https://orcid.org/0000-0002-6553-421X
ABSTRACT
Drawing on a dataset following the careers of 214 early-career popular music acts in the Dutch live music industry over a period of eight years, this paper maps trends in the number of live shows early-career acts play and the fees they receive. In addition, it investigates factors that may explain success in live music. Descriptive statistics indicate that a small number of acts manage to perform a lot of shows and receive high fees. However, most acts are only able to play a few shows per year for a relatively low fee, indicating a winner-takes-all market mechanism. A multilevel analysis showed that critical recognition and popular recognition are positively related to the number of shows acts performed. Furthermore, label and booker representation and attending a pop academy has a positive effect on success in live music, adding insights to our conceptualization of the structural dynamics of live music industries.
Introduction
Throughout the history of popular music, recorded music and performing live have been the two main pillars for earning a living as a musician. But now, due to technological changes during the last 20 years, the music industries have been characterized by a decline in income from recording music, as audiences are buying fewer physical carriers such as CDs (IFPI, Citation2021). Although the recording industry has been on the rise again in recent years due to increasing revenues from streaming (IFPI, Citation2022), it remains difficult for musicians to earn a substantial income from streaming (Hesmondhalgh et al., Citation2021). As a result, live music continues to be an important source of income for musicians, accounting for around 31 percent (ibid.) or even as much as 47 percent (Fuhr, Citation2015) of their total income from music.
Yet, for most musicians in the Netherlands, the revenues from live music remain moderate (8,500 euros per year on average) (Fuhr, Citation2015). Moreover, as is in recorded music (Hesmondhalgh et al., Citation2021), live music revenues are distributed very unevenly (Krueger, Citation2019) and live music markets are characterized by a winner-takes-all economy (Hughes et al., Citation2016). Research has shown that out of the 12,000–15,000 acts headlining small venues, only 176 will go on to play large venues (Mulder, Citation2022). Due to its significance for securing an income as a musician, the fact that many musicians struggle to turn live music into a profitable business model and the apparently unequal distribution of live music revenues, it is important to map the dynamics of how careers in live music develop over time and which proportion of new acts manages to achieve success in live music.
In addition, because of the low odds of achieving success in live music, this paper investigates factors associated with becoming successful. For example, previous research has shown that recognition, e.g. critical, professional and popular recognition (Kersten & Verboord, Citation2014; Schmutz, Citation2016; Schmutz & van Venrooij, Citation2021), is an important prerequisite for artistic careers (Jensen & Kim, Citation2020), particularly careers in music (Everts et al., Citation2022). In addition, research has identified a wide range of other characteristics that may influence a musician’s chances of becoming successful. For example, gender continues to function as a strong predictor of a successful career in music (Berkers et al., Citation2019), along with the phase a musician’s career is in (Williams et al., Citation2019), their alliances with cultural intermediariesFootnote1 (Everts et al., Citation2022) or whether they attended a popular music programme (CBS, Citation2017). In addition, the genre in which a musician is active might affect their opportunities to perform (Hitters & van de Kamp, Citation2010). Yet, there is a lack of quantitative research to investigate the relationships between these factors and success in live music.
Therefore, this paper aims to answer the question what the dynamics are in the number of live shows early-career actsFootnote2 play and the fees they receive, and which factors explain success in live music. For this, the paper draws from a dataset that follows the careers of 214 early-career popular music actsFootnote3 in the Netherlands over an eight-year period. First, by means of descriptive statistics mapping the number of shows per year and the annual average fees paid per show as two measures of “sustained productivity” (Williams et al., Citation2019), this paper demonstrates dynamics and differences in the volume of their live performances and the fees they receive for their live shows. Second, a multilevel analysis is performed to determine the relationship between the number of live shows acts play with a series of variables measuring critical, professional and popular recognition and other characteristics. The next section will discuss which analyses were employed in order to answer the research question, after which relevant theory and formulated hypotheses are discussed.
