INTELLECTUAL PROPERTY RIGHTS AND THE SOUTH KOREAN MUSIC INDUSTRY: ARE COPYRIGHT LAWS STRENGHTENING MAJOR LABEL MARKET POWER?

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Noah Fabie

March 2019

i

INTELLECTUAL PROPERTY RIGHTS AND THE SOUTH KOREAN MUSIC INDUSTRY: ARE COPYRIGHT LAWS STRENGHTENING MAJOR LABEL MARKET POWER?

Noah Fabie

March 2019

Economics

Abstract

SM Entertainment, JYP Entertainment, and YG Entertainment, often referred to as “The Big Three”, are accredited with launching K-Pop onto the global scene. Before “The Big Three”, the South Korean music industry acclimated to tighter copyright policies between the 1960s and the late 2000s. Popular economic theory suggests that intellectual property right laws negatively impact social welfare and increase monopoly power. Using music chart data from 2011 to 2017, this analysis investigates whether or not these effects are prevalent in the South Korean music industry. KEYWORDS: (Intellectual Property Rights (IPRs), Copyrights, Music Industry, K-Pop)

ii

ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS

Signature

iii

TABLE OF CONTENTS

ABSTRACT ii ACKNOWLEDGEMENTS iv 1 INTRODUCTION 1 1.1 The Shift from Loose to Strong Copyright Laws……………………………… 1 2 UNDERSTANDING THE K-POP BUSINESS MODEL 6 3 BACKGROUND ON THE ECONOMICS OF THE MUSIC INDUSRTY AND 10 COPYRIGHTS 4 DATA COLLECTION AND METHODOLOGY 16 5 DATA ANALYSIS 23

5.1 Data Analysis: Streaming………………………………………………………. 23 5.2 Data Analysis: Downloads...... 27 5.3 Data Analysis: Social Media Plays…………………………………………….. 32 6 INTERPRETATION 36 7 CONCLUSION 37 8 WORKS CITED 38

iv Thank you, Mom and Dad.

v INTRODUCTION

“The Korean Wave” describes the massive increase in South Korean music exports beginning in the mid 2000s. Prior to South Korea’s explosion onto the global music scene, the country underwent a variety of copyright reforms between 1960 and

2009. These reforms mirrored regulations in western entities, such as the United States and the European Union. Such policies were enacted to protect intellectual property and help secure foreign investment in South Korea. Although the regulations replicate the west, South Korea’s music industry grew under its own structure. South Korean labels utilized piracy and “the 360 deal” to grow their business during a period of lax copyright laws. In the modern industry, “The Big Three”, South Korea’s three major music industry firms, hold power in both the labor and output market. Popular economic thought on intellectual property rights (IPRS) indicates that intellectual property laws create monopolistic distortions in market output. Given “The Big Three’s” market power, and economic thought pertaining to copyright laws creating distortions in the market place, is there evidence to suggest that modern copyright laws are strengthening “The Big

Three’s” oligopoly?

THE SHIFT FROM LOOSE TO STRONG COPYRIGHT LAWS

Between 1960 and 1990, the Korean music industry grew under extremely lax copyright laws and rampant piracy. Following U.S. occupation, and the election of authoritarian President Park Chung-hee in 1963, South Korea introduced laws banning

U.S. and Japanese cultural imports (John Lie, 343-44). Consequently, during 1960 and

1970, pirated albums of American artists were extremely popular among young people.

Pirated albums were on average one-third the price of an officially released album (Parc

1 and Messerlin, 141). Multiple albums on South Korea’s all-time-best-selling album list were created during this time and are identifiable by their distinct western influences

(Shin, Lee, and Choi, 2005). In addition to imposing western influences on Korean pop music, piracy was also responsible for financially stimulating the domestic music industry.

