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Communication Research

125

The Author(s) 2016

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DOI: 10.1177/0093650215623834

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Article

The Globalization of PopularMusic, 1960-2010: A Multilevel

Analysis of Music Flows

Marc Verboord1and Amanda Brandellero2

Abstract

This study offers a cross-national multilayered analysis of music flows between1960 and 2010. Advancing on previous empirical studies of cultural globalization,it attends to the global and country level, while adding the individual level of musicflows. Concretely, the authors analyze the international composition of pop chartsin nine countries by (a) mapping trends, (b) comparing countries, and (c) conductingmultivariate analyses. The results show that pop charts increasingly contain foreignmusic, with the exception of the United States. Explanatory analyses of foreignsuccess confirm that limited cultural distance results in greater flow as found infilm and television studies, while revealing additional positive impacts of centrality

of production (e.g., artists from more central countries in music production aremore likely to chart abroad) and the star power of artists. Both the innovativemethodological approach and findings of this article offer promising research avenuesfor globalization, media industry, and celebrity studies.

Keywords

globalization, media flow, popular music, cultural centrality, cultural distance, starpower, celebrity

Introduction

Over the past decades, we have witnessed a clear increase in the exchange of culturalproducts across the globe, as evident in media trade studies of film (e.g., Fu, 2006; Fu& Sim, 2010) and music (Moon Barnett, & Lim, 2010), cross-national comparisons of

1Erasmus University Rotterdam, The Netherlands2University of Amsterdam, The Netherlands

Corresponding Author:

Marc Verboord, Department of Media and Communication, Erasmus School of History, Culture and

Communication, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands.

Email: [email protected]

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at Middle East Technical Univ on February 14, 2016crx.sagepub.comDownloaded from
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2 Communication Research

the newspaper coverage of culture (Janssen, Kuipers & Verboord, 2008), and com-parative analyses of popularity rankings of media products (e.g., Achterberg, Heilbron,Houtman, & Aupers, 2011; Fu, 2013). Yet empirical investigations of these trendsmostly focus on the macro level (by analyzing aggregated data), thereby underestimat-

ing the multilayered structure and complexity of cultural globalization (Appadurai,1996; Straubhaar, 2014), and neglecting the role of individual agents. Taking the caseof pop music, we advance on previous studies of global media flow in at least threeways. First, our focus on productsin our case, music hitsand their related artists,rather than on country aggregates of successful products, allows a more precise analy-sis of flows. Second, we conduct a multilevel analysis that estimates the relativeimportance of factors shaping international music flow at the global, country, andindividual level. Third, we compare explanatory variables from various theoreticalperspectives, while adding the understudied factors of music television and individualstar power.

Our aim is twofold. First, we set out to describe the trends in global music flow inthe period 1960 to 2010 that can be observed in nine Western countries differing insize, language, and relevance in the field of music production (the United States, theUnited Kingdom, France, Germany, Austria, Italy, Netherlands, Norway, Australia).More specifically, this article analyzes the international composition of pop chartsone of the most dominant market information regimes (Anand & Peterson, 2000)through which producers and consumers assess what is popular in the music market.Notwithstanding the various ways in which charts have been compiled over time (viasales, airplay, downloads) and methodologies (e.g., store surveys, product scans), theyhave become institutions featured in most mediatized societies, from the magazine tothe iTunes era (Anderton, Dubber, & James, 2012; Burnett, 1996).

Our second aim is to explain these trends, combining several explanatory models ofhow cultural media products spread across the world. The model of cultural proximity(or distance) perceives global media flow to be in close alignments to geo-cultural andcultural-linguistic identities (cf. Straubhaar, 2014). Economic models of media trademarkets place more emphasis on the importance of market size, yet also adopted cul-tural distance and language as predictors of media flow (e.g., Fu, 2013; Fu & Sim,2010). The model of cultural centrality, in contrast, accentuates symbolic productionand views transnational cultural exchange in the light of competition for dominantaesthetic position-takings and status in the global field (Janssen et al., 2008). Finally,more politically oriented scholarship has pointed to the role of media systems (e.g.,Norris & Inglehart, 2009) and to wider political sentiments that may impact a societysopenness toward foreign culture (Achterberg et al., 2011; Bekhuis, 2013).

Our contribution lies in combining these macro-level factors in one explanatorymodel to estimate their relative importance. We also add two factors that have beenassociated with globalization in cultural economics and celebrity studies, yet that haveso far largely been ignored in the study of media flow. First, we specify how televisioncontributes to spreading music across the globe by investigating the impact of theavailability of music television channels in a country. Second, we analyzeat themicro levelthe impact of individual superstars (Adler, 2006), that is, celebrities

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Verboord and Brandellero 3

whose brand-name recognition benefits sales (Turner, 2013), and the effect of partici-pation of artists in television talent shows (Redmond, 2014).