Data and methods
To answer the research question of what the dynamics are in the number of live shows early-career acts play and the fees they receive, and which factors explain success in live music, a dataset was created of acts participating in the Dutch showcase festival Noorderslag. A showcase festival is “an event whose main goals are the promotion of emerging artists, and the networking of musicians and music industry representatives” (Galuszka, Citation2021, p. 56). As such, showcase festivals function as entry points in a market (ibid.). Indeed, Noorderslag is widely perceived as such (Kamer, Citation2016). Acts are selected to perform at Noorderslag based on the fact that acts are early-career and have a chance to become successful. In addition the showcase festival aims for a diverse and inclusive selection with regard to genre, gender and ethnicity of acts (RvC, Citation2022). While this paper does not aim to investigate the effect of the actual festival on their career, taking this showcase festival as a starting point for careers in the Dutch live music industry allowed me to select early-career musicians, and gives us insight into how the careers of early-career live music acts develop.
Research units
A hierarchically structured dataset, where years were nested within an act, was constructed in order to map the career of acts in the years after their participation in the showcase festival. This made it possible to collect variables on both the level of the year (primary level) and the level of the act (secondary level). The dataset of acts participating in Noorderslag was compiled using an online archive.Footnote4 As the dataset that contained the number of shows acts perform (see dependent variables below) offered reliable data from 2008 onwards, that year was selected as the starting point of the dataset. As the live show dataset was disrupted by the Covid-19 crisis in 2020, 2019 was selected as the last year of the dataset. To be able to follow acts over a substantial number of years, and to create a substantially sized sample, all acts were followed of the five first editions of Noorderslag from 2008 to 2012 (n = 214) over a period of eight years,Footnote5 resulting in 1712 research units.
Next, for each research unit two dependent variables were collected which can be understood as measures of the career success of acts in live music, and a series of primary and secondary level independent variables. The next sections will discuss these variables and the way data. A summary of the research units, dependent and independent variables and the most prominent used data sources to collect data can be found in .
Table 1. Descriptive statistics of the research units.
Dependent variables
First, the number of shows that the sampled acts performed in the eight years after their performance at Noorderslag were collected from two databases from the websites PodiuminfoFootnote6 and Festivalinfo.Footnote7 Together, these websites offer an overview of all popular music shows performed in venues and at festivals in the Netherlands over the measured period.
The second collected dependent variable is the annual average of the fees paid for shows of each act. For this, the annual average of the fees that these acts received for shows that had been booked by Mojo Concerts (Mojo) were collected. Mojo is the Netherlands’ largest concert promoter and organizes over 150 concerts per year as well as organizing several of the largest festivals in the Netherlands. As only a small part of the acts and the measured years in the sample was found in Mojo’s booking data (289 out of 1712 research units), the dataset proved unsuitable for statistical analysis of hypothesized relationships. Yet, taking this limitation into account, the data collected is useful for a descriptive analysis.Footnote8
Primary level independent variables
On the primary level of the measured years, one control variable was created measuring the number of years after Noorderslag. Then, in line with the theoretical framework and formulated hypotheses (see next section), a series of explanatory variables were collected measuring the amount of recognition each act received in the measured years. First, critical recognition was measured in a number of ways: (1) the number of album and concert reviews and interviews that each act received in De Volkskrant (Schmutz & van Venrooij, Citation2021), a widely circulated newspaper which is considered an opinion leader with regard to popular music; (2) the number of live television performances that each act gave on De Wereld Draait Door, a popular daily talk show in that period that showcased acts and was believed to kick-start music careers (Pisart, Citation2020); (3) the number of points acts received in the annual list of popular music magazine Oor, which publishes an annual list of the best releases each year based on a survey of 60 music journalists and radio DJs; (4) how often acts were nominated for the Dutch MTV awards (known as the TMF Awards until 2011) of Dutch music television channel MTV, and before 2011 its competitor TMF; (5) how often acts were nominated for the 3FM Awards, an award show organized by 3FM, a prominent public radio station dedicated to popular music.
Professional recognition was measured by looking at how often in a given year acts were nominated for three national awards that are awarded annually by juries of industry professionals: (1) the 3voor12 Awards, organized by popular music website 3voor12, (2) the Buma Awards, organized by the national collecting society for composers and publishers Buma/Stemra, and (3) the Edison Awards, organized by the Dutch association for the recording industry (NVPI) (Schmutz, Citation2016).