The late 1980s marked a striking transition in Korean copyright law. In order to expand into new markets, South Korea adopted a series of regulations and reforms suggested by the United States and various western countries. Anti-piracy measures and standardized western copyright laws were included in these reforms (Parc and

Messerlin, 140). Immediately following these reforms, South Korea saw a large influx of cultural imports from the United States and Japan (Parc and Messerlin, 142). In order to compete, Korean labels began to sell pirated albums of their own artists. Since these pirated albums did not pass through any government regulation, they usually hit the market a week before an official release. Piracy quickly became more profitable than selling retail albums. The impact of selling pirated records was twofold. The increase in capital allowed Korean labels to invest in new music production technologies (Parc and

Messerlin, 142), and paradoxically, also stimulated the sales of full-priced retail albums

(Kim, 2012). Ironically, the reforms demanded by the United States pushed American labels out of the market. Tighter anti-piracy laws reduced the available amount of available pirated American albums, thus pitting full-price western albums against cheaper pirated Korean albums (Parc and Messerlin, 143). Therefore, the foundation of the modern South Korean music industry emerged out of piracy. South Korea would

2 continue to turn a blind eye to its copyright regulations through the early 2000s, using similar tactics to grow the industry in the digital age.

In the early 2000s, South Korea’s music industry enjoyed a global first-mover- advantage in digital music distribution. By the end of 2002, 69% of the population in

South Korea had a mobile communication device, with mobile internet capabilities on

90% of cell phones (Takeishi and Lee, 293). Service providers offered three music- related services for consumers to purchase ringtones, “ringback” tones, and direct MP3 download packages. In 2003, South Korea managed to capture 110 million USD of the

230 million USD mobile music market (Takeishi and Lee, 294). A main factor of South

Korea’s first mover-advantage was the ability to set the rules at the table. At the time, only Japan rivaled South Korea in the mobile music market. Although Japan and South

Korea shared similar mobile content distribution systems, South Korea was able to leverage its weaker copyright laws to its advantage.

Weak copyright collection systems created unique business opportunities in

Korea’s mobile music market. In both Japan and South Korea’s content distribution systems, a consumer paid their carrier a fee or subscription for purchasing a ringtone,

“ringback tone”, or MP3 download. This general system is illustrated in the figure below.

From there, the mobile carrier would pay the content provider a subscription fee to license the content. The difference between the systems occurred at royalty distribution.

3

Source: Takeishi and Lee (2005) In Japan, the content provider paid a 5-7 percent fee per transaction to JASRAC;

Japan’s royalty collection service. In Korea, however, the content provider would pay

6.5-8% of total monthly revenue to the Korean Music Copyright Association (KOMCA);

South Korea’s royalty collection service. By paying a percentage of royalty revenues monthly, “content providers in Korea [had] more flexibility in designing their music business models and pricing than their counterparts in Japan” (Takeishi and Lee, 299).

Asymmetrical information between service providers and KOMCA also hindered royalty collection. Carriers held ringtone transaction data but did not make the information public. Therefore, there was no way to verify if the royalties paid to KOMCA were accurate. From 1970 to 2008, lax copyright enforcement allowed firms to raise capital and develop South Korea’s music industry (Takeishi and Lee, 299). These practices would not last for long.

4 K-pop began to form as a prominent genre by the mid 2000s. During this timeframe, there were frequent copyright and plagiarism disputes between K-pop labels . As a response, South Korea passed strict copyright revisions into order in 2009.

Revisions to Korea’s Copyright Act intended to crack down on piracy and to regulate royalty collection in South Korea’s “insanely popular norabangs, or karaoke rooms”. The reforms shut down a variety of websites that were distributing content illegally, extended the term of a copyright to 20 years, and instituted a “work for hire concept in which the employer owns the copyright rather than the employee”

(Marchand, 18, 21). The Economist referred to these new anti-piracy laws as the toughest in the world, yet South Korea “was still considered to be the country with the second largest number of illegal music downloads in the world” (Parc, Messerlin, and

Moon, 132). Since then, piracy has steadily declined. According to the International

Federation of the Phonographic Industry, piracy via cyberlock (websites that allow mass-upload and download of content) decreased 38% in 2012 alone. Additionally, the

Ministry of Culture, Tourism, and Sports has blocked 41 copyright infringing websites since 2011 (IFPI).

Between 1960 and 2009, South Korea’s music market evolved under various copyright regimes. Under lax copyright laws, the industry was able to raise capital through pirated records and profit by underpaying royalties in the new mobile market.

Music imports have steadily increased since 2009, indicating that copyright reforms

5 have restored confidence of foreign music labels.

Source: Stata Korean Music Industry Dossier (2019) Before discussing the economic implications of these laws, it is necessary to explain the K-Pop business model.