Methodologically, we contribute to the explanation of cultural globalization byapplying a multilevel design that enables us to analyze simultaneously the impact of

attributes of multiple origin countries and destination countries, country ties, and indi-viduals. Moreover, our research design explicitly takes into account that many culturalproducts do not attain global success. We do so by modeling artist-country dyads thatindicate whether an artist had success abroad or not (estimated separately for everydestination country in the design).

We first conceptualize cultural globalization, build our explanatory model based onprevious research, and develop hypotheses. We then provide a detailed methodologyand outline our findings and conclusions.

Theory and Hypotheses

Global Level Explanations of Music Flows: Centrality and Cultural

Distance

Over the past decades, scholars of cultural globalization have focused on defining thephenomenon and explaining its causes and effects (e.g., Nederveen Pieterse, 2004;Tomlinson, 1999). In general terms, cultural globalization can be conceptualizedaccording to two dimensions: first, the spatial transnational diffusion and availability

in any given location of cultural goods and media products, such as films, televisionprograms, books, and music singles, generated from elsewhere in the world (Crane,2002); second, the process of reflexivity and articulation of disparate cultures withinindividuals and cultural and media products, whereby multilayered identities emerge(Nederveen Pieterse, 2004; Straubhaar, 2014). Our research centers primarily on theformer conceptualization, and a number of models have been developed to explainsuch diffusion.

While earlier theorizations propounding a model cultural imperialist modelinwhich monopolistic core countries prevail over peripheral and semi-peripheral ones

(Wallerstein, 1979)have been largely dispatched (see Crane, 2002), media econo-mists have further scrutinized the impact of structural and economic conditions oncultural trade. The buying power of marketsoften measured by market size, popula-tion size, or prosperityhas been identified as a predictor of international culturaltrade dynamism (e.g., Fu, 2013; Fu & Sim, 2010; Hoskins, McFadyen, & Finn, 1997).More specifically, a large domestic market, often accompanied by a wider and morevibrant local cultural production, accrues a home market advantage (Waterman,2005). For instance, the larger a countrys size, the more its media consumption con-cerns domestic content (Oh, 2001; Puppis, 2009). Because of this advantage, produc-

ers can invest more and achieve higher quality products, in turn strengthening theirposition in the international market (Fu & Sim, 2010; Hoskins et al., 1997; Marvasti,1994). In short, according to this perspective, producers from a large domestic market,with large economic power, are better able to export their products abroad.

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Such economic explanations have been extended with symbolic dimensions by thetheory of cultural centrality. Starting from the concept of a global cultural field(Bourdieu, 1993) in which certain countries have higher concentration of symboliccapital and prestige than others (Casanova, 2004; De Swaan, 1999), this theory con-

tends that countries possess a differential ability to entice the imagination and interestof other countries through their cultural production (Appadurai, 1996; Heilbron,1999). Cultural centrality, thus, concerns the extent to which a countrys cultural pro-duction interests foreign producers, experts, and audiences. The more countries suc-ceed in attaining a central position in cultural production, exemplified by theinternational circulation or consecration of their products, the more they themselvesengage in domestic cultural consumption, and vice versa ( Heilbron, 1999; Janssenet al., 2008). In a similar vein, studies on media production have noted the distinctionbetween centers and peripheries, and how this informs the way producers search forinternational audiences (Mayer, Banks, & Caldwell, 2009). More specifically, in thepop music field, research continues to find strong domination of Anglo-American pro-ductions (L. Marshall, 2013; Negus, 1992; Regev, 2013; Robinson, Buck, & Cuthbert,1991). Based on these models, we draw the following hypothesis:

Hypothesis 1:Chances of foreign chart success are (a) larger for music productscoming from origin countries with large centrality in music production, but (b)smaller for music products going to destination countries with large centrality inmusic production.

A third set of explanations zooms into the importance of country ties based on theircultural proximity or distance (Straubhaar, 1991), incorporatingmore than otherapproachesthe agency of audiences and articulation of layered identities. Countriesfrom the same region often share history and cultural elements. At the same time, geo-graphically distant countries can display communalities by, for instance, having thesame language. Such geo-cultural and cultural-linguistic ties provide a shared basis ofunderstanding of and identification with cultural expressions that in turn facilitatesexchanges (Straubhaar, 2007, 2014). Meanwhile, audiences in countries that are cul-turally distant will draw on different experiences, taste cultures (e.g., aesthetic prefer-ences, genre conventions), and value structures (e.g., themes or moral messages in thecontent)to help them decode and digest foreign media content (see Morley, 1992;Straubhaar, 2014). Such cultural distance can negatively impact exchanges (Fu, 2013;Straubhaar, 2014). Indeed, studies of media flows have drawn on the national cultureframework of Hofstede (2001), which highlights several spatially enduring and col-lectively shared systems of values in societies, to show how such values can translateinto localized consumer preferences for foreign cultural products such as film andpopular music (Fu, 2013; Fu & Sim, 2010; Moon et al., 2010). Based on these theoreti-cal insights, we postulate the following hypotheses:

Hypothesis 2:Chances of foreign chart success are inversely related to the (a)cultural distance and (b) geographic distance between origin and destinationcountry.