Popular recognition was measured by taking an annual average of Google TrendsFootnote9 score for each act (Montoro-Pons & Cuadrado-García, Citation2019), which shows a monthly number relatively measuring the frequency with which people search for an act, ranging from 0 (no searches) to a 100 (highest level of popularity). In addition, the “Song of the year award”, organized by 3voor12, was used where the audience can vote for their favourite song. The 100 most popular songs are ranked in a hierarchy, where 1 is the least popular song and 100 the most popular song.
Secondary level independent variables
On the secondary level of the acts, the data was enriched with several characteristics of acts that may influence career success in live music. First of all, dummy variables were created for all-female acts, mixed acts and all-male acts (included as the reference variable). In addition, to measure the age of an act, based on online music database Discogs,Footnote10 two variables were created for measuring the year an act released their first album, and the year they released their first EP. Here a value was calculated by subtracting the year the record was released from the year the act appeared at Noorderslag (similar to Schmutz, Citation2016). Next, to measure the level of alliances with industry intermediaries, drawing once again from Discogs, dummy variables were created indicating whether musicians self-released their music (reference variable), were signed with an indie label or were signed with a major label.Footnote11 Furthermore, drawing from their websites, social media, or the Internet Archive,Footnote12 dummy variables were created indicating whether acts were signed with a booking agency. As seven larger booking agencies represented half of the signed acts, and 66 agencies represented the other signed acts, a dummy variable was created for the smaller agencies and a dummy for the larger agencies to see whether there was a difference with regard to their effects. Next, a dummy variable was created measuring whether acts contained graduates of popular music programmes, for which data was collected from their websites, social media, the Internet Archive, online social network LinkedIn,Footnote13 and interviews accessible online. Lastly, once again relying on Discogs, dummy variables were created for the different genres acts performed, namely pop (reference category), electronic, rock, indie/alternative, hip-hop/rap, jazz/blues, singer-songwriter, funk/soul/world and folk/country.
Analyses
First, to visualize the distributions over time for the number of performed shows and the yearly average of fees paid to acts, a descriptive table () and two graphs ( and ) were created. Next, a multilevel regression analysis was performed to measure the relationships between the independent variables and the number of performed shows. First, a correlation matrix was created (see Table A1 in Appendix), where no multicollinearity between independent variables was identified. Furthermore, the distribution of the variables was inspected for normality and outliers by creating scatterplots. Then, as the dataset is hierarchically structured a series of multilevel models were created (see ) using the lmerTest package (Kuznetsova et al., Citation2017), with tables constructed using the stargazer package (Hlavac, Citation2018). First, a basic two-level intercept-only model was created (model 1) to which the fit of the subsequent models were compared. Then, the primary level independent variables were included (model 2). Lastly, all secondary level independent variables were added (model 3). In , for all models the log-likelihood was reported, which is a measure of the model fit where a higher value indicates a better fit. Histograms indicated a normality of the conditional residuals.
Figure 1. Distribution of number of shows over time.
Figure 2. Distribution of fees over time.
Table 2. Descriptive statistics of shows and fees per year.
Table 3. Multilevel effects models.
Limitations
In narrowing the scope of this study, the following limitations exist. First, the annual average of the fees must be interpreted with caution: as said, the dataset is only a small subset of the total number of shows in the Netherlands, and there may be biases in the sample. For example, Mojo might only book more established acts, inflating the fees that they receive. The other dependent variable does not include performances abroad, which can be an important part of earning a living in live music as the Dutch live music sector is relatively small. In addition, taking the number of performances in a year as a measurement of live career success has its shortcomings as the most successful acts may only have to play a relatively low number of shows in very large venues. Furthermore, because the number of performances and the primary level variables are measured in the same year, this study cannot say anything about the direction of the causal effect. While models were tried out in which the primary level variables were lagged for a year, this proved to be insufficiently accurate to find causal effects. Therefore, on the primary level the paper speaks of correlation, whereas on the secondary level the paper speaks of causal effects, as these characteristics preceded the measurement moments. The next sections will discuss theory on factors associated with becoming successful in live music, starting with different forms of recognition, that have informed the hypotheses that were tested by means of the discussed analyses.
