time. Taking only the significant values in Model 4-6 and averages

from Table 5, the average artist has an 5.1 to 17.2 percent increase in price associated with their

exhibitions within a given year. Yet, again, it is worth noting that while these are average levels,

most artists included in the sample have some years in which there are no reported exhibitions

within the auction record sample period. Thus, the differential impact of these exhibitions is likely

close to their association with art prices.

However, these results might be biased because of differing reporting standards since

exhibition information is collected from individual artist CVs from a variety of sources.

Unfortunately, the direction of the bias in unclear since often there will be increased reporting of

lesser exhibitions for new artists, which might bias downward the estimates since reported

exhibitions will likely have less branding effects. Yet, if more experienced or renowned artists are

Wilkinson 64

not reporting all of their exhibitions on their CVs, there could be a bias of the coefficients upwards

since it is capturing effects granted through other exhibitions. While for these reasons the rest of

this analysis will focus on those exhibitions deemed “majors”, understanding the price

associations of “lesser” exhibitions of varying types with better standards of collection is thus an

important area of future study.

6.3 The Associated Impact of Major Exhibitions Contemporaneous to the Year of Sale

Now turning to major exhibitions, as defined above, Table 6 reports regressions based on

the exhibition counts contemporaneous to the year of sale for the artworks in sample, again with

the standard errors clustered by the auction house of sale. Each of the contemporaneous major

institution variables is run separately and then all together.115 Run separately, a single solo

exhibition at a major museum within the year of sale corresponds to about a 7.5 percent price

increase. Solo major gallery exhibitions and group major museum exhibitions are not significant

explanatory factors in the auction price of a work. A contemporaneous group major gallery show

in the year of sale creates a 2.5 percent increase in price significant at the five percent level. In

the model including all of the major variables, the only show type which maintains significance is

the contemporaneous group major gallery show. This is an interesting result since in most other

models discussed later, as well as alternative clustering styles (shown in Appendix 11), it is solo

major museum shows which correspond to the highest significance as well as coefficients.116

However, many of the other significance levels are consistent with what was expected through

the exposure afforded by the various types of exhibitions as far as the museum exhibitions are

concerned.

As mentioned previously, solo museum exhibitions highly publicize the individual artist.

This generates wide exposure for the artist due to the prestige of the museum. Thus a bump in

prices within the specific year is to be expected – a result which is clear in Model 1 wherein it is

run alone even if not in Model 5. However, museum group shows tend to highlight themes or

115 This general format is followed throughout the reported analyses in large part due to the certain amount of collinearity which exists between the exhibition types (These correlations are reported in Appendix _). As one would expect, an artist with major exhibitions in certain museums will also be included in major galleries, etc. which creates a certain amount of overlap (although not as extensive as one might think). Thus, in order to get a more complete understanding of the roles of these exhibition types, then, it makes the most sense to look at models which both separate the types and which maintain their coefficients and signs once they are run together. 116 This result is also largely seen in other clustering types, seen in Appendix 11.

Wilkinson 65

issues over the individual artist, and therefore it is not surprising that being included in a

contemporaneous group show is not associated with a significant increase in price.

In contrast, the estimated effects of the solo and group gallery exhibitions for these major

institutions is somewhat surprising. For these so-called “mega-galleries”, the general art world

considers their exhibitions to have similar effects to that of major museums. They are highly

acclaimed institutions, some of them with multiple worldwide locations, and are thought to

convey artistic taste in a similar way to museums, i.e. “museums which sell art.”117 However, this

analysis suggests otherwise, at least for the contemporaneous secondary market. While a solo

gallery exhibition at one of these major institutions potentially increases prices within the primary

market, it does not significantly impact the contemporaneous sales of work by the artist at

auction. Especially when compared to the extremely large significance of solo exhibitions at a

117 In fact, there has been speculation in recent years that these major galleries create shows of an even greater caliber than museums. As quoted in The Guardian, the former exhibitions secretary of the Royal Academy and the man who staged the controversial Britart show Sensation in 1997, Norman Rosenthal, said, "These galleries put on some amazing shows. Sometimes more amazing than the subsidised museums do.” ; Thorpe (2010, December 12).

Wilkinson 66

major museum, this suggests that the exposure afforded even by a major gallery operates

differently within the fine arts market.

Then, looking to the group gallery exhibitions, these exhibitions run separately and with

the other exhibition types have an about 2 percent impact on price. While this is weakly significant

run alone and insignificant run with the other exhibition types, the fact that it has any significance

more than the solo gallery exhibitions is interesting as to the impact of these major art galleries

on the secondary market. It implies that being included in a group major gallery museum can

impact the price of work for a living artist more than a solo exhibition at a major gallery. Yet, these

results may also be due to the fact that these contemporaneous exhibitions do not give the

market enough time to internalize the information and branding conveyed by the exhibitions.

Table 7 displays the price associations of major exhibitions lagged by one year. This would

hypothetically allow the market time to internalize the signaling conveyed by these exhibitions.118

We see evidence for this concept in the estimations in the table above. When run separately, all

118 This is particularly true since the data for the year of exhibition is recorded at the start year of the exhibition. Thus, this means that even for the lagged variables, some of the exhibitions will be ongoing, but it just means there will have been at least a few months for information about the exhibition to disseminate.

Wilkinson 67

of the exhibition types become significant at the one percent level, except for group major

galleries which are only significant at the ten percent level. Even when all included together in

Model 5, everything but group major gallery exhibitions are significant for at least the ten percent

level. In this case, major solo museum exhibitions are associated with a 4 to 12 percent increase

in price, solo gallery exhibitions are 2 to 5 percent, group museums 2 to 3 percent, and group

galleries 1 to 2 percent. This represents both a slight shift in coefficient value from the

contemporaneous model as well as estimate precision. In terms of significance, there is an almost

reversal from only group galleries maintaining significance in the total model to, in the lagged

table, all exhibition types except for group galleries maintaining significance. In terms of thinking

about how to consider these percentage values, as displayed in the summary statistics in Table 8,

these exhibitions are relatively rare. In any given year, an artist will likely not have any of the

exhibition types. If they do have one of these types of exhibitions, it will be a shock to the system,

and they will likely only have one within a specific year.

Because of the increased precision gained from the lagged values, these variables will be

the standard single-year variable type for the rest of the presented regression analysis. While the

values shift to become somewhat more significant, most of the associated price impacts are what

one would expect with the types of exhibitions, although others are somewhat surprising.

As mentioned previously, solo museum exhibitions highly publicize the individual artist,

which gains a wide exposure due to the prestige of the museum, and thus a bump in prices within

the specific year is to be expected – a result which is clear in lagged models. Since museum group

shows tend to highlight themes or issues over the individual artist, it might be expected that these

inclusions would not significantly impact the price of the work, seen in the contemporaneous

model. However, in the lagged model, they have a 2.4 to 2.7 percent associated price increase.

This would suggest that museum interest in an artist’s work, even included with an array of other

artists, is associated with price increases. If one considers the general clout of museums in

influencing opinions surrounding artists, this would make sense. This is especially true if this

Wilkinson 68

inclusion represents a signal that the museum plans to acquire an artist’s work for their

permanent collection – this serves to restrict supply and as an expert signal of quality which would

point to appreciation of the artist’s other works. Additionally, the exposure and signaling prestige

afforded by a solo exhibition versus the group exhibition at the museum, and thus the greater

increases in price associated with the former than the latter, are to be expected.

The estimated effects of the solo and group gallery exhibitions for these major institutions

also have elements which are both expected and surprising. Solo major gallery exhibitions in the

lagged model are associated with an about 1.9 to 4.8 percent increase in price. This was more of

an expected result than the insignificant levels in the contemporaneous model. It would also make

sense that this would take at least a small amount of time for these levels to reach the secondary

auction market from the primary gallery sphere. Yet, this still reports them as lesser impacts than

major museums. However, this analysis suggests that even if they have enough clout to affect the

secondary market, their affects are always less than those of solo museum exhibitions, and

potentially less or near the impact of inclusion in a group museum exhibition (looking to Model

5). This suggests that the exposure afforded even by a major gallery operates differently within

the fine arts market.

Looking to group gallery exhibitions, while these exhibitions have an about 2 percent

impact on price in the contemporaneous model, in the one-year lagged model it is the only

exhibition type that has a weak significance when run alone and no significance in the total model.

This suggests that major group gallery exhibitions have less correlation with prices in the

secondary market than any of the other exhibition types. These varying significance levels are also

important when considering the stages in the artist’s career in which they occur.

As seen in the histograms in Figure 11 on the next page, the age at which artists have their

first major shows for each of the respective exhibitions types is different. The system in which

artists have their first shows in each seems fairly systematic in the career of a contemporary artist:

first, they have a group major museum show (age median 33, mean 34.9), then a group major

gallery show (age median 35, mean 36.7), then a solo major gallery exhibition (age median 37,

mean 40.0), and finally, only a small amount above solo major museum exhibition (age median

38, mean 40.1).119 In a sense, it appears that these exhibitions act as stepping stones to each other

within the career of an artist – the former major exhibitions type must be accomplished before

119 Both mean and median values are included in here. Thus, the median values are perhaps more accurate in determining at about what age these exhibitions occur in an artist’s career.

Wilkinson 69

they may be considered for the next exhibition type. Thus, each exhibition’s ability to confer

status, foreshadow price appreciation, and signal expert assessment of quality might also be

thought of as increasing through these various exhibition types. If this is true, we would expect

price correlations with exhibition types to increase through this list.

The one caveat to this conception is that major museum group shows, while technically

earlier in an artist’s career, are consistently correlated with prices more than major gallery group

exhibitions. The fact that gallery exhibitions tend to highlight their artists more, as discussed

previously, also makes this result surprising. However, there are a few potential reasons for this

result. One is simply the separation between the primary and secondary markets. It is possible

that these exhibitions are correlated more closely with increases in the price of art in the primary

market, and that they simply don’t confer the status necessary to increase prices in the secondary

market. Yet, even if we take this as part of the narrative, group museum exhibitions would also

likely confer status for the primary market as well, and therefore would probably still be

associated with higher price increases than group major gallery exhibitions. It is likely that the

Figure 11: Histograms of the Age of First Group Major Gallery (GMG) Exhibition, First Solo Major Gallery (SMG) Exhibition, First Group Major Museum (GMM) Exhibition, and Solo Major Museum (SMM) Exhibition for Sample Artists

Wilkinson 70

true cause of this difference comes from the indirect channel of exhibitions contributions to buyer

willingness to pay: their signaling of expert assessment of artist reputation.

While major galleries do convey a certain amount of status and foreshadow price

appreciation for artists, they are still a commercial enterprise. Although they choose which artists

they represent, the idea is that they are involved to make money, not confer creative quality. In

contrast, museums are generally held to be outside the commercial art market (which is why their

increasing associations with the art market discussed earlier seems particularly troubling). As

largely non-profit institutions, they are considered purveyors of unbiased taste. Beyond

academics as universities, museums and curators are foremost sources of art historical

scholarship. Thus, even if a major museum group exhibition does not highlight the individual

artists as much within these exhibitions, their signal of expert assessment of the artist’s reputation

is stronger because they ideally have no financial stake in the subsequent development of the

artist’s career. Indeed, part of the reason why a mega-gallery may attempt to represent a specific

artist is because of expert signaling of value such as through inclusion in group major museum

exhibitions.

The development of an artist’s career to the point of being the subject of solo shows first

at major galleries and then at major museums would also make sense. Since both focus on an

individual artist, it would make sense that this emphasis would be associated with a larger

increase in price than inclusion in an exhibition with a wider array of artists. Solo major gallery

exhibitions highlight the artist as much as solo major museums, but they have the similar lesser

conveyance of expert assessment described for group major gallery exhibitions above. As such,

solo major museum exhibitions are considered the ultimate confer of status on an individual

artist. These exhibitions are likely to publicize the artist most and from the most reputable source

of expert opinion for contemporary art. An artist must have a considerable reputation of

exhibition success and quality to be the subject of such a solo exhibition. Thus, it would both make

sense that they would first become represented by a major gallery to the point of having a solo

show and that this would be associated with the highest increases in price in the year following

an exhibition of this type. Beyond associations between prices and these varying exhibition types,

it is also instructive to look at the price correlations for cumulative exhibition types. This could

give some insight as to the relationship between prices and artist reputation over time.

Wilkinson 71

6.4 The Associated Impact of Cumulative Counts with 1 Year Lag Major Exhibitions on the Year

of Sale

Looking to cumulative counts of major exhibitions conveys greater insight into the impact

on art price through the development of artist reputation. These regression estimates seem to

convey that both exhibitions directly preceding the sale and the historical record of exhibitions

are important indicators for price increases. This section proceeds in stages, first looking to the

year preceding the sale with the five years prior cumulative. Then, more regression analysis

utilizes cumulative totals from the six to ten years prior to the year before the sale in addition to

these earlier variables. These two regression tables help understand the associated price values

with exhibition histories in comparison to those related to those exhibitions directly preceding

the sale of the work.

Wilkinson 72

Table 9 displays the major museum types with their corresponding 5-year variable for the

exhibitions in the five years preceding the one-year lagged variable (so the cumulative total for

the 2-6 years before the year of the sale). These models have each exhibition type individually as

well as a final model with all variable types together. The overwhelming result of these models is

that exhibition histories are significantly correlated with price increases. As one would expect,

solo major museum exhibitions have the highest associated price increases: 6.3 to 12 percent

increases associated with the year before the sale and 6.3 to 9.3 percent increases associated

with each exhibition in the 2-6 years before the sale. For these solo museum exhibitions, those

which occur in the year preceding the sale of an artwork have a larger associated price increase

than those within the five years prior. Thus, the closer these exhibitions are to the year of sale,

the more they are associated with a price increase.

Solo major gallery exhibitions on the other hand, while having significant values for both

variable categories, when run alone have greater correlations with price for the five year

cumulative totals (6.5 percent) than the year preceding the sale (5.8 percent). In the total model,

this switches and the values become relatively similar (2.3 for the lagged year and 2.2 for the

cumulative variable). This suggests that the price association of a solo major gallery exhibition is

not significantly different whether it happens directly preceding the sale or in the years before.

Thus, it may contribute to the general reputation of the artist in a way which is not particularly

time-dependent.

Group major museum shows follow the same general form as solo museum exhibitions

in that price associations are larger and stronger in the directly preceding year than the years

before. This is potentially because the indirect effects of expert signaling of quality is relatively

ephemeral, becoming weaker after a year or two, while the status conveyance and the idea of

price appreciation lingers for a longer period through the artist’s reputation.

