The geography of music preferences

There is a long line of research and theory on the class and geographic bases of political and cultural divides. In the main, this literature finds that denser, more affluent and educated locations are more open-minded and politically liberal and that less dense, less affluent and less educated places skew more traditional, more closer-minded and more conservative. Here we argue that these geographic divides are not only economic and political but reflect and are reinforced by cultural preferences, attitudes and predispositions.

Indeed, there is a somewhat older vein of research and theory that identifies both the geographic variation in cultural preferences and connects that variation to underlying economic and political divides. Zelinsky (1974) examined cultural differences in the USA using magazine subscriptions and found a strong divide between the preferences of residents of cities and metro areas with higher concentrations of professional, white-collar workers, college-educated residents, and higher levels of diversity, on the one hand and those with high shares of blue-collar workers, and less advantaged residents and low levels of diversity. Fox and Wince (1975) found that individuals from small farm towns preferred folk, rock, and country music, while individuals from larger regions preferred jazz and blues music.

Weiss (1988) found a divide between a bi-coastal preference for theater and public broadcasting on the one hand and a preference for hunting, fishing and conservative radio in the Midwest and Southeast.

This research focuses on the consumption of books, magazines, newspapers, film and television (Zelinsky 1974; Weiss 1988). Our research examines the extent to which preferences for music both reflects these divides and can help us understand them. Indeed, music plays a central role in culture and cultural consumption. The typical American, for example, listens to roughly 18 h of music in an average week (Motion Picture Association of America 2007). In other words, people on average spend roughly a fifth of their waking hours with music playing. Music is a very heterogeneous art form. There are myriad musical styles and genres from classical to jazz and blues, R&B and hip-cop, country and religious, rock and pop. Furthermore, research shows that people’s tastes and preferences for music vary widely not just across demographic groups but also across cities and geographic areas.

More recent research identifies connections between music preferences and socioeconomic class, with upper class and well-educated individuals preferring “high-brow” music genres, such as classical, opera, and big band, and working class and less educated individuals preferring “low-brow” music, such as country, gospel, and rap (Katz-Gerro 1999; Mark 1998; Van Eijck 2001). Musical preferences have been linked to personality factors as well as (Colley 2008; Delsing et al. 2008; Dunn et al. 2012; Rentfrow and Gosling 2003). Several studies indicate that individuals with strong preferences for sophisticated musical styles, like classical, opera, or jazz, score high on psychological measures of creativity, curiosity, intelligence, and political liberalism (Rentfrow and Gosling 2003). There is also evidence that people who enjoy intense styles of music, like rock, heavy metal, and punk, score high on psychological measures of thrill-seeking, openness, and also value freedom and independence (Rentfrow and Gosling 2003, 2006; McNamara and Ballard 1999; Zweigenhaft 2008).

Our research examines the geographic variation in preferences for popular music and the economic and political factors that account for such variation. Drawing from previous research in psychology (e.g., Delsing et al. 2008; North and Hargreaves 1996; North et al. 1999; Rentfrow and Gosling 2003; Zweigenhaft 2008), our research examines geographic variation in five music-preference dimensions—referred to by the acronym MUSIC—mellow, unpretentious, intense, sophisticated and contemporary (Rentfrow et al. 2011).

The central hypothesis is that geographic variation in music preferences reflects and reinforces related political and economic divides by class and geography. As noted above, we believe that geographic preferences for music will vary based on density and socioeconomic class, as measured by income, education and occupation. We also test for the role of other demographic factors such as race and marital status as well as for personality factors which the psychology literature has found to play a role in the variation in music preferences. The research is based on a large nationwide survey of approximately 120,000 individuals that assessed individual differences in music preferences. We map music preferences by metro region, and provide both a correlation analysis and a regression analysis of music preferences and economic, demographic and psychological variables across 95 US metros with a population of 500,000 or more.

The key findings provide support for the hypotheses. Music preferences vary geographically in line with America’s broader economic and political divide. Broadly speaking, two musical types—sophisticated and contemporary—are associated with more affluent, more educated, more knowledge-based places that are also denser, more diverse, and politically liberal. Two other musical types—unpretentious and intense—are associated with less advantaged, less educated, more working class places that are more politically conservative and have larger white populations. Thus, overall, we find that the geography of music preferences across US metropolitan areas reflects and reinforces broader divides of economic, class and politics.

Our study examines the geographic divide in music preferences across 95 of the largest US metropolitan regions. We expect that: (1) music preferences will vary substantially across metro regions, and (2) that such variation will reflect and be shaped by regional variation in underlying economic and political divides. To test these claims, we employ data from a large-scale Internet survey involving approximately 120,000 individuals. 92,000 of these live in big metropolitan regions with more than 500,000 in population, and these regions are the focus of our analysis.