UNDERSTANDING THE K-POP BUSINESS MODEL

Firms in South Korea’s music industry have cultivated a unique approach unlike major labels in the United States. South Korean music labels almost exclusively sign artists under the pretense of a “360 deal”. 360 deals provide a means of vertically integrating a label’s business. In the United States, an artist may have a business manager, an attorney, a booking agent, a manager (different from business manager, primarily responsible for coordinating all partners connected to the artist), a tour manager, and a music label responsible for distribution. Under a 360 deal, all components are employed within the record label. The 360 deal first came onto the scene in the early 2000s, when record sales in the U.S. dropped by 58% from $14.6 billion to $7.0 billion(Tsai, 328). Under a 360 deal, a record label can obtain 35% of

6 income from ancillary sources, such as ticket sales or merchandise, as opposed to an industry standard 10% (Tsai, 328). South Korea’s industry leaders, “The Big Three”,

JPY Entertainment, SM Entertainment, and YG entertainment took the 360 to a new extreme.

The Big Three designed the K-pop industry around the 360 format. Lee Sooman,

Founder of SM Entertainment, summarized the staunch difference between South

Korean 360 deals and U.S. 360 deals in a speech at Stanford University. Sooman stated that “even America has been unable to establish a management system like ours. Recruiting young trainees, signing them to long-term contracts and putting them through years of extensive training – this just can’t happen here” (Marchand 29). In the

U.S, artists are recruited by A&R representatives who help groom their talent. In Korea,

K-pop “idols” are recruited on average at ages 12-13, packaged with other idol hopefuls into a boy-band or girl-band, and groomed into a final product that could make their potential debut around ages 18 to 25. Unfortunately, these deals are not always beneficial for the artists involved.

An intense criticism of 360 K-Pop deals is that they can overstep the boundaries of a standard employer and employee relationship. “Slave Contract” is a term occasionally used to describe a 360 contract with a long term and restrictive clauses.

No time-off for illness, a demanding daily fitness and dance training schedule, unpaid public appearances, restrictions on an artist’s love life, and mandatory plastic surgery are all examples of terms that are occasionally found in 360 deals (Tsai, 337). The subject was brought to the attention of the Korean Fair-Trade Commission (KFTC), following various high-profile lawsuits between idols and their labels. The KFTC ruled

7 that 13 years, the standard length for a 360 contract at the time, was too long. Following this ruling the KFTC deemed that seven years is the maximum length for a contract of this nature (Tsai, 337-38). In addition to pioneering the 360 deal as the standard form of artist contract, the Big Three also revolutionized music distribution and royalty collection.

In 2008, SM Entertainment CEO Kim Young-min revolutionized the future of the

Korean Music industry after realizing that YouTube came automatically installed on new iPhones at that time (Oh and Park, 2012). YouTube, as a distribution service, is free-to- upload, free-to-share, and collects royalties for the copyright holder. Although YouTube faced criticism for its inability to take-down videos violating copyright laws, and for not properly disseminating ad revenue to users, the platform still outshines radio or television in terms of reach and royalty collection. A popular, and potentially overused, example of this is the K-Pop idol PSY. PSY’s viral sensation, “GANGNAM STYLE” has garnered over 3.2 billion views since 2012. YouTube is often credited with spreading K-

Pop internationally. The correlation between the two can be seen in Figure 3 below, which demonstrates K-pop’s explosive growth since 2010.

8

Source: Stata Korean Music Industry Dossier (2019) All of this growth raises the question: “what about the artists”? How is this money divided up for the artists who are actually creating and performing the art. In their annual report for 2017, KOMCA, one of two royalty collection agencies in the modern South

Korean music industry, described the current state of the music industry. Total royalty collections rose 20% in 2017, for a gain of $156,400,000 on the year. According to the

2018 IFPI Global Music Report, South Korea is now the 6th largest music market in the world, despite their rank as the 10th largest market in 2015. KOMCA recently reported that royalties collected from album sales are up 45% in 2018. Granted, these figures hide distortions within the market. According to tax filings from Korea’s National Tax

Service (NTS), 89% of artists earned an average income of $7,172 annually in 2015.

The top 10% of artists accumulated $541,564, and the top 1% accumulated $2,787,737.

