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Music is one of the greatest cultural common denominators. Nothing unites people from different backgrounds more than music, and at medimops (a subsidiary of momox SE), we witness this everyday through the community of music-lovers that buy and sell albums through our platforms. And while certain languages and genres have dominated the musical landscape more than others, the exchange of music between countries and cultures continues to have an enormous influence on what we listen to today. As an online shop for music lovers, we wanted to dig a little deeper into this topic and use a data-driven approach to determine which country has the most musical influence worldwide.
To conduct this study we first needed to understand how musical influence has shifted and evolved throughout the decades. Naturally the advent of European classical music continues to hold the greatest influence over music to this day. However, it was only when music in other continents like the Americas incorporated African beats and European melodies into a cultural melting pot of local and global sounds, that the concept of what we perceive as modern music really began to take shape. Musical exchange rapidly increased in the 20th century due to technological advancements such as the invention of the radio, the television and most recently the internet, which has allowed sounds and genres to transcend location and reach audiences in the most far-flung areas of the globe.
Given this historical convergence of local and global sounds, determining musical influence is not an easy task. However, modern streaming services have made it possible to measure it in more concrete ways than ever before. Having access to this data, nevertheless, is not enough to determine influence, so we needed to find out not only where the most popular artists in the world originated, but also where, how far, and how much their music was being listened to outside their country of birth.
Once we were able to identify the most played acts worldwide, we used algorithms with a scoring system to determine their popularity in other countries across several music platforms*. Next, we collected the number of globally renowned artists that originated in each country, reaching all the way from classical musicians like Bach, to modern day artists like Beyoncé. To add further historical context, we consulted an ethnomusicological study on the uniqueness of local sounds, identifying how distinct each country’s traditional and folk music styles were from others.
It was also important to understand the extent to which each country supported the development of a local music industry as a measure of the symbiotic relationship between their artists and institutions. We did this by analysing each country’s musical infrastructure, ranging from the number of music schools and major labels, to the percentage of people working in the music industry. To determine local demand and support for live music, we investigated how popular that country was as a destination for major artist tours / concerts over the last five years.
Finally, to also determine which type of music is produced most in each country, we measured the musical output based on four different genres - pop, electronic, rock, and classical. By combining these indicators of historical development, global music reach and local artist support, we were able to calculate the first-ever Global Influential Music Index, which uses data to showcase the worldwide influence of each country’s musical output.
Asking which country has had the most influence on music is no doubt an extremely subjective question. Modern music wouldn’t exist without classical Germanic composers like Mozart and Bach, however they could not have created the music they did without instrumentation adopted from other important cultures and countries. Each country has its own unique classical traditions as well, like India’s Carnatic and Hindustani music, or China’s Yayue and literati styles, which all have had some impact on the music we hear today. Knowing this, we decided to focus on the result of all this incredible musical exchange by evaluating the status of a country’s influence in the modern musical landscape. While the results show that the US is by far the most influential musical country today, we can see that other nations are beginning to close the gap. The debate as to who influenced whom will of course continue, however there is no doubt that the digital age has made the exchange of sounds happen faster than ever before, and we look forward to seeing which countries will shape the musical landscape of the future.
The final ranking displays the countries with the most musical influence around the world in order from highest score to lowest. Each individual column is filterable, and the full methodology explaining how each factor was evaluated can be found underneath the table.
*Note on streaming data and country selection: data was collected for music artists from all countries worldwide, however in some countries with large local industries such as China, global streaming data was not accessible and therefore unavailable for comparison. Similarly, despite having a vast musical heritage, countries such as Jamaica did not have sufficient local data about music infrastructure for comparison, which may become available for future iterations of of index.
