J Cult Econ DOI 10.1007/s10824-012-9196-0 ORIGINAL ARTICLE
Trust the artist versus trust the tale: performance implications of talent and self-marketing in folk music Brinja Meiseberg
Received: 16 September 2012 / Accepted: 14 November 2012 Springer Science+Business Media New York 2012
Abstract Based on a unique dataset of artists that are active in the German market for folk music—the third largest music genre in terms of popularity and sales—I study what factors determine the artists’ success. Following Rosen (Am Econ Rev 71(5):845–858, 1981), I test if differences in artistic performance have a direct effect on financial rewards as regards physical and digital record sales (‘‘direct superstar effect’’). Following Adler (Handbook on the economics of art and culture. Elsevier, Amsterdam, 1985), I also study sales effects of a media presence of artists (‘‘classical superstar effect’’). Controlling for various contingency factors (e.g., record labels’ support, artists’ socio-demographics), I deal with an economic issue of general interest: Does it pay more to develop your skills in your core business to perfection or to maintain the current level of skills and invest in self-marketing; and do these effects apply to all folk artists alike? Rather contrary to studies on pop and rock genres, I find that higher ability increases artists’ revenues disproportionately, but simultaneously, openly competing for the recognition of one’s talent holds substantial economic risk. I also observe a positive effect of various types of media presence on financial rewards. However, these income determinants have different impacts on sales in physical versus in digital markets, and their effects vary across the success distribution from low- to top-selling artists as well. Keywords Cultural industries Music industry Superstar theory Sales distribution Talent contest Self-marketing JEL Classification
Z11 L82 D31 D83 L10
B. Meiseberg (&) Institute of Strategic Management, Westfa¨lische Wilhelms-Universita¨t Mu¨nster, Leonardo-Campus 18, 48149 Mu¨nster, Germany e-mail:
[email protected]
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1 Introduction In the music industry and other cultural industries, only a very small number of people ever become really successful (Strobl and Tucker 2000; Towse and Moser 1993). Tervio¨ (2009) recently suggested that 80–90 % of records by new artists make losses, while the proportion of highly profitable albums in the market continues to be marginal. Many more musicians never get any record deals. Likewise, the income distribution across artists is highly skewed and departs from normality assumptions about random errors (Abbing 2002; Borghans and Groot 1998; Pitt 2010; Wetzels 2008). Besides, the popularity of most music is short-lived, so copyright owners must maximize the value of creations during a brief window of opportunity (Giles 2007; Pitt 2010). Caves (2000) called this ‘‘reward uncertainty’’ the ‘‘nobody knows’’ property, as the first on a list of striking characteristics of cultural industries. Based on the belief that there is a close connection between personal reward and the size of one’s market, the music industry has become a testing ground for the socalled superstar phenomenon. Superstar theory explains how, over time, artists emerge who dominate the market and earn disproportionate rents (Rosen 1981; Adler 1985; Giles 2006). For artists, record labels, and other industry professionals, understanding how to crowd out competition and decrease reward uncertainty is essential (Abbing 2002; Borghans and Groot 1998; Wetzels 2008). Rosen (1981) proposed a strong tendency for market size and artists’ rents to be skewed toward the most talented people in a particular activity, so that small differences in ability would translate into enormous differences in income. In contrast, Adler (1985, 2006) pointed out that skewness of rewards was not necessarily related to ability, as art consumption follows the notion of ‘‘the more you know, the more you enjoy’’: Consumers enjoy discussing art with friends and acquaintances for social exchange, and knowledge, either by direct consumption experience or by learning from others, adds to their consumption capital (Giles 2006, 2007; Throsby 1994). Accordingly, stardom is a market device that economizes on consumers’ learning costs and offers them an efficient mechanism to expand consumption capital (Stigler and Becker 1977). Therefore, factors other than ability explain the individual success of artists. Building on these two opposing views and a unique database of artists who sell physical or digital folk music recordings in the German market (n = 1,623), I study effects of artists’ abilities and of self-marketing through media presence, on their respective market success. Our main research questions are as follows: (1) In the folk genre, what factors determine individual artists’ success? Does it pay more to develop your skills in your core business to perfection or to maintain the current level of skills and invest in self-marketing? (2) Do these effects apply to all artists alike, across the various segments of the sales distribution from low- to top-selling artists? (3) What are the implications toward a more strategic buildup of artist careers in the music business? This setting is particularly attractive to study for several reasons. First, typically, there is little digital alteration to the artist’s song performance in folk, so musical ability is more observable than when extensive alteration is the norm. Thus, the folk
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context is apt to provide clearer evidence on drivers of stardom than previous pop or rock genre environments (e.g., Chung and Cox 1994; Crain and Tollison 2002; Connolly and Krueger 2005; Giles 2006; Hamlen 1991, 1994). Second, folk music is a vital cornerstone of Central European cultures and traditions, reinforcing the understanding of national identity for a wide range of people (Engeler 1996). As the folk genre appeals to many layers of society, it is also sufficiently large to be of both cultural and economic interest, and at the same time, small enough to allow data collection on a (nearly) complete population of artists rather than some (more or less representative) subsample. Thereby, I can also study input–output linkages across the segments of the market’s entire sales distribution. In addition, there is a dearth of studies on market success of music artists based on real sales data. Giles (2006) pointed out the sensitivity of conclusions to the (non)adequacy of the chosen stardom measure. Still, most studies apply variables intended as a sales proxy, for example, chart listings, airplay time, awards, or consumer-survey self-reports (e.g., Chung and Cox 1994; Krueger 2005; Montoro-Pons and Cuadrado-Garcı´a 2011; Prieto Rodrı´guez and Ferna´ndez-Blanco 2000). Besides, I study determinants of physical and digital market success jointly, instead of focusing on one distribution type alone. This is interesting as perceived ‘‘riskiness’’ of purchase options can depend on distribution channels (Montoro-Pons and Cuadrado-Garcı´a 2011) and as digital formats increasingly supersede physical recordings. The paper is organized as follows. Section 2 presents the theoretical framework and provides background on the folk music context. Section 3 explains our hypotheses. Section 4 describes data and methods. Results are reported in Sect. 5. The final section concludes.
2 Conceptual background 2.1 Superstar theory ‘‘Superstar systems’’ are characterized by a highly skewed distribution of income, market share, and public attention in favor of a few artists. But how do these individuals come into such extreme demand? On the supply side, technology permits mass duplication of content at constant marginal costs.1 On the demand side, given that talent determines success, increasing levels of education in society lead to consumer preferences for high quality (Malmendier and Tate 2009). Then, the highest quality producers can attain disproportionate rewards. I term this ‘‘direct linkage’’ between ability and success (Rosen 1981), a ‘‘direct superstar effect’’. In contrast, Adler (1985) suggested that appreciation of an artist’s output rather increases due to consumers’ learning processes: Consumers strive to accumulate ‘‘consumption capital’’, that is, knowledge about artists and their art that can be 1
Marshall (1947) offered that linkages between quality and rewards were skewed in many markets, but not in music, because of an inability to mass-produce sound back then, which has certainly changed. In this study, I use the terms ‘‘talent’’ and ‘‘ability’’ interchangeably, suggesting that talent translates into an artist’s ability—particularly, if supported by training and experience, both of which factors I study as well.
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discussed with other consumers for social entertainment. If search costs for finding discussion partners are inversely related to artist popularity, patronizing the most popular artists is efficient no matter if these are more talented than others. In the following, I call this path to stardom a ‘‘classical superstar effect’’. Concerning previous research on the determinants of star evolution in the music industry, MacDonald (1988) demonstrated that often, the presence of multiple market stages (e.g., singles and albums in music; minor and major leagues in sports) can filter out low-quality individuals and increase the likelihood that direct superstardom occurs. However, advocating ‘‘voice quality’’ as central ability indicator for popular music, Hamlen (1991) rejected the Rosen (1981) form of stardom based on US demand for music recordings. Subsequently, Hamlen (1994) replicated this result by showing that rewards do increase with ability, but that the relationship is less than proportional. Towse (1992) proposed bandwagon effects as an explanation for skewness of rewards. Such effects occur when consumers’ choice of a certain behavior grows with the number of others who will undertake the same action, so that snowballing increases demand for some artists’ outputs, independent from their ability (Grant and Wood 2004; Strobl and Tucker 2000). Chung and Cox (1994) focused on the stochastic model of Yule (1924) and Simon (1955) as the mechanism underlying consumers’ choices and showed that for US Gold Record awardees, demand concentrated on a few lucky individuals without requiring superior ability. In contrast, Giles (2006) did not find support for the Yule-Simon form of superstardom for both the life-lengths and the number of recordings that reach the top of Billboard 100 charts. Subsequently, Spierdijk and Voorneveld (2009) established that the snowball effect explains the emergence of lesser stardom concerning record sales, but not for superstars. In contrast, Georges and Sec¸kin (2012) show that for classical music manuscripts, top-prices at Sotheby’s auctions do depend on the ‘‘artistic value’’ of the composition. Taken together, the superstar model has proven difficult to test, and evidence on determinants of stardom in the music industry is mixed. This may also be attributed to the fact that objective measures of ‘‘talent’’ or ‘‘ability’’ for musicians are hard to define and harder to quantify. Moreover, despite the importance of music as a cultural industry, there has been remarkably little use of objective, accurate data to study returns in the market (Strobl and Tucker 2000). For artists to build up careers successfully, and for producers and record companies to identify those artists worthwhile to launch and promote, understanding the factors driving market success is essential. This obviously applies to superstar creation, yet improving prospects for artists in second tier that can still perform profitable if careers were managed properly, would be of value as well. A more informed foundation for approaching career decisions, based on individual achievement prospects, would also be desirable to those artists whose market success has proven marginal, or who are pondering getting started in the business. Accordingly, focusing on objective sales performance data, I test the two theories on star formation by Rosen (1981) and Adler (1985) and establish what criteria drive success in folk music.