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Hypothesis 3:Chances of foreign chart success are larger if the same language isspoken in the origin and destination country.

Country-Level Explanations of Music Flows: Media Systems, CulturalPolicy, and Political Sentiment

The way national cultural and media fields and markets are organized, and how theyare situated vis--vis other relevant institutions (Janssen et al., 2008; Mayer et al.,2009; Peterson, 2001), can also influence international music flows. Hence, countrieswith very different media systems or cultural policies will vary in how foreign mediaproducts reach audiences, irrespective of similarities in country size, prosperity, orcultural values (Norris & Inglehart, 2009). Therefore, we supplement our analysiswith three additional country-level factors to help explain popular music flows.

First, we propose that the media system of a particular destination country caninfluence which music becomes successful, since media agents act as gatekeepers whodecide which products and artists receive attention and which do not (Janssen,Verboord & Kuipers, 2011). Throughout its history, television has been an importantmediator in the marketing of popular music (Anderton et al., 2012; Frith, 2002),thereby moving music from the point of manufacture . . . to the point of sale (S.Jones, 2002, p. 217). Previous research found that more commercialized media sys-tems are often associated with stronger reliance on output from large multinationalcorporations, thus increasing foreign imports (for television, see Biltereyst, 1992). Wetherefore expect that in destination countries in which commercial television is moredominant, there will be more foreign music. In addition, the arrival of music televi-sion, starting with MTV in 1981, and the video clip in particular enabled artists to bebroadcast simultaneously across the globe. Yet criticisms of the strong Anglo-Americanfocus of MTV (see Banks, 1997) soon spurred the ascent of local versions of MTV aswell as local competitors (e.g., Viva, The Music Factory) that opted for more localmusic (Roe & De Meyer, 2001). We expect that this has stimulated demand for localproduct and thus hinders the influx of foreign music. We draw the followinghypotheses:

Hypothesis 4:The larger the audience share of commercial television broadcastersin a destination country, the larger the chances of chart success in that country.Hypothesis 5:Chances of foreign chart success are smaller if local music televi-sion is present in a destination country.

Second, the flow of popular music can also be affected, and indeed countered, by gov-ernmental policies (Bekhuis, 2013; Crane, 2002; Janssen et al., 2008). Various countrieshave reacted to the increasing import of foreignparticularly Americancultural prod-ucts by introducing quota for national products. For instance, since 1993, radio stations inFrance have been obliged by law to ensure that at least 40% of the songs they play areFrench. Also, in countries such as Australia and Canada, radio quotas have been in usesince the 1970s (Homan, 2012). Since quotas limit media exposure to foreign products,we expect that they, if present, hinder success of foreign music in destination countries.


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Hypothesis 6:Chances of foreign chart success are smaller if radio quota for for-eign music is present in a destination country.

Finally, we consider the political context of the destination country. Recent studies

have suggested the strong influence of a countrys political climate on its culturalpreferences. Achterberg et al. (2011) observe renewed interest in local music in France,Germany, and the Netherlands during the 1990s and interpret this renaissance ofnational music (p. 603) in terms of the changing political constellation after the fallof the Iron Curtain. Other scholars point at the importance of (right-wing) nationalismfor explaining the popularity of domestic culture (Bekhuis, 2013; Meuleman &Lubbers, 2013). These results imply that chances of foreign chart success may besmaller if the political climate in the destination country is more populist or right-wingoriented.

Hypothesis 7:The more prominent far-right political parties are in a destinationcountry in a given year, the smaller the chances of chart success in that country.

Individual-Level Explanations of Music Flows: Star Power and Language

Last, global and country-level explanations of flows overlook the individual compo-nent, in spite of it having been recognized as a determinant for success (Redmond,2014). Record companies partake in the celebrity industry where individuals arecommodified through promotion, publicity and advertising (Gamson, 1994; Turner,2013, p. 4), resulting in the branding of performers through an articulation of theirunique and distinctive identity (Negus, 1992; Wikstrm, 2009). For organizations inthe music industry, this implies controlling risks in an uncertain market (Caves, 2000;Negus, 1992), by developing formats that maximize audience share (Hesmondhalgh,2012; M. Jones, 2013), and strategically maximizing profits by investing in the devel-opment of a smaller number of highly recognizable celebrity-commodities in onelocation, and spreading the product to others (Jenkins, 2006; D. P. Marshall, 1997;Turner, 2013, p. 34). Strong brand-name recognition can lead to superstardom, wherethe higher an individuals star power associated with his or her previous success, thehigher the chances of further success (e.g., Adler, 2006; Basuroy, Chatterjee, & Ravid,2003; Canterbery & Marvasti, 2001; Verboord, 2011).