Group major gallery deviates from the general overall significance found for the exhibition

types throughout the other included models. These exhibitions, consistent with the lagged

variable model in Table 7, are insignificant for the on-year lagged variables both when they are

regressed alone as well as in Model 5. However, major galleries over the 2-6 years before the year

of sale are statistically significant at the one percent in both models of their inclusion. This

suggests that, while a major group gallery show may not affect prices directly following the

exhibition, a history of group gallery exhibitions can impact price. This could be a lagging effect in

Wilkinson 73

the market transitioning impact from the primary to the secondary market. Alternatively, it could

be that the history of an artist’s reputation built through these exhibitions helps them develop

their specific artist brand, conferring status over time, but does not generate enough buzz

surrounding the exhibition to impact prices.

In thinking about these kinds of cumulative exhibitions, it is also instructive to look at their

summary statistics, displayed above in Table 10. The top four variables are the cumulative totals

included in Table 9 while the following four variables will be used in Table 11. While these

exhibition types, particularly the two solo exhibitions, are rare, cumulative totals are relatively

large in comparison with the single year values. The fact that these mean values are as high as

they are even ten years before the one-year lagged variable suggests a well-established sample

of artists. Additionally, since these mean values are all greater than one, this would suggest that

while individually museum exhibitions closer to the time of sale are associated with higher price

increases, this could potentially shift to the cumulative exhibition histories being more important,

depending on how many are included. Particularly for major museum exhibitions, with means of

over 5, this would mean the associated price increases would be higher for the cumulative

exhibition history than the recent exhibition. This suggests that the reputation of the artist

through exhibition history over even a brief period has at least as much correlation with price as

recent exhibitions and perhaps more.

Testing this idea by extending even farther into the past, Table 11 looks at the variables

included in the models in Table 9 as well as including cumulative totals that extend to ten years

before the year prior to the sale of the work (in avoiding overall, this means the 6-10 years prior

to the single year variables). Again, we largely find significant results across all categories with

similar coefficient values as those in the models included in Table 9. In fact, if anything, it tends

Wilkinson 74

to slightly increase the coefficient values and thus the associated price increases for each variable

category. Additionally, the magnitude of the correlations with price for the lagged year and the

five year cumulative totals are relatively consistent with the models in Table 9.

However, rather than having differing descending magnitudes, the coefficients associated

with the cumulative totals 6 to 10 years before the lagged year ubiquitously have a decrease in

Wilkinson 75

magnitude at least in the models where the variables are run separately, though they are also

significant. For solo major museum exhibitions, still with the highest price correlations, the lagged

year is associated with a 7.7 to 12.9 percent increase, the five year cumulative a 7.8 to 10.4

percent increase, and the six to ten year cumulative a 4.3 to 6.4 percent increase. Solo major

gallery exhibitions have a 2.2 to 6.2 percent increase for the year prior to sale, 2.8 to 7.1 for five,

and 3.3 to 3.9 on six to ten. The total model thus shifts the weights of these exhibitions so that

the farther back they are, the more they impact pricing. This suggests more of a reputation

mechanism for the artist’s development through these gallery exhibitions is more important than

the associated buzz of the exhibition.

Group major museum exhibitions maintain a high level of significance for each variable

when included individually (Model 3) with descending magnitudes as the exhibitions get farther

from the time of sale. However, in the total model, while the year preceding the year of sale is

still strongly significant (associated with a 2 percent increase), the five-year cumulative loses

significance and the six to ten year cumulative (0.74 percent increase) is only significant at the ten

percent level. This suggests that while group museum exhibitions are useful indicators of price,

they are less strongly correlated than the solo exhibition variables. In contrast, group major gallery

exhibitions are insignificant for both the year prior to the sale of the work and those from 6 to 10

years prior, even becoming negative (while still not significant) in the total model. However, the

significance found in Table 9 for the five-year cumulative is again seen here. This suggests that

while these group major gallery shows confer status and price appreciation, this is really only true

in the recent past. It does not create buzz or a long-term indication of artist reputation, but rather

is only correlated with price in the medium term.

These explorations of the correlations between price and different types of major

museum exhibitions display a high amount of positive significance between the two for all

exhibitions except group major gallery. The results of these models are the premier set of

estimates of the paper. As would be largely expected, solo major museum exhibitions have the

highest correlations with price (as high as 12.9 percent for an exhibition before the year of sale)

across all models. Solo major gallery exhibitions have the second highest magnitudes, generally

about half those associated with major museum exhibitions. Despite the fact that they do not

tend to specifically highlight individual artists, inclusion in major museum exhibitions is also fairly

consistently correlated with price increases of 0.5 to 2.5 percent. Major group gallery exhibitions

Wilkinson 76

are the only exhibitions which are not correlated with price for exhibitions in the year prior to that

of the sale.

Exhibition histories also appear to be important indicators of price for contemporary

artists. Extending both five years and six to ten years into the past, these cumulative totals

maintain a strong statistical significance for both categories specifically for solo exhibitions. For

solo major museum exhibitions, the exhibitions have higher correlations with price increases the

closer they are to the year of the sale and decrease in magnitude the farther away they are from

the year of sale. Solo major gallery exhibitions have greater associated price increases within the

five years prior to the year of sale than the year prior, although six to ten years is considerably

lower than these magnitudes. This could indicate some price sticky-ness in the influence from the

primary to the secondary market. Group major museum exhibitions follow a similar trajectory as

solo museum exhibitions in their correlations with price, but with lower magnitudes as well as

weaker significance in the total model. Finally, group major galleries seem to only be significant

in the five year cumulative variables, suggesting that they are only correlated with price in the

medium-run.

There are a few major insights derived from this analysis. One, the development of an

artist through their career in each exhibition type seems to be largely associated with a greater

correlated price increase at each stage (with the one exception being the switch of major

museums and major gallery group exhibitions). As such and as was expected, solo exhibitions

ubiquitously have higher magnitudes associated with price than group exhibitions. If exhibitions

are defined as contributing to buyer willingness to pay through conferring status, signaling price

increases, and as expert assessment of artist reputation, solo exhibitions would highlight the

individual artist more and thus be associated with higher levels of each of these things than simple

inclusion in a wider exhibition of artists. Along a similar concept, museums consistently are

associated with higher price increases than galleries. This is likely because museums are seen as

unbiased purveyors of taste since they hypothetically have no financial stake in the subsequent

development of the artist’s career.

Thus, each of the discussed mechanisms would be greater for museums than galleries.

While mega-galleries may have more prestige than a lesser gallery and are considered “museums

that sell art” by some, they still do not reach the level of influence extended over the market by

major museums. This is seen in that the highest associated price increases for museums are in the

year prior to the year of sale and for galleries are in the years prior to that. Perhaps this is

Wilkinson 77

indicative of a price stickiness between the influences of the primary market on the secondary

auction market. Yet, this differential between museums and gallery coefficient magnitudes still

emphasizes that museums exercise more control over the market generally – in both the

secondary market and probably within the primary market as well. However, galleries have

understood this concept for a significant period of time. This is why the commercialization of

museums and the overlap between mega-galleries and major museums has been occurring over

time, as discussed in a previous section. Galleries understand that having artists they represent

exhibit at major museums greatly increases the price of their art, and with increasing economic

pressures on museums, they are more easily monetarily influenced, as will be further discussed

in the conclusion.

6.5 Examining the Differential Impacts of Exhibitions on Price for Female Artists and Artists

with Masters in Fine Arts (MFA) Degrees

Now that the price association of exhibitions for the overall fine arts market has been

established, it is interesting to determine the impact of these different exhibitions on sections of

the overall art market. Here, the differential effects explored are whether or not the artist is

female and whether they have earned a Masters of Fine Arts (MFA) degree. These two aspects

are of particular interest in the art world today. Impacts surrounding the influence on the price of

art for female artists is of particular importance because of gender discrimination in the fine arts

world. Despite the fact that women earn over half the MFAs earned in the United States and 51

percent of visual artists today are women, only about 28 percent of museum contemporary

exhibitions spotlighted women throughout the 2000s.120 This may be easily found in the

institutions which constitute the major institutions within this analysis. Of all the solo exhibitions

since 2007 at the Whitney Museum, 29 percent went to women artists – which unfortunately is

one of the higher statistics. In the year 2000, the Guggenheim in New York had zero solo shows

by women. In 2014, 14 percent of the solo exhibitions were by women. Of all the solo exhibitions

at the Centre Pompidou since 2007, only 16 percent went to women, and this is an improvement.

In 1980 it was 1.1 percent, in 1990 it was 0.4 percent, and in 2000 it was 0.2 percent. At MoMA

April 2015, only 7 percent of the works on display were by women. 121 This kind of apparent sexism

in the discrepancy in the display of female artists versus male artists is extremely controversial in

120 National Museum of Women in the Arts (2014). 121 Reilly (2015, May 26)

Wilkinson 78

the art world. Understanding the impact on prices for the exhibited women, which represent

about 23 percent of this sample, is therefore of particular interest.

Masters in Fine Arts degrees are also being marketed as a necessity to young

contemporary artists for breaking into the contemporary art sphere. Whether or not an artist

needs an MFA to be successful is a topic of heated discussion in the art world, with passionate

defendants on both sides of the issue. Some argue that the network and branding knowledge

gained through an MFA, even more than the artistic knowledge, are necessary to make it as a

contemporary artists. At this point, some view an MFA as an essential credential, which would

explain the record-setting enrollments in MFA programs for fine artists. Others say that there is

no clear-cut advantage to earning an MFA from a monetary perspective and can even make one’s

artistic practice worse. Based on a survey performed by Jane Chafin, owner of Offramp gallery in

Pasedena, California, the degree to which artists are able to make a living as a working artist has

no substantial difference between those with an MFA and those without the degree.122 From an

artistic perspective, many critics feel that art schools are also directly responsible for a decline in

the quality of art. ''When I go to the New York galleries, all I see is art-school art,'' says Barbara

Rose, the art historian. ''The art is either feminist or deconstructionist, and basically it looks like

homework, because what is homework but learning how to follow the teacher's rules?'123 Thus,

the idea of how an MFA contributes to the artist is also of particular interest.

In order to explore the differential impacts of exhibitions for if the artist is female or if

they possess an MFA degree, the regression analysis included here will incorporate interaction

terms as represented in Equation 8 below:

ln𝑃𝑃𝑓𝑓𝑖𝑖𝑠𝑠𝑠𝑠𝑗𝑗𝑗𝑗 = α1𝐸𝐸𝑀𝑀ℎ𝑖𝑖𝑠𝑠𝑖𝑖𝑠𝑠𝑗𝑗+α2𝐸𝐸𝑀𝑀ℎ𝑖𝑖𝑠𝑠𝑖𝑖tt ∗ 𝐶𝐶ℎ𝑀𝑀𝑓𝑓𝑀𝑀𝑠𝑠𝑠𝑠𝑠𝑠𝑓𝑓𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑠𝑠 + α3𝑊𝑊𝑡𝑡𝑓𝑓𝑊𝑊𝑊𝑊𝑊𝑊𝑠𝑠t +

α4 𝑎𝑎𝑎𝑎 𝑀𝑀𝑓𝑓𝑠𝑠𝑀𝑀j + α5𝑠𝑠ℎ𝑓𝑓𝑠𝑠𝑠𝑠𝑟𝑟𝑗𝑗 + ∑µi𝑀𝑀𝑠𝑠𝑎𝑎𝑖𝑖𝑢𝑢𝑀𝑀j +∑βi𝑌𝑌𝑠𝑠𝑀𝑀𝑓𝑓𝑡𝑡𝑓𝑓𝑌𝑌𝑀𝑀𝑎𝑎𝑠𝑠 +

∑γi𝑊𝑊𝑓𝑓𝑠𝑠𝑖𝑖𝑠𝑠𝑠𝑠 + ∑σi𝐶𝐶𝑡𝑡𝑢𝑢𝑎𝑎𝑠𝑠𝑓𝑓𝐶𝐶𝑡𝑡𝑓𝑓𝑌𝑌𝑀𝑀𝑎𝑎𝑠𝑠+ ϵ ( 8 )

This model is exactly the same as the model outlined in Section 5.2 Methodology in Equation 7

except for the interaction term after Exhibit, denoted by Exhibition*Characteristic. The two

characteristics which are explored in this section are Female, an indicator variable for whether or

not the artist is female, and MFA, an indicator variable for whether or not the artist has reported

earning an MFA. We can consider the original term (Exhibit) to be the effect of the exhibition on

the base case for artists and the interaction term (Exhibition*Characteristic) to be the differential

122 Chafin (2011, July 10). 123 Quoted in Soloman (1999, June 27).

Wilkinson 79

influence of either being a female or having an MFA on the impact of having an exhibition on the

price of the artist’s work. There are no baseline indicators for either MFA or female because there

are still artist indicator variables in the model which capture these effects. In order to explore

these differential effects, the rest of this section will look at female and MFA interacted with both

the one-year lag variables and then five year cumulative totals.

The results of the female interaction analysis associated with the exhibition counts for

the year prior to the year of sale is represented by Table 12. Like the other tables, it includes

regressions run with each exhibition type and then all of them together in the final model. Looking

to solo major museum exhibitions, the single model reports a much higher price differential

associated with a female artist versus a male artist of about 15 percent higher (with male artists

being associated with a 4.4 percent increase). Similarly, solo major gallery shows report a 12.6

higher price correlation for female artists (in addition to a 2.5 percent price increase associated

Wilkinson 80

with male artist exhibitions). Particularly in light of the sexism in the art market previously

discussed, each of these results could be a huge development in the consideration for exhibiting

female artists. However, the interaction terms for each of the group exhibition types are

insignificant, and all of the interaction terms are insignificant in the total model. This suggests that

for the short run, exhibitions may be somewhat differentially influential to price for female artists,

but this may not be the best measure of influence. Thus, it is interesting to examine this effect

over time.

Table 13 displays results interacting female with Last5 variables, which are cumulative

exhibition totals over the last five years. Here, we see much stronger results which maintain

significance in the total model. Cumulative totals for solo major museums are associated with a

6.4 to 8.1 percent increase in price for women artists more than male artists (with increases of

Wilkinson 81

about 4.3 percent for male artists), and solo galleries are 18.5 to 5.2 percent increases. For group

exhibitions, museums are associated with a 1.3 to 1.4 percent increase and the interactions for

group gallery major shows is even 2.3 percent higher when run separately, although it does not

maintain significance in the overall model. These female increases maintain their significance

even when some of the exhibition variables lose their significance. Thus, this suggests that there

is an overall greater relationship between a major exhibition and the price of work for a female

artist than a male artist, particularly as they develop in their exhibition histories.