We assess geographic variation in music preference across five broad categories of types of music: mellow, unpretentious, sophisticated, intense, and contemporary, referred to the convenient moniker “MUSIC” (Rentfrow et al. 2011, 2012). Mellow reflects music that is romantic, relaxing, unaggressive, sad, slow, and quiet; unpretentious is relaxing, unaggressive, soft, and acoustic; sophisticated is inspiring, intelligent, complex, and dynamic; intense is distorted, loud, aggressive; and contemporary is percussive and electric.

We use factor analysis to identify the geographic structure of these broad musical types. We then map the geographic variation in each of the five major types of music. We also conduct bivariate correlation analysis and regression analysis of the associations between these music preferences and key economic, demographic, political, and psychological variables.

The music preferences data were collected as part of an ongoing study of music preferences involving volunteers assessed over the World Wide Web (http://www.outofservice.com/music-personality-test/). The web site is a non-commercial, advertisement-free web site containing a variety of psychology measures. Potential respondents could find out about the site through several channels, including search engines, or unsolicited links on other web sites. The data reported in the present research were collected between 2001 and 2013.

Respondents volunteered to participate in the study by “clicking” on the music-preference test icon and were then presented with a series of questions about their music preferences, personalities, demographic characteristics, and place of residence. After responding to each item and submitting their responses, participants were presented with feedback about the music preferences based on their responses to the items.

We use a variety of secondary data to examine the relationships between music preference and regional economic, demographic, political, cultural and social psychological characteristics.

We were interested in metro-level trends in musical preferences, so we aggregated individual-level preferences for each genre among respondents who reported living in each of the metropolitan areas. Then we examined the factor structure of the metro-level musical preferences. Specifically, we conducted a principle components analysis with varimax rotation on the metro-level music preferences to determine whether preferences can be characterized in terms of a set of meaningful dimensions. We used the results from the factor analysis to map geographic variation in musical preferences. We then used bivariate correlation analysis to relate the variation in these music preferences to key economic, social, political and psychological variables described above, followed by a regression analysis. Because of the large number of explanatory variables, the relatively low number of observations (N = 95), and the high level of linearity between the explanatory variables, we run a principal component analysis for the economic, social, political and psychological factors to reduce the number of explanatory variables and avoid issues with multicollinearity.

We now turn to the geographic distribution of music preferences. To get at this, we computed metro-level factor scores to represent each metropolitan region’s degree of preference for each of the MUSIC factors. The maps below show the geographic distribution for each of the five music-preference dimensions based on the factor analysis. The color scheme in the maps is based on the generated genre score from the principle component analysis. The higher the score, the stronger is the average preference for this specific music genre.

7.1 Mellow

Figure 1 maps mellow music preferences by metropolitan region. The top ten metros on this dimension are Provo-Orem, UT; McAllen-Edinburg-Mission, TX; Salt Lake City, UT; El Paso, TX; Ogden-Clearfield, UT; Fresno, CA; Palm Bay-Melbourne-Titusville, FL; Boise-Nampa, Idaho; Tampa-St. Petersburg-Clearwater, FL; Albuquerque, NM; and Miami-Ft. Lauderdale-Pompano Beach, FL.

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7.2 Unpretentious

Figure 2 maps unpretentious music preferences by metropolitan region. The top ten metros on this dimension are Jackson, MS; Charleston-North Charleston-Summerville, SC; Little Rock-North Little Rock-Conway, AR; Memphis, TN-MS-AK; Oklahoma City, OK; Chattanooga, TN-GA; Birmingham-Hoover, AL; Greenville-Mauldin-Easley, SC; Provo-Orem, UT; and San Antonio, TX.

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Unpretentious metros are mainly clustered in the traditional south, Mississippi, South Carolina, Arkansas, Tennessee, Georgia, Alabama and Texas as well as Utah.

7.3 Sophisticated

The map of metropolitan-wide preferences for sophisticated music is plotted in Fig. 3. The top ten metros on this dimension are Austin-Round Rock, TX; San Francisco-Oakland-Fremont, CA; Jackson, MS; Greensboro-High Point, NC; Madison, Wisconsin; Rochester, NY; Columbia, SC; Charleston-North Charleston-Summerville, SC; Nashville-Davidson-Murfreesboro-Franklin, TN; and Albuquerque, NM.

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Sophisticated metros are mainly clustered in the Sun Belt and West. Austin is first and San Francisco second, while Nashville (a leading center for music production) is ninth.

7.4 Intense

The map of preferences for intense music is shown in Fig. 4. The top ten metros on this dimension are Scranton-Wilkes-Barre, PA; Chattanooga, Tennessee; Las Vegas-Paradise. NV; Youngstown-Warren-Boardman, OH-PA; Greenville-Mauldin-Easley, SC; Albuquerque, NM; Portland-South Portland-Biddeford, ME; Colorado Springs, CO; Allentown-Bethlehem-Easton, PA-NJ; and Albany-Schenectady-Troy, NY.

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Intense music is more broadly distributed with clusters across the Northeast, Midwest, South and West.