Hypothesis 8:The more individual star power a performer holds, the larger thechances of foreign chart success.

Celebrity engineering has gone hand in hand with the strengthening of transnationalconglomerations and the fluid exchange of products across countries and media platforms(Redmond, 2014; Turner, 2013). In the music industry, this has translated into globallycirculating music repertoires backed by major labels (Anderton et al., 2012; Burnett,1996; Redmond, 2014). The U.S. and U.K markets appear to be leading in establishingreputations in pop music (L. Marshall, 2013; Negus, 1992; Peterson, 2001; Regev, 2013),


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suggesting that success in these markets is crucial for achieving global success. Whilelocation could be a factor here, some evidence suggests that use of English is an advan-tage: Performers who use languages other than English receive less marketing support,media attention, and thus have smaller chances of foreign success (Hitters & Van de

Kamp, 2010; Negus, 1993). We therefore expect performers who use English to havemore foreign success compared with those performing in other languages. However,while in the 1980s and 1990s, record companies actively removed most local performersfrom their catalogue (Negus, 1992, p. 7ff), the 2000s saw renewed opportunities for localartists via cross-media collaborations such as franchised television talent showsAmericanIdol and Pop Idol (Redmond, 2014; Wikstrm, 2009). Given different countries havetheir own version of the format, we do not expect success in one countrys show to trans-late into foreign successbut we do expect it to provide a platform for local success.

Hypothesis 9:Chances of foreign chart success are smaller for performers singingin languages other than English.Hypothesis 10:Chances of foreign chart success are smaller for performers whoparticipated in television talent shows.

Method

Our data consist of all weekly top 15if unavailable top 10pop music charts in ninecountries for 11 sample years starting in 1960 with 5-year intervals.1The countriesunder investigation are the United States, France, Germany, the United Kingdom,Netherlands, Austria, Norway, Italy, and Australia. We selected these countries for acombination of reasons. First and foremost, we chose countries that differ in terms ofthe amount of music production, population size, and language. Nathaus (2011), forinstance, shows how the music markets in the United States, the United Kingdom, andWest Germany took significantly different trajectories after World War II. Then thereis the issue of availability: Charts needed to be accessible (via the Internet or archiveswithin range of the primary researchers) for the period 1960/1965-2010. Limitationsin time and material resources confined our sample to nine countries. For this article,we chose to emphasize diversity rather than resemblance (e.g., selecting Australiainstead of Canada; Italy instead of Switzerland).

Research units. The collected charts yielded a sample of 11,513 songs by 3,760 uniqueperformers. In our multivariate analyses, we analyze performer-country dyads toaddress the fact that our data do not comprise a random sample of the total populationof performers. Our sample only comprises successful acts: artists who managed toenter the top 15 in a particular country and a particular year. Since data on all singlereleases in our sample countries and years is beyond reach, we address the selectionproblem by constructing an artificial population as was done in previous studies ofculture (see Schmutz & Faupel, 2010, who only examined performers who receivedsome form of cultural consecration or popular recognition). We argue that from across-national perspective, many successful performers are actually not successful


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because they fail to chart (in the top 15) in other countries. For every country, chartedperformers will therefore be complemented by uncharted performers (as found in ourother sampled countries). Hence, for each sample year, we identified all unique songsfound in our nine countries: These comprise the population of the year.2Then, we

established for each song-country combination whether the song charted or not. Thesedata were then aggregated to the performer level since looking at individual songstends to underestimate the success of performers who often chart (as their smaller hitswill dampen their overall impact).

In sum, the advantage of analyzing performer-country dyads is that unobservednon-hits (e.g., Dutch hits that did not chart outside of the Netherlands) are turned frommissing into observed non-hits, while global hits remain recognized as such.Consequently, the research units from the data set grow from 11,513 (songs) to 40,860(performer-country combinations). A final step involved selecting only performer-country combinations that referred to foreign success. This left us with 36,719 researchunits. In the explanatory analyses, we use the dummy variable chart entry as ourdependent variable (thus, having not charted in a country is the reference category).

Measurements. For each unique individual performer (including bands or orchestras)found in our sample, a range of background characteristics was retrieved using popencyclopedias and various Internet sources (artists and record companies websites,Allmusic.com, Rateyourmusic.com, etc.), in addition, cross-checking for increasedreliability. We looked up the performers nationality, defined in terms of the countrythat the performer is legally a national of, rather than place of birth or place of resi-dence. One exception to this measurement concerns projectsthat is, collaborationsbetween one or more producers and a varying set ofsometimes unidentifiablestudio musicians. We coded these projects according to the location in which theywere established. The same applies to non-persons such as cartoon characters. Othertypes of artist collaborations (e.g., duets) were coded for multiple nationalities; how-ever, in our analysis, we use the dominant nationality (e.g., most frequent one amongband members, the performer on whose album the duet is featured).