Music Influence | Music Infrastructure | Genre Output | |||||||||||||||
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# | Country | Subregion |
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TOTAL | ||
1 | USA | Northern America | 100 | 100 | 100 | 57.9 | 100 | 2 | 24506 | 8421 | 100 | 100 | 100 | 81 | 100 | ||
2 | UK | Northern Europe | 65.2 | 66.3 | 74.3 | 93 | 94.7 | 2.7 | 11910 | 1652 | 58.3 | 69.8 | 58.9 | 55.9 | 75.3 | ||
3 | France | Western Europe | 54.9 | 53.8 | 69.5 | 66.7 | 88.5 | 1.7 | 4163 | 654 | 55.9 | 63 | 51.9 | 69.9 | 62.7 | ||
4 | Germany | Western Europe | 54.8 | 53.7 | 71 | 65.1 | 86.9 | 1.4 | 7274 | 652 | 52.9 | 70.7 | 52.1 | 66.4 | 62.7 | ||
5 | Canada | Northern America | 56.3 | 55.4 | 52.9 | 57.8 | 87.8 | 1.8 | 2346 | 665 | 53.9 | 55.9 | 54.5 | 52.7 | 61.4 | ||
6 | Japan | Eastern Asia | 54.1 | 51.9 | 54.9 | 88.4 | 86.5 | 1.3 | 6469 | 254 | 55.5 | 68.4 | 54.9 | 63.7 | 61.3 | ||
7 | Italy | Southern Europe | 53.6 | 51.8 | 70.9 | 82.5 | 85.3 | 1.4 | 2854 | 225 | 54.1 | 57.3 | 51.1 | 69.3 | 61.3 | ||
8 | Sweden | Northern Europe | 52.6 | 52.9 | 54.5 | 72.7 | 81.9 | 2.7 | 2193 | 157 | 51.9 | 54.4 | 51.9 | 50.9 | 61.3 | ||
9 | Australia | Australia and New Zealand | 53.5 | 53.2 | 51.8 | 72.8 | 91.6 | 1.7 | 1613 | 657 | 53.3 | 55.3 | 53.4 | 51.6 | 60.7 | ||
10 | Ireland | Northern Europe | 51.8 | 51.3 | 51.9 | 93.6 | 83 | 2.3 | 359 | 193 | 52.1 | 51.3 | 51.3 | 50.8 | 60.6 | ||
11 | Spain | Southern Europe | 52.6 | 53 | 55.9 | 75.8 | 79.8 | 2 | 1761 | 517 | 59.5 | 55 | 51.5 | 68.8 | 60.3 | ||
12 | South Africa | Sub-Saharan Africa | 51.4 | 51 | 51 | 85.4 | 76.7 | 2.9 | 319 | 35 | 50.9 | 53.2 | 50.6 | 50 | 60.2 | ||
13 | Brazil | Latin America and the Caribbean | 54.8 | 51.5 | 52.3 | 100 | 85.5 | 0.7 | 815 | 930 | 51.7 | 51.7 | 50.4 | 50.5 | 59.8 | ||
14 | Netherlands | Western Europe | 52.5 | 52.9 | 53.3 | 54.8 | 83.2 | 2.1 | 2062 | 592 | 51.6 | 58 | 50.7 | 51.5 | 59.5 | ||
15 | South Korea | Eastern Asia | 53.6 | 54.3 | 52.7 | 50.5 | 87.9 | 1.8 | 801 | 131 | 62.3 | 56.2 | 50.3 | 60.4 | 59.2 | ||
16 | Puerto Rico | Latin America and the Caribbean | 55.5 | 53.4 | 50.7 | 78.4 | 69.5 | 1.9 | 140 | 26 | 50.3 | 50.2 | 50 | 50 | 59.2 | ||
17 | Russia | Eastern Europe | 51.7 | 51.6 | 59 | 60.8 | 82.1 | 2 | 1197 | 83 | 50.7 | 55.9 | 50.8 | 100 | 58.9 | ||
18 | Colombia | Latin America and the Caribbean | 54 | 52.1 | 50.7 | 95.8 | 75.9 | 1.2 | 226 | 115 | 50.3 | 51 | 50.1 | 50 | 58.8 | ||
19 | Latvia | Northern Europe | 51 | 50.5 | 51.8 | 72.2 | 69.5 | 3 | 105 | 7 | 50 | 50 | 50.2 | 51.2 | 58.7 | ||
20 | India | Southern Asia | 58.1 | 50.6 | 52.2 | 87.3 | 85.3 | 0.2 | 242 | 27 | 52.5 | 50.4 | 50.1 | 52.5 | 58.4 | ||
21 | Belgium | Western Europe | 51.5 | 51.5 | 54.8 | 77.8 | 76.1 | 1.6 | 1098 | 320 | 50.9 | 53.4 | 50.6 | 50.1 | 58.3 | ||
22 | Denmark | Northern Europe | 51.6 | 51.4 | 52.5 | 50.5 | 81.5 | 2.3 | 698 | 137 | 50.7 | 52.4 | 50.7 | 51.2 | 58.2 | ||