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2.2 The market for folk music Popularity and diffusion of folk music is high throughout Central Europe. Folk music is characterized by several archetypical elements, including pleasant melodies, easy-to-grasp content, repetition, mass-compatible dialect, and informal address, thereby offering an emotionally oriented, identity-creating environment (Engeler 1996; Stein et al. 2008). For example, the enormous popularity of the male duo ‘‘Kastelruther Spatzen’’ (‘‘Sparrows from Kastelruth’’, Kastelruth being a village in South Tyrol, Italy) is based on songs that mostly center on stereotype sceneries. The latter feature small villages, narrow lanes, neat churches, idyllic nature including blue skies, snowy mountains, lush green meadows, well-fed cattle, and colorful flowers (Engeler 1996). TV broadcasts of folk song performances offer picturesque mountain hut settings (staging huts in the Alps), decorated with saddles, scythes, hay-forks, dulcimers, floral arrangements, and artists wear stylized traditional costumes including dirndl dresses. Millions of Central Europeans, particularly Germans, Austrians, Swiss, and Northern Italians, are glued to their TV sets on prime time Saturday, when folk shows are on the state channels. Consequently, some shows gain around 20 % market share (Lu¨cke 2010; Stein et al. 2008). Germany is the second biggest market in terms of overall record sales in Europe, and the fourth largest worldwide (after the US, Japan, and the UK). In Germany, physical recordings (predominantly, CDs) are still the main sales generator (83 % of 1.67 m total revenues), and sales have remained stable over the last decade (Bundesverband Musikindustrie 2011). For years, folk music has held a market share of around 11 % in sales, coming in third place of all genres (pop: 37 %, rock: 20 %, classic: 7 %; Bundesverband Musikindustrie 2011; Musikwirtschaftsforschung 2010). Folk music has also proven rather immune to economic crisis and to digital formats replacing physical recordings, so that the latter should continue to be the primary income source over the next decade at least (Musikwoche 2010; Schulz 2006). The folk genre is characterized by a comparatively small group of active artists in a rather stable environment, unlike, for example, in pop music where artists frequently ‘‘rise and fall’’ (Montoro-Pons and Cuadrado-Garcı´a 2011; Schulz 2006). Folk artists who become successful once can often expand their careers long-term (Schulz 2006). Many folk fans are ‘‘mainstream’’ consumers (Engeler 1996). Currently, 63 % of people interested in folk are aged 50 and above, 16 % are 40–49, 7 % are 30–39, 11 % are 20–29, and 3 % are below the age of 20 (Bundesverband Musikindustrie 2011). In general, folk is an amazingly popular genre in Central European markets.2 Next, I develop hypotheses on factors driving success for artists in this market.
2
Two positive trends that have promoted the folk genre’s market prospects are first, the number of consumers aged 30 and over who frequently buy recordings has grown steadily over the last decades Bundesverband (Musikindustrie 2009), and recently, younger consumers’ interest in folk music has risen rapidly, as young folk artists have emerged that soften the ‘‘granddad music’’ image that folk music held in earlier years (Bundesverband Musikindustrie 2009).
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3 Hypotheses 3.1 Superstar effects by differences in ability (Rosen 1981) ‘‘Musical talent’’ has been controversially discussed as a capacity that remains unobservable and depends on cultural, historical, and social norms (Engeler 1996; Gembris 2002, 2005; Sloboda 1993; Stefani 1987). Various methods of assessing musical talent in ‘‘objective’’ ways have been proposed, but most allow judging abilities only to a limited extent as they are oriented toward hearing capacity alone (Seashore 1919; Bentley 1968; Gembris 2005; Gordon 1965, 1989; Preusche et al. 2003; Wing 1961). Thus, subjective assessments may prove more suitable: Scholars have argued if and how experts can judge the quality of artists’ output, and whether the taste of the public also has merit in judging musical ability (Haan et al. 2005). When unsure whether a good or a service is of high quality, consumers often consult expert opinions (Gingsburgh and van Ours 2003; Franck and Nu¨esch 2010).3 For example, experts point out the best performer in some music competitions. Studying the Eurovision song contest—an annual festival organized by the European Broadcasting Union (EBU), where a range of countries compete each with one song—Haan et al. (2005) find that the outcome of finals judged by experts is less sensitive to factors unrelated to ability than the outcome of finals judged by public opinion. Yet, many commentators and music professionals bemoan that ratings follow political interests rather than musical abilities and that the participating singers and songs are of dismal quality anyway (Haan et al. 2005; Kultur 2012). That is, some musical contests are not based on a particular interest to identify talent, but rather aim at offering an entertaining media spectacle to consumers, so the ‘‘value’’ of expert ratings may be hard to discern in such contexts. In contrast, Glejser and Heyndels (2001) study the ‘‘Queen Elisabeth International Music Competition’’, a prestigious classical music contest that is intended as a ‘‘true’’ talent identification contest held annually in Brussels and show that inefficiency occurs in expert jury ratings as well. They conclude, along with others (Haan et al. 2005; Glejser and Heyndels 2001; Amegashie 2009), that experts do not necessarily judge quality better than consumers. In other competitions, experts only make a pre-selection among contestants, and consumers subsequently decide on the contest winners based on the majority’s perception of musical quality. In the latter case, expert opinions can function as a cut-off that initially differentiate the more from the less able candidates (Haan et al. 2005). Amegashie (2009) has recently shown that when experts are involved in the pre-selection of artists, but the final decision about the contestants’ ranking is transferred to a collective of consumers instead—which potentially increases the degree of noise, or ‘‘luck’’, in a contest—an increase in artists’ aggregate efforts will result. Amegashie (2009) argues that contestants will particularly focus energy on 3
Wijnberg (1995) distinguishes three types of selection systems: market, peer, and expert selection. Market selection means that producers are the selected and consumers are the selectors. In peer selection, the selectors and selected belong to the same group. In case of expert selection, selectors are neither producers nor consumers, but will select by virtue of specialized knowledge and distinctive abilities (see also, Wijnberg and Gemser 2000).
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improving their singing performance, even though they know that consumers care about non-singing attributes (e.g., dance, looks) as well, as preferences for nonsinging activities largely vary in the audience and are thus harder to fulfill than singing preferences. Thereby, consumers’ noisy preferences for non-singing elements paradoxically lead to an increase in the ability shown in talent contests in terms of singing efforts. This, in turn, helps consumers pick the ‘‘true’’ best quality performance as a winner. In consequence, as all artists increase their efforts based on their respective ability, transferring the final decision to non-experts leads to greater efficiency and a ‘‘more adequate’’ ranking. Hamlen (1991) also argued that consumers do indeed discern quality in their singers, refuting the common notion against this view. Then, competing successfully in a renowned folk talent contest, especially when experts pre-select and consumers decide about the final ranking, should indicate some true ability. In addition, being able to produce high quality will not depend on talent alone, like musical sensitivity, musical or intellectual capacity, and fast learning, but also on influencing factors like education, experience as a musician, or individual factors (e.g., efforts and involvement) (Gembris 2005; Hamlen 1991). That is, talent requires development to manifest itself in strong ability and high output quality, both for professionals as well as for lay artists (Gembris 2005; Sloboda et al. 1994). Accordingly, research in various fields of expertise formation has explained output quality with goal-oriented and time-intensive training, often from early childhood onwards (Ericsson et al. 1993; Krampe 1994). Ability to produce superior quality should then translate into great demand and market success (Rosen 1981). Thus, I expect: H1 High ability—as indicated by gaining recognition as a top-artist in a renowned talent contest—increases an artist’s sales performance disproportionately. H2 Investments in talent development increase an artist’s sales performance disproportionately. H2a Education in the field increases an artist’s sales performance disproportionately. H2b Experience in the field increases an artist’s sales performance disproportionately. 3.2 Superstar effects by differences in media presence and self-marketing (Adler 1985) Previous findings on effects of an artist’s media presence, in terms of higher consumer interest and opinion of the artist’s output and increased market success (Adler 1985; Amegashie 2009; Ehrmann et al. 2009), may hold for folk music as well—particularly, as the average consumer of folk music has been found to be above-average mainstreamoriented and susceptible to TV influence (Engeler 1996). Then, consumer choice does not necessarily promote the survival of the most talented, but of the most popular (Crain and Tollison 2002). Folk music shows on TV are frequent. The entertainment value of
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artists’ appearances on TV is spurred by a range of stage props, by ventriloquists, magicians, and conferenciers (Engeler 1996). Other visual aspects accompanying the song—like body language and staffage—additionally enhance the ‘‘show factor’’ of song presentations (Amegashie 2009; Haan et al. 2005; Engeler 1996). Thereby, artists’ appearances in such shows offer plenty to talk about to consumers. Besides, TV appearances in non-music shows may obviously increase artists’ interestingness as a conversation topic and in turn, their popularity as well. Then, consumers can build consumption capital by patronizing those artists that regularly appear on TV, and the artists can absorb parts of consumers’ ‘‘savings’’ in search costs for finding discussion partners and consequently, become disproportionately successful over time. H3 Frequent media disproportionately.