Country characteristics. We thus distinguish between origin countriescountries wherethe performers come fromand destination countriesthe nine countries our chartinformation refers to. Most variables we use were modeled for both types separately,since a characteristic that enhances export success of performers coming from thatcountry may at the same time limit the tendency of that country to import music fromother countries.

Our measure of centrality of production is based upon Heilbron (1999) and Janssenet al. (2008). We count how many successful music performers a country generated inthe year prior to the ones in our sample. We achieved this by calculating for everyorigin country the share of all performers in the other destination countriesthusexcluding domestic successin the end-of-year charts of the year preceding our sam-ple year (e.g., 2009 was used to determine the centrality of countries for 2010). Thispercentage was used as an indicator of the centrality of production.


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Cultural distance is established using Hofstedes (2001) cultural indicators, whichprovide indexes for how cultural values differ across various countries (cf. Fu & Sim,2010). We use the four core dimensions Hofstede, and others after him, distinguishthat is, power distance, masculinity, individualism, and uncertainty avoidanceand

calculate for each dimension the difference between the index scores of origin anddestination country. The mean difference is used as a variable: the larger the differ-ence, the greater the cultural distance. Countries that were absent from the data (par-ticularly, some African and Caribbean countries) were imputed using the scores ofneighboring countries. This variable is treated as a time constant. Geographical dis-tance is measured calculating the distance in kilometers between the capitals of theorigin and destination countries (via the website www.timeanddate.com). Presence ofa language tie is recorded if the countries share an official language (observed in theCentral Intelligence Agencys World Factbook) (0/1). The prominence of far-rightpolitical parties in the destination countries was measured by retrieving the share thatextreme right-wing or populist right-wing political parties had in parliament in thesample year. We relied on Lubbers (2001) and Ignazi (2003) for our definition ofextreme right-wing political parties, and used the following online sources: http://www.parties-and-elections.eu/countries.html (Europe), www.elections.uwa.edu.au(Australia), and www.loc.gov/rr/program/bib/elections/statistics.html(United States).

Commercial orientation of the media field in the destination countries was opera-tionalized by the market share of mainstream commercial broadcasters per country persample year. Various Internet sources were used to find these data. Note that for theUnited States and Australia, we used invariable estimates based on the scarce informa-tion available (1997 and 1985, respectively). The impact of music television wasexamined by creating a variable that measured for each country for each year whetherthere was no music television (0), regional music television (1), or local televisionavailable (2). Regional music television refers to the situation when only music televi-sion channels are available that target other countries or broader country areas (e.g.,MTV Europe, MTV Nordic, Music Box in continental Europe), while local musictelevision refers to channels targeting the country under investigation (e.g., MTV inthe United States, TMF in the Netherlands, Music Box in the United Kingdom).Finally, we retrieved the existence of radio quota for domestic music. In our sample ofdestination countries, only in France and Australia do such quota exist (respectively,since 1994 and 1973). This variable is measured as the percentage, scaled between 0and 1.

Control variables. Effects of production centrality may be associated with the popula-tion size or the economic prosperity of a country. We thus control for these variables.For each country in each sample year, we retrieved information on the population size(in millions) and GDP per capita using Internet databanks of the United Nations Edu-cational, Scientific and Cultural Organization (UNESCO) and World Bank.

Individual level characteristics. We operationalized the star power of each performer asthe number of end-of-year hits in the 5 years preceding the sample year in the U.S.

http://www.timeanddate.com/http://www.parties-and-elections.eu/countries.htmlhttp://www.parties-and-elections.eu/countries.htmlhttp://www.elections.uwa.edu.au/http://www.loc.gov/rr/program/bib/elections/statistics.htmlhttp://crx.sagepub.com/http://crx.sagepub.com/http://crx.sagepub.com/http://www.loc.gov/rr/program/bib/elections/statistics.htmlhttp://www.elections.uwa.edu.au/http://www.parties-and-elections.eu/countries.htmlhttp://www.parties-and-elections.eu/countries.htmlhttp://www.timeanddate.com/

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and the U.K. singles charts. Because chart success is associated with wider star-celebrity status (Redmond, 2014, p. 52), this measure provides us with an indicationof how much brand-name recognition performers had immediately prior to the chartsuccess we analyze (see also Schmutz & Faupel, 2010; Verboord, 2011). Note that

for this indicator, we only used U.S. and U.K. charts given these countries centralpositions both in the history of pop music and in the contemporary field of musicproduction. Not only are these countries generally considered to set standards withregard to music innovation, genre development, and production logics, but they alsoexert their influence through the increasing globalization of media and culturalindustries, in which the English language is often dominant. Finally, we have infor-mation on the participation of performers in television talent shows (e.g.,AmericanIdol, Pop Stars). These data come from artist profiles on the Internet (personalpages, pop encyclopedias, etc.). Since the relevance of such shows appears larger forrecent years, and (from a practical viewpoint) artist profiles of older performers aregenerally more limited, we only use this variable for the period after 1985.