Theory and hypotheses
In order to explore which factors explain success in live music, based on existing literature hypotheses were formulated on the relationship between the number of live shows acts play with the primary and secondary level independent variables introduced in the previous section. These are the hypotheses that were tested in the multilevel regression analysis:
H1. Acts with critical, professional and popular recognition and the number of shows perform more shows;Footnote14
H2. All-male acts will perform more shows than all-female or mixed acts;
H3. The age of an act will have a negative effect on the number of shows it performs;
H4. Acts with industry representation will perform more shows than acts without representation;
H5. Acts with music education graduates will perform more shows than others;
H6. Significant effects exist of different genres on the number of shows acts play.
H1: acts with critical, professional and popular recognition and the number of shows perform more shows
The music industries are marked by a winner-takes-all distribution, indicating that more acts will fail than succeed. Yet, it is difficult for industry intermediaries to predict who will become successful (Darr & Mears, Citation2017). This uncertainty is amplified by the high economic risks due to an unknown audience preference, and a constant oversupply of new acts (Hirsch, Citation1972; Negus, Citation2013). This uncertainty might be further increased by the digital technologies that social media and home recording software that have lowered the barriers for outsider musicians to enter the music industry (Wikström, Citation2009). Therefore, cultural intermediaries, such as booking music acts for live shows, (at least partially) base their decision on whether these products have acquired forms of recognition, as this increases their legitimacy (Janssen & Verboord, Citation2015). As such, artistic products are filtered based on the recognition they have received (Montoro-Pons & Cuadrado-García, Citation2019).
Three types of recognition can be distinguished: critical recognition, which is awarded by critics; professional recognition, which is awarded by peers; and popular recognition, which is awarded by audiences (Kersten & Verboord, Citation2014). Depending on how economically or artistically oriented all or a part of a cultural field is, we may find that different types of recognition relate differently to success. In the classic model of Bourdieu (Citation1993), cultural fields have an autonomous pole (which is relatively autonomous from economic logic) and a heteronomous pole (where an economic logic often prevails). In the autonomous pole, success is awarded by critics and peers based on artistic criteria (Dubois & François, Citation2013). In the heteronomous poles of cultural fields success is defined in economic terms and measured by popular recognition in the forms of sales, downloads or views.
However, not all cultural markets have such a clear dichotomous structure. For example, Velthuis (Citation2013) showed that even though actors in visual arts markets are confronted with these different logics, these cultural and economic values overlap and reinforce each other in a process of value creation. In other words, the relationship between these forms of recognition and success may differ based on the make-up of the specific subsection of the industry. Research specific to the music industry suggests that the autonomous part of the music industries has become part of the heteronomous part (Hesmondhalgh, Citation2006) and that the differences between autonomous and commercial approaches to work in music have eroded (Keunen, Citation2014; Klein et al., Citation2017). As a result, both professional, critical, and popular recognition may be positively related to success. For example, Schmutz (Citation2016) has shown that critical and professional recognition overlap with popular recognition in the coverage of popular music in newspapers, also in the Netherlands. Schmutz and van Venrooij (Citation2021) found a high degree of consensus regarding critical and professional recognition and commercial success in the American and British music industries. On the basis of these studies, it is hypothesized that there will be a positive correlation between all three forms of recognition and the number of shows that an act performs.
H2: all-male acts will perform more shows than all-female or mixed acts
First, in the creative industries, men tend to have more successful careers than women. While research in music has shown that audiences are receptive to female acts, there is still a glass ceiling. A survey of a 1000 pop songs on Spotify reported that women make up only 21.8 percent of the artists, 12.7 percent of the songwriters and 2.8 percent of the producers of those songs (Hernandez et al., Citation2021). In the Netherlands, only thirteen percent of the members of Buma/Stemra, the Dutch collecting society for songwriters, composers, and music producers, identifies as female (Berkers et al., Citation2019), and only 21.7 percent of live music performances is done by all-female acts (versus 78.3 percent for all-male acts) (Mulder, Citation2022). Furthermore, research indicates that women’s average income is lower (Fuhr, Citation2015): 17,835 euros compared to 32,611 euros for men (Berkers et al., Citation2019). There are different possible explanations for this discrepancy in representation and earnings for women, including the fact that women are expected to play different industry roles (Fuhr, Citation2015), existing stereotypes (Hesmondhalgh & Baker, Citation2015) or a lack of media attention (Berkers et al., Citation2016). Therefore, it is hypothesized that all-male acts will perform more shows than all-female or mixed acts.