While there is no baseline female variable in the above models since they include artist

fixed effects, coefficients associated with being a female artist without artist fixed effects (seen in

Appendix 10) are consistently negative and often statistically significant. Although models without

Wilkinson 82

artist fixed effects are problematic for a variety of reasons (discussed in Appendix 10), these

negative price associations for female artists may be taken as an overall art market trend. Thus,

these strong premiums for female artists become even more important. They suggest that the

bias against women may be more with the institutions than with the collectors. It appears that if

women are able to surpass the relative barriers placed in their way to being exhibited in large

institutions, any of these show types, except potentially group major gallery exhibitions, create a

larger price differential for female in comparison to male artists.

Looking to the results of artists with MFA degrees interacted with exhibitions lagged by a

year in Table 14 above, the significance levels are not as high or as consistent as the female

interaction variables even in their lagged variable results in Table 12. The only interaction in the

single exhibition type models is for solo museum exhibitions, and this values is negative –

Wilkinson 83

representing a 5.5 percent lower price increase for artists with MFAs than those without. None of

the MFA interactions maintain significance in the total model. Yet, looking to the female

interactions, which only become particularly strongly significant with the cumulative totals over

time, it is worthwhile to interact artists with MFAs with a similar five year cumulative exhibition

count.

The results of this longer-term interaction are displayed in Table 15 above. Here, too, they

largely display negative or insignificant results. When each variable type is run separately, the

interaction is only significant for major solo museum and gallery exhibitions, and in both cases it

is negative (5 percent and 3.9 percent less of a price increase associated with each respectively).

In the total model, only the major solo gallery interaction term is significant at the five percent

level, and this sign switches to positive, giving this specific result mixed results. Thus, for the

overall model, this gives either negative, insignificant, or mixed results regarding the impact of an

artist having an MFA on the price of their art.

The results in Table 15 do not conclusively display that having an MFA is detrimental to

one’s career, since there is no baseline art prices for artists with MFAs within these models. Yet,

coefficients associated with having an MFA in models without artist fixed effects (seen in

Appendix 10) are consistently negative and strongly statistically significant. While these models

are arguably subject to omitted variable bias, these correlations are consistent with the idea that

an MFA is potentially detrimental to one’s career. For exhibitions specifically, based on this

analysis, if these artists make more money per piece of art without exhibitions, then MFAs might

still make sense for a lot of artists who are unable to get exhibits or in the early stages of their

career before they have exhibits. However, from this limited analysis, at best, having an MFA adds

no additional value to being exhibited; at worst it can decrease the advantage of an extra solo

major museum exhibition by 5 percent. Thus, having an MFA does not appear particularly useful,

and is sometimes detrimental, for the associated price increases of exhibitions for artists.

6.6 Further Explorations

Of course there are many other approaches to considering exhibitions as exposure to the

market. While only a selection of these concepts are addressed in the analysis above, further

analysis is included in Appendixes 9-12. As a robustness check, Appendix 9 displays models which

incorporate extra hammer prices by including 75 percent of the auction house estimation of

bought-in works, based on cultural economic papers such as Ashenfelter (2011), to attempt to

Wilkinson 84

counteract some of the selection bias and determine whether or not this actively changes the

impacts of exhibitions on prices. In this analysis, I find that it does not greatly change the

associated coefficients. Appendix 10 examines regression analysis of the records without artist

fixed effects in order to discuss further biographical information. This biases some of the

estimates significantly, which is why artist fixed effects are used in all of the included regression

models, but does give some baseline information for overall associations with various

nationalities, gender, MFA, and city of residence. Of particular note are the significantly negative

correlations between price and MFAs across all models as well as for group exhibition types and

the total model for female artists. Appendix 11 explores models with career-to-date exhibition

variables as a long-term reputation examination of the impact on price. These career variables

tend to have weaker significance than the cumulative year models included in the body of the

paper. Exploring these impacts further through logarithmic models, quadratics, and weighting

closer to the time of sale are recommended for future research. Finally, Appendix 12 includes

various alternative error clustering methods to the auction house clustering used in all of the

above analysis. They do not tend to have substantially different point estimates than the ones

included with auction house clustering, although sometimes significance levels for different

exhibition types vary.

7. Conclusion

In this thesis I have used data on auction prices to investigate the impact of exhibitions

on the price of work for living, contemporary artists, focusing specifically on “major” institutions

and varying effects for galleries and museums as well as solo versus group exhibitions. The

analysis utilizes over 46,000 auction sales of artwork by 375 artists and sold in 53 different

countries during the period 1986 to 2015. The types of exhibitions explored in this analysis were

total reported solo exhibitions, group exhibitions, and the same separation for major museums

and major galleries. The ways in which these variables were counted for analysis were

contemporaneous to the year of sale, lagged by a year, cumulative totals for the five years prior

to the single year variable, and the cumulative six to ten year total exhibitions of each type prior

to the lagged year variable.

Although it does not prove a causal relationship, this analysis supports the conventional

wisdom that exhibitions increase the price of work for artists and that there are differential

impacts according to the type of exhibition. The relationship of exhibition exposure to this sector

Wilkinson 85

of fine arts indicates that there exist positive, significant correlations between certain kinds of

exhibitions and the price of work for living artists. In particular, focusing on the exhibitions the

year prior to the year of sale, major solo museum, solo gallery, and group museum exhibitions

maintain strong significance across all models, with only group gallery shows displaying weak

significance. Ubiquitous across all models, solo exhibitions are associated with higher magnitudes

than group exhibitions, and within these types, major museums have higher price correlations

than major galleries. This would suggest that market trends at least follow museum exhibitions

more closely than gallery exhibitions. This calls into question the idea that these major galleries

function in the market similarly to museums.

Exhibition histories seem to also strongly correlate with price particularly with the solo

exhibition types across all models. These price correlations tend to be weaker for the group

exhibition histories. Additionally, for museum exhibitions, each individual exhibition tends to have

a larger price correlations when they are closer to the year of sale. For gallery exhibitions, the

highest price correlations tend to happen in the five years prior to the year prior to the sale. This

suggests potentially both some price stickiness from the influence of exhibitions in the primary

market on the secondary market as well as greater price associations with museums in the short

term. However, because in the regression models these are cumulative models that tend to be

higher than a single year of exhibitions, exhibition histories tend to have higher overall

correlations with the price of an artwork than recent exhibitions.

Across all of these models, solo major museum exhibitions are associated with the highest

increases (7 to 13 percent), then solo major galleries (2-4 percent), group major museums (1 to 2

percent), and finally group major galleries (0.7 to 2 percent). These significant associated

percentage changes in price have serious implications for controversies surrounding art

commercialization, investors, collectors, museum acquisitions, and cultural economic literature.

7.1 Museum Exploitation by and Relationships to Commercial Galleries

“Nothing seems to me so like a whorehouse as a museum.”

– Michel Leiris, French surrealist writer and ethnographer124

By empirically linking specifically solo museum exhibitions to large increases in art prices,

museums can no longer deny their activities are at least associated with art market trends. Since

124 Quoted in Torgovnik (1990).

Wilkinson 86

this link can now be analytically shown, the thinning divide between museums and sellers of art

becomes more problematic. As museums become more connected with galleries and other sellers

of art, museums may be forced to abandon the concept of organizations outside the economic

sphere. Museums have the highest measured associations with prices. Even if this is not causal,

the assumption that it is within the art market makes museums a target for exploitation, as well

as potentially willingly exploited, as they struggle to operate as expenses in the art world increase

dramatically.

This demonstrable impact of exhibitions on the price of work for living artists is

particularly important to the conception of moral corruption in various forms for museums,

whose supposed purpose is the cultural education of the general public. This manifests itself in

many different ways already in the museum context, and may become a further issue as people

begin to understand the real impacts of exhibitions on the price of works. For example, a previous

director of a major New York museum with a global presence relayed a story about how this

distortion of museum function is already impacting the museum market.125 This director hired an

Italian art historian as a contemporary curator for the museum. In his role, this curator had to

organize and arrange two or maybe three exhibitions every other year. For this position, at

$100,000 a year, the curator was being paid less than he originally thought he would be paid,

especially since this is not a huge amount for living in New York City. Yet, despite this, the curator

owned apartments in New York City as well as Italy, had plenty of money, and lots of fine art. It

turned out that a bunch of major contemporary artists would give him major works to try to get

a show at the Guggenheim, and then he did with them whatever he wanted – often sold them to

supplement his wages at the Guggenheim.126 While it is unclear how common this kind of

phenomenon is at other museums, or even within the Guggenheim since this is just one anecdotal

example, this story is demonstrable of the kinds of backdoor-dealing which this measurable

influence on prices can have for museums.

Beyond corruption of museum function through individuals at museums, these same

hazards exist for the museums themselves and are actually manifesting in similar ways. As recent

article by Robin Pogrebin at The New York Times entitled, “Art Galleries Face Pressure to Fund

Museum Shows,” discusses how non-profit museums are pressuring commercial art galleries for

125 Due to the confidential nature of this exchange, in respect to the former director who told this story, both the director in question and the museum are kept anonymous. 126 Sheppard (2016, February).

Wilkinson 87

increasing amounts of money – anywhere from $5,000 to $200,000 – to help pay for exhibitions

featuring artists they represent.127 For example, the installations outside the Whitney’s popular

Frank Stella retrospective mentioned earlier in this thesis was made possible by funds from the

Marianne Boesky and Dominique Levy galleries which jointly represent Stella. Quoted in the

article, Jeffrey Deitch, the longtime dealer and former director of the Museum of Contemporary

Art, Los Angeles (LACMA – one of the major museums in this sample) said of this phenomenon,

“Museums are giving these galleries the best platform in the art world for free, where they can

sell work to their clients on the walls of the greatest museums. If the galleries can contribute, why

not?”

This concept becomes particularly poignant in relationship to the findings of this analysis.

Solo major museum exhibitions always positively impact prices within the year of sale, even for

works that are not included in these major exhibitions, whereas group gallery exhibitions have

weakly significant impacts and solo gallery exhibitions have no significant impact at all. Even if this

is not as true for the primary market as for the auction market, the fact that solo major museum

exhibitions have such an impact for these secondary market price levels even outside the

exhibited work is indicative of their large impact on the fine arts market as a whole. Seeing as

these museum exhibitions have such a large positive impact on prices and gallery exhibitions even

at major institutions do not seem to highly impact price, it would behoove the galleries to

influence museums to exhibit the artists they represent since this boosts the price of these works

so significantly and galleries tend to earn between 20 and 50 percent commission on each sale of

the work.

Yet, this direct connection between museums and galleries can distort museum functions

by influencing what is exhibited. If certain galleries have more prerogative to pay the museum,

these museums may have a greater incentive to exhibit those artists because of the monetary

incentive more than the quality of the work – which is supposedly these nonprofit museums’

major function. This concept is laid out by Maxwell Anderson, who served as the director of

institutions like the Whitney Museum of American Art and the Dallas Museum of Art (both major

museums in the sample): “Gallery-supported exhibitions commingle inventory that may be for

sale with museum inventory. The self-interest of the gallery can compromise the independence

and integrity of the curatorial voice.”128

127 Pogrebin (2016). 128 Quoted in Pogrebin (2006).

Wilkinson 88

Beyond these payments for single exhibitions, exploitation of museums is also arising in

museums with this increasing number of gallery directors sitting on the boards of major museums.

Even though it costs a certain amount of money (sometimes extremely large) to buy themselves

onto these boards, more and more major gallery directors are getting on the boards of museums

like LACMA and MOMA.129 Yet, with the results of this analysis as to the greater impact of solo

major museum exhibitions on the price of work far above that of just the gallery exhibitions, this

makes sense. With the amount of commission they take on each work, the influence they would

hold on the board of these major museums to make sure the artists they represent are exhibited

would likely make sure they made their money back potentially multiple-fold over the years.

This is particularly an issue as internal economic pressures mount for museums. Museums

have long turned to outside support for their exhibitions, namely corporations and collectors, but

the cost of mounting exhibitions has grown, largely from increasingly pricy items like insurance.

As such, museums have started reaching out to sources like these commercial galleries for

funding. When faced with these kind of pressures, it seems more and more museums are

following Deitch’s opinion of “Why not?” Yet, the reason why not is because museums, rather

than being a somewhat unbiased purveyor of taste, become biased towards the artists

represented by galleries with the deepest pockets. As previously mentioned, nearly a third of

major solo exhibitions at museums in the United States between 2007 and 2013 featured artists

represented by just five galleries, those five which contributed to the compilation of the current

artist sample: Gagosian, Pace, Marian Goodman, David Zwirner, and Hauser & Wirth.

In light of the current analysis, this distinction of “solo” is even more important. Since it

has been shown that group major museum shows have negligible (or even negative if looking at

career-to-date measures) on overall artist work prices, solo museum exhibitions always have

positive, significant impacts on price and would therefore be those exhibitions for which galleries

would be more likely to pay. As dealer James Cohan said, “Gallery involvement in museum

exhibitions is part of the ecosystem of the art world. The competition to get one’s artist seen in a

noncommercial context like a museum or international survey is quite intense but ultimately

hugely gratifying. It’s part of our job to step up and support our artists.”130 Thus, dealers

understand the positive price impact of their artist being in a major museum solo show as well as

the increasing economic pressures on museums and are exploiting these pressures.

129 Knight (2015). 130 Quoted in Pogrebin (2006).

Wilkinson 89

7.2 Implications for the Art Market

For economic actors in the art market, having a predictable increase in art prices due to

exhibition exposure impacts time-sensitive decisions about buying artwork moving forward. Since

there is a demonstrable price association, particularly for major solo museum exhibitions, this

may influence the timing of buying works of art for various contemporary, living artists.

Beyond how museums act with their increased power over the gallery representation due

to their impact on increasing the price of work, it also impacts their acquisition of work. Since they

know that these exhibitions increase the price of the work of living artists by 4 to 8.7 percent,

museums should acquire works they want to have in their collection before exhibitions either

within their institution or in the art world more widely. Especially due to the increasing economic

pressures on museums for not just exhibition but acquisition because of the soaring price of art,

being able to get art at a relative discount before the price increases due to these exhibitions

could be a major asset.

Art collectors should time their acquisitions in a similar manner for the same reason. It

makes their decision to buy a work of art more time sensitive, since if they wait on a work of art

until after the announcement of the exhibition, it could make the work out of reach. However,

the difficulty would likely be knowing about the exhibition before the price increase as these two

aspects are probably fairly simultaneous – as soon as the general art market knows the show is

happening or going to happen the price will likely raise somewhat.