presence
increases
an
artist’s
sales
performance
H3a Frequent presence in TV music shows increases an artist’s sales performance disproportionately. H3b Frequent presence in other non-music TV entertainment shows increases an artist’s sales performance disproportionately. Stars emerge because much of the pleasure in consuming art lies in discussing it with others (Adler 1985). For social interaction, consumers create interpersonal ties and exchange units of discourse in face-to-face encounters. In addition, increasing numbers of consumers are active on the internet or join virtual networks of people sharing some same interest (Dwyer 2006). To provide consumers with easy opportunities for building up consumption capital and to fuel interpersonal discussion, setting up an online presence where consumers can gather information and latest news about the artist’s work may prove valuable. Then, an artist’s online presence can become a convenient multiplier of popularity. In addition, Elberse and Oberholzer-Gee (2008) and Brynjolfsson et al. (2006) pointed out that online communication can help consumers locate, evaluate, and purchase a wider variety of products that match their specific tastes and change the variety of products that consumers purchase. Brynjolfsson et al. (2011) highlighted that information technology in general and the Internet in particular have the potential to substantially increase the share of ‘‘niche’’ products, which could provide advantages in particular to less known folk artists. Besides, offering merchandise online may increase the artist’s homepage attractiveness, the online traffic directed to that homepage, and its visibility in search engines, reinforcing the discussed effects. H4
Online presence increases an artist’s sales performance disproportionately.
H4a A personal homepage increases an artist’s sales performance disproportionately. H4b Online merchandise activity increases an artist’s sales performance disproportionately. Kamphuis (1991) suggested that artist names function analogous to brand names. Brand names are a powerful source of inference for consumers concerning the attractiveness of an offering and are often significantly related to product popularity.
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When needing cues to assess the appeal of a product or service, consumers search for brand information more than for any other type of information (Dawar et al. 1996). Research in various cultural settings, for example, in the literature market, shows that well-known artist names have a positive impact on sales success (Clement et al. 2007; d’Astous et al. 2006; Janssen and Leemans 1988; Kamphuis 1991; Liebenstein 2005). Hence, stars creating a recognizable image, like a brand, may strongly profit from such behavior. Image creation may include aspects like adopting a stage name or signature feature that increase attention and serve as a recognition factor for consumers (see Fig. 1). H5 Establishing a brand image increases an artist’s sales performance disproportionately. H5a Adopting a stage name increases an artist’s sales performance disproportionately. H5b Adopting a signature feature increases an artist’s sales performance disproportionately.
4 Sample, variables, and methods 4.1 Sample I use proprietary data from MediaControl Handelspanel (‘‘MediaControl Retail Panel’’), the most comprehensive source of data on the music industry in Germany. MediaControl is a commercial market intelligence company that collects market research data across a range of cultural industries, including music. For gathering physical record sales, the panel is based on automatically recorded real-time sales data from 2,800 large retailers across the country. Each time an album is purchased, the retailer runs a scanner over the product bar-code, and this information is automatically sent to Media Control’s database. In addition, the panel stores download data provided by the largest digital music retailers selling to the German market. In 2010, the year for which I acquired the sales data, the Panel covered 90 % of all physical recordings sales in the German market, and 97 % of all download sales, so market coverage is almost complete. Our sample covers folk music artists (soloists, duos, groups) that sold at least one physical recording in Germany in 2010. The final sample size, after excluding around 300 artists that did not match this condition, is 1,318 (if counting groups or duos as ‘‘one’’ artist; the total number of individuals in the final sample amounts to 3,500). 4.2 Variables 4.2.1 Dependent variable: sales Radio airplay, album sales and concerts provide revenues to songwriters, music labels, and musicians (Black et al. 2007). Previous literature on music artists’
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Fig. 1 Proposed model
economic success has typically focused on chart presence (although many artists never make it into the charts), like duration or ranks reached, on airplay time, or record awards (Bhattacharjee et al. 2007; Black et al. 2007; Hamlen 1994; Giles 2006; Ordanini 2006; Strobl and Tucker 2000). Yet, the central income source for most artists (for those who have record deals) are album sales (Musikwoche 2010). However, studies using actual sales data are rare, as such data is often unavailable to the public or considerably expensive to acquire. To provide more rigor to the analysis of artist success in music, our study is based on comprehensive sales data of folk albums across two distribution forms: physical recordings and downloads. First,
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official statistics define ‘‘total’’ sales per artist (relevant for e.g., gold record awards) as the sum of physical and digital recordings sales (Bundesverband Musikindustrie 2010). I follow this procedure to study total sales success. Second, even though physical distribution holds a much larger market share in the folk genre than downloads currently have, I investigate success drivers in both channels, as they may vary by distribution form. Besides, online distribution has been shown to crowd out recorded music consumption for, for example, pop music (Montoro-Pons and Cuadrado-Garcı´a 2011), an effect that might become relevant to folk in the future. Thus, integrating insights into sales drivers in both channels should add relevance to the study results. One hundred and thirty-four sample artists had digital sales in 2010, but no physical sales. For 42 artists who sold physical albums, there are no digital sales. Physical and digital sales are highly correlated (0.630, p \ 0.001; Table 2). For the analyses, variables are logged. To counter endogeneity issues, the dependent variable is measured for the year of 2010, and the independent variables are measured prior to 2010 (i.e. 2009 and before). A summary of the variables is presented below (Table 1). 4.2.2 Star effects: ability Depending on the type of talent competition, being ranked highly in a talent contest can be interpreted as an indicator of musical ability (Amegashie 2009; Gingsburgh and van Ours 2003). I focus on the most renowned talent competition in folk music in Central Europe, ‘‘Grand Prix der Volksmusik’’ (‘‘Grand Prix of Folk Music’’). From 1986 and 2010, this contest was held on an annual basis. Artists from Germany, Austria, Switzerland, and Italy (South Tyrol) would compete for talent recognition. Both groups and soloists could participate. The contest was organized as follows: In each country, contestants were pre-selected by expert juries. For the pre-selection procedure, artists were required to hand in an anonymous audio tape of their song. From all the anonymous tapes received, the expert juries on each national committee pre-selected 10–15 contestants for their own country whom they deemed the most talented. Subsequently, the power to select four final contestants per country was handed over to each nation’s consumers, who televoted on the occasion of a one-time TV show that presented the respective 10–15 national preselected songs (i.e. each country had its own show). The final Grand Prix with four artists from each country was broadcasted live in all the participating nations, and consumers could rate contestants’ talents by calling the studios. Artists were then ranked according to the respective number of votes received (note: consumers were not allowed to vote for songs from their home country). As previous studies show, consumers can command the capacity to discern and rate talent, particularly after expert pre-selection, so noticing ability is not systematically out of consumers’ grasp. Although the consumer-selection part of the contest, after the artists’ filtering by expert pre-selection, is broadcasted on TV, I suggest that the contest rankings are an adequate talent proxy for several reasons. First, consumers vote independently from one another—interim results are not presented while the show is running, and they vote by phone, so they cannot observe (many) others’ votes. Second, the vote
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J Cult Econ Table 1 Overview of variables Variables
Measure
Mean/ratio
(1) Sales (total)
Physical and digital album sales in 2010 combined
6,569.49
(2) Sales (physical)
Physical album sales in 2010
4,670.38
(3) Sales (digital)
Digital album sales in 2010
1,899.13
(4) Talent competition: winner
Talent competition: grand prix-winner (1st rank)
1.80 %
(5) Talent competition: awardee
Talent competition: grand prix-awardee (2nd–4th rank)
5.46 %
(6) Talent competition: participant
Talent competition: grand prix-participant (all other ranks, i.e. 5th–16th)
18.11 %
(7) Education
Dummy variable, 1—artist with vocal/instrument education, 0—otherwise