H3: the age of an act will have a negative effect on the number of shows it performs
In addition, historical trends of the music industry demonstrate that audiences are characterized by a taste for the hip and trendy, and therefore pop acts have a high turnover rate (Caves, Citation2000). To satisfy this customer demand for novelty (Hirsch, Citation1972) and “to ensure that no large unsated demand among consumers materializes” (Lopes, Citation1992, p. 70) music industry intermediaries look for new trends when booking acts (Foster et al., Citation2011). This is also displayed in the fact that workers in the creative industries such as musicians tend to have more work at the beginning of their career (Williams et al., Citation2019). Yet, the question remains whether a novelty effect can also be measured for live music acts. Circumstantial evidence does suggest that in the Dutch music industries has reported that on average, musicians play the most shows in their twenties, and this number declines steadily in the following decades (Fuhr, Citation2015) – but this might not be caused by age, but rather issue of musicians shifting out of full-time music careers because of their life stage. In any case, it is hypothesized that the age of an act will have a negative effect on the number of shows it performs.
H4: acts with industry representation will perform more shows than acts without representation
Furthermore, success in creative industries should to a large extent be understood as a network effect (Williams et al., Citation2019). For example, earlier research has indicated that the music industries, especially in the Netherlands (Everts & Haynes, Citation2021), consist of a small, closely-knit network of cultural intermediaries like labels, venues and media that are able to connect acts to an audience (Keunen, Citation2014). As a result, being accepted by such networks has a huge effect on artists’ careers (Bielby & Bielby, Citation1994; Hirsch, Citation1972). Therefore, musicians collaborate with managers, bookers, and labels that can help them gain access to this network (Everts et al., Citation2022). These intermediaries use their network (Lizé, Citation2016) and wager their reputation (Bourdieu, Citation1993) to help further the careers of these musicians. Because of the importance of industry connections, it is hypothesized that acts with industry representation, such as being signed with a label or booker, will perform more shows than acts without representation.
H5: acts with music education graduates will perform more shows than others
Next, popular music programmes at music education institutions aim to prepare students for a career in the music industries. Evidence suggests a moderately positive relationship between music programme enrolment and career success. For example, research shows that overall a smaller percentage of high arts education graduates earns a low income (less than 30,000 euros) than the whole population of artists (CBS, Citation2017). Focusing on the music industries, a survey amongst Dutch musicians has shown that musicians with a higher music degree earn more from music than all other musicians (Fuhr, Citation2015). The reasons for this positive effect might be sought in the fact that attending popular music programmes, and art programmes in general, may have a symbolic value (Fine, Citation2017), help students to acquire the artistic and entrepreneurial competences necessary to perform well in the music industries (Bennett, Citation2015; Toscher, Citation2019) and assist them with obtaining a network that helps them to reach new audiences (Ballico, Citation2015) and access the resources and jobs required to build a career (Fine, Citation2017). Therefore, it is hypothesized that acts with music education graduates will perform more shows than others.
H6: significant effects exist of different genres on the number of shows acts play
Lastly, if genres increase in popularity, labels tend to increase their investments in those genres (Hitters & van de Kamp, Citation2010). Therefore, being active in well-recognized genres might result in more recognition (Negus, Citation2013), which has also been documented in other parts of the creative industries (Hsu, Citation2006). This effect might be especially strong as these acts are not well known (Hesmondhalgh, Citation2012). In the Netherlands, research has shown a huge variance in the number of live shows between genres: for example, rock, pop and electronic music are the most popular genres with 29.5, 23.7 and 20.4 percent of the total number of live shows, while genres such as indie, hip-hop, and funk (respectively 9.2, 8.1 and 4.8 percent) are much less popular (Mulder, Citation2022). Consequently, it is hypothesized that significant effects of different genres can be seen on the number of shows acts play. The next section will explore the outcomes of the performed statistical analyses, after the conclusions of this paper are summarized.
Results
To answer the research question, first is discussed how the number of shows and acts develop over time, before turning to the explanatory analysis to explain success in live music.