Art investors may consider the number of exhibitions for a single artist happening within

a particular year in determining when to sell a piece they’ve acquired or whether to buy an

artwork with similar projections. Say an art investment firm has a work by David Hockney in their

possession and is considering whether or not to sell it. If David Hockney has a major solo museum

exhibition taking nothing else into account, this would suggest a 4-8.7 percent higher return than

selling in a year in which David Hockney had zero exhibitions, based on this analysis. At the very

least, this analysis shows that there is potential in determining the economic impact of exhibitions

for acquisition and selling decisions by actors in the art market. However, on the opposing side of

investing in art, the price index for living, contemporary artists generated by this analysis also

suggests that fine art is not as good a hedge as people tend to believe. While the tends upwards

and downwards may be a bit stickier than other markets, the overall trends in the fine arts market

tend to follow the trends in the stock market as well. This means that it may not be as great an

asset to one’s portfolio as generally believed.

Wilkinson 90

7.3 Implications for Cultural Economic Literature

Within the cultural economic sphere, this increase in price due to a factor even potentially

external to the quality of the work calls the work of Galenson and followers into question with

price acting as a standard of value. The price increases in this analysis possibly change with the

status signaling and reputation development of exhibitions rather than specific work qualities.

Thus, using price as a standard of quality is likely time-dependent and therefore inaccurate over

the long-term. Additionally, hopefully this foray into the determinants of pricing outside the

standard set often included in cultural economic papers will encourage others to experiment with

other factors impacting the price of art which have yet to be explored. Thus, this analysis

displaying a significant increase in the price of work for living artists at auction in relationship to

different exhibitions displaying the artist’s work has significant impacts on economic actors in the

art market, the controversy surrounding commercialization, and cultural economic literature.

7.4 Suggested Further Research

My investigation has documented that exhibitions, as a function of building artist acclaim,

are highly significantly associated with art prices. Even though this is hardly a novel insight for

those in the art market, it is worth emphasizing that the mechanisms underlying the art market

cannot be fully understood without looking at both exhibitions close to the year of sale as well as

exhibition histories. The empirical literature has a tendency of downplaying the influence of artist

reputation or coming at it through strange, cherry-picking means because it is hard to measure.

Future empirical research into art price formation would enormously benefit from examining

various reputation measures within the art market and the ways in which such measures function.

Examining the effect of exhibitions for deceased artists of various artistic sectors and time

periods could come to different results. Posthumous exhibitions may be associated with even

higher magnitudes than those for living artists, compounding on the death effect, different artistic

sectors other than the rapidly expanding contemporary are likely associated with smaller

increases in price due to exhibitions, and exhibitions during various historical periods may have

had less of an impact on the market since information on exhibitions could not be disseminated

as easily. Additionally, by looking at prices in more specified time ranges, such as month to month

within years, one could map how soon correlations with price increases appear surrounding

exhibitions.

Wilkinson 91

One could also examine the impact of having exhibitions in different global regions on the

effect of being sold both within the country of the exhibition and across the globe as a function

of impact on global and national artist reputations. Having an exhibition in Latin America could be

associated with different price increases than exhibitions in China, and it would be interesting to

tease out these differences. One could also determine how the number of artworks featured in

an exhibition or how many times a specific work has been featured in various exhibitions affects

art pricing. It might greatly increase the value of the exhibition on the artist’s prices or the value

of a specific work, but it would be equally interesting if it had no varying impact at all. Additionally,

weighting the various exhibitions by their amount of attendance, number of pieces the artist

exhibited, the amount of publicity surrounding the exhibition, or whether the exhibition was well-

received or not could also lend interesting insights into the development of artist acclaim on art

pricing.

Beyond exhibitions, other measures of artist acclaim, such as those originally collected by

Bongard for his artist ranking system, could lead to viable data: looking at an artist winning general

awards as well as prestigious awards (such as the Turner Prize), the amount in which the artist

was mentioned or reviewed well in various publications (the art section of or art-specific

magazines, newspapers, etc.), and which museum permanent collections of which their work is a

part effects on price. Another interesting factor would also be looking at the impact of artists’

various levels and kinds of education on the pricing – as a reflection of both their network and

their artistic influences – such as art historical training, fine arts training, MFA, working under a

master artist, etc.

These are just a few suggestions based in the vast potential of this under-developed

sector of cultural economics for further research; all of which could greatly impact understanding

of the development of art prices within the art market.

Wilkinson 92

Appendix 1. Museums and Galleries for Artist Sample Creation

List of Contemporary U.S. Museums for Artist Sample Art Institute of Chicago Brooklyn Museum LA MOCA Los Angeles County Museum of Art (LACMA) MassMOCA MoMA

Pompidou Tate The Soloman R. Guggenheim Walker Art Center Whitney

List of Galleries for Artist Sample Pace Marian Goodman

Gagosian Hauser & Wirth

David Zwirner

Appendix 2. Major Museums and Galleries List of Major Museums

Acropolis Museum ATHENS Art Gallery of Ontario TORONTO Art Gallery of South Australia ADELAIDE Art Institute of Chicago CHICAGO Australian Centre for Moving Image MELBOURNE British Museum LONDON Brooklyn Institute NEW YORK CaixaForum Barcelona BARCELONA CaixaForum Madrid MADRID Centre Pompidou PARIS Centro Cultural Banco do Brasil BELO HORIZONTE Centro Cultural Banco do Brasil BRASÍLIA Centro Cultural Banco do Brasil RIO DE JANEIRO Centro Cultural Banco do Brasil SÃO PAULO Cleveland Museum of Fine Arts CLEVELAND Dallas Museum of Art DALLAS Detroit Institute of Arts DETROIT Deutsches Historisches Museum BERLIN FAMSF SAN FRANCISCO Galleria degli Uffizi FLORENCE Galleria dell’Accademia FLORENCE Gallery of Modern Art GLASGOW Getty LOS ANGELES Grand Palais PARIS Guggenheim Museum BILBAO Guggenheim Museum NEW YORK Gyeongju National Museum GYEONGJU

Huntington Library SAN MARINO Imperial War Museum LONDON Instituto Tomie Ohtake SÃO PAULO Israel Museum JERUSALEM Istanbul Modern ISTANBUL Kelvingrove Art Gallery and Museum GLASGOW Kunsthistorisches Museum VIENNA LACMA LOS ANGELES Louisiana Museum of Modern Art HUMLEBÆK Louvre PARIS Martin-Gropius-Bau Museum BERLIN Meijer Gardens and Sculpture Park GRAND RAPIDS Metropolitan Museum of Art NEW YORK Minneapolis Institute of Arts MINNEAPOLIS MMCA GWACHEON MMCA SEOUL Montreal Museum of Fine Arts MONTREAL Mori Art Museum TOKYO Moscow Kremlin Museums MOSCOW MuCEM MARSEILLES Musée d’Orsay, PARIS Musée de l’Orangerie PARIS Musée du Quai Branly PARIS Musées Royaux des Beaux-Arts BRUSSELS Museo Nacional del Prado MADRID Museo Soumaya MEXICO CITY Museo Thyssen-Bornemisza MADRID Museu Nacional

Wilkinson 93

Museu Nacional d’Art de Catalunya BARCELONA Museu Picasso BARCELONA Museum of Fine Arts BOSTON Museum of Fine Arts HOUSTON Museum of Modern Art NEW YORK National Art Center Tokyo TOKYO National Folk Museum of Korea SEOUL National Galleries of Scotland EDINBURGH National Gallery LONDON National Gallery of Art, WASHINGTON, DC National Gallery of Australia CANBERRA National Gallery of Victoria MELBOURNE National Museum in Krakow KRAKOW National Museum of Korea, SEOUL National Museum of Scotland EDINBURGH National Museum of Western Art TOKYO National Palace Museum, TAIPEI National Portrait Gallery LONDON National Portrait Gallery/SAAM WASHINGTON Neues Museum BERLIN Österreichische Galerie Belvedere VIENNA Palais de Tokyo PARIS Palazzo Ducale VENICE Pergamonmuseum BERLIN

Philadelphia Museum of Art PHILADELPHIA Queensland Art Gallery/GoMA BRISBANE Reina Sofía MADRID Rijksmuseum AMSTERDAM Royal Academy of Arts LONDON Royal Ontario Museum TORONTO Saatchi Gallery LONDON Seattle Art Museum SEATTLE Serpentine Galleries LONDON Shanghai Museum SHANGHAI Somerset House LONDON State Hermitage Museum ST PETERSBURG State Tretyakov Gallery MOSCOW Stedelijk Museum AMSTERDAM Tate Britain LONDON Tate Modern, LONDON Teatre-Museu Dalí FIGUERES Tokyo National Museum TOKYO Triennale di Milano MILAN Ullens Center for Contemporary Art BEIJING Van Gogh Museum AMSTERDAM Vatican Museums, VATICAN CITY Victoria and Albert Museum LONDON Whitney Museum of Modern Art NEW YORK

List of Major Galleries Bitforms Blain Southern Castelli David Zwirner Gagosian Gavin Brown Gladstone Hauser Wirth

Lisson Luhring Augustine Marian Goodman Matthew Marks Maureen Paley Pace Paula Cooper Sadie Coles

Sonna Bend Stephen Friedman The Approach Victoria-Miro White Cube Yvon-Lambert

Appendix 3. Artists in Data Set

Abbas Kiarostami Abelardo Morell Adam Fuss Adam McEwen Adam Pendleton Adrian Ghenie Ai Weiwei Albert Oehlen Alberto Di Fabio Aleksandra Mir

Alessandro Pessoli Alex Israel Alexis Rockman Alfredo Jaar Alice Aycock Allan McCollum Amy Sillman Andrea Bowers Andreas Gursky Anj Smith

Anna Gaskell Anna Maria Maiolino Anri Sala Anselm Reyle Anslem Keifer Antonio Lopez Garcia Any Hope 1930 Arnold Odermatt Arturo Herrera Barbara Chase-Riboud

Wilkinson 94

Barry Frydlender Berlinde De Bruyckere Beth Campbell Bharti Kher Bill Viola Bjarne Melgaard Boris Mikhailov Bosco Sodi Brian Clarke Brice Marden Bridget Riley Bruce Davidson Bruce Nauman Cai Guo-Qiang Carlos Cruz-Diez Carol Bove Carrie Mae Weems Carsten Höller Carsten Nicolai Catherine Opie Cecilia Edefalk Cecily Brown Cerith Wyn Evans Chan Chao Charles LeDray Chiho Aoshima Chitra Ganesh Christian Jankowski Christian Marclay Christoph Ruckhäberle Christopher Williams Christopher Wool Chuck Close Collier Schorr Cory Arcangel Daidō Moriyama Damián Ortega Dan Cohen Dan Graham Dan Walsh Danh Vo Danny Lyon David Diao David Hockney David Robbins David Schnell Dawoud Bey Dayanita Singh

Diana Thater Dieter Rams Dike Blair Doris Salcedo Douglas Gordon Edmund de Waal Edward Burtynsky Eija-Liisa Ahtila El Anatsui Elad Lassry Elisa Sighicelli Elizabeth Peyton Ellen Gallagher Ernesto Neto Erwin Redl Ewan Gibbs Florian Maier-Aichen Francesco Vezzoli Francis Alÿs Frank Gohlke Fred Tomaselli Fred Wilson Gabriel Orozco Gary Hill Georg Baselitz Ghada Amer Giuseppe Penone Glenn Brown Glenn Ligon Gohar Dashti Gregory Crewdson Guillermo Kuitca Hai Bo Hans-Peter Feldmann Hellen van Meene Henry Wessel Hernan Bas Hiroshi Sugimoto Hirsch Perlman Hong Hao Howard Hodgkin Ian Wallace Ida Applebroog Iñigo Manglano-Ovalle Isa Genzken Jacqueline Humphries Jakub Julian Ziolkowski James Casebere

James Siena James Turrell James Welling Jamie Wyeth Jean-Michel Othoniel Jeff Wall Jennifer Bolande Jennifer Steinkamp Jenny Holzer Jenny Saville Jimmie Durham Jitish Kallat Jockum Nordström Joel Otterson Joel Sternfeld John Baldessari John Currin Jonas Wood Jordon Wolfson Joseph Grigely Joseph Kosuth Judy Chicago Julian Schnabel Julie Mehretu Kara Walker Karin Kneffel Kate Shepherd Katharina Wulff Katy Schimert Kehinde Wiley Keith Coventry Keith Edmier Keith Mayerson Keith Sonnier Keith Tyson Ken Lum Kerry James Marshall Kiki Smith Kim Sooja Konstantin Grcic Kristin Baker Lalla Essaydi Lawrence Weiner Lee Bontecou Lee Friedlander Lee Ufan Li Songsong Lisa Yuskavage

Wilkinson 95

Liu Jianhua Lois Conner Loretta Lux Loris Gréaud Lorna Simpson Louise Fishman Luc Tuymans Lucas Samaras Lucy McKenzie Luis Gispert Mai-Thu Perret Mamma Andersson Manolo Valdes Mao Yan Marc Newson Marcel Dzama Marcel Wanders Marilyn Minter Marina Abramović Mark Bradford Mark Cohen Mark Grotjahn Mark Manders Mark Wallinger Marlene Dumas Martin Creed Martin Kobe Martin Puryear Mary Heilmann Matthew Barney Matthew Day Jackson Matthias Weischer Maureen Gallace Maurizio Cattelan Maya Lin Meg Webster Michaël Borremans Michael Craig-Martin Michael Heizer Michael John Hunt Michael Raedecker Michael Wesely Michal Rovner Mickalene Thomas Miguel Ángel Rios Miwa Yanagi Mona Hatoum Monica Bonvicini

Monika Baer Monir Shahroudy Farmanfarmaian Mungo Thomson Nan Goldin Nancy Rubins Naoya Hatakeyama Neil Jenney Neo Rauch Nicholas Nixon Nigel Cooke Nilima Sheikh Olafur Eliasson Oscar Murillo Ouattara Watts Pablo Bronstein Pae White Paul Graham Paul Noble Paul P. Paul Sietsema Peter Campus Peter Halley Peter Lindbergh Phil Collins Philip Taaffe Philip-Lorca diCorcia Phyllida Barlow Piero Golia Pierre Huyghe Piotr Uklanski Pipilotti Rist Qiu Xiaofei R. (Robert) Crumb Rachel Feinstein Rachel Harrison Rachel Whiteread Rania Matar Raqib Shaw Rashid Johnson Raymond Pettibon Rebecca Warren Renzo Piano Ricci Albenda Richard Aldrich Richard Hawkins Richard Jackson Richard Long

Richard Misrach Richard Phillips Richard Prince Richard Serra Richard Tuttle Richard Wright Ridley Howard Rita Ackermann Robert Bechtle Robert Gober Robert Irwin Robert Lazzarini Robert Mangold Robert Morris Robert Ryman Robert Therrien Robert Whitman Rodney Graham Roe Ethridge Roger Hiorns Roman Signer Ron Arad Ron Mueck Roni Horn Ross Bleckner Roxy Paine Rudolf Stingel Ryan Gander Sabine Hornig Sally Mann Sam Taylor-Wood Sanford Biggers Sarah Morris Sean Scully Seth Price Shadi Ghadirian Sharon Lockhart Sheila Hicks Sherrie Levine Shirin Neshat Simryn Gill Song Dong Spencer Finch Stan Douglas Stephen Dean Steve Wolfe Subodh Gupta Sui Jianguo

Wilkinson 96

Susan Rothenberg Suzan Frecon Suzanne McClelland T.J. Wilcox Takashi Murakami Takesada Matsutani Tara Donovan Taryn Simon Tatiana Trouvé Terrance Koh Thomas Demand Thomas Houseago Thomas Nozkowski Thomas Ruff Thornton Dial Tilo Baumgärtel Tim Eitel Tim Gardner Tim Hawkinson

Timothy Greenfield-Sanders Toba Khedoori Todd Eberle Tomma Abts Tony Lewis Tony Tasset Tracey Moffatt Trenton Doyle Hancock Uta Barth Vera Lutter Vija Celmins Vik Muniz Wade Guyton Walton Ford Wang Guangle Wang Jianwei Wangechi Mutu Wilhelm Sasnal William Eggleston

William Kentridge William Pope.L William Wegman Xiao Yu Y.Z. Kami Yang Fudong Yayoi Kusama Yin Xiuzhen Yinka Shonibare Yto Barrada Yue Minjun Yutaka Sone Zaha Hadid Zak Smith Zeng Fanzhi Zhang Enli Zhang Huan Zhang Xiaogang Zilvinas Kempinas

Appendix 4. Sample Artist CV

Included beginning on the next page is a sample artist CV. The specific artist is Richard

Aldrich with his CV reported by Gladstone Gallery, the commercial gallery representation for the

artist. The information enclosed in the blue squares represents how I derived the biographical

information, and the red squares are a few examples in this CV of major exhibitions.