Many of the used variables have skewed distributions. Therefore, we log-trans-formed the following variables: centrality of production, population size, GDP percapita, cultural distance, geographical distance, and performers star power.Subsequently, these variables were rescaled between 0 and 10 by dividing through themaxim and multiplying by 10 for the sake of comparability.

Statistical analyses. Our data are both hierarchically and non-hierarchically struc-tured. Performers are nested within countries, but destination countries and origincountries cannot be ordered as the latter do not exclusively situate themselves underthe former. To correct our standard errors for these clusterings, we thus use cross-classified multilevel models in which Level 1 consists of the performers, Level 2 theorigin countries, and Level 3 the destination countries. We used the Markov ChainMonte Carlo (MCMC) option in MLwin (see Hox, 2010; Raudenbush & Bryk,2002). Note that modeling performers at Level 1 is necessary to handle the multipleobservations of artists in combinations with destination countries in a statisticallycorrect way. While we find 76 unique origin countries in our data, many of thesecountries only represent one or two artists, which may lead to unstable effects(Raudenbush & Bryk, 2002). We thus created a new country of origin variable inwhich some geographically and culturally proximate countries were clustered, andother countries (for which no meaningful cluster was possible) were left out of theanalyses. As a criterion, at least four unique performers needed to represent thecountry or cluster of countries in order to be included in our data set. Our final dataconsist of 41 origin countries, of which 31 are unique countries, and 10 areclusters.3

Results: Overall Trends

Figure 1 shows the trend lines for all nine destination countries between 1960 and2010 with regard to the extent to which the pop charts are globalized, seen from


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percentage of hits of foreign origin they contain. These results are weighted for thenumber of weeks in the chart. In the period from 1960 to 1980, we note that Norway,the Netherlands, Austria, Germany, and Australia have the most globalized pop charts(about 70% to 95% foreign acts), whereas the United States, the United Kingdom,Italy, and France have the least (mostly below 50% or even 40%). After 1980, how-ever, this pattern changes. The year 1985 is pivotal: Being the first year in our samplein which the rise of the video clip and music television is observable, it stirs globaliza-tion in almost every country in our sample. After 1985, the United Kingdom, Italy,and, to a lesser extent, France resemble more and more the other European countriesand Australia. The United States, in contrast, increasingly turn to their own music, hit-ting an all-time low of 8.8% foreign hits in 2005.

The timing of globalization of popular music differs per country. In France, thebiggest leap toward more international hits was taken as early as 1980, while inItaly, we only observe this in the following sample year, 1985. Most of this global-ization should be attributed to American and British hits as these countries com-prise between 57% and 75% of the market between 1960 and 2010 (see Figure 2).Still, in the 1980s, also more peripheral countries launched successful artists via thevideo clip (e.g., A-Ha from Norway, Falco from Austria), and in the 1990s, weobserved many internationally successful Eurodance performers from Germany,Italy, and the Netherlands. It should be noted that most of these acts perform inEnglish.

While the general trend is one of globalization, the United States is not the onlycountry where domestic music becomes more present after 1985. In Germany, therehas been a slow rise of domestically produced music since the 1970s. In the Netherlands,

Figure 1. % Hits by foreign artists, for destination countries (weighted by weeks in chart).


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we observe peaks in the popularity of domestic music in 1995 (35%) and 2005 (36%),while also in Norway, domestic music becomes slightly more popular after 2000

(25%-30%). Of course, these market shares of domestic music are still very modest ifwe compare them to those in the United States, and they do not counter the prevailingtrend toward increasing globalization since 1960.

Results: Explanatory Analyses

The descriptive analyses of trends in globalization of pop charts demonstrate howvarious factors are intertwined: Some countries of destination are more open toforeign pop music, while some countries of origin are more successful in export-ing their music. In a multilevel analysis, we now explain chart success across ournine sample countries, distinguishing variables for destination countries, origincountries, country ties, and individual performers (see the Appendix for thecorrelations).

The empty model, containing only random intercepts, is used to calculate the rela-tive importance of the distinguished levels through the decomposition of the variance.Note that in a logistic regression, the variance at the individual level equals 2/3 = 3.29(Hox, 2010, p. 130). Since the variance in the empty model is .209 at the destination

level and .810 at the origin level, in total, 76.4% of the variance is located at the indi-vidual level (3.29 / [3.29 + 0.209 + 0.810] = 0.764). About 4.7% of the individualdifferences in charting can be attributed to the destination country and 18.9% to thecountry of origin.

Figure 2. % Hits abroad, for origin countries, 1960-2010 (weighted by weeks in chart).