Overall dynamics
First, when looking at the number of shows in , the data is marked by a huge variation: the minimal value is zero performed shows in a year and the maximum is 89 shows. On average, acts played 9.09 shows per year over the whole measured period. However, at three shows, the overall median is much lower. This indicates a heavily skewed distribution whereby most acts only play a couple of shows per year, while a few go on to become very successful. A second aspect that stands out is that for most acts a decline over time can be seen. In the first year, the average number of shows is 22.1 and this number fell until it reached 3.8 shows in the eighth year. While the maximum number of shows is relatively stable, the same holds for the median, which drops from seventeen shows to zero in year six. This trend becomes clearer in , which depicts the distributions of the number of performed shows over time. For the lower three quartiles a strong drop after the first year can be noted, which further decreases towards zero until year eight. Although for the upper quartile a decline can be noted as well, this decline seems to stabilize around 30 shows per year. Overall, while the most successful acts seem to persevere over the years, the acts in the long tail stop performing over time, suggesting a winner-takes-all economy.
Although the fee data should be interpreted with caution, in a similarly skewed distribution can be seen of the annual average of fees paid to acts. The lowest fee average is 0 euros, whereas the maximum average is 60,000 euros. However, as the mean is 4530 euros and the median 2030, it becomes clear that most acts receive a relatively low average fee for their live shows, whereas a few earn a very high income from live shows. Remarkably, the most successful quartile of acts seems to be able to expand their lead with regard to the fees they receive between year one and year seven, and to a lesser extent the 75 percent quartile is also characterized by a small but steady growth (see also ). Part of the explanation might be sought in the fact that the percentage of missing values increases over the years, suggesting that acts in the sample that had previously received a low fee were no longer booked by Mojo in the later years, leading to an increase in the average of the fees paid by Mojo. All in all, for most acts it is difficult to earn a substantial income from live music, while a small part is able to profit and expand their lead. However, interestingly, a sharp drop was observed in the highest quartile of acts in year eight as well. While we must be cautious as we cannot see whether this is an outlier or part of a longer trend that persists after year eight, this change might indicate that there is an expiration date for even the most successful acts.
Explanatory analysis
provides the result of the multilevel analyses. First, a two-level intercept-only model was run (model 1). Here, the intraclass correlation coefficient (not included in ), which is a measure of the proportion of the variance that is explained on the secondary level (Hox, Citation2002), showed that 45.54 percent of the variance of the dependent variable is explained on the level of the acts. Next, model 2 included the primary level independent variables. As the log-likelihood value is higher than model 1 (), this suggests that the model has a better fit compared to the intercept-only model. This was confirmed by a χ2 (chi-square) test (not included in ) which compared the model fit based on these log-likelihood values: results indicated that the explained variance of this model is significantly higher than the two-level intercept-only model (χ2Change = 586.54, dfChange = 10, p < 0.001). Furthermore, the R2 (not included in ) indicated that the primary level independent variables explained 25 percent of the variance on the primary level and 57 percent of the variance on the secondary level. Lastly, model 3 included all primary and secondary level independent variables. Once again, the log-likelihood score indicates a better model fit. However, fewer research units were included here (1,360 versus 1,712 research units), as I was unable to obtain information for all acts for each secondary level independent variable, and therefore no χ2 test could be performed. For this model, no primary level R2 was calculated as only secondary level independent variables were added. However, 4.34 percent of the variance on the secondary level is explained by the secondary level independent variables, which suggests that the effect size of those variables is relatively low. As such, this indicates that the primary level independent variables are more important for understanding success in live music than the secondary level independent variables.
Primary level independent variables
Overall, the models suggest a positive correlation between critical and popular recognition and the number of shows that acts perform, but when it comes to professional recognition, the effects are less convincing. Both in model 2 and 3, primary level independent variables measuring forms for critical recognition note positive results with strong significance, except for being in the year list of Oor, which only had a moderately significant effect. Noteworthy here is an appearance in De Wereld Draait Door and being nominated for MTV and 3FM awards, all of which have high positive correlations with the number of performed shows. As such, these findings indicate that acts which receive critical recognition play more shows than acts which do not: therefore being reviewed, praised or covered by the media goes hand-in-hand with success in live music. Next, variables that measure forms of professional recognition paint a less clear picture. Whereas being nominated for the 3voor12 awards has a highly positive and strongly significant effect, this is not the case for other variables measuring professional recognition; winning an Edison or Buma award does not yield significant results. Therefore, these models do not unambiguously provide evidence of a positive relation between professional recognition and the number of performed shows. Lastly, the two variables measuring popular recognition, namely Google Trends and nominations for the Song of the year award both yield positive and strongly significant results in both models. In other words, these findings suggest that receiving forms of audience attention and appraisal are correlated with playing more shows.