Biographical Information – Blue

Major Galleries – Green

Major Museums – Red

Wilkinson 97

Wilkinson 98

Wilkinson 99

Wilkinson 100

Wilkinson 101

Appendix 5: Variable Interpretations for Hedonic Impacts

An abbreviated version of the hedonic being used is given by:

0 1 2 3 4

0 1 2 4 3

[1] ln[ ] ln[ ][1] ln[ ]

in price exhibit workage area otherstuffout exhibit workage otherstuff area

α α α α αα α α α α

= = + + + += + + + +

This implies that:

0 1 2 3 4

0 1 2 4 3

ln[ ]

ln[ ]1

[2][2]

exhibit workage area otherstuff

exhibit workage otherstuff area

in price eout e

α α α α α

α α α α α α

+ + + +

+ + + +

= =

=

Thus the dollar-value impact of adding one extra exhibit should be the estimated parameter 1α

x (predicted price). This would be the change in price, so the percentage change in price would

be this divided by the predicted price, which just leaves 1α .

This means that if 1α =0.075, then the percentage impact on the value of the work from adding

one exhibition would be .075 or 7.5 percent. Thus if the change in the value of the work was $75

and the value of the work was $1000, the percent increase in value expressed as a decimal would

be:

75[4]1000

[4] 0.075

in N

out

= =

=

This increase is obviously 7.5% of the value.

For variables that enter as the logarithm (like area) the dollar-value impact would be:

0 1 2 4 3 ln[ ]

3

[5]

[5]

areaexhibit workage otherstuff area

in price

eoutarea

α α α α α

α+ + + +

= ∂

=

This gives the dollar value change in the work of art per unit of added area. To get the percentage

increase you would need to divide through by the predicted price as before.

For variables that enter in this form, it is easy to express the impact in the form of an elasticity.

Recall that:

33area

area areaelasticity price priceprice area price

α α= ∂ × = × =

Wilkinson 102

where the middle equality uses the partial derivative with respect to area obtained above, and the

final equality is the result after simplification.

Thus for variables that enter as logs (which here are all natural logarithms) the estimated

parameter is the elasticity, and thus when multiplied by 100 gives an approximation of the

percentage impact of doubling the characteristic (in this case area).

Appendix 6. Artist Coefficients (As Related to Chan Chao)

TABLE 16: Artist Coefficients Derived from Regression Model including All One-Year Lagged Exhibition Types (Table 7 Model 5)

Abbas Kiaostami 2.830** Mark Grotjahn 4.920*** (1.107) (1.094)

Abelardo Morell 2.150** Mark Manders 2.551 (1.090) (1.716)

Adam Fuss 2.914*** Mark Wallinger 2.571** (1.093) (1.128)

Adam McEwen 3.127*** Marlene Dumas 3.809*** (1.116) (1.096)

Adam Pendleton 2.248* Martin Creed 2.749*** (1.147) (1.061)

Adrian Ghenie 3.459*** Martin Kobe 2.281** (1.128) (1.102)

Ai Weiwei 3.297*** Martin Puryear 3.725*** (1.105) (1.114)

Albert Oehlen 2.964*** Mary Heimann 2.477** (1.091) (1.092)

Alberto Di Fabio 1.483 Matthew Barney 3.615*** (1.137) (1.099)

Aleksandra Mir 1.138 Matthew Day Jackson 3.549*** (1.142) (1.110)

Alessandro Pessoli 1.553 Matthias Weischer 3.143*** (1.137) (1.096)

Alex Israel 3.883*** Maureen Gallace 2.136** (1.103) (0.999)

Alexis Rockman 0.565 Maurizio Cattelan 3.601*** (1.117) (1.106)

Alfredo Jaar 2.174** Maya Lin 1.912 (1.102) (1.275)

Alice Aycock 0.270 Meg Webster -0.382 (1.172) (1.266)

Amy Sillman 1.782 Michael Heizer 1.303 (1.085) (1.074)

Andrea Bowers 0.832 Michael Raedecker 2.747** (1.213) (1.093)

Andreas Gursky 4.377*** Michal Rovner 2.722** (1.094) (1.093)

Andreas Hoffer 2.068* Michael Borremans 4.497***

Wilkinson 103

TABLE 16: Artist Coefficients Derived from Regression Model including All One-Year Lagged Exhibition Types (Table 7 Model 5)

(1.102) (1.106) Anj Smith 3.059*** Mikalene Thomas 2.473**

(1.125) (1.115) Anna Gaskell 2.151* Miguel Angel Rios 0.932

(1.096) (1.161) Anna Maria Maiolino 2.525** Miwa Yanagi 2.723**

(1.144) (1.091) Anri Sala 2.331** Mona Hotoum 2.731**

(1.041) (1.089) Anselm Kiefer 3.895*** Monica Bonicini 1.027

(1.090) (1.140) Anselm Reyle 2.519** Monika Baer -0.113

(1.090) (1.406) Antonio Lopez Garcia 4.251*** Monir Farmanfarmaian 2.442**

(1.107) (1.115) Arnold Odermatt 2.473** Mungo Thomson 1.503

(1.106) (1.050) Arturo Herrera 2.366** Nan Goldin 2.583**

(1.117) (1.084) Barry Frydlender 3.720*** Nancy Rubins 1.362

(1.110) (1.226) Berlinde De Bruycke 2.876*** Naova Hatakevama 2.451**

(1.046) (1.125) Beth Campbell -0.430 Neil Jenney 3.139***

(1.103) (1.089) Bharti Kher 3.144*** Neo Rauch 3.755***

(1.102) (1.095) Bill Viola 3.828*** Nicholas Nixon 2.670**

(1.142) (1.108) Bjarne Melgaard 1.452 Nigel Cooke 2.413**

(1.104) (1.115) Boris Mikhailov 3.128*** Nilima Sheikh 1.978*

(1.111) (1.113) Bosco Sodi 1.432 Olafur Eliasson 2.950***

(1.140) (1.113) Brice Marden 4.555*** Oscar Murillo 2.983***

(1.106) (1.102) Bridget Riley 3.577*** Ouattara Watts 0.303

(1.095) (1.142) Bruce Nauman 3.543*** Pablo Bronstein 2.642**

(1.082) (1.124) Carlos Cruz Diez 3.002*** Pae White 0.921

(1.103) (1.080) Carol Bove 2.706** Patty Chang 1.314

(1.106) (1.094) Carrie Mae Weems 2.554** Paul Graham 1.504

(1.087) (1.118) Carsten Holler 2.819** Paul Noble 2.526**

Wilkinson 104

TABLE 16: Artist Coefficients Derived from Regression Model including All One-Year Lagged Exhibition Types (Table 7 Model 5)

(1.109) (1.084) Carsten Nicolai 1.369 Paul P. 1.734

(1.099) (1.115) Catherine Opie 2.606** Paul Sietsema 2.867***

(1.098) (1.106) Cacilia Edefalk 2.969*** Peter Campus -0.381

(1.096) (1.135) Cecily Brown 3.593*** Peter Halley 2.121*

(1.102) (1.103) Cerith Wyn Evans 2.822** Peter Lindbergh 3.644***

(1.119) (1.095) Charles LeDray 2.739** Phil Collins 1.450

(1.134) (1.241) Chiho Aoshima 2.516** Philip Taafe 2.196**

(1.109) (1.093) Chirta Ganesh 1.899* Phyllida Barlow 0.815

(1.106) (1.096) Christian Marclay 2.929*** Pierre Huyghe 4.082***

(1.117) (1.200) Christoph Ruckhaberle 1.835* Piotr Uklanski 3.329***

(1.110) (1.113) Christopher Williams 3.558*** Pipilotti Rist 2.097*

(1.059) (1.089) Christopher Wool 3.788*** Qiu Xiaofei 2.288**

(1.092) (1.104) Chuck Close 3.673*** R. Crumb 3.029***

(1.095) (1.113) Cory Arcangel 3.428*** Rachel Feinstein 0.280

(1.145) (1.427) Daido Moriyama 2.826** Rachel Harrison 1.948*

(1.096) (1.073) Damian Ortega 2.606** Rachel Whiteread 3.285***

(1.108) (1.092) Dan Colen 3.458*** Rania Matar 1.107

(1.084) (1.093) Dan Graham 2.744** Raquib Shaw 3.244***

(1.090) (1.104) Dan Walsh 1.622 Rashid Johnson 2.593**

(1.101) (1.101) Danh Vo 3.518*** Raymond Pettibon 2.815***

(1.176) (1.071) David Hockney 3.568*** Rebecca Warren 2.731**

(1.102) (1.137) David Robbins 3.270*** Renzo Piano 1.100

(1.107) (1.123) Dawoud Bey 1.324 Ricci Albenda 1.163

(1.156) (1.100) Dayabita Singh 1.740 Richard Aldrich 1.655

Wilkinson 105

TABLE 16: Artist Coefficients Derived from Regression Model including All One-Year Lagged Exhibition Types (Table 7 Model 5)

(1.107) (1.110) Diana Thater 1.875* Richard Hawkins 1.196

(1.096) (1.143) Dike Blair -0.266 Richard Jackson 0.351

(1.459) (1.099) Doris Salcedo 3.043*** Richard Long 2.415**

(1.091) (1.092) Edmund de Waal 2.796** Richard Misrach 2.276**

(1.097) (1.096) Edward Burtynsky 2.705** Richard Philips 2.693**

(1.087) (1.098) Elad Lassry 3.597*** Richard Prince 4.065***

(1.106) (1.088) Elisa Sighicelli 2.296** Richard Serra 3.292***

(1.094) (1.088) Elizabeth Peyton 4.262*** Richard Tuttle 3.156***

(1.094) (1.098) Ellen Gallagher 3.104*** Richard Wright 2.169*

(1.115) (1.134) Ernesto Neto 1.461 Ridley Howard 0.789

(1.074) (1.120) Erwin Redl 0.101 Rita Ackermann 1.274

(1.111) (1.110) Francesco Vezoli 4.277*** Robert Bechtle 2.549**

(1.108) (1.157) Frank Gohlke 2.444** Robert Gober 4.282***

(1.121) (1.059) Fred Tomaselli 3.167*** Robert Irwin 2.141*

(1.102) (1.198) Fred Wilson 2.302** Robert Lazzarini 3.727***

(1.151) (1.137) Gabriel Orozvo 3.353*** Robert Mangold 3.009***

(1.065) (1.078) Gary Hill 2.017* Robert Morris 2.041*

(1.088) (1.067) Georg Baselitz 3.734*** Robert Ryman 4.453***

(1.092) (1.096) Ghada Amer 2.605** Robert Therrien 2.722**

(1.092) (1.070) Giuseppe Penone 2.994*** Robert Whitman 1.045

(1.095) (1.100) Glenn Brown 4.166*** Rodney Graham 2.831***

(1.113) (1.062) Glenn Ligon 3.826*** Roe Ethridge 2.151**

(1.110) (1.052) Gohar Dashti 2.728** Roger Hiorns 0.497

(1.096) (1.105) Gregory Crewdson 2.830*** Roman Signer 2.155**

Wilkinson 106

TABLE 16: Artist Coefficients Derived from Regression Model including All One-Year Lagged Exhibition Types (Table 7 Model 5)

(1.086) (1.075) Guillermo Kuitca 2.794** Ron Mueck 5.245***

(1.091) (1.091) Hellen van Meene 2.237** Roni Horn 3.511***

(1.124) (1.069) Henry Wessel 2.832*** Ross Bleckner 2.075*

(1.093) (1.097) Hernan Bas 2.935*** Roxy Paine 1.296

(1.107) (1.102) Hiroshi Sugimoto 3.843*** Rudolf Stingel 3.736***

(1.091) (1.092) Hirsch Perlman 0.748 Ryan Gander 2.991***

(1.107) (1.098) Howard Hodgkin 3.003*** Sally Mann 3.269***

(1.095) (1.097) Ian Wallace 1.487 Sanford Biggers 1.435

(1.128) (1.099) Isa Genzken 2.313** Sarah Morris 1.952*

(1.093) (1.082) Jacqueline Humpries 1.022 Sean Scully 3.648***

(1.086) (1.084) Jakub Julian Ziolkowski 2.013* Seth Price 3.227***

(1.105) (1.131) James Casebere 2.467** Shadi Ghadirian 2.267**

(1.088) (1.098) James Siena 2.995** Sharon Lockhart 2.013*

(1.180) (1.106) James Welling 2.219** Sheila Hicks 3.599***

(1.091) (1.248) Jeff Wall 4.165*** Sherrie Levine 3.361***

(1.033) (1.088) Jennifer Bartlett 2.582** Shirin Neshat 3.643***

(1.103) (1.088) Jennifer Bolande -0.436 Simryn Gill 2.675**

(1.249) (1.154) Jennifer Steinkamp 2.170* Song Dong 4.010***

(1.127) (1.162) Jenny Saville 3.794*** Spencer Finch 1.974*

(1.131) (1.125) Jimmie Durham 1.635 Stan Douglas 2.179**

(1.109) (1.098) Jitish Kallat 2.157** Stephen Dean -0.728

(1.095) (1.102) Jockum Nordstrom 2.957*** Sterling Ruby 2.683**

(1.082) (1.112) Joel Otterson -0.537 Steve Wolfe 4.024***

(1.195) (1.095) Joel Sternfeld 2.492** Sui Jianguo 1.816

Wilkinson 107

TABLE 16: Artist Coefficients Derived from Regression Model including All One-Year Lagged Exhibition Types (Table 7 Model 5)