Thankfully, there is some hope. In January of 2019, a law went into effect stipulating

9 that artists must be paid 65%, a 5% increase, of royalty income from streaming services

(Sanchez, 2019).

BACKGROUND ON THE ECONOMICS OF THE MUSIC INDUSTRY AND

COPYRIGHTS

The music industry has a long history of interactions with international copyright law. Intellectual property rights (IPRS) have evolved in the music industry since the early days of phonographic recordings, when recording technology patents were the main barrier to entry for artists (Peterson and Berger, 40). In the music industry’s modern digital landscape, IPRs typically refer to copyrights. Copyrights in the music industry are defined by a few basic terms: term, which refers to how long the copyright holder retains exclusive access to an idea, right of first sale, which ensures the creator an exclusive right to sell an idea on the primary market, and downstream licensing, which pertains to the original author’s right to control the use of an idea in all markets subsequent to the primary (Boldrin and Levine, 2). Of these basic principles, term and downstream licensing are the most controversial.

The nature of downstream licensing implies that an author holds a monopoly over an idea. Proponents of strong IPRs, especially in the music industry, argue that a monopoly over a song or album is necessary in order to recoup the “cost of expression”.

The cost of expression is defined as all sunk-costs involved in bringing an idea to market. According to popular theory, innovation will only take place if the innovator receives economic rents as reimbursement for assuming risk associated with the cost of expression (Bensen and Raskind, 5). In the modern music industry, the cost of expression is extremely low for the average artist. Recording technologies are cheap

10 and readily available, as are digital distribution services (Rayna and Striukova, 4).

Nonetheless, major recording labels advocate for strong IPRs on the basis of their cost of expression being higher than average, operating under the assumption that marketing and distributing costs may never be recouped (Rayna and Striukova, 4). The assertion that a monopoly is necessary to foster creation of new works is highly contested.

A major critique of allowing recording labels monopolistic privilege is that the justification for their monopoly ignores the input market. Theirry Rayna and Striukova dubbed this term as a “monometapoly”, in which the firm holds a monopsony in the labor market and a monopoly in the output market. The term’s use in the music industry is appropriate. Only 1% of artists are given the opportunity to sign a contract in the U.S.

Additionally, the price artists are paid is extremely low. Typically, “artists only start earning royalties after the initial advance made by the recording companies has been reimbursed” (Rayna and Striukova, 8). The monopsonist’s trademark is their ability to set cost and amount of inputs. Therefore, the comparison is an apt fit.

In a “metamonopoly”, the input market is marked by upward sloping marginal factor costs and a steeper marginal cost than the monopolist ( Rayna and Striukova, 6).

Given these rising costs, the monopsonist finds it profitable to decrease inputs. Granted, a “metamonopsonist” does not exist in a competitive output market. Since a

“metamonopoly” holds a monopoly in the output market, the firm’s marginal revenue and marginal revenue product are downward sloping. Consequently, a “metamonopoly” will use even fewer inputs than a monopsonist and produce even less than a

11 monopolist. Socially, this is the worst possible market structure in terms of welfare, with deadweight welfare losses in both the input and output market.

A record label’s control over the labor market should give pause to policy makers. Do record labels need monopolist privileges to recoup sunk costs if they have complete control over the price and quantity of their inputs? In addition to critiquing the labor market, it’s important to also investigate the notion that record labels need protection from sunk costs. Unlike most monopolists, record labels have access to an extensive back catalogue of products which accrue compounding revenue. Back catalogue royalties provide a constant cash-flow and allow major labels to take on large risks while maintaining a safety-net (Rayna and Striukova, 4). In order to protect their back catalogue, major labels use IPRs to hunt rival and unsigned artists who cover, or are strongly influenced by, an artist on their roster. A prime example of this is YouTube, where artists’ videos covering or influenced by artists are hunted with “takedown notices” by major labels.

ANALYSIS OF COPYRIGHT PROTECTION AND YOUTUBE

Kristofer Erikson and Martin Kretschmer studied video takedowns of parody videos, or covers, of artists in the UK. In 2012, a proposal was made to exempt parody from UK copyright law. A variety of groups opposed the motion, stating that parodies might “deprive rightsholders of a legitimate stream of licensing revenue” and that these parodies might “ might compete unfairly with original works in the marketplace, either by substituting for the original, or by causing unwanted reputational damage” (Erikson and

Kretschmer, 81-2). It is important to note that YouTube is not obligated to seek out and takedown parodies. YouTube was defined in both U.S. and E.U. courts as a “service

12 provider”, which excludes YouTube from liability pertaining to copyright infringement.