33.15 %
(8) Experience
Career years as a musician in years up to 2009
24.64
(9) TV presence music
Number of appearances in TV music shows in 2009
9.76
(10) TV presence non-music
Number of appearances in non-music TV shows in 2009
1.36
(11) Online presence
Dummy variable, 1—internet presence via webpage, 0—otherwise
73.36 %
(12) Online merchandise
Dummy variable, 1—merchandise provided via homepage, 0—otherwise
22.76 %
(13) Pseudonym
Dummy variable, 1—artist has adopted a stage name, 0—otherwise
17.26 %
(14) Signature feature
Dummy variable, 1—artist has adopted a signature feature, 0—otherwise
(15) Brand support
Dummy variable, 1—major label, 0—otherwise
(16) Nationality 1
Dummy variable, 1—artist is Austrian, 0—otherwise
29.15 %
(17) Nationality 2
Dummy variable, 1—artist is Swiss, 0—otherwise
13.51 %
(18) Nationality 3
Dummy variable, 1—artist is Italian, 0—otherwise
8.49 %
(19) Nationality 4
Dummy variable, 1—artist is of another nationality (German is the default category for the nationality variables), 0—otherwise
1.35 %
(20) Profession
Highest education level achieved (5 = doctorate, 4 = (applied) university degree, 3 = apprenticeship, 2 = A-levels, 1 = secondary school or less)
2.48
(21) Age
Measured in years in 2009
47.70
(22) Gender
Dummy variable, 1—soloist, female, 0—otherwise
20.27 %
(23) Duo
Dummy variable, 1—duo, 0—otherwise
(24) Group
Dummy variable, 1—group (soloist, male, is the default category for the gender and organizational form variables), 0—otherwise
4.25 % 29.85 %
9.46 % 39.96 %
collection for the Grand Prix is a one-time-approach, that is, there is no repeated presentation of artists like in many media spectacle-‘‘contests’’ where contestants participate repeatedly in a variety of music (and non-music) activities over a season. Taken together, there is little opportunity for consumers to observe others’ interests
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(which would potentially cause votes for the popular rather than the truly talented), so that the evolution of ‘‘lemming’’ behavior that results from social interaction feedback loops is prevented. Third, the purpose of the contest as a talent contest, and the fact that artist rankings are intended to reward talent and reflect music quality, is explained to consumers repeatedly during the competition. Fourth, as a technicality, taking part in the competition in earlier years, for example, 20 years ago, could hardly be argued to be a suitable measure of ‘‘recent’’ popularity that affects present sales. Recently-running TV-shows would be more central to consumers’ TVinduced awareness of an artist. Thus, recent TV presence would obviously be a more appropriate measure of popularity, so the (timeless) talent-rating character of doing well in the contest should exceed any potential TV presence character the variable might also have. Fifth, many contestants are unknown newcomers when entering the contest, so previous popularity should not bias votes substantially. Besides, all contest songs have to be new in the sense that they have not been presented or recorded earlier, which could influence consumers if they feel familiar with some tunes and unfamiliar with others. Moreover, the songs artists present are usually not their own works, but by professional composers and songwriters. So, contestants basically bring their singing ability to the table, and unprofessional composing or texting done by the artists that could skew results should not be an issue. Therefore, I suggest that the talent contest rankings used here, despite all potential shortcomings, are the best available indicator for basically unobservable ‘‘true’’ talent. I collected data on Grand Prix contestants since the initiation of the talent competition in 1986. I recorded all rankings ever received—1st place (winner), 2nd–4th place (awardee), 5th–16th place (participant) for our sample artists. Data were obtained from the Internet listings of the Grand Prix rankings (hitparade.ch, wikipedia.de, gpvolksmusik.at). Following Krueger (2005) and Gembris (2005), I also collected information on whether artists had received an education in music. Additionally, I count the years of experience in the market (measured in years since their first album). Data were acquired from ‘‘Lexikon der Volksmusik’’ (‘‘Encyclopedia of Folk Music’’), volksmusik.de, artists’ homepages, and interviews with artists themselves or their management. 4.2.3 Star effects: media presence and self-marketing In line with previous studies (Hamlen 1991, 1994), I counted each artist’s TV appearances in folk music shows in 2009 based on the Prisma online TV guide, homepages of the largest German folk music shows (‘‘Die Feste der Volksmusik’’, ‘‘Willkommen bei Carmen Nebel’’, ‘‘Musikantenstadl’’, ‘‘Musikantendampfer’’, ‘‘Servus Hansi Hinterseer’’, ‘‘Wenn die Musi spielt’’, ‘‘Gut Aiderbichl’’, ‘‘Marianne and Michael’’, ‘‘Hit auf Hit’’, ‘‘Krone der Volksmusik’’ etc.), artists’ homepages, and volksmusik.de. I also recorded the number of appearances in non-music shows in the same year based on Prisma, homepages, and volksmusik.de. Next, following Lehmann and Schulze (2005) and Franck and Nu¨esch (2010), I checked whether an artist runs a homepage (using google search, with the artist’s name, and the name
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plus ‘‘Volksmusik’’, i.e. ‘‘folk music’’). I also investigated whether a page offers merchandise. Concerning image creation, I ascertained whether artists had adopted a stage name (data from Lexikon der Volksmusik, volksmusik.de, artists’ homepages). Based on these sources and including additional press releases, I also check whether there was a signature feature applied by an artist (e.g., a signature move, a reappearing piece of clothing, conspicuous hairstyle, a co-performing stage pet). 4.2.4 Controls In line with Bhattacharjee et al. (2007), I control for record labels. Some studies in cultural industries argued that the distributor’s reputation impacts consumers’ perceptions of product quality. For example, Clement et al. (2006) found that wellestablished publishers increase consumer interest in book releases. D’Astous et al. (2006) and Kirmani (1990) argued that consumers know that launching and advertising a cultural product is a substantial commercial risk, and well-known brands would not take such risks without believing in the product’s quality. Besides, well-known labels have the financial means and know-how to promote an artist professionally, via online and offline advertising, bringing the artist on TV shows etc. Ordanini (2006) found that big labels make artists find a faster path to a strong success in the charts, although the same artist will have a shorter charts presence. However, whether a well-known label rather indicates performance quality of an artist or enhances that artist’s self-marketing effectiveness is hard to disentangle. Therefore, I do not pose a hypothesis but include the label as a control only. Data are from the artists’ and record labels’ homepages. I also control for artists’ nationalities (Smith 2007; Ordanini 2007; Gingsburgh and van Ours 2003; Krueger 2005; Franck and Nu¨esch 2010); for their highest educational degree, as a proxy for opportunity costs involved in choosing a folk music career versus a ‘‘standard’’ occupation, which may drive efforts elsewhere than into music; for age (Gingsburgh and van Ours 2003; Hamlen 1991; Franck and Nu¨esch 2010), gender (Gingsburgh and van Ours 2003; Hamlen 1991, 1994; Krueger 2005), and organizational form (Bhattacharjee et al. 2007; Hamlen 1991, 1994). Nationality and gender have sometimes been overlooked in other studies, although, dependent on genre, some subgroups obtain much higher market success (Smith 2007; Ordanini 2007). Data were collected from Lexikon der Volksmusik, hitparade.ch, volksmusik.de, artists’ homepages, press releases, and interviews with the artists or their management. 4.3 Methods To study the effects of the independent variables on sales in detail, I use both ordinary least squares (OLS) regressions (controlling for absence of multicollinearity, heteroscedasticity, and the distribution of disturbance terms) and quantile regressions (QR). While the majority of regression models analyze the conditional mean of a dependent variable, there is an increasing interest in methods of modeling other aspects of the conditional distribution. QR estimates a specified conditional
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quantile of the response variable as a function of the covariates and permits a better description of the conditional distribution than conditional mean analysis alone. QR allows describing how the median, or perhaps the 10th or 95th % of the response variable, are affected by a regressor (Koenker and Bassett 1978; Koenker and Hallock 2001). As the QR approach does not require strong distributional assumptions, it offers a distributionally robust method of modeling these relationships (Koenker and Bassett 1978; Koenker and Machado 1999). Here, the Koenker and Bassett (1982) test for the equality of the slope coefficients across quantiles shows a Wald-test v2 statistic value that is statistically significant at conventional levels (p \ 0.001). Coefficients differ across quantile values, and the conditional quantiles are not identical. I use Huber Sandwich calculations, valid under independent but non-identical sampling, and obtain the individual scalar sparsity estimates using kernel residuals. Bootstrap resampling is applied to calculate the covariance matrix (Buchinsky 1995; Jones 1992). Huber Sandwich and bootstrap techniques produce concurring results. QR and OLS results correspond, indicating result reliability.