(1.096) (1.122) John Baldessari 3.121*** Susan Rothenberg 3.050***

(1.093) (1.113) John Currin 4.502*** Suzan Frecon 2.818***

(1.101) (1.064) Jonas Wood 3.358*** Suzanne McClelland 0.316

(1.123) (1.113) Jordan Wolfson 3.041** T.J. Wilcox 1.671

(1.264) (1.173) Joseph Kosuth 3.491*** Takashi Murakami 2.954***

(1.113) (1.101) Judy Chicago 1.580 Takesada Matsutani 1.459

(1.208) (1.123) Julian Schnabel 2.516** Tara Donovan 2.464**

(1.089) (1.145) Julie Mehretu 3.990*** Taryn Simon 2.439**

(1.114) (1.096) Kara Walker 3.173*** Tatiana Trouve 2.378**

(1.111) (1.124) Karin Kneffel 2.986*** Thomas Demand 3.587***

(1.110) (1.096) Kate Shepherd 1.014 Thomas Houseago 2.822**

(1.133) (1.096) Katharina Wulff 0.789 Thomas Nozkowski 1.680

(1.196) (1.149) Katy Schimert -0.402 Thomas Ruff 3.109***

(1.106) (1.082) Kehinde Wiley 2.046* Thornton Dial 1.339

(1.108) (1.099) Keith Coventry 1.784 Tilo Baumgartel 1.487

(1.090) (1.111) Keith Edmier 0.581 Tim Gardner 2.684***

(1.085) (1.033) Keith Mayerson 0.206 Tim Hawkinson 2.120*

(1.008) (1.116) Keith Sonnier 0.896 Toba Khedoori 2.695**

(1.090) (1.146) Keith Tyson 2.241** Todd Eberle 1.309

(1.100) (1.102) Ken Lum 1.502 Tomma Abts 2.316**

(1.126) (1.104) Kerry James Marshall 3.321*** Tony Lewis 3.042***

(1.108) (1.114) Kiki Smith 2.689** Tony Tasset -0.359

(1.089) (1.196) Kristin Baker 1.590 Tracey Moffatt 2.780**

(1.131) (1.099) Lalla Essaydi 2.869*** Trenton Doyle Hancock 1.629

Wilkinson 108

TABLE 16: Artist Coefficients Derived from Regression Model including All One-Year Lagged Exhibition Types (Table 7 Model 5)

(1.096) (1.016) Lawrence Weiner 2.414** Uta Barth 2.455**

(1.091) (1.099) Lee Ufan 3.530*** Vera Lutter 2.991***

(1.098) (1.097) Li Songsong 3.052*** Vija Celmins 4.588***

(1.105) (1.096) Lisa Yuskavage 3.733*** Vik Muniz 3.376***

(1.104) (1.092) Liu Jianhua 2.598** Wade Guyton 4.090***

(1.118) (1.113) Lois Conner 1.254 Walton Ford 3.932***

(1.144) (1.129) Loretta Lux 3.694*** Wang Jianwei 1.972

(1.097) (1.204) Loris Greaud 2.753** Wangechi Mutu 3.506***

(1.327) (1.102) Lorna Simpson 2.351** Wilhelm Sasnal 2.817**

(1.111) (1.098) Louise Fishman 0.949 William Eggleston 3.685***

(1.109) (1.097) Luc Tuymans 3.681*** William Wegman 3.542***

(1.090) (1.102) Lucas Samaras 3.031*** William Pope.L 2.984***

(1.095) (1.110) Lucy McKenzie 1.715 William Wegman 2.376**

(1.136) (1.087) Luis Guispert 1.501 Y.Z. Kami 2.409**

(1.263) (1.108) Mamma Andersson 3.581*** Yang Fudong 2.628**

(1.099) (1.106) Manolo Valdes 3.545*** Yin Xiuzhen 2.486**

(1.097) (1.153) Mao Yan 3.225*** Yinka Sonebare 2.387**

(1.106) (1.106) Marc Newson 2.317** Yue Minjun 3.768***

(1.156) (1.093) Marcel Dzama 2.055* Yutaka Sone 0.693

(1.079) (1.127) Marilyn Minter 2.771** Zak Smith 1.528

(1.111) (1.149) Marina Abramovic 2.684** Zeng Fanzhi 4.324***

(1.105) (1.097) Mark Bradford 4.032*** Zhang Enli 2.841**

(1.114) (1.112) Mark Cohen 2.240** (1.140)

Wilkinson 109

Appendix 7. Country-of-Sale Coefficients (As Related to the Czech Republic)

TABLE 17: Country-of-Sale Coefficients Derived from Regression Model including All One-Year Lagged Exhibition Types (Table 7 Model 5)

Argentina 0.613746 Monaco 1.195216 Australia 0.90206 Morocco 1.441701 Austria 0.627217 Netherlands 0.391874 Belgium 0.182578 New Zealand 0.4426396 Brazil 1.026006 Norway 1.045957 Canada 0.286355 Poland 0.0757893 China 1.277294 Portugal -0.1271182 Denmark 0.283063 Qatar 1.963208 Finland 1.520473 Scotland 0.7249248 France 0.429396 Singapore 0.766605 Germany 0.284184 South Africa 0.6901593 Greece 1.361064 South Korea 0.862387 Hong-Kong 1.557287 Spain 0.5284923 India 1.23107 Sweden 0.6177145 Iran 2.27052 Switzerland 0.3940181 Ireland 0.963768 Taiwan 1.005599 Israel 0.940064 Turkey 1.364264 Italy 0.159211 United Arab Emirates 1.695597 Japan 0.474596 United Kingdom 1.05325 Korea 1.102416 United States 1.072447 Lebanon 2.767187 Venezuela 0.4951418 Mexico 0.686054

Wilkinson 110

Appendix 8. Year-of-Sale Coefficients (As Related to 1987)

TABLE 18: Year-of-Sale Coefficients Derived from Regression Model including All One-Year

Lagged Exhibition Types (Table 7 Model 5) 1988 0.822464 2002 0.613508 1989 1.268195 2003 0.694102 1990 0.781717 2004 0.893532 1991 0.326576 2005 0.947712 1992 0.248758 2006 1.06055 1993 0.274722 2007 1.348444 1994 0.153489 2008 1.208048 1995 0.102972 2009 0.822592 1996 0.239986 2010 1.04031 1997 0.244995 2011 1.037087 1998 0.35771 2012 1.057049 1999 0.299338 2013 1.12079 2000 0.498954 2014 1.149897 2001 0.449226 2015 1.072917

Wilkinson 111

Appendix 9. Robustness Check: Extra Hammer Prices

Table 19 above represents a robustness check of the results presented in Section 6

through the inclusion of extra hammer prices to attempt to get rid of some of the bias in the data.

The data for much of this analysis only looks at artworks which were successfully sold at auction,

and therefore have a hammer price. In contrast, this analysis includes more auction sales by

counting artworks that were bought-in by making 75 percent of their reported low estimate the

hammer price for analysis. While it is not entirely accurate, as spelled out in Ashenfelter (2011),

and therefore cannot be used as a substitute for all models, it is useful for checking the results

and including further previously excluded observations. Looking to Table 19, while the coefficients

are biased lower to a small degree, these results of the impact of exhibition counts over the last

ten years largely uphold the results found in the rest of the analysis.

Wilkinson 112

Appendix 10. No Artist Fixed Effects Models and Why They Are Not Used There are many reasons why artists fixed effects are necessary indicator variables in the

models included in this analysis. There are three main reasons for this necessity. First, using artist

indicator variables is similar to the widely accepted and employed method of using tract or block

group indicators in housing price models, and a minor theme of this thesis is that analysis of the

market for art is, like housing, analysis of a market for a heterogeneous good with complex

characteristics. Second, including artist indicator variables is standard practice in models that

present such estimates. Galenson's paper in the Journal of Political Economy, all of his NBER

working papers on the subject, and almost all of the other papers on the topic do this. Finally,

artist indicator variables (like tract or block group indicator variables in housing markets) are a

useful technique for accounting for variables that: a) Are important determinants of price, b) Are

not recorded separately in the available data, and c) Do not vary (or do not vary too much) for a

set value of the indicator variable.

For example, we know that the size, medium, age of the work (or age of the artist when

the work was done) support material, etc. are important determinants of market price. So are the

style of the art (abstract or representational) and the execution of the work in a manner that

makes it instantly recognizable as the work of a particular artist. This can range from reliance on

a particular palette of colors, brush technique, etc. Whatever the qualities that make us look at a

work and say, "Oh, that must be the work of artist x."

Unfortunately, most art market data do not include information on color palette, brush

style, or even a reliable description that can reveal whether the work is abstract or a depiction of

some recognizable object. In this situation, the economist is confronted with moving forward to

estimate a model that is clearly corrupted by omitted variable bias, or to make a working

assumption that the value of these unmeasured variables will be constant for a given artist (at

least for those works that are in sufficient demand to be observed in auction sales). Conditional

on the truth of this hypothesis (point (c) above) then using an indicator variable for artist's name

will represent (we sometimes say "absorb") the variance in market price that is due to these

unmeasured values, and permit unbiased estimation of the impact on market price of size and

other characteristics.

Previous work made this assumption and used artist indicator variables, but there was at

least one variable that was NOT likely to have a fixed value for a given artist: the number of

exhibitions of the artist's work. This number will naturally change over the course of an artist's

Wilkinson 113

career and change more for some than for others. This thesis research is the very first to address

and try to correct this problem. Not only will it permit a better estimate of an art price hedonic

(useful for valuation and understanding the market) but interesting in its own right for what it can

teach us about the differences between museums and galleries and the incentives that these

institutions have to go to the trouble and expense of arranging these exhibitions. To exclude artist

indicator variables would be to discard a central contribution of this thesis, and accepting

increased omitted variable bias (from the lack of information on unmeasured style features that

characterize an artist's work).

However, while for all of these reasons artist indicator variables were a necessary function

of the thesis model, it is interesting to explore this model which does not incorporate them in

order to also explore the effects of other aspects of the artist, such as their gender, whether or

not they have an MFA, and their country of residence, without confusing the interpretation of

variable coefficients.

While this table represents a model which still includes indicator variables for medium,

area, and the age of the work at time of sale as incorporated in other models, since these variables

are largely consistent, they have been excluded from the output for the sake of brevity. As

expected, the variable coefficients are biased upwards from the models with the artist fixed

effects and the adjusted R-squared values lower, capturing 10-20 percent less variance than the

models with the fixed effects, supporting the concept that these values are necessary to the

model in capturing unobservable elements. Yet, this model is useful in looking at variables which

would be otherwise captured by the artist fixed effects.

TABLE 20: 1-Year Lagged Major Exhibition Counts Correlations with Price without Artist Indicator Variables

OLS Regression, Clustered Standard Errors by Auction House (1) (2) (3) (4) (5) VARIABLES Model 1 Model 2 Model 3 Model 4 Model 5 MMsoloLag1 0.189*** 0.127***

(0.0185) (0.0157) MGsoloLag1 0.121*** 0.0652***

(0.0125) (0.0145) MMgroupLag1 0.0907*** 0.0691***

(0.00789) (0.00685) MGgroupLag1 0.106*** 0.0684***

(0.0129) (0.0124) female -0.0776 -0.0356 -0.189*** -0.199*** -0.159***

(0.0748) (0.0845) (0.0494) (0.0509) (0.0499)

Wilkinson 114

TABLE 20: 1-Year Lagged Major Exhibition Counts Correlations with Price without Artist Indicator Variables

OLS Regression, Clustered Standard Errors by Auction House MFA -0.290*** -0.309*** -0.279*** -0.273*** -0.266***

(0.0405) (0.0426) (0.0377) (0.0357) (0.0356) NYCres 0.121*** 0.0650 0.205*** 0.174*** 0.189***

(0.0470) (0.0479) (0.0520) (0.0515) (0.0515) Parisres -0.471*** -0.481*** -0.467*** -0.420*** -0.455***

(0.0630) (0.0665) (0.0612) (0.0635) (0.0599) Berlinres -0.875*** -0.835*** -0.791*** -0.825*** -0.812***

(0.0730) (0.0764) (0.0646) (0.0714) (0.0675) LAres 0.118 0.0740 0.165* 0.123 0.159

(0.0957) (0.0935) (0.0959) (0.0964) (0.0967) Londonres -0.0312 -0.0915 0.0888 -0.0406 0.0914

(0.0807) (0.0764) (0.0850) (0.0815) (0.0866) Albanian 1.992*** 1.977*** 4.696*** 4.769*** 4.636***

(0.538) (0.530) (0.262) (0.275) (0.263) American 1.050** 1.091** 3.834*** 3.772*** 3.732***

(0.483) (0.473) (0.130) (0.130) (0.128) Argentinian 0.574 0.613 3.424*** 3.336*** 3.372***

(0.496) (0.489) (0.161) (0.162) (0.157) Australian 0.907* 0.944* 3.564*** 3.593*** 3.500***

(0.509) (0.499) (0.226) (0.226) (0.226) Austrian -1.476*** -1.393*** 1.320*** 1.232*** 1.293***

(0.493) (0.486) (0.0625) (0.0625) (0.0595) Belgian 1.630*** 1.658*** 4.470*** 4.454*** 4.350***

(0.487) (0.476) (0.141) (0.138) (0.139) Brazilian 1.127** 1.247*** 3.773*** 3.832*** 3.754***

(0.487) (0.478) (0.106) (0.109) (0.108) Canadian 0.591 0.617 3.389*** 3.311*** 3.313***

(0.486) (0.477) (0.135) (0.134) (0.135) Canadian-Amer 0.912* 0.955* 3.797*** 3.685*** 3.749***

(0.529) (0.523) (0.275) (0.274) (0.270) Chilean -0.0464 0.0473 2.641*** 2.577*** 2.612***