Granted, this does not exclude YouTube from all takedown liability. Instead, the liability is placed on the rights holder to identify and contact YouTube with take-down notices.

There is “no set of consistently applied set of rules governing the removal of derivative online use of copyright work” (Erikson and Kretschmer,80).

Erikson and Kretschmer’s study consisted of 1,839 parody videos containing tracks which charted in the UK’s top 100 song charts in the 12 months preceding

January of 2012 (when the study took place). The parodies were all derivatives of an officially licensed video and found by searching “ ‘song title’ + parody”. A total of 8,299 videos were found and a random sample of 1,839 videos were selected for the study.

Each video was recorded with information pertaining to number of views, parodic content, and production. Homemade karaoke videos, or covers, were often labeled as

“low production”. After recording video information in 2012, the study reconvened in

2013 and 2016 to assess the results.

Percentage of Videos Taken Down After Experiment

Source: Erikson and Kretschmer (2018)

13 Regression Results of Possible Takedown Factors

Source: Erikson and Kretschmer (2018) Results of the study show that four years after initially posting, 40.8% of parodies were taken down. The study ran a regression on genre, production quality, and parodic content to see which factors played into videos receiving a takedown notice.

Interestingly, of the reasons listed by right holders for opposing parodies, none were statistically significant. In fact, the inverse was true. Videos with a smaller view count and poor production content, which were usually cover videos, were statistically significant. Unsurprisingly, videos that contained the original audio, were also taken down at a significantly significant rate.

IMPACT OF INTELLECTUAL PROPERTY LAWS ON SOCIAL WELFARE

The cost of these welfare losses is both social and monetary. Interpretation of these results indicate that a record label’s monopoly has negative effects on output pertaining to creativity and welfare. Surely, cover videos with a low view count are not responsible for draining royalty revenues from right-holders. Social welfare losses are not the only result of a label’s monopoly. Paul Romer states that “a crude estimate

14 suggests that the welfare loss created by the excess of price over marginal cost could be comparable to total revenue for the recording industry” (Romer, 214). Looking to the pre-streaming era of early 2000s, Romer used Napster as a reference to create a straight-line approximation of deadweight-welfare-loss. As of February 2001, Napster accrued an average of 1.5 billion illegal MP3 downloads per month. The majority of these tracks were singles, one-off tracks rather than complete albums. At the time, the price of a physical single (pertaining to CDs) was $4 on average. A straight-line approximation comprised of $2 (($4-$0)/2),multiplied by 1.5 billion per month over 12 months, equals $37 billion USD. In 2000, global music industry revenue was $37 billion.

In other words, deadweight-welfare-loss imposed by monopolistic pricing was equivalent to global industry revenues in 2000 (Romer, 215).

Finally, in addition to the deadweight loss associated with a major label monopoly, IPRs have overextended their original purpose. Boldrin and Levine illustrate this in a separate paper entitled Market Size and Intellectual Property. Boldrin and

Levine state that it was an incredible feat for an album to sell one million copies in the

1960s. Yet, in the modern market, it’s incredibly common for an album to sell 10 to 20 million copies. Additionally, modern recording costs are roughly one-fifth of their 1960 levels. Therefore, the term length of copyright protection should have decreased by a factor of 12 since 1960. If the 1960s term length of 28 years was socially optimal, the current length should be slightly over a year. Instead, the current term length is nearly

100 years. Therefore, copyright protection is currently 100 times greater than the socially optimal level (Boldrin and Levine, 876).

15 DATA COLLECTION AND METHODOLOGY

Although the term “The Big Three” was widely popularized in the literature, the grouping of “the big four” may be more appropriate. Below are figures highlighting the big three’s net profit since 2015. Additionally, I’ve included a figure with net profit data for CJ&EM Entertainment. Perhaps CJ&EM is excluded from the conversation due to its relatively late arrival onto the South Korea’s music scene. Although CJ&EM entered the music industry in 2013, with its acquisition of Jellyfish Entertainment, it held a strong presence before then through its popular television network: Mnet. Mnet doubles as both a popular television service and as a music content provider. Although music is not

Mnet’s primary business, Mnet still captured 10% of the digital music market in 2017

(Stata, 2019). Since 2013, CJ&EM continued to purchase or form extensive partnerships with a variety of labels. Currently, CJ&EM owns or has extensive partnerships with eleven different labels. A majority of these labels were consolidated into Stone Music Entertainment, which was founded in 2017 and houses all eleven subsidiary labels.