7.5 Contemporary

The map of preferences for contemporary music is shown in Fig. 5. The top ten metros on this dimension are Charleston-North Charleston-Summerville, SC; San Francisco-Oakland-Fremont, CA; Modesto, California; Richmond, VA; Des Moines-West Des Moines, IA; Orlando-Kissimmee, FL; Omaha-Council Bluffs, NE-IA; Memphis, TN-MS-AK; Birmingham-Hoover, AL; and Rochester, NY.

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Leading contemporary music metros are located in the South and West, especially California and Iowa as well as the Northeast.

We now move on to the multivariate OLS regression analysis. Since we only have 95 observations and a large number of explanatory variables, we first performed a principal component analysis to reduce the number of explanatory variables—the same technique that was used above when the music genres were generated. This allows us to reduce the number of variables to a limited number of extracted factor variables, which in turn reduces concerns about multicollinearity and degrees of freedom in the regression analysis. The new variables (factors) capture the metropolitan socioeconomic structures included in our study from a number of dimensions (Factor 1–7). Table 3 illustrates a summary of the seven extracted factor variables (a detailed table is provided in Table 9 Appendix). The extraction ranges from 0.595 to 0.918 and with an average of 0.796.

The principle component analysis reduces the number of explanatory variables to seven factors, which are as follows.

Factor 1 This factor captures large affluent, diverse and creative metros. Metros in this group have larger populations, higher incomes, more highly educated populations, and higher levels of creative class workers. They have larger gay and bohemian populations and higher levels of well-being.

Factor 2 This second factor are metros with higher levels of Hispanic and foreign-born or immigrant populations. These metros also tend to have fewer singles, and they marry earlier in life. These metros also have relatively open personalities.

Factor 3 Metros with high factor 3 scores have a lower share of marriages, are more likely to vote Democratic in the latest election and less likely to be religious. These metros have relatively high levels of population density and lower shares of working class jobs.

Factor 4 This factor is closely related to the personality variables, and metros with high scores here are more agreeable and conscientious, but less neurotic.

Factor 5 This next factor is closely related to race, and metros with high Factor 5 scores have higher shares of black population and lower shares of white population.

Factor 6 Metros with high Factor 6 scores tend to work more hours per week and have higher divorce rates.

Factor 7 This last factor is most strongly related to the share of extroverts in the metro. It is also negatively related to the share of neurotic individuals.

Our research was based on the basic hypothesis that geographic differences in music preferences would reflect underlying economic and political divisions in American society. Our findings provide significant support for this hypothesis.

Broadly speaking of the five broad musical types, two—sophisticated and contemporary—are associated with more affluent, more educated, more knowledge based, more liberal, more diverse and denser places. Two others—unpretentious and intense—are associated with less advantaged, less educated, more working class, more conservative, and less diverse or whiter places. One remaining type, mellow music—is mainly associated with race and ethnicity, than class or economics.

These results broadly track and reflect America’s more basic political and economic divide, where more affluent, educated, knowledge based, denser and more diverse places trend liberal and less advantaged, less educated, working class, whiter, and more religious places trend conservative. We can say that broadly speaking musical preferences line up with these lines of class and political cleavage. Places where people prefer sophisticated and contemporary music are more affluent educated and liberal. Places where people prefer unpretentious and intense music are less advantaged, more working class and more conservative. Geographically, our preferences for music reflect and reinforce our political-economic divides.

Several caveats should be kept in mind in considering these findings and results. For one, they are based on a sample of self-selected participants who completed a self-report survey on the Internet. It could be that the individuals who volunteered to complete a survey about their music preferences may be more committed to music than the average person, so data based such participants may not be representative. That said, research on Internet-based studies indicates that Internet users are not perfectly representative of the general population (Lebo 2000; Lenhart 2000), but Internet-based samples are much more diverse and considerably more representative than the convenience samples commonly used in social science research (Birnbaum 2004; Gosling et al. 2004; Skitka and Sargis 2006). Furthermore, similar results are typically obtained across Internet and non-Internet samples, including studies of music preferences (Rentfrow et al. 2011, 2012; Rentfrow and Gosling 2003), and Internet-based studies tend to yield data that are comparable or of better quality than studies relying on paper and pencil, face-to-face, and telephone surveys (Richman et al. 1999; Skitka and Sargis 2006).

Still, our findings and results align previous research on cultural preferences more broadly. They are similar to the divides found by Zelinsky (1974) and Weiss (1988). Interestingly, given the fact that these studies were conducted several decades ago, it appears that these cultural divides are long-standing and deeply ingrained in America’s political-economic and geographic divides. The similarly between the current findings and those from previous research suggests that our results are robust.

We hope that our research stimulates future research on this topic. Future research could obtain music-preference data for a nationally representative sample to evaluate the generalizability of the current results to other samples. Additionally, it would be useful to map music preferences using behaviorally revealed music-preference information. Geographic information on music sales, digital downloads, or listening data from music-based online social networks would provide compelling data to compare with the current results. We look forward to more research on this important subject.