(0.510) (0.499) (0.209) (0.219) (0.222) Chinese 1.601*** 1.594*** 4.504*** 4.392*** 4.414***

(0.477) (0.465) (0.175) (0.174) (0.173) Columbian 1.320*** 1.466*** 4.243*** 4.235*** 4.085***

(0.492) (0.482) (0.135) (0.133) (0.138) Cuban 0.398 0.481 3.179*** 3.144*** 3.128***

(0.504) (0.495) (0.181) (0.176) (0.180) Danish 1.997*** 2.073*** 4.800*** 4.764*** 4.691***

(0.513) (0.505) (0.211) (0.217) (0.214) Dutch 1.431*** 1.478*** 4.371*** 4.454*** 4.292***

(0.504) (0.494) (0.173) (0.174) (0.172) Egyptian 0.860* 0.871* 3.699*** 3.693*** 3.615***

(0.496) (0.486) (0.171) (0.171) (0.168) English 1.118** 1.186** 3.932*** 3.943*** 3.822***

Wilkinson 115

TABLE 20: 1-Year Lagged Major Exhibition Counts Correlations with Price without Artist Indicator Variables

OLS Regression, Clustered Standard Errors by Auction House (0.485) (0.476) (0.117) (0.117) (0.117)

Nigerian 0.482 0.662 3.289*** 3.339*** 3.178*** (0.506) (0.504) (0.205) (0.211) (0.191)

Ethiopian 2.111*** 2.119*** 5.038*** 4.954*** 4.876*** (0.498) (0.491) (0.144) (0.151) (0.149)

French 1.863*** 1.925*** 4.513*** 4.615*** 4.444*** (0.719) (0.698) (0.430) (0.520) (0.468)

French-Am -2.740*** -2.667*** (0.494) (0.486)

German 1.482*** 1.471*** 4.305*** 4.225*** 4.224*** (0.483) (0.474) (0.123) (0.122) (0.122)

Greek 0.685 0.714 3.355*** 3.211*** 3.211*** (0.497) (0.491) (0.151) (0.151) (0.149)

Hungarian -0.661 -0.617 2.256*** 2.144*** 2.182*** (0.518) (0.511) (0.205) (0.208) (0.202)

Indian 0.734 0.762 3.616*** 3.570*** 3.549*** (0.497) (0.487) (0.212) (0.214) (0.210)

Israeli 0.503 0.482 3.335*** 3.229*** 3.227*** (0.488) (0.479) (0.142) (0.137) (0.140)

Iranian 1.575*** 1.656*** 4.423*** 4.403*** 4.314*** (0.490) (0.481) (0.150) (0.148) (0.145)

Iraqi 1.418*** 1.461*** (0.528) (0.520)

Israeli 1.389*** 1.478*** 4.321*** 4.247*** 4.266*** (0.512) (0.505) (0.152) (0.152) (0.146)

Italian 1.490*** 1.524*** 4.326*** 4.213*** 4.241*** (0.487) (0.478) (0.154) (0.155) (0.151)

Italian-Brazilian 0.728 0.717 3.646*** 3.615*** 3.601*** (0.560) (0.559) (0.280) (0.301) (0.279)

Ivorian -1.808*** -1.734*** 0.985*** 0.885*** 0.960*** (0.541) (0.529) (0.229) (0.234) (0.231)

Japanese 1.128** 1.188** 3.897*** 3.816*** 3.771*** (0.485) (0.475) (0.145) (0.146) (0.141)

Kenyan 1.626*** 1.722*** 4.488*** 4.534*** 4.404*** (0.510) (0.503) (0.244) (0.238) (0.233)

Korean 1.721*** 1.721*** 4.831*** 4.600*** 4.685*** (0.490) (0.480) (0.158) (0.160) (0.154)

Latvian 2.450*** 2.541*** 5.313*** 5.285*** 5.230*** (0.497) (0.485) (0.165) (0.173) (0.171)

Lebanese 1.053** 1.093** 3.854*** 3.874*** 3.742*** (0.515) (0.506) (0.220) (0.221) (0.220)

Lithuanian 0.963* 1.158** 3.856*** 3.648*** 3.733*** (0.550) (0.547) (0.299) (0.290) (0.291)

Mexican 1.319*** 1.465*** 4.172*** 4.030*** 4.025***

Wilkinson 116

TABLE 20: 1-Year Lagged Major Exhibition Counts Correlations with Price without Artist Indicator Variables

OLS Regression, Clustered Standard Errors by Auction House (0.495) (0.490) (0.184) (0.183) (0.173)

Moroccan 1.311*** 1.390*** 4.169*** 4.093*** 4.095*** (0.502) (0.494) (0.174) (0.181) (0.172)

Norwegian -0.519 -0.505 2.306*** 2.224*** 2.249*** (0.509) (0.499) (0.180) (0.184) (0.178)

Polish 0.959* 0.969* 3.766*** 3.653*** 3.682*** (0.511) (0.503) (0.185) (0.192) (0.188)

Scotish -0.359 -0.262 2.702*** 2.679*** 2.578*** (0.565) (0.542) (0.279) (0.253) (0.290)

Serbian 0.766 0.757 3.594*** 3.647*** 3.538*** (0.513) (0.505) (0.205) (0.198) (0.205)

South African 1.256** 1.329*** 4.295*** 4.180*** 4.110*** (0.498) (0.488) (0.159) (0.161) (0.158)

Spanish 1.450*** 1.542*** 4.275*** 4.200*** 4.250*** (0.494) (0.486) (0.131) (0.132) (0.131)

Swedish 1.111** 1.093** 4.110*** 3.976*** 4.011*** (0.490) (0.481) (0.140) (0.139) (0.137)

SwedAm -0.662 -0.585 2.108*** 2.257*** 2.058*** (0.585) (0.575) (0.345) (0.363) (0.350)

Swiss 0.304 0.300 3.189*** 3.084*** 3.070*** (0.507) (0.501) (0.190) (0.196) (0.195)

Ukranian 1.705*** 1.679*** 4.426*** 4.393*** 4.438*** (0.542) (0.541) (0.256) (0.256) (0.259)

Venezuelan 1.421*** 1.372*** 4.202*** 4.005*** 4.046*** (0.489) (0.479) (0.144) (0.147) (0.142)

Welsh 1.098* 1.220** 3.917*** 3.869*** 3.763*** (0.576) (0.576) (0.332) (0.336) (0.321)

Controls (Table 3) YES YES YES YES YES Country of Sale, Year of Sale FE YES YES YES YES YES Constant 2.786*** 2.716*** -0.0958 0.130 -0.0266

(0.548) (0.533) (0.275) (0.291) (0.276)

Observations 46,718 46,718 42,099 42,099 42,099 R-squared 0.623 0.621 0.630 0.629 0.633

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Specifically, this model examines variables such as indicator variables for whether or not the artist

is female, has an MFA, lives in NYC, London, Berlin, Paris, or LA respectively, and the artist’s

nationality, here represented by the country names. Again, while there are not any artist fixed

effects involved in this analysis since they would capture this biographical information, it still

Wilkinson 117

means that some of these estimates can be biased by which artist is included in each category.

Thus, these estimates, particularly for residence and country should be considered carefully since

they are directly affected by artist omitted variable bias. Yet, some interesting results do emerge

from this analysis in terms of this biographical information.

According to this analysis, if an artist is female, this does not impact their prices in Models

1 and 2, looking at major solo exhibitions. However, in Models 3-5, which have the major group

measures and the total model with all of the exhibition variables, being a female artist is

correlated with an 15.6 to 18.9 significant decrease in price. Here, one can see the sexism in the

art world expressed in Section 6.5, in the interaction term analysis. Having an MFA as an artist

corresponds to a 26.6 to 29.0 decrease in price across all models, which would support the idea,

also discussed in Section 6.5, that not only is an MFA unnecessary to an artistic career, but it could

be detrimental.

Looking to the correlations of artist residency to their art prices, both New York City and

Los Angeles in the United States are associated with large price premiums of 15 to 20 and 28 to

32 percent price increases respectively. This in itself is an interesting result since, while one would

expect being based in the U.S. to have a positive impact on price since it is now largely considered

the global art center, one might expect that New York City to have larger premiums than Los

Angeles since this tends to be more the global art center. Additionally, more artists as a whole live

in New York City, although since there are not artist fixed effects, this might be part of the reason

the premium for NYC is lower – since it has a higher range of people or a higher percentage of

lower quality artists attempting to “make it” in New York than LA. For Models 1,3, and 5, being

based in London has an associated 24 to 32 percent increase in price. This is also somewhat to be

expected since this is a location of high volumes and quality of auction sales. In contrast, being

based in Paris or Berlin comes with extremely large, 50 to 90 percent, negative correlations.

In terms of the nationalities of artists, some of the coefficients are higher or lower than

one might expect. However, this may, in part, be because some of them basically act as proxies

for the artist fixed effects since Nigeria or Albanian may only represent a single artist. In this sense,

the trends represented in this table are interesting, but should not necessarily be considered

entirely accurate even in terms of correlations.

Wilkinson 118

Appendix 11. Models with Career Exhibition Variables While career-to-date variables are not included in the main body of the results, this does

not mean that exhibitions over the course of an artist’s career up to the year of sale not worthy

of study. It just means that the initial limited attempts to analyze these effects came to relatively

weak significance levels. More important than that, I felt that these results were not indicative of

the overall price correlations for career-to-date levels of exhibitions, and that further research

would be necessary to fully flesh out these differences. For example, weighting career-to-date

variables closer to the time of the exhibit or exploring various quadratic or logarithmic forms of

the variable might be able to more closely approximate these career-to-date exhibition

correlations. However, I still felt that these initial attempts to understand these correlations were

worthy of noting here. These models do not include the other cumulative variables or lagged

variables as they would overlap with the career-to-date variables. This would create collinearity

which would distort the estimates in the reported regressions and have thus been left out.

11.1 Career-to-Date Models

This first model looks at career-to-date variables. As in the tables throughout the analysis

presented in this paper, each of the models is first run with the four different types of major

exhibitions, and then a total model with them altogether. The career-to-date variables are a count

of all of the reported exhibition type from the start of the artist’s career up until the year of sale.

Thus, this includes contemporaneous exhibition counts within the variable type.

As seen in Table 21 below, these career-to-date variables are significant for major solo

museum exhibitions, solo gallery exhibitions, and group gallery exhibitions. For major group

museum shows, the coefficient is both not statistically significant and negative. The only variable

which retains its significance in this model is the group major gallery exhibition, significant at the

one percent level and representing about a 1.6 percent associated increase in price. These results

are actually somewhat close to the contemporaneous model of exhibitions in Table 7 of the

results section in terms of significance, although their coefficients have less magnitude as would

be expected.

Wilkinson 119

Wilkinson 120

11.2 Career-to-Contemporaneous Models

The model included here is career-to-contemporaneous, or all of the exhibitions

accumulated by an artist for each exhibition type up to the year prior to the year of sale. In a

sense, it may be considered the one-year lagged form of the career-to-date variable. In this model,

again, we see that the solo exhibitions and group major gallery exhibitions are strongly significant

in their individual models. However, here, both gallery exhibition types maintain their significance

in the total model. This is a somewhat surprising result since, throughout the rest of the analysis,

we have found that museums tend to be the greater, more significant indicators of price. Results

such as these are worthy of being teased out to explain these discrepancies in another analysis

which focuses on career totals’ associations with price.

Wilkinson 121

Appendix 12. Alternative Clustering for Standard Errors

If one thinks about the process that generates random fluctuation in auction outcomes,

it seems less likely to be the artist and more likely to be circumstances of the auction which effects

the clustering of errors. This is why this analysis looks at models clustered by the auction house.

However, other forms of clustering, such as using country of sale, year of sale, nationality of the

artist, or artist name were examined as potential forms of clustering and are presented here.

Many of them have similar point estimates to those included in Section 6, but some of them have

variations on the significance levels for each exhibition type. Overall, however, these differing

cluster types largely support the results included in the main body of the paper.

12.1 Basic Robust Clustering

The point estimates associated with this small amount of general error clustering do not

appear significantly different from those estimated by the models clustered by auction house. The

main difference here is the levels of significance. Particularly for the gallery estimates, this model

shows them to be more significant. The group major gallery exhibitions are significant at the one

Wilkinson 122

percent level in their single model and maintain at least a weak significance at the ten percent

level in the total model. Additionally, solo major gallery exhibitions have a slightly higher

associated significance in these models.

12.2 Country of Sale Clustering

When the regressions are clustered by the country of sale, the estimated point values

tend to be biased a bit higher than in the auction error clustered data. Solo major galleries are

associated with a smaller statistical significance both when run alone and in their single model. In

contrast, group major galleries are associated with higher levels of significance.

Wilkinson 123

12.3 Year of Sale Clustering

When the regressions are clustered by the year of sale, the estimated point values tend

to be biased significantly higher than in the auction error clustered data (3 percent higher for solo

major museums, 2 percent for solo major gallery, and about 1 percent for both group exhibition

types). However, the only variable which maintains high levels of significance across both models

of its inclusion are solo major museum exhibitions. In the models where only single variable types

are included, other than solo major museum exhibitions, only solo major gallery exhibitions are

significant, and they lose significance in the final model.

Wilkinson 124

11.4 Artist Clustering without Artist Fixed Effects

The artist name may not be the most sensible grouping to think of for clustering standard

errors since if we think about the process that generates random fluctuation in auction outcomes,

it seems less likely to be the artist and more likely to be circumstances of the auction. Additionally,

it is generally not considered in good form to cluster by errors with the inclusion of artist indicator

variables. However, it seemed worth inclusion here for some of the general outcomes of this

method of clustering. Without artist fixed effects and with artist error clustering, the results are

biased far higher than any other model in comparison to the regression results included in Section

6.

Wilkinson 125

11.5 Artist Clustering without Artist Fixed Effects

In these models, which include artist error clustering and artist fixed effects, the

estimated results are similar in magnitude, although slightly lower, and with lower associated

significance levels than the main results included in Section 6. Here, in the models with each

exhibition run separately, similar to the man results, both solo exhibition types and group major

gallery shows are significant. Here, even in the single model with group major gallery exhibitions

is insignificant. Then, in the total model, only the museum exhibition types retain weak

significance levels.

Wilkinson 126

11.6 Nationality Clustering

While nationality may have a similar problem as the artist in terms of clustering errors

based on artist information rather than aspects of the auction itself as the process of random

fluctuation, the results of this clustering are included here as well.