5 Results Studying the distribution of success in the folk genre, I observe that the 25 % most successful artists account for 88.24 % of all sales, and the TOP10 account for 38.74 % (for a schematic representation, see Fig. 2).4 Hence, superstardom clearly exists in folk music, driven either by ability, media presence, or both. Results are presented below. Table 2 shows the variables’ statistics and correlations. Table 3 displays estimates for the OLS regressions. Table 4 presents the QR models. First, I introduce the controls (Model 0). Model 1 shows the results of the variables on overall sales. Model 2 displays the effects on physical recordings sales. Model 3 presents effects for digital sales. A number of our hypotheses are supported. A very important factor for enhancing sales in folk music is demonstrating ability by winning a highly renowned talent competition. The coefficient (1.094 for overall success, 1.564 for physical, 0.823 for digital sales) is about 19 times higher for total sales than the TV music show coefficient (0.057). The effects of talent recognition exceed those other influential variables, that is, online presence (0.832) and merchandise (1.063), particularly for physical record sales. Musical education (1.199) as a catalyst for talent development underlines the essential importance of ability. Hamlen (1991) estimates a log–log model and argues that coefficients far below one provide evidence against the Rosen (1981) star phenomenon. Here, the same rule holds, yet as only the dependent variable is logged coefficients above 0.7 would indicate a more than 100 % increase in the dependent variable if the independent variable increases by one unit (Maddala 1983). Thus, concerning the question of whether ability or self-marketing induce disproportionate rewards, coefficient comparisons point more strongly to ability (H1) (although music show 4
The most successful artists in our sample are Helene Fischer, Die Flippers, Amigos, Semino Rossi and ¨ tzi. DJ O
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Fig. 2 Sales distribution across artists
presence explains much of the variance according to its standardized coefficient). This holds for total sales but particularly, for physical sales; downloads sales react less sensitively to changes in the independent variables. The impact of ability on rewards is backed by Gingsburgh and van Ours (2003) study, who reported that doing well in the Queen Elisabeth talent competition significantly helps classical musicians in their careers. Yet, effects of winning a talent competition on digital sales are only just significant. The reason could be first, that download albums are often cheaper than physical albums, and second, that downloads are often played on electronic consumer equipment (PC, iPod etc.) that cannot bring out voice nuances as ll as high-performance home equipment playing physical albums—so consumers may be more tolerant as regards quality requirements toward artists in digital formats. In this context, note that despite radically changing the way people buy and access music following Apple’s introduction of digital music equipment and iTunes, late Steve Jobs himself reportedly preferred vinyl albums and vacuum tube amplifiers at home. Moreover, Neil Young, who has long been a critic of the music quality offered by MP3 and iPod, plans on soon releasing ‘‘Pono’’, a high-resolution music service designed to compete against the MP3 standard. Pono aims to introduce a line of portable players, a music-download service, and digital-to-analog conversion technology intended to present songs as they sound first during studio recording sessions (Flanary 2012). The results from this paper may imply a demand for this new technology. As another argument, it may be that physical albums are rather obtained with the purpose of showing them around during social entertainment, or for presenting them as a gift, so that high quality may be more prestigious and important in the context of records than for downloads that rather serve easylistening purposes for one’s private pleasure. Being awarded second, third, or fourth place in a talent competition also drives overall sales. Interestingly, it enhances downloads more than physical sales.
123
-0.11**
-0.00
-0.10**
-0.02**
(21) Age
(22) Gender
0.11**
-0.03**
0.11**
(19) Nationality 4
-0.07*
-0.18****
-0.00**
0.35****
0.19**
0.22****
0.00
0.54****
-0.03****
-0.05**
(18) Nationality 3
(20) Profession
-0.01*
-0.22****
(16) Nationality 1
(17) Nationality 2
0.35****
(15) Brand support
0.16****
0.16****
0.22**
0.27****
0.33****
(11) Online presence
(12) Online merchandise
(13) Pseudonym
0.02**
(10) TV presence nonmusic
(14) Signature feature
0.31****
0.57****
(9) TV presence music
0.16****
0.15**
-0.08***
0.15****
(6) Talent competition: participant
0.13**
0.17****
-0.14***
(5) Talent competition: awardee
0.63****
0.24****
(8) Experience
0.14***
(4) Talent Competition: winner
(2)
(7) Education
0.78****
0.26****
(3) Sales (digital)
0.88****
(1)
(2) Sales (physical)
(1) Sales (total)
Variables
Table 2 Correlations
-0.04**
-0.11**
-0.02****
0.10**
-0.04**
-0.15****
-0.03
0.36****
0.20**
0.16****
0.33****
0.21****
0.02
0.57****
0.16****
0.10***
-0.11**
0.12***
0.11**
(3)
0.06
0.00
-0.07**
-0.02
0.05
0.07*
0.01
0.13***
0.11**
0.05
0.14***
0.12***
0.10**
0.36****
-0.02
0.08***
-0.10**
-0.16
(4)
0.03
-0.10**
0.00
-0.03
0.07
-0.01
0.01
0.05
-0.03
0.03
0.09*
0.03
0.34****
0.06
-0.09**
0.04**
-0.14***
(5)
-0.01
0.07
-0.04
-0.06
0.08*
0.15****
-0.02
-0.03
0.02
-0.00
-0.08*
-0.04
0.30****
-0.07
-0.03
-0.03
(6)
0.18****
0.01
0.44****
0.01
-0.05
-0.02
-0.13***
0.01
0.02
-0.07
0.08*
0.10**
0.03
0.07
0.03
(7)
-0.08*
0.65****
0.04
0.06
-0.03
-0.05
-0.07*
0.05
0.02
0.05
0.10**
-0.05
-0.10**
0.01
(8)
-0.01
0.07
-0.01
0.14***
-0.01
-0.09**
0.04
0.31****
0.28****
0.09*
0.30****
0.16****
-0.05
(9)
0.11***
-0.09*
0.02
-0.12***
0.17****
0.14***
-0.02
-0.06
-0.06
0.01
-0.08*
-0.04
(10)
-0.10**
-0.13***
-0.09**
-0.01
-0.02
-0.04
0.06
-0.01
0.11**
-0.02
0.32****
(11)
-0.02
-0.14***
-0.10**
0.07
-0.03
0.01
0.01
0.04
0.09**
-0.01
(12)
J Cult Econ
123
123
0.09**
0.01
0.09*
(17) Nationality 2
(18) Nationality 3
(19) Nationality 4
0.08*
0.11**
(21) Age
(22) Gender
-0.04
-0.09**
(16) Nationality 1
(20) Profession
0.09**
0.09*
(15) Brand support
(13)
(14) Signature feature
(13) Pseudonym
(12) Online merchandise
(11) Online presence
(10) TV presence non-music
(9) TV presence music
(8) Experience
(7) Education
(6) Talent competition: participant
(5) Talent competition: awardee
(4) Talent Competition: winner
(3) Sales (digital)
(2) Sales (physical)
(1) Sales (total)
Variables
-0.05*
-0.05**
-0.11**
(23) Duo
(24) Group
-0.12***
(2)
(1)
Variables
Table 2 continued
-0.08*
0.06
0.03
0.06
-0.07
0.00
-0.05
0.14***
(14)
-0.09*
-0.03*
(3)
-0.05
0.06
0.01
0.01
-0.09**
-0.03
-0.03
(15)
-0.05
0.04
(4)
0.03
0.01
(5)
-0.08*
-0.10
-0.13***
-0.08*
-0.20****
0.25****
(16)
-0.03
0.01
(6)
0.18***
**
-0.01
-0.05
-0.12***
(17)
-0.14***
-0.09**
(7)
-0.09*
-0.08*
-0.02
-0.04
(18)
0.06
0.01
(8)
-0.02
-0.08*
0.04
(19)
0.12***
0.05
(20)
-0.14***
0.02
(9)
0.10**
0.00
(11)
-0.11****
0.06
(21)
-0.05
0.04
(10)
(22)
(23)
0.10**
-0.20
(12)
J Cult Econ
0.04
0.26****
(23) Duo
(24) Group
-0.04
-0.04
(14)
-0.06
-0.09**
(15)
0.18****
-0.05
(16)
-0.14***
-0.03
(17)
Significance levels (two-tailed): **** p \ 0.001; *** p \ 0.01; ** p \ 0.05; * p \ 0.1
(13)
Variables
Table 2 continued
0.09**
0.02
(18)
-0.06
-0.04
(19)
-0.07
-0.07
(20)
-0.07*
0.02
(21)
-0.41****
-0.16****
(22)
-0.27****
(23)
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123
Pseudonym
Online merchandise
Online presence
TV presence non-music
TV presence music
Experience
Education
Talent competition: participant
Talent competition: awardee
Talent competition: winner
Constant Term (0.554)
(0.260)
0.788***
(0.256)
1.063****
(0.205)
0.832****
(0.066)
0.176**
(0.008)
0.057****
(0.013)
0.053****
(0.259)
1.199****
(0.224)
-0.407*
(0.355)
0.709**
(0.514)
1.094**
3.306****
5.180****
(0.567)
0.098***
0.157****
0.130****
0.083**
0.349****
0.186****
0.168****
-0.057*
0.067**
0.073**
Std. coeff.