23 | Austria | Western Europe | 51.3 | 50.9 | 57.8 | 60.8 | 84.9 | 1.7 | 547 | 77 | 50.7 | 51.6 | 50.2 | 58.8 | 58.1 | ||
24 | Argentina | Latin America and the Caribbean | 51.8 | 50.9 | 51.9 | 66.5 | 78.3 | 2 | 418 | 159 | 52.3 | 50.5 | 51.1 | 52.8 | 58 | ||
25 | Mexico | Latin America and the Caribbean | 52.5 | 51.1 | 51.7 | 72.3 | 84.4 | 0.9 | 413 | 395 | 53.6 | 51.3 | 50.7 | 51.6 | 57.3 | ||
26 | Hungary | Eastern Europe | 51.2 | 50.7 | 53 | 62 | 78.8 | 1.8 | 247 | 53 | 50 | 50.7 | 50.2 | 52 | 57.2 | ||
27 | Czechia | Eastern Europe | 51.2 | 50.6 | 56.9 | 54.1 | 76.5 | 1.9 | 382 | 40 | 50.3 | 50.5 | 50.6 | 54.3 | 57 | ||
28 | Greece | Southern Europe | 51.1 | 50.9 | 51.5 | 74 | 77.2 | 1.4 | 520 | 13 | 51.1 | 50.4 | 50.2 | 51.2 | 57 | ||
29 | New Zealand | Australia and New Zealand | 51.6 | 51.2 | 50.7 | 59.1 | 86.2 | 1.2 | 437 | 152 | 50.6 | 50.5 | 50.3 | 50.2 | 56.9 | ||
30 | Poland | Eastern Europe | 51.9 | 50.7 | 54.7 | 68.5 | 74.4 | 1.3 | 824 | 71 | 50 | 51.5 | 51.1 | 53.5 | 56.8 |
The Global Influential Music Index uses data to analyse and compare the musical influence and supporting infrastructure of 30 countries around the world. The countries were chosen for having the most world-renowned musicians, as well as being countries of origin of the most globally-recognised musical styles.
The core of the analysis was performed on music charts available from global streaming platforms. Although extensive international streaming data exists, it is not yet publicly available from these sources for every single country. As a result, some notable countries are excluded from this iteration of the index to make the data comparable, such as China, Cuba and Jamaica.
The index comprises two main categories - Music Influence and Music Infrastructure - containing eight indicators outlined below which measure the global reach of and local support for music in each country. The third category - Genre Output - measures the musical output of a country in the four most common music genres.
Each factor consists of one or more indicators which were scored and averaged. The equation for scoring is as follows:
z-Score = x - mean(X)Standard deviation(X)in short x - μσ
For columns where a low value is better, the score is inverted such that a high score is always better:
>z-Scoreinverted = -1*x - mean(X)Standard deviation(X) in short -1 *x - μσ
Data is normalized to a [50-100] scale, with 100 being the best score. Therefore, the higher the score, the better the country ranks for that factor in comparison to the other countries in the index. The formula used is min-max normalisation:
score = (100-50) *x - min(X)max(X) - min(X)+50
The final score was determined by calculating the sum of the weighted average score of all of the indicators and normalizing to a 50-100 scale
All factors are based on the latest available data.