16

Source: Wall Street Journal, CJ&EM Income Statement

17

In order to observe the influence of “The big four” in South Korea, chart data was gathered from South Korea’s government chart service: Gaon. Gaon chart is a government-run website that was founded in 2010 amidst the explosion of K-pop onto the international scene. Prior to 2010, accurate chart data was withheld from public record. Gaon chart provides weekly top 100 chart data for album sales, digital MP3 single downloads, streaming data for singles, and YouTube plays (or “Social Plays”).

18 Additionally, there are weekly charts for karaoke, background music, and ringtones, as well as an aggregate chart comprised of digital downloads, streaming, and background music. For the purposes of this study, monthly download and streaming data were imported from the Gaon Chart website. YouTube play data was imported in weekly charts, due to Gaon Chart only offering the information in weekly format.

Scraping data into Google Sheets involved use of Sheet’s “importHTML” function. By using the function importHMTL, with parameters “table, 1” on a Gaon Chart link, chart data from that link would be pulled into Sheets. Each iteration of

“importHTML” contained 100 rows of data, with each row containing a song’s (or album’s) rank in the top 100, movement within the top 100, an artist name and song title, and an artist’s label or distributor. Each year was given its own sheet within a master spreadsheet for each chart. For example, the Monthly Digital Download sheet contains 8,400 rows of data, with each year holding 1,200 rows of chart data. The

Monthly Digital Download sheet, Monthly Albums Sold sheet, and the Monthly

Streaming sheet all follow the same format and contain data ranging from 2011 to 2017.

Each sheet contains 8,400 rows of data. The only exception pertains to weekly

YouTube chart data, which contains 5252 rows of data per year. YouTube chart data ranges from 2013 to 2018 and contains 31,512 rows of data.

Once imported into sheets, the data needed to be thoroughly cleaned up. Every imported set of data contained two columns with combined data. Artist, song title, and album were all packaged within a single cell in one column. Label and distribution data were also combined into a single cell. In order to unpack these data points into individual columns, the data tool “split text to column” and the function “SPLIT” were

19 utilized. First, artist, song title, and album were split into separate columns by using the

SPLIT function with the parameters (cell with column containing text, “(RETURN)”)).

These parameters indicate that the function split text into the adjacent column where a return fell within the cell. The same principle was applied to an artist’s label and distributor. Besides splitting the data into multiple columns, other adjustments were needed. Following an artist’s name usually fell a divider, “|”, which would signify a break between an artist’s name and the album’s name. In order to remove these breaks, the

“split text to column” command was employed using the parameters “split text at ‘|’”, which would break the divider marker into the next column.

Another issue pertained to gathering uniform variables across each dataset.

Gaon charts does not list label data within its Social Media chart. Naturally, this posed an issue, since label information is the crux of the data analysis. In order to obtain label

20 information, all download and streaming data was aggregated into one sheet. This reference sheet contained basic information such as artist name, song title, album, and label. Utilizing the VLOOKUP function, and using an artist’s name as the search criteria, the formula was successfully able to pool all available label information associated with an artist. After using VLOOKUP, each row contained label data of from 2011 to 2017, for both streaming and downloads, which yielded a max of 14 potential matches.

In order to trim this down, I created a function consisting of INDEX, MODE, IF, and MATCH functions. The purpose of this function was to look across the range of potential matches and return the value that was occurred most frequently. In order to account for the possibility that a row only contained one label value, the list of potential matches was doubled to 28. Doubling the dataset allowed the function to account for single observations, since the function requires a duplicate to aggregate a potential match. Due to the prevalence of 360 deals in the South Korean music industry, it is not unreasonable to assume that an artist would remain on the same label for seven years or more. Given the aforementioned KFTC court ruling which instituted a seven-year term maximum for recording contracts, an artist matched to a label within a seven-year period seems fair.