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Table 1 presents the results of the multilevel logistic analyses. We present analysesof the complete data set, and separate analyses for the early period (1960-1980, beforethe introduction of MTV) and the post-MTV period (1985-2010), because we areinterested in testing the explanatory power of music television. Variables are ordered

according to theoretical relevance (global, country, individual level) with the excep-tion of GDP and population size, since these are control variables for centrality ofproduction (of both destination and origin country).

Global Level Explanations

Origin effects. There is a huge positive effect of the centrality of production of the origincountry. That is, the difference in success abroad between performers coming from themost central countries and the least central ones is almost (1/1 + e (0.226)) 55.6 percentpoints. This effect remains strong when controlling for other variables in Models 3 to 5.Comparing 1960 to 1980 with 1985 to 2010 (Models 6 and 7) suggests that the impact ofcentrality has become less important over time. These results corroborate Hypothesis 1a,which states that music originating from more central countries has higher chances of suc-cess abroad. In addition, GDP also sorts an effect for origin countries: Coming from moreprosperous countries seems to decrease chances of chart entry abroad. Population size ofthe country of origin does not influence the performers chances of foreign success.

Destination effects. At the global level, attributes of the destination country impact thechances of chart entry in a limited way. We find no significant effect of centrality ofproduction, which implies that the position that a destination country has in the inter-national music market neither hinders nor facilitates access to its chart. Both controlvariablesGDP and population sizedo have an impact implying that economic fac-tors matter. The larger the population, the less accessible the chart is to foreign artists.At the same time, we find that richer countries seem more accessible than poorercountries, suggesting that larger buying power of audiences makes them more inclinedto look for foreign products. When we leave out population size to test whether thisvariable suppresses the effect of centrality (in Model 5), we observe this is not thecase. Although the latter effect of centrality becomes a bit stronger, it is not significant.We thus reject Hypothesis 1b: Destination countries that take a central position inmusic production are no more difficult to enter than less central countries.

Country ties effects. The country ties effects test the importance of cultural and geo-graphic distance as well as language sharing. There is a clear negative effect of culturaldistance (B = .122), which corroborates previous research in film and television:Larger cultural distance between countries of origin and destination thwarts chances ofglobal success for pop artists. Hypothesis 2a is supported. Comparing Models 6 and 7suggests, however, that the impact of cultural distance is declining slightly in timeinline with the trends we reported earlier. Geographical distance and language ties haveadditional impacts on foreign chart entry. The larger the physical distance between


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14

Table1.Cross-ClassifiedMultilevelLogisticRegressionofChartEnt

ryAbroad,forNineCountrie

s,1960-2010.

1

960-2010Model1

Model2

Model3

Model4

Model5

FixedPart

Year=19

60

Ref

Ref

Ref

Ref

Ref

Year=19

65

.233(.086)*

.312

(.108)***

.337(.095)***

.278(.093)***

.30

4(.099)***

Year=19

70

.366(.079)***

.293

(.129)**

.342(.096)***

.357(.104)***

.41

9(.109)***

Year=19

75

.127(.082)

.053

(.216)

.059(.135)

.082(.146)

.23

4(.176)

Year=19

80

.373(.080)***

.006

(.309)

.200(.185)

.323(.197)

.56

0(.252)*

Year=19

85

.682(.079)***

.325

(.293)

.530(.174)**

.704(.190)***

.91

8(.237)***

Year=19

90

.405(.078)***

.166

(.403)

.166(.237)

.434(.249)

.73

8(.329)*

Year=19

95

.467(.079)***

.110

(.439)

.290(.255)

.459(.271)

.77

7(.364)*

Year=20

00

.528(.077)***

.038

(.435)

.443(.248)

.599(.272)*

.91

5(.358)*

Year=20

05

.221(.078)**

.357

(.505)

.079(.290)

.297(.309)

.67

3(.418)

Year=20

10

.442(.081)***

.215

(.534)

.244(.302)

.532(.329)

.93

1(.445)*

Globallevel

Origineff

ects

Centra

lityproduction(0-10)

.226

(.014)***

.227(.014)***

.171(.014)***

.17

6(.015)***

GDPpercapita(0-10)

.109

(.039)**

.119(.038)***

.167(.037)***

.19

0(.032)***

Populationsize(0-10)

.150

(.068)

.204(.071)**

.097(.059)

Destinationeffects

Centra


.016

(.014)

.004(.015)

.016(.015)

.02

7(.015)

GDPpercapita(0-10)

.190

(.069)**

.149(.056)**

.137(.044)**

.08

6(.065)


.380

(.063)***

.479(.111)**

.226(.044)***

(continued)


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15

1

960-2010Model1

Model2

Model3

Model4

Model5

Countrytieseffects

Culturaldistance(0-10)

.122(.021)***

.122(.017)***

.12

2(.017)***

Geogra

phicaldistance(0-10)

.230(.032)***

.274(.027)***

.29

7(.036)***

Languagetie(0/1)

1.395(.093)***

1.439(.081)***

1.43

8(.081)***

Countrylevel

Destinationeffects

Far-rightpolitics(0-10)

.015

(.009)

.024(.010)*

.026(.010)**

.02

3(.010)*

Audien

cecom.TV(0-10)

.038

(.013)*

.004(.021)

.003(.015)

.00

9(.015)

Individuallevel

Starpower(0-10)

.265(.008)***

.26

6(.008)***

Language

=Eng.