Coefficient (SE)
Coefficient (SE)
Std. coeff.
Model 1: sales (total)
Model 0: sales (total)
Table 3 Results: OLS regressions
(0.265)
0.809***
(0.250)
1.137****
(0.223)
0.463**
(0.079)
0.091
(0.008)
0.057****
(0.013)
0.050****
(0.249)
0.366
(0.249)
-0.491**
(0.397)
0.702*
(0.455)
1.564****
(0.554)
2.863****
Coefficient (SE)
0.106***
0.177****
0.076**
0.045
0.362****
0.185****
0.050
-0.072**
0.070*
0.110****
Std. coeff.
Model 2: sales (physical)
(0.246)
0.701***
(0.221)
0.920****
(0.197)
0.500***
(0.053)
0.099*
(0.007)
0.046****
(0.012)
0.045****
(0.240)
0.791****
(0.210)
-0.047
(0.257)
0.707***
(0.446)
0.823*
(0.476)
2.908****
Coefficient (SE)
0.103***
0.160****
0.092***
0.056*
0.333****
0.186****
0.131****
-0.008
0.079***
0.07*
Std. coeff.
Model 3: sales (digital)
J Cult Econ
Group
Duo
Gender
Age
Profession
Nationality 4
Nationality 3
Nationality 2
Nationality 1
Brand support
Signature feature
Table 3 continued
2.079****
(0.275)
-0.760***
(0.416)
-0.818**
(0.326)
-0.085
(0.010)
-0.012
(0.239)
-1.157****
(0.966)
1.781
(0.419)
-0.426
(0.347)
-1.937****
(0.268)
-0.345
(0.261)
-0.132***
-0.085**
-0.012
-0.050
-0.191****
0.073
-0.042
-0.254****
-0.055
0.317****
0.527
(0.230)
-0.540**
(0.313)
58**
-0.
(0.237)
-0.285
(0.011)
-0.011
(0.254)
-1.279****
(0.853)
0.151
(0.272)
-0.603**
(0.300)
-1.745****
(0.226)
-0.380
(0.244)
1.166****
(0.515)
-0.094**
-0.068**
-0.041
-0.048
-0.211****
0.006
-0.059**
-0.211****
-0.061
0.178****
0.038
Std. coeff.
Coefficient (SE)
Coefficient (SE)
Std. coeff.
Model 1: sales (total)
Model 0: sales (total)
(0.219)
-0.341*
(0.316)
-0.513*
(0.254)
-0.374
(0.011)
-0.011
(0.227)
-0.366*
(0.623)
-0.082
(0.321)
-0.551*
(0.271)
-1.197****
(0.225)
-0.460**
(0.245)
1.167****
(0.573)
0.223
Coefficient (SE)
-0.063*
-0.056*
-0.056
-0.049
-0.064*
-0.004
-0.057*
-0.153****
-0.078**
0.188****
0.017
Std. coeff.
Model 2: sales (physical)
(0.230)
-0.430*
(0.290)
-0.526*
(0.232)
-0.135
(0.010)
-0.006
(0.204)
-0.776****
(0.844)
0.342
(0.300)
-0.688**
(0.260)
-1.251****
(0.200)
-0.212
(0.230)
1.044****
(0.476)
0.217
Coefficient (SE)
-0.088*
-0.064*
-0.023
-0.032
-0.151****
0.017
-0.080**
-0.179****
-0.040
0.188****
0.018
Std. coeff.
Model 3: sales (digital)
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0.118
Adj. R2 0.545
0.564
30.508**** 0.456
0.478
21.619****
Coefficient (SE)
Std. coeff.
Model 2: sales (physical)
Significance levels (two-tailed): **** p \ 0.001; *** p \ 0.01; ** p \ 0.05; * p \ 0.1
Single performer, male is the default category for gender (single performer, female) and organizational forms 1 (duo) and 2 (group)
German nationality is the default category for the nationality variables
Dependent variables are logged
8.681****
0.134
R2
Std. coeff.
Coefficient (SE)
Coefficient (SE)
Std. coeff.
Model 1: sales (total)
Model 0: sales (total)
F
Table 3 continued
0.456
0.478
21.619***
Coefficient (SE)
Std. coeff.
Model 3: sales (digital)
J Cult Econ
Signature feature
Pseudonym
Online merchandise
Online presence
TV presence non-music
TV presence music
Experience
Education
Talent competition: participant
Talent competition: awardee
Talent competition: winner
Constant term
-0.054
(0.748)
-0.734
(3.393)
(0.356)
(0.442)
(0.386)
0.496
0.732
(0.410)
(0.260)
0.988**
0.434
(0.315)
(0.106)
0.428
(0.168)
0.033
0.133
(0.017)
0.221
0.056***
0.067*
(0.031)
(0.046)
(0.037)
0.058*
0.034
(0.368)
(0.403)
(0.304)
1.353****
1.475****
(0.497)
(0.475)
-0.100
-0.455
(0.771)
(1.167)
1.532***
(6.375)
0.429
2.150*
(0.872)
1.213
1.546*
1.788
Q20
(1.254)
Q10
Table 4 Results: quantile regression
(0.990)
-0.290
(0.360)
0.588
(0.334)
1.012***
(0.240)
0.655***
(0.094)
0.216**
(0.009)
0.067****
(0.018)
0.057****
(0.312)
1.413****
(0.284)
-0.578**
(0.414)
1.026**
(0.721)
0.946
(0.708)
1.401**
Q30
(0.787)
0.450
(0.361)
0.923***
(0.338)
0.866**
(0.265)
0.747***
(0.089)
0.254***
(0.008)
0.067****
(0.018)
0.051****
(0.331)
1.393****
(0.303)
-0.642**
(0.396)
0.851**
(0.628)
0.839
(0.712)
1.725**
Q40
(0.599)
0.263
(0.372)
0.956***
(0.324)
1.004***
(0.275)
1.006****
(0.094)
0.183*
(0.009)
0.071****
(0.017)
0.048****
(0.340)
1.081***
(0.203)
-0.553**
(0.405)
0.997**
(0.716)
0.988
(0.737)
2.234***
Q50
(0.747)
0.259
(0.358)
0.969***
(0.349)
1.211****
(0.280)
1.234****
(0.087)
0.124
(0.008)
0.060****
(0.017)
0.054****
(0.391)
0.927***
(0.305)
-0.690**
(0.413)
0.988**
(0.718)
1.482**
(0.700)
3.309****
Q60
(0.656)
0.542
(0.311)
0.586*
(0.327)
1.487****
(0.290)
1.071****
(0.090)
0.161
(0.014)
0.062****
(0.015)
0.052****
(0.340)
1.204****
(0.300)
-0.811***
(0.392)
0.513
(0.605)
1.318**
(0.617)
3.908****
Q70
(0.811)
0.711
(0.293)
0.625**
(0.341)
1.461****
(0.303)
1.082****
(0.087)
0.017
(0.011)
0.049****
(0.013)
0.044****
(0.340)
0.966***
(0.342)
-0.558*
(0.422)
1.050**
(0.613)
1.550***
(0.600)
4.589****
Q80
(1.425)
1.627
(0.336)
0.366
(0.396)
1.060****
(0.303)
1.141****
(0.105)
-0.052
(0.018)
0.055****
(0.014)
0.035**
(0.425)
0.358
(0.529)
-0.074
(0.393)
1.158***
(0.617)
1.525***
(0.700)
4.913****
Q90
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123
123 0.29
(0.292)
-0.285
(0.432)
-0.555
(0.330)
-0.048
(0.012)
-0.002
(0.242)
-1.507****
(0.671)
1.088
(0.432)
-0.697
(0.303)
-1.305****
(0.268)
-0.552**
(0.332)
1.372****
Q30
0.33
(0.301)
-0.154
(0.413)
-0.254
(0.326)
-0.173
(0.015)
-0.001
(0.269)
-1.502****
(0.756)
0.752
(0.446)
-0.685
(0.322)
-1.534****
(0.267)
-0.553**
(0.322)
1.287****
Q40
Significance levels (two-tailed): **** p \ 0.001; *** p \ 0.01; ** p \ 0.05; * p \ 0.1
0.26
(0.293)
0.26
(0.376)
(0.444)
-0.403
(0.554)
-0.312
-0.053
-0.429
(0.321)
(0.366)
(0.014)
0.114
(0.022)
0.032
-0.011
(0.251)
-0.023
-2.068****
(0.328)
(0.634)
(8.675)
-1.467****
1.617**
-1.533
(0.410)
(0.547)
(0.433)
-0.483
-0.424
(0.498)
(0.259)
-1.416***
(0.377)
-1.501***
-0.394
(0.385)
-0.180
0.976**
1.043**
Q20
(0.430)
Q10
Robust standard errors in parentheses
Dependent variable ln(sales)
Pseudo R2
Group
Duo
Gender
Age
Profession
Nationality 4
Nationality 3
Nationality 2
Nationality 1
Brand support
Table 4 continued
0.35
(0.328)
-0.267
(0.427)
-0.677
(0.345)
-0.265
(0.015)
-0.005
(0.274)
-1.570****
(0.746)
0.633
(0.555)
-0.513
(0.325)
-1.727****
(0.268)
-0.642**
(0.273)
1.022****
Q50
0.32
(0.349)
-0.834**
(0.430)
-1.264***
(0.356)
-0.508
(0.015)
-0.004
(0.321)
-1.218****
(0.807)
-0.190
(0.238)
-0.403*
(0.359)
-1.947****
(0.317)
-0.489
(0.274)
0.926****
Q60
0.32
(0.300)
-0.926***
(0.425)
-1.388***
(0.336)
-0.423
(0.013)
-0.008
(0.298)
-1.255****
(0.996)
-0.361
(0.154)
-0.329**
(0.357)
-1.937****
(0.285)
-0.142
(0.250)
1.009****
Q70
0.33
(0.323)
-0.938***
(0.474)
-0.705
(0.301)
-0.667**
(0.014)
-0.001
(0.338)
-0.845**
(0.980)
0.513
(0.332)
-0.599*
(0.408)
-1.620****
(0.280)
-0.208
(0.287)
1.047****
Q80
0.28
(0.359)
-0.706**
(0.444)
-0.451
(0.401)
-0.381
(0.015)
0.007
(0.438)
-0.426
(0.767)
0.558
(0.345)
-0.724**
(0.495)
-1.404***
(0.324)
-0.096
(0.364)
1.360****
Q90
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Consequently, it seems that consumers focus on ability in digital formats as ll, only peak performances are not required (potentially, for the reasons above). Remarkably, participation in a talent competition that results in being ranked behind the Top4 is disadvantageous for overall sales, and particularly, for physical sales. Thus, it seems that consumers do not wish to make purchases from artists that have been ‘‘officially branded’’ as ‘‘second rate’’ concerning their musical performance quality (see Mol and Wijnberg 2007 on legitimation processes in the music industry and changing ways in which consumers acquire information about music products and their quality). Education is highly significant for overall performance (H2a), but the effect is stronger for digital than physical record sales. Maybe for physical records, deficiencies in talent development are smoothed by the record label’s professional presentation of the artist and the CD, whereas digital albums lack both the haptic and visual qualities a CD offers. Also, they are sometimes marketed by the artists themselves, some of whom may not have developed a sufficiently professional label. Then, lacks in ability may be more obvious if songs are offered for download, so that albums are less interesting to buy, even for in-private consumption. Experience is highly significant across physical and digital sales (H2b). Thus, artists that are in the market longer tend to have higher sales (Hamlen 1991 found the same result for physical record sales in popular music), which is intuitive insofar as folk music is a steady market, artists that have achieved market success once tend to sale successfully over a long period, and learning effects will occur for artists as ll. Yet, compared with the other ability variables, the income effect of experience is small in terms of its standardized coefficient (0.013). Turning to the media presence variables, although TV presence in music shows is highly positively significant, its coefficient value is small (H3a). That is, whereas talent contest wins help gain ‘‘instant’’ disproportionate rewards, regular TV presence may be a very time-intensive, arisome road to gaining market recognition (yet much less risky than contesting). TV presence in non-music shows is slightly significant for overall and download sales (H3b). Online presence and merchandise have both significant positive sales effects (H4a, 4b). Still, I suggest that ability remains the central success driver for folk artists, as the aggregated effects of ability (including rankings, education) exceed both TV and online marketing effects. Besides, participation in contests (Grand Prix and others) could, theoretically, be repeated and its impact reinforced, whereas online marketing is either absent or there, so its upside potential, at least as measured here, is limited. As regards image creation, a pseudonym has a positive effect on physical records and download sales (H5a). The signature feature variable, although positive in sign, is insignificant (H5b). This result supports Amegashie (2009) arguing that the nonsinging attributes consumers care about may include various elements like hairstyle, smile, sense of humor, tone of voice when speaking (as opposed to singing), choice of clothing, etc. and that due to diverging and uncertain consumer preferences in non-singing activities, from the standpoint of the artists, it would be reasonable to treat these preferences simply as noise. Concerning the control variables, brand support by a major label enhances physical and digital sales. Being of Swiss nationality is a disadvantage for both