After aggregating all potential label matches into one column, I needed to clean the sheet once more and create a new variable. Starting with clean-up, I deleted all rows which did not contain a label observation. This brought the total number of rows containing observations from 31,512 down to 18,762, which is still suitable for our analysis. Analyzing the effect of major labels on the marketplace implies the need for a dummy variable, which can return a 1 if the song was released by a member of the “big

21 four”, or a 0 for all other observation. The formula used to create these dummy values contained an IFERROR function, which contained an IFS function, which contained an

ISNUMBER(SEARCH()) function for each major label and their sub-labels. Essentially, the function searched a cell for the name of a major label, or a subsidiary, and returned a 1 or a 0 accordingly.

Before subjecting the data to econometric analysis, a new tab was created within each dataset. Annual data from each tab was aggregated into one tab labeled composite. All datasets, monthly download data, monthly streaming data, and weekly social media data, contain a composite tab which contains all data for that sheet. The composite tab allows econometric analysis to run on the aggregate data, instead of exclusively on data within a given year.

Each sheet was trimmed down once more before analysis. Since Gaon charts reports data weekly or monthly, there are many duplicate artists charting over the course of a year. Rather than cut data out from the analysis, I decided to aggregate all data pertaining to an artist. Using COUNTIF and SUMIF functions, I compiled all streams, downloads, or social media plays related to an artist. A new variable,

Averanking, was created, which counted an artist’s chart rating and divided it by their number of Gaon chart appearances. Naturally, the variable Appearances, which aggregates how many times an artist appeared in the charts, was also created. From

22 there, I downloaded the sheets into Excel where I was able to remove duplicate artist

listings. By end of this process, each artist was listed with their aggregate value of

streams, plays, or downloads, average chart ranking, and number of chart

appearances.

DATA ANALYSIS : STREAMING

Source: Gaon Chart Data (2019)

The “big four” have accounted for 35% to nearly 50% of all streams in the Korean

Music industry since 2013. We’d expect to see this trend reflected in the annual histograms below. Each histogram contains streaming data for a particular year, on a logarithmic scale, sorted by industry label streams and “big four” streams (0 for the industry, 1 for the “big four”). Looking at the histograms, it’s clear that nearly every distribution is skewed to the right. Intuitively, this makes sense, since songs within the top ten of streams usually garner a larger portion of streams versus the bottom ninety.

Generally speaking, if the left tail does exist, it exists in both industry and “big four” histograms. Between 2011 and 2013, both the industry and the “big four” exhibited relatively normal distributions in the top 100 charts. From 2014 onward to 2017, both

23 charts have, for the most part, lacked a lower tail. Notably, during this time, the big four’s upper-tail has consistently out-performed the general industry. Perhaps what it more interesting is how these distributions have behaved on the aggregate.

ANNUAL HISTOGRAM ON PERCENT OF LNSTREAMS: INDUSTRY VS. “BIG FOUR”

Source: Gaon Chart Data (2019)

24 AGGREGATE HISTOGRAM ON PERCENT OF STREAMS: INDUSTRY VS. “BIG FOUR”

Source: Gaon Chart Data (2019)

Above is a table comprised of aggregate histogram analyses. Generally, all industry histograms reflect a “more normal” distribution. Granted, there are still upper-tail outliers, but the presence of some lower-tail observations is notable. Looking at aggregate charts for “the big four”, nearly every chart has a large percentage of observations in the upper-tail. The histogram pertaining to LnTotalStreams actually reflects a normal distribution until it spikes in the upper-tail, with multiple instances of ten to eighteen percent observations occurring near the end of the scale. Notably, both the number of

25 chart appearances and average ranking for an artist is much higher for “big four” artists.

Compared to the industry at large, both distributions are shifted to the right.

Simple linear regression analysis was performed on each of variables listed in the aggregate histogram analysis. The Gaon chart data is inherently highly correlated, with streams factoring into ranking and ranking factoring into number of chart appearances. In order to avoid heteroskedasticity, simple robust regressions were used instead of multiple regression analyses. Future models may want to consider building a model including an artist’s gender, genre, age, and ticket sales. Typically, due to the kurtosis exhibited in the histogram distributions, a single-sided log-scale variation and a robust simple linear regression was employed to help reduce the impact of outliers on the analysis.