Ref

R

ef

Language

=Fra./Spa./Ita./Ger.

.658(.074)***

.66

3(.075)***

Language

=small

1.187(.169)***

1.18

9(.161)***

Language

=instrumental

.019(.107)

.02

1(.103)

Intercept

2.126(.175)***

1.337

(.295)***

1.879(.450)***

1.459(.260)***

1.01

4(.467)

Random

par

t

Var.Level3(destinationcountry)

.211(.134)

.807

(.585)

.839(.670)

.273(.219)

.45

1(.315)

Var.Level2(origincountry)

.862(.222)

1.236

(.365)

1.685(.514)

1.066(.287)

.89

5(.246)

Var.Level1(artist)

Deviance(M

CMC)

27,431.980#

27,0

71.115#

26,110.021#

24,741.401#

2

4,768.099

Observation

sdestinationcountry

9

9

9

9

9

Observation

sorigincountry

41

41

41

41

41

Observation

scountryties

2,414

2,414

2,414

2,414

,2414

Observation

sindividual

36,680

36,680

36,680

36,680

36,680

Table1.(

continued)

(continued)


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16

19

60-1980Model6

1985-2010Model7

1985-2010Mode

l8

FixedPart

Year=19

60

Ref

Year=19

65

.333(.119)***

Year=19

70

.557(.170)***

Year=19

75

.442(.327)

Year=19

80

.902(.481)

Year=19

85

Ref

Ref

Year=19

90

.216(.101)*

.285(.106)**

Year=19

95

.169(.120)

.230(.121)

Year=20

00

.056(.130)

.004(.133)

Year=20

05

.329(.166)

.218(.170)

Year=20

10

.064(.182)

.029(.182)

Globallevel

Origineff

ects

Centra


.169(.032)***

.137(.017)***

.137(.022)***

GDPpercapita(0-10)

.430(.076)***

.129(.040)***

.135(.050)**


.128(.064)*

.145(.073)*

.082(.074)

Destinationeffects

Centra


.029(.029)

.010(.020)

.016(.025)

GDPpercapita(0-10)

.218(.101)*

.074(.043)

.117(.046)*


.244(.100)*

.178(.070)**

.281(.101)**

Countrytieseffects

Culturaldistance(0-10)

.163(.030)***

.092(.023)***

.100(.021)***

Geographicaldistance(0-10)

.301(.038)***

.271(.037)***

.267(.044)***

Languagetie(0/1)

1.552(.149)***

1.431(.103)***

1.417(.104)***

Countrylev

el

Destinationeffects

Far-rightpolitics(0-10)

.039(.020)

.029(.013)*

.031(.013)*

Audien

cecommercialTV(0-10)

.097(.047)*

.024(.024)

.011(.024)

(continued)

Table1.(

continued)


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17

19

60-1980Model6

1985-2010Model7

1985-2010Mode

l8

Radioquota(0-1)

.322(.171)

Nomu

sicTV

Ref

RegionalmusicTV

.059(.133)

Localm

usicTV

.312(.157)*

Individuallevel

Starpower(0-10)

.252(.012)***

.277(.010)***

.277(.010)***

Language

=Eng.

Ref

Ref

Ref

Language

=Fra./Spa./Ita./Ger.

.490(.138)**

.931(.101)***

.936(.104)***

Language

=small

1.615(.298)***

1.121(.201)***

1.124(.205)***

Language

=instrumental

.093(.140)

.062(.188)

.077(.180)

Participat

ionTVtalentshow

.585(.147)***

Intercept

3.465(.858)***

1.569(.356)***

2.223(.670)***

Random

par

t

Var.Leve

l3(destinationcountry)

.415(.339)

.256(.212)

.479(.422)

Var.Leve

l2(origincountry)

.695(.255)

1.147(.358)

1.026(.359)

Var.Leve

l1(artist)

Deviance(M

CMC)

9,768.374#

14,815.014#

14,788.419#

Observation

sdestinationcountry

9

9

9

Observation

sorigincountry

38

39

39

Observation

scountryties

2,414

2,414

2,414

Observation

sindividual

15,088

21,592

21,592

Note.Random

InterceptModels.Unstandardizedco

efficients,betweenbracketsstandarderror;Dep.variablescaled0/1;allindependentvariablesscaled0-10,except

whenindicated.

#=Significantmodelimprovement(firstmodelcom

paredwithemptymodel).MCMC=MarkovChainMonteCarlo.

*p

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