Overall, H1 is partially confirmed. For all primary level independent variables no substantial changes can be seen in estimated regression coefficients between model 2 and 3, which suggests that the relationship between these forms of recognition and the number of shows musicians perform cannot be explained away by the secondary level independent variables.
Secondary level independent variables
The secondary level independent variables added in model 3 do not show the same convincing results as the primary level variables. Overall, this confirms that the relationship between forms of recognition and the number of performed shows is stronger than the relationship between individual characteristics of acts and their success in live music. To start, effects were measured of gender on the number of performances. Here, in contrast to the expectations, women or mixed acts did not perform significantly fewer shows than men (rejecting H2). While the descriptive statistics show that female and mixed acts are a minority in the sample, there is no significant difference in how well these acts are doing. Furthermore, no effect of the age of an act was found, as the time between their first album and first EP release and their appearance at Noorderslag did not have a significant effect (rejecting H3). However, a negative effect was found of the measurement moment on the primary level, suggesting the existence of some sort of novelty effect, but this is not caused by the time between the release of the first album or EP and participation in Noorderslag. Next, acts that were signed with an indie or major label performed more often than acts without label representation, although considering the small statistical significance (p < 0.1) this is only weak evidence of an effect. While the same applied to being signed with a small booker (noting a positive but weak effect), having a large booker did have a moderately significant positive effect. In other words, being signed with one of the larger booking agencies has a positive effect on the number of shows that acts performed. Overall, based on these findings H4 is partially confirmed, as they suggest that some forms of industry representation have a positive effect on success in live music. Furthermore, whether members of acts had attended popular music programmes also has a positive effect on the number of performed shows. Once again, this effect has a relatively small statistical significance (p < 0.1), which means that it only provides weak evidence of an effect (partially confirming H5). Next, the inclusion of genres did not yield notable results. The only exceptions here are rock music and folk and country, both of which have a positive effect. This suggests that no genres are booked more or less often than pop music, but that rock acts and folk/country acts get to perform more shows. However, considering the weak significance scores and the lack of effects for other genres, H6 was only partially confirmed.
Conclusion
In this paper, the paper mapped how the careers of live music acts develop over time. Results indicate that a few acts manage to perform a lot of shows and command high fees. However, most acts are only able to play a few shows per year for a relatively low fee. In addition, there are considerable differences between successful and unsuccessful acts in the first year of their post-showcase careers, which only increase over time. Overall, the number of performed shows decreases over the years, but, although we must be careful in view of the limitations of this part of the dataset, an increase can be seen in the fees these musicians earn over time.
There may be several explanations for the trend as found here. To begin, as acts struggle to find sufficient shows, musicians might break up their act and look for other ways to earn a living in music, causing the number of performances in the lower quartile to drop to zero. In addition, the decrease in the number of shows by superstars in combination with the increase in their fees may indicate that these acts have reached a level of success and corresponding fees where they can afford to play fewer shows as they receive higher fees. Lastly, the overall decrease in the number of shows that acts played in the years after Noorderslag might indicate that audiences lose interest and shift their allegiance to newer, trendier acts (Hirsch, Citation1972), confirming other findings that productivity in the creative industries tends to be concentrated at the start of careers (Williams et al., Citation2019).
Second, relationships were mapped between the number of live shows acts performed in the eight years after participating in Noorderslag and a range of possible factors associated with success in live music. In short, strong evidence was found that critical recognition and popular recognition are positively related to the number of shows performed. For industry recognition, mixed results were found. Including secondary level independent variables measuring other characteristics that may influence an act’s overall success only had a small effect. Remarkably, no evidence was found of an effect of gender. Furthermore, no effect was found for the age of an act, although a negative effect was found for the number of years that had passed after Noorderslag. Industry representation yielded positive effects with weak to moderate significance scores, as did attending a popular music programme. Regarding genre, only rock and folk/country acts outperformed pop acts, but overall genre did not seem to be an important predictor of success in live music.