To determine which artists have achieved the largest number of global sales and streams, data from Spotify, YouTube Music, Apple Music, Apple iTunes sales and positions on national sales charts were compiled for each artist. Through this process, more than 10,000 artists were identified. Each artist was assigned an ‘Origin Country’ based on their actual country of birth.
For each artist, the performance of all songs they released was aggregated on a per-country basis. To measure global performance, the sum of all country performance was combined with global sales data from kworb.net and charts2000.com. The following datasets were considered in estimating global performance:
This is presented as the factor ‘Most Played Acts (Score)’.
For each artist, the above four classifications were then scored and tallied to construct a combined ‘Global Influence (Score)’ (further details on the factor below).
Reference for designation of culture groups
Definition of culture groups: Mensah, Yaw M. and Chen, Hsiao-Yin, Global Clustering of Countries by Culture – An Extension of the GLOBE Study (April 14, 2013).
Other sources
Country-based streaming data from Spotify and YouTube Music; Analysis of iTunes/Apple Music, Spotify and YouTube charts performed by kworb.net; analysis of record sales country charts performed by chart2000.com.
The sum of global performance of all musical acts originating in each country, expressed as a score, calculated as described above (see “Analysing the Global Performance of Musical Acts”).
Sources: kworb.net; charts2000, YouTube Music.
The sum of global influence of all musical acts originating in each country, expressed as a score, calculated as described above (see “Analysing the Global Performance of Musical Acts”). The score reflects the ability of acts originating from each country to achieve chart success outside of their home, country and culture group.
Sources: kworb.net; charts2000, YouTube Music.
This factor is based on the sum of the Historical Popularity Index (HPI) scores for famous musicians in each country, as computed by the Pantheon biographical database. Pantheon assigns HPI scores to musicians based on visitors to Wikipedia webpages and the availability of translated entries across multiple Wikipedia languages. This method only takes into account inviduals and does not include musical groups.
The World Music Uniqueness Score is based on M. Panteli’s (2017) Computational study on outliers in world music which investigates music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world, with the aim of identifying music recordings that are the most distinct compared to the rest. These unique recordings are referred to as ‘outliers’ and are considered to contribute to the ‘uniqueness’ of the music of each country.
Sources: Panteli M, Benetos E, Dixon S (2017) A computational study on outliers in world music. PLoS ONE 12(12): e0189399.
Sources: QS World University Rankings: Performing Arts 2017- 2020, CEOWorld Best Music Schools, IDMMEI database.
The share of employed people working in the ‘arts, entertainment & recreation’ industries at a national level, using 2019 data from the International Labor Organization (ILO). As there is no standardised statistical designation or definition for what constitutes the creative arts sector, for some countries the designation most closely resembling the ILO definition was taken. In some cases, definitions encompassed more than the ILO standard. 2019 data (unless otherwise specified) for the following statistical categories were taken for these countries:
Sources: ILOStat, national statistical departments.
The number of record label headquarters in each country, based on the label’s geographical origin. Data correct as of November 2020.
Sources: MusicBrainz Database.
Sources: Setlist.fm, kworb.net.
The estimated output of four popular music genres per country, based on an analysis of releases tagged with genre information on Discog.com. The analysis provides a split of genres within each country. To achieve an index for country-to-country comparisons, we multiplied this share with the estimated cultural GDP of each country, resulting in an indicator of the economic value of release output.
These scores were provided as a comparative index of output for four of the most influential genres. However, in order to not penalise countries who specialise in a particular genre (for example, Puerto Rico and Reggaeton), the Genre Output Scores were not considered when calculating the total score for each country.
Sources: tickx.com, discogs.com.