In these models, the estimated results are similar in magnitude, although slightly lower,

than the main results included in Section 6. However, the significance levels are similar. In the

models with each exhibition run separately, similar to the man results, both solo exhibition types

and group major gallery shows are significant while, even in the single model, group major gallery

exhibitions is insignificant. Then, in the total model, the solo types and group major gallery

exhibitions are all strongly significant while group major galleries are not. Thus, this clustering

type is probably closest, of those examined, to the reported results.

Wilkinson 127

References

Anderson, R. C. (1974), Paintings as an investment, Economic Inquiry 12, 13-25.

Armstrong, R., Marshall, R., & Philips, L. (1989). Catalogue 1989 Biennial Exhibition. Whitney

Museum of American Art. Ashenfelter, O., and K. Graddy (2006). Art Auctions, in Hand-

book of the Economics of Art and Culture, Vol. 1, edited by V. A. Ginsburgh and D. Throsby.

Elsevier, pp 909–46.

Artprice. (2014). 2014 Art Market Report. Retrieved from http://imgpublic.artprice.com/pdf/

rama2014en.pdf

Ashenfelter, O., & Graddy, K. (2006). Art auctions. Handbook of the Economics of Art and

Culture, 1, 909-945.

Bailey, M., J.F. Muth, & H.O. Nourse (1963). A regression method for real estate price index

construction, Journal of the Americn Statistical Association 58, 933-942.

Bakhouche, A., & Thebault, L.P. (2011). What Determines Cézanne’S Art Pricing? A Hedonic

Regression Method. Analele Stiintifice ale Universitatii" Alexandru Ioan Cuza" din Iasi-

Stiinte Economice, 58, 515-532.

Baumol, W.J. (1986). Unnatural value: or art investment as floating crap game.The American

Economic Review, 10-14.

Bonus, H., & Ronte, D. (1997). Credibility and economic value in the visual arts. Journal of

Cultural Economics, 21(2), 103-118.

Bonus, H., & Ronte, D. (1993). I. Rechenhaftigkeit, Vertrauen und Leidenschaft 1.

Rechenhaftigkeit. Hamburger Jahrbuch für Wirtschafts-und Gesellschaftspolitik, 38, 243.

Wilkinson 128

Candela, G., & Scorcu, A. E. (2001). In search of stylized facts on art market prices: Evidence from

the secondary market for prints and drawings in Italy. Journal of Cultural Economics,

25(3), 219-231.

Case, K.E. (1986). The Market for Simple Family Homes in the Boston Area, New England Economic

Review, May-June, 38-48.

Case, K.E. & R.J. Shiller (1979). The efficiency of the market for single family homes, American

Economic Review 79, 125-137.

Case, K.E. & R.J. Shiller (1987), Prices of single family homes since 1970: New indices for four cities,

New England Economic Review, 44-56.

Chanel, O., Gérard-Varet, L.A., & Ginsburgh, V. (1996). The relevance of hedonic price

indices. Journal of Cultural Economics, 20(1), 1-24.

Chafin, J. (2011, July 10). MFA: Is It Necessary? Lecture presented at Artillery Magazine's "Artillery

Sets the Standard" in The Standard Hotel, Los Angeles.

Childs, M. (Nov. 10 2015). Chinese billionaire buys Modigliani nude for $170m at Christie's.

Financial Times. Retreived http://www.ft.com/intl/cms/s/0/f3946616-8773-11e5-90de-

f44762bf9896.html#slide0

Court, A.T. (1939) "Hedonic Price Indexes with Automotive Examples", in The Dynamics of

Automobile Demand. New York: The General Motors Corporation, pp. 99-117.

Crane, D. (2009). Reflections on the global art market: implications for the Sociology of

Culture. Sociedade e Estado, 24(2), 331-362.

Dobrzynski, J. H. (2013, December 31). End Of The Year Thoughts On Museums And Money.

Retrieved March 11, 2016, from

Wilkinson 129

http://www.artsjournal.com/realcleararts/2013/12/end-of-the-year-thoughts-on-

museums-and-money.html

Edwards, S. (2004). The Economics of Latin American Art: Creativity Patterns and Rate of Return

(Working Paper No. 10302). Retrieved from National Bureau of Economic Research

website: http://www.nber.org/papers/w10302.

"Exhibitions: Multiplication" (2006). Arts Council of England. Retrieved 2 April, 2016.

Finkel, J. (May 9, 2013). Getty Museum buys 'Rembrant Laughing': tiny portrait, huge value. L.A.

Times. Retreived http://www.latimes.com/entertainment/arts/culture/la-et-cm-getty-

rembrandt-laughing-20130509-story.html

Findlay, M. (2012). The value of art. Prestel Verlag.

Finkel, J. (2014, July 31). The story of the original and greatest art fund, The Art Newspaper.

Retrieved December 7, 2015, from http://old.theartnewspaper.com/articles/ The-story-

of-the-original-and-greatest-art-fund/33141

First Research (2015). Art Dealers & Galleries Industry Profile. Retrieved from

http://www.firstresearch.com/Industry-Research/Art-Dealers-and-Galleries.html.

Frey, B. S., & Pommerehne, W. W. (1989). Muses and Markets: Explorations in the Economics of

the Arts. Blackwell.

Galenson, D.W. (1997). The careers of modern artists: evidence from auctions of contemporary

paintings (No. w6331). National Bureau of Economic Research.

Galenson, D.W. (1999). The lives of the painters of modern life: The careers of artists in France

from Impressionism to Cubism (No. w6888). National Bureau of Economic Research.

Wilkinson 130

Galenson, D.W. (2001). Painting outside the Lines: Patterns of Creativity in Modern Art,

Cambridge: Harvard University Press.

Galenson, D.W., & Jensen, R. (2002). Careers and canvases: The rise of the market for modern art

in the nineteenth century (No. w9123). National Bureau of Economic Research.

Galenson, D.W., & Weinberg, B.A. (1999). Age and the quality of work: The case of modern

American painters (No. w7122). National Bureau of Economic Research.

Ginsburgh, V., J. Mei, & M. Moses (2006). “The Computation of Prices Indices,” in Handbook of

the Economics of Art and Culture, Vol. 1, edited by V. A. Ginsburgh and D. Throsby.

Elsevier, 947–82.

Ginsburgh, V. A., & Throsby, D. (Eds.). (2006). “Chapter 3 The History of Art Markets.” Handbook

of the Economics of Art and Culture (Vol. 1). Elsevier, p. 69-122.

Goetzmann, W.N. (1990), Estimating price trends for residential property: a comparison of repeat

sales and assessed value methods, Working Paper, University of Connecticut.

Goetzmann, W.N. (1993), Accounting for taste: art and financial markets over three centuries,

American Economic Review 83, 1370-1376.

Grampp, W. D. (1989). Pricing the priceless: art, artists, and economics. Basic Books.

Griliches, Z. (1971), ed. Price Indexes and Quality Change: Studies in New Methods of

Measurement. Cambridge, Mass.: Harvard University Press.

Guerzoni, G. (1994, August). Testing Reitlinger's sample reliability. In 8th Conference on Cultural

Economics, Witten, August.

http://www.guggenheim.org/exhibition/zero-countdown-to-tomorrow-1950s60s-2

Wilkinson 131

Halperin, J. (2015, April 2). Almost One Third of Solo Shows in US Museums Go to Artists

Represented by Just Five Galleries, The Art Newspaper. Retrieved November 20, 2015

from http://old.theartnewspaper.com/articles/Almost one third of solo shows in US

museums go to artists represented by just five galleries/37402.

Hawkins, R. (2015, April 12). Art Funds Survey 2015. Private Art Investor. Retrieved December 7,

2015, from http://www.privateartinvestor.com/art-finance/art-funds-survey-2015

Hensher, P. (2006, February 13). When even the most monstrous works of art cost millions, it's

time for a price crash, The Guardian. Retrieved December 7, 2015, from

http://www.theguardian.com/artanddesign/2006/feb/13/art.culture

Hoffmann, M., Worthington, A., & Higgs, H. (2006). Urban water demand with fixed volumetric

charging in a large municipality: the case of Brisbane, Australia*. Australian Journal of

Agricultural and Resource Economics, 50(3), 347-359.

Horowitz, N. (2014). Art of the deal: Contemporary art in a global financial market. Princeton

University Press.

http://www.artprice.com/client/subscriptions/

Hutter, M., Knebel, C., Pietzner, G., & Schäfer, M. (2007). Two games in town: a comparison of

dealer and auction prices in contemporary visual arts markets. Journal of Cultural

Economics, 31(4), 247-261.

Knight, C. (July 17, 2015). Museums' Disturbing Transformation: Relentless Commercialization,

Los Angeles Times. Retrieved November 20, 2015 from

http://www.latimes.com/entertainment/ arts/la-ca-cm-knight-art-commercialization-

20150719-column.html#page=1.

Korteweg, A., Kraussl, R., & Verwijmeren, P. (2015), Does it Pay to Invest in Art? A Selection-

corrected Returns Perspective, Available at SSRN: http://ssrn.com/abstract=2280099.

Wilkinson 132

Lind, M. (2012). Preface, in Contemporary art and its commercial markets: a report on current

conditions and future scenarios, edited by M. Lind and O. Velthuis. Sternberg Press, pp.7-

14.

Maddison, D., & Jul Pedersen, A. (2008). The death effect in art prices: evidence from

Denmark. Applied Economics, 40(14), 1789-1793.

Mark, J.H. and Goldberg, M.A. (1984), Alternative housing price indices: an evaluation, AREUEA

Journal 12, 31-49.

McAndrew, C. (2009). Globalisation and the art market: emerging economies and the art trade in

2008. European Fine Art Foundation (TEFAF).

Mei, J., & Moses, M. (2002). Art as investment and the underperformance of masterpieces:

Evidence from 1875–2002. American Economic Review, 92(5), p. 1656–1668.

McAndrew, C. and Arts Economics (2014) The European Fine Art Fair (TEFAF) 2014 Art Market

Annual Report. Retrieved from http://artseconomics.com/project/tefaf-art-market-

report-2015/

Murphy, Kevin. Lay People in the Art Market [Personal interview]. (2016, January).

National Center for Charitable Statistics (U.S.), & Center on Nonprofits and Philanthropy (Urban

Institute). (1999). National Center for Charitable Statistics: NCCS : a project of the Center

on Nonprofits & Philanthropy at the Urban Institute. Washington, D.C: National Center for

Charitable Statistics.

Palmer, L. (2015, August 13). Top 10 Most Expensive Living American Artists. Retrieved December

7, 2015, from https://news.artnet.com/market/artnet-newss-top-10-expensive-living-

american-artists-2015-323871

Wilkinson 133

Palmquist, R.B. (1980). Alternative Techniques for Developing Real Estate Price Indices, Review of

Economic and Statistics 62, 442-448.

Pesando, J.E. (1993). Art as an investment. The market for modern prints, American Economic

Review 83, 1075-1089.

Pesando, J., & Shum, P. M. (1996). Price anomalies at auction: Evidence from the market for

modern prints. CONTRIBUTIONS TO ECONOMIC ANALYSIS, 237, 113-134.

Pogrebin, Robin (06 Mar. 2016.). Art Galleries Face Pressure to Fund Museum Shows. The New

York Times. The New York Times. Retrieved Mar. 21, 2016, from

http://www.nytimes.com/2016/03/07/arts/ design/art-galleries-face-pressure-to-fund-

museum-shows.html?_r=0

Ridker, R.G. and Henning, T.A. (1967) The Determinants of Residential Property Values with

Special Reference to Air Pollution. Review of Economics and Statistics 44: 2d6-257.

Reilly, M. (2015, May 26). Taking the Measure of Sexism: Facts, Figures, and Fixes. Artnews.

Retrieved April 05, 2016, from http://www.artnews.com/2015/05/26/taking-the-

measure-of-sexism-facts-figures-and-fixes/

Taylor, D. & Coleman, L. (2011). Determinants of Aboriginal Art, and Its Role as an Alternative

Asset Class, Journal of Banking and Finance, 35(6), p. 1519–29.

Sagot-Duvauroux, D. (2011), Art Prices, in A Handbook of Cultural Economics, Ed. Ruth Towse, 2nd

Ed., Northampton, MA: Edward Elgar Publishing.

Schneider, F., & Pommerehne, W. W. (1983). Analyzing the market of works of contemporary fine

arts: An exploratory study. Journal of Cultural Economics, 7(2), 41-67.

Wilkinson 134

Sheets, H. H. (2015). Blurring the Museum-Gallery Divide. Retrieved March 11, 2016, from

http://www.nytimes.com/2015/06/21/arts/design/curators-straddle-the-museum-

gallery-divide.html

Sheppard, S. (2016, February). The Art Market According to Tom Krens [Telephone interview].

Sheppard, S. (1999). Hedonic analysis of housing markets. Handbook of regional and urban

economics, 3, 1595-1635.

Soloman, D. (1999, June 27). How to Succeed in Art. The New York Times. Retrieved April 5, 2016,

from http://www.nytimes.com/1999/06/27/magazine/how-to-succeed-in-

art.html?pagewanted=all

Thompson, D. (2009). The $12 million stuffed shark: The curious economics of contemporary art.

Anchor Canada.

Thorpe, V. (2010, December 12). The Mega-Galleries Battling It out for Control of the Global Art

Market. The Guardian. Retrieved April 5, 2016, from

http://www.theguardian.com/artanddesign/2010/dec/12/mega-galleries-global-art-

market

Ursprung, H.W., & Wiermann, C. (2011). Reputation, price, and death: an empirical analysis of art

price formation. Economic Inquiry, 49(3), p. 697-715.

Van der Post, L. (2010, February 27). Terms of Engagement. How to Spend It: Financial Times

Weekend Magazine. Quoted in Velthius, p. 33.

Velthuis, O. (2012). Introduction. Contemporary art and its commercial markets: a report on

current conditions and future scenarios. Sternberg Press, p. 15-46.

Wilkinson 135

Visitor Figures: 2014 Exhibition & museum attendance survey. (2015, April). The Art Newspaper,

Special Report Number 267. Retrieved December 7, 2015, from http://www.museus

.gov.br/wp-content/uploads/2015/04/TheArtNewspaper_Ranking2014.pdf

Waxman, O. (2013, November 14). A $58.4M orange balloon dog and 10 other Jeff Koons balloon

pieces, Time. Retrieved December 7, 2015, from http://newsfeed.time.com/2013/11/14/

an-orange-balloon-dog-sold-for-58-4-million-so-here-are-10-cool-jeff-koons-balloon-

pieces/

Witkowska, D. (2014). An Application of Hedonic Regression to Evaluate Prices of Polish

Paintings. International Advances in Economic Research,20(3), 281-293.

Yip, P. S., & Tsang, E. W. (2007). Interpreting dummy variables and their interaction effects in

strategy research. Strategic Organization, 5(1), 13-30.