All in all, this analysis fills a research gap on the careers of musicians in the live music industry, by showing the dynamics regarding how careers in live music develop in the early stages and musicians’ chances of establishing a successful live music act. In addition to the present winner-takes-all dynamics (Hughes et al., Citation2016), this study adds quantitative evidence on the relationships between success in live music and a range of characteristics associated in the literature with success in artistic careers. Secondly, this study adds various insights to our conceptualization of the structural dynamics of the live music industries. For example, the simultaneous importance of critical recognition and popular recognition (Schmutz, Citation2016; Schmutz & van Venrooij, Citation2021) suggests that cultural and economic logics may overlap in this part of the music industries (Hesmondhalgh, Citation2006; Keunen, Citation2014; Klein et al., Citation2017). In addition, the importance of industry representation for success in live music suggests that the live music industry is networked and that cultural intermediaries rely on contacts with other intermediaries who can advocate on behalf of acts (Bourdieu, Citation1993; Lizé, Citation2016).
A few possible relationships which were not explored may be interesting to pursue in future research. To begin, while critical recognition and popular recognition both correlated with the number of performances, the effects may differ if you differentiate for shows played in more commercial and more artistic venues or genres. Furthermore, while the relationship between being signed with a label and live performances was measured, it might also be interesting to investigate the relationship with other forms recorded music success, such as radio hit charts or streaming success. Lastly, as it was not relevant in light of the research question, this study did not look at random slopes, and as a result could not examine interaction effects such as between critical recognition and gender, or between critical and popular recognition and different genres. Nevertheless, this study offers insights to understand the dynamics involved in building a career in live music.
Acknowledgements
The researcher would like to thank Jay Lee for his support in the process of data collection, Joran Jongerling for his support with the analysis, and Pauwke Berkers, Erik Hitters, Joep Hofhuis and Kees de Glopper for their comments on earlier versions of this manuscript. The study upon which this work draws was supported as part of the project Staging Popular Music: Researching Sustainable Live Music Ecologies for Artists, Music Venues and Cities (POPLIVE) by the Netherlands Organization for Scientific Research (NWO) and the Taskforce for Applied Research (NRPO-SIA) [grant number 314-99-202, research programme Smart Culture – Arts and Culture]. Partners in this project are Mojo Concerts and The Association of Dutch Pop Music Venues and Festivals (VNPF).
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Notes
1 Cultural intermediaries can be defined “as those involved in the mediation between the production of cultural goods and the production of consumer tastes” (Janssen & Verboord, Citation2015, p. 440).
2 Early-career acts refer to acts that have transcended the position of amateur, but are not established acts yet (Everts & Haynes, Citation2021).
3 This paper focusses on the broader field of popular music which includes a plurality of genres such as pop, electronic, rock, indie/alternative, hip-hop/rap (Mulder, Citation2022).
4 http://www.iprecom.nl/noorderslag/archief/index.php, visited on 9-9-2022.
5 If acts participated more often than once in Noorderslag (which happened 10 times), the first appearance in the dataset was included.
6 https://www.podiuminfo.nl/, visited on 9-9-2022.
7 https://www.festivalinfo.nl/, visited on 9-9-2022.
8 To ensure the privacy of acts, the dataset has been anonymized after enriching the data and the paper will not refer to specific acts in this paper.
9 https://trends.google.com/trends/, visited on 9-9-2022.
10 https://www.discogs.com/, visited on 9-9-2022.
11 Major labels “represent a large copyright firm with operations in several countries and in control of a well-established distribution machinery” whereas indie labels “are the opposite of everything above, and have a stronger focus on the text, the creativity and the art, rather than the commerce” (Wikström, Citation2009, p. 28).
12 https://archive.org/, visited on 9-9-2022.
13 https://www.linkedin.com, visited on 9-9-2022.
14 Hypotheses are numbered H1, H2, H3 etc.
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Appendix
Table A1. Correlation matrix.
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