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physical and digital sales. Being Austrian or Italian is slightly worse than being German or of any other nationality. In addition, having high opportunity costs is strongly negatively related to sales—eventually, as it drives artists to concentrate efforts on pursuing a career elsewhere. Being a male soloist is better than presenting as a duo or a group, which supports Haan et al.’s (2005) findings. As the age variable turned out insignificant, I added a squared age term to the regressions. In effect, ‘‘age’’ as ll as ‘‘age squared’’ become significant (\ 0.001); the first term displays a negative, the second one a positive sign. So apparently, there is a U-shaped relation beten age and sales—being either very young or very old is most positively related to sales. Given the steep rise in the distribution of sales success, I next study which factors drive sales in the various segments from high- and low-selling artists, and whether these factors differ in their impact across segments. In fact, I observe major differences as regards the impact of the independent variables across the sales distribution: For upper- and top-performer positions (Q60–Q90), winning a talent contest largely boosts sales. Coming in 2nd to 4th place at the talent contest is beneficial for most artists, but particularly for those positioned in lor (Q20, Q30) quantiles (as shown by very high coefficients), and for ll-selling artists (Q80, Q90). Hover, having participated in a competition and not won anything decreases sales particularly for those artists that otherwise reach a lor or middle (Q30–Q50) or upper (Q60, Q70) sales position. Thus, there is not only an upside opportunity involved in talent competitions, but also a substantial risk for those either that are not unknown anyway (Q10, Q20) or that have already made it in the market (Q80, Q90). A reason that artists in the medium segments miss out on substantial income if not finishing in the top ranks could be, as suggested, that consumers remember these artists and now also recollect that they re officially rated as being worse than others, so such artists become ‘‘certified failures’’. Similarly, an experiment by Salganik et al. (2006)—also cited by Frank (2012) in a recent New York Times article on whether consumers like the music because of perceived music quality or simply because others like it—shod that if a few early listeners disliked a song that usually spelled its doom. But if a few early listeners happened to like the same song, it often nt on to succeed. Similarly, artists’ success in later years can be pathdependent on their earlier contest performance. An education in the field is positive for most artists, but loses relevance for those that are top-sellers, where the coefficient is insignificant. Experience is beneficial for most artists as ll, but not for the lost-performers; possibly, unsuccessful artists in the lost sales quantile drop out of the market after a short time anyway, so they simply do not stand a chance to gather much experience. TV presence in music shows presents an advantage for most artists, particularly for those in lor, middle, and upper quantiles (Q30–Q70). TV presence in non-music shows is beneficial for artists advancing toward the distribution’s middle (Q30–Q50), but the variable changes sign for the top quantile (Q90), indicating that non-music TV presence turns disadvantageous once an artist has gained substantial success already. Possibly, highly successful artists trying to have a finger in every pie lose time they had better invest in music-related activities to further enhance sales. For reaching the quantiles higher up (Q50–Q90), online presence via
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a personal homepage is valuable, and using a pseudonym increases sales as ll (Q40–Q80), although it is not important for the top segment (Q90). Engaging in online merchandise is positive almost across the entire distribution, unless in the lost quantile. Although using a signature feature is insignificant, the coefficient changes sign across the distribution as the negative effect it shows in the lost quantiles is reversed with increasing success of artists. Being supported by a major label enhances sales across quantiles, and particularly, for top-performing artists. The result supports Amegashie’s (2009) claim that marketing and promotion agencies can boost the success of, particularly, high-ability singers after they have won a music competition (through the choice of clothing, facial make up, appearances on shows, etc.), while it is much harder to improve the singing ability of a mediocre talent, and any popularity of a mediocre singing talent will eventually wane. Austrian nationality is disadvantageous for lor and middle segments (Q30–Q50), being Swiss is highly disadvantageous across the distribution, and being Italian may hinder advancing in upper segments (Q60–Q90). Having high opportunity costs for pursuing a career in the music industry is particularly negatively related to sales in the low and middle quantiles (Q10–Q50), yet the effect rather decreases across the distribution to higher selling artists (starting from Q20 onwards to Q80) and becomes insignificant for top-sellers— eventually so, because top artists are financially sufficiently profitable that pursuing a different career track is less attractive or less necessary compared with lowsuccess artists. This result mirrors a finding for the consumer side reported by Montoro-Pons and Cuadrado-Garcı´a (2011), where the implicit cost of leisure time explains concert attendances. Age and gender are almost nonsignificant across quantiles, but presenting as a duo hinders sales in upper segments (Q60, Q70), and presenting as a group is adversely related to sales performance in all the upper and top quantiles (Q60–Q90). As regards robustness checks for our findings, being with a ‘‘major label’’ warrants some further investigation. At first, I was concerned that, for example, having won a talent competition might have made an artist obtain a subsequent major label’s contract, and so, endogeneity issues could arise when using both variables together as predictors of sales. Similarly, major labels might push their artists into TV music shows or might initiate homepages and selling merchandise. Hover, although being with a major label correlates with TV presence and homepage use, the respective coefficients are comparatively low (0.30 and 0.32, see Table 2), and multicollinearity is not an issue in any analysis (VIFs are below 2). Moreover, when omitting the label variable and re-running all the estimations, results do not change substantially in signs or significance levels. Thus, I conclude that although labels can have an impact on the professionalization of artists, any linkage does not bias overall results. As additional analyses, having an album featured in the charts has been claimed to be a measure of artist success. Thus, I also ventured to incorporate chart success as a dependent variable; hover, few folk artists are ever present in the charts, and results remain inconclusive. I also considered winning music awards (Echo, Grammy, Comet), but most artists never won such awards, so sample size decreases drastically (data from mix1de, mediabiz.de, artist, and award homepages). I also
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controlled for the number of an artist’s fan clubs (schlager-fanbook.de), with positive but nonsignificant effects. As another measure for experience, I counted the number of previously recorded albums. Results are comparable to findings for the number of years spent in the market, yet slightly aker in significance (5 %-level). Besides, some believe that prominent achievers tend to subsequently underperform. In academia, Paul Samuelson describes the ‘‘Nobel Prize disease’’—winners ‘‘withering away’’ ‘‘into vainglorious sterility’’ and ‘‘preaching to the world on ethics and futurology, politics and philosophy’’ (Samuelson 2002). In business, the media have coined the term ‘‘CEO disease’’ to refer to CEO underperformance after gaining a top position in their organization (Byrne et al. 1991). In sports, the ‘‘Sports Illustrated jinx’’ is believed to negatively affect athlete careers if they appear on the magazine’s cover. In cultural industries, the term ‘‘sophomore jinx’’ refers to onetime successful new performers who never live up to meet the quality of their debuts (Malmendier and Tate 2009). Applying additional analyses including moderator terms and controlling for time periods passed since taking part in a talent contest, I do not find any significant time-related effects. So, the suggested jinx seems to spare this of all sectors at least.
6 Conclusion Based on real sales data for folk music artists in Germany, I study factors that determine physical and digital recording sales and how these factors apply across the sales distribution from low- to top-selling artists. I find that highly successful artists in the folk genre exhibit some recognized ability (particularly for physical record sales), while musical education and experience support sales success across the distribution. Hover, taking part in talent contests and not being recognized for talent is largely disadvantageous, particularly, for otherwise medium-successful artists. Thus, there is not only an upside opportunity in attempting to gain recognition through contests for the average artist, but also a substantial risk. As regards income effects of self-marketing, the average artist benefits moderately from TV presence which is less advantageous for established top-performers. For better selling artists, the value of online presence is more pronounced, and recordings sales also benefit from offering merchandise online. Besides, support by a major label is positively, and alternative career options are negatively, related to a career in folk music. Demographics play a role as ll. The impacts of these variables do not only vary across physical and digital formats, but also vary across segments from low- to top-successful artists. The findings offer some practical implications for a successful buildup of folk music careers. Given the risk involved in talent contest participation, where success would be a prerequisite particularly for physical record sales, the average successful artist may rather focus on download distribution strategies and push digital sales rather than physical recordings. Artists striving for instant recognition need to contest successfully. Yet, contesting may carry a lor risk in early career stages, before consumers’ collective memory does not ‘‘forgive’’ some ranking far behind anymore. Furthermore, career development is supported by marketing activities.
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Here, providing consumers with an online domain that also offers merchandise (in addition to direct, ancillary income), pays off better than, for example, selfmarketing by TV show participation. For low-performers and newcomers who could not position a hit album yet, there is nothing to lose in terms of career prospects if pursuing talent contests or self-marketing, but signature features seem awkward to the average folk fan. Still, based on the folk genre’s market inertia (successful artists stick around long-term) and artists’ opportunity costs, careers getting stuck in the lor segments will probably never develop into anything more than a hobbyhorse or require a shift to download markets. Moreover, major label support helps sales, yet ll-established labels may come at the cost of lor artist royalties than smaller labels (or self-publishing). Last, presenting as a sole artist is more promising than organizing into a duo or group. However, common wisdom holds that many ‘‘artists are poor’’ (Abbing 2002; Wetzels 2008), which applies to folk artists as well—and even to the top artists when compared with rock and pop stars (Musikwoche 2010). Our study provides several contributions: First, I show the expected pay-offs of two different career paths, (1) to develop one’s skills in one’s core business to perfection versus (2) to maintain the current level of skills and invest in selfmarketing. For the folk music genre, I find that higher ability strongly increases artists’ revenues, which hints at a direct superstar effect rather than a classical, TVdriven effect. Similarly, Minor et al. (2004) identified ability as the primary element that drives customer satisfaction with musical performances. Although online marketing is also beneficial to some extent, our findings are partly contrary to previous studies that stress a more fundamental importance of popularity and selfmarketing, and mostly focus on pop and rock genres (Adler 1985; Frith 2006; Gander and Rieple 2004; Montoro-Pons and Cuadrado-Garcı´a 2011; Ordanini 2006, 2007; Smith 2007; see Favaro and Frateschi 2007; Prieto Rodrı´guez and Ferna´ndezBlanco 2000; Vaubel 2005, on classical music). Second, I establish how these effects apply to artists’ rewards in different segments across the sales distribution. As a theoretical implication, the QR results also suggest that commonly applied regression-to-the-mean models may not provide the best solution to assess linkages between inputs and outcomes in music markets, as effects vary across segments. Third, I contribute to the literature on the music business by offering a first study that is based on actual sales data and on two different distribution formats, and also, considers an entire population of artists rather than some subsample, all of which should increase result relevance and reliability. Besides, as most studies focus on pop and rock, folk has gone largely overlooked, although it is of considerable cultural and economic importance [Georges and Sec¸kin (2012) argue a similar case for classical music, the fourth largest genre in Germany—behind folk music]. Fourth, our study contributes to the recent economics literature by arguing that even low powered incentives (Francois and Vlassopoulos 2008), as present for artists when consumers are allowed to get involved in talent-rating, strongly affect market outcomes. Fifth, our findings may provide some policy implications as regards the long-standing question of whether there is a market failure in the provision of the arts—particularly, in an art genre that is so central to the cultural heritage of Central Europe. As economic incentives encourage artists to address the needs and interests of the audience (Haan et al. 2005), some scholars argue that artists will ‘‘aim their
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offerings at the lowest common denominator thereby degrading cultural products by catering to the relatively uncultivated tastes of ordinary consumers’’ (Holbrook 1999). Yet, our results indicate that a systematic crowding-out of talent does not occur, contradicting claims that official intervention alone can preserve cultural heritage. As Cowen (1998) puts it, ‘‘aesthetic judgments that divide ‘high’ culture from ‘low’ culture fail to appreciate […] the efficacy of market forces in stimulating and sustaining creativity.’’ There are several limitations to this study. First, artists are motivated by intrinsic or extrinsic values (the individually perceived attractiveness of the activity where their talent is employed versus the lure of money), or some combination of both—as the economics of awards literature points out, people do not only strive for higher incomes, but also strive for gaining social distinction or peer group acceptance (Frey 2005; Frey and Neckermann 2008). Data on artists’ motivations is unavailable, so I cannot extend the analysis to cover psychological rewards. Besides, any measure of musical ability, or ‘‘talent’’, comes with limitations due to the inherently unobservable nature of the concept. I suggest that compared with hardly quantifiable, doubtfully discussed approaches proposed in earlier literature (e.g., supposed measures of concepts like ‘‘voice quality’’), being recognized as a topartist in a renowned talent contest—that even involves a two-sided selection process involving both experts and consumers—should present a more reliable proxy at least. Moreover, career paths may differ among artists and individual careers may not only depend on individual factors; for example, successful soloists may have started in duos or groups, using others’ talents as a stepping stone for achieving individual market success later on. Finally, despite the essential ingredient of musical ability, there will still be some degree of arbitrariness and chance involved in the genesis of a real star (see Salganik et al.’s (2006) study indicating that the best songs rarely did poorly, and the worst rarely did well, but any other result was theoretically possible). Thus, future research could investigate additional linkages between artist characteristics, career-enhancing mechanisms, contingency factors, and subsequent market outcomes, in order to help further demystify the process of successful career launches in the arts.
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