Averanking is an average between 1 and 100, therefore there was no need to utilize a log scale or robust regression.

SIMPLE LINEAR REGRESSION ANALYSIS: STREAMS

Dependent Variables Individually 95% Regressed Against N Prob > F R2 P > | t | Coef. Conf. BigFourStreams

LnTotalStreams 911 0.1965 0.0018 0.197 0.1051471 [-0.545236, .2648178] * LnAboveAveStreams 1,135 0.0331 0.0039 0.033 0.1152419 [0.009276, 0.2212079] * AveRankingStream 1,244 0.0001 0.0116 0.000 -2.669889 [-4.043093, -1.296685] * LnAveApperanceStream 1,244 0.0432 0.0032 0.043 0.1379148 [0.0042046, 0.271625]

The first analysis on the natural log of total streams yielded a p-value of .197, which is not significant at the .05 level. Rather than concluding the analysis of totalstreams, a new variable was created which contained all observations greater than or equal to the mean. The regression on above average streams held a p-value of .033, which is significant at the .05 level. The Prob > F value is a little high for this model, with a value of .0331, indicating that this model may not be the best fit. Yet, given our limitations

26 to run any multiple least squares analyses, the model is still useful to illustrate that there is a weak positive relationship (R^2 = .0039) between above average performing artists and “the big four”.

Looking at average ranking and number of chart appearances, there is a relationship between “the big four” and an artist’s average chart rating. With a R^2 of .01, and a p-value of 0, the model interpreted that “big four” artists rank roughly 2.6 chart positions lower than industry artists. Yet, there is also a positive relationship between number of chart appearances and “the big four”. Therefore, since average ranking is comprised of an artist’s aggregate ranking divided by their number of appearances, this is most likely the byproduct of “big four” artists having a larger denominator in the average ranking equation.

DATA ANALYSIS: DOWNLOADS

Source: Gaon Chart Data (2019)

Unlike global trends, MP3 downloads are still prevalent in South Korea. This will not be the case for long. Due to the aforementioned Ministry of Culture Tourism and

Sports statute imposing a mandatory 5% royalty return increase to artists, download packages are no longer offered at the old package rate. Previously, consumers could opt to buy a 30 song MP3 download package, in addition to their streaming subscription, for

27 50% of the standard price when bought in conjunction with a streaming subscription. With the new royalty stipulation, streaming services have raised their prices and reduced the discount rate on MP3 packages to 40%. The discounted price is expected to gradually diminish until MP3 downloads are ultimately no longer offered on the market. This mindset is reflected in the global outlook for MP3 download revenue through 2023, below.

Forecasted Global Digital Download Revenue

Annual histogram results for digital MP3 downloads tell a similar story to the annual histogram outputs for streaming. The “big four’s” streaming and download market share increased between 2013 and 2017. This growth is reflected in the histogram output, under the assumption that right-tail observations are responsible for the increase in download market share. Consistently, the decay of the right-tail is more gradual for “the big four” in comparison to the general industry. This suggests that an increase in right-tail observations, in contrast to the general industry’s right-tail, is responsible for the increase in market share.

28 ANNUAL HISTOGRAM OF LNDOWNLOADS: INDUSTRY VS. “BIG FOUR”

Source: Gaon Chart Data (2019)

29 Aggregate download histogram results highlight extreme right-hand tails for total downloads and number of appearances in the top 100. In both the total download and appearances in the top 100 chart, the general industry’s charts followed a more normal distribution. Although the general industry also had outliers in its right-tail, the outliers pale in comparison to the “big four’s” outliers. In both total download and appearance charts,

“the big four” contains right-tail observations which account for at least 20% of all observations.

AGGREGATE HISTOGRAM OF DOWNLOADS: INDUSTRY VS. “BIG FOUR”

Source: Gaon Chart Data (2019)

30 SIMPLE LINEAR REGRESSION ANALYSIS: DOWNLOADS

Dependent Variables Individually N Prob > F R2 P > | t | Coef. 95% Regressed Against Conf. BigFourDownload LnTotalDonwloads 1,244 3.02 0.0023 0.083 .1332441 [-.0172, .2837] * AboveAveDownlaod 530 0.00 0.0791 0.000 -4684395 [-6055699, -3313090] * AppearancesDownload 1,244 0.00 0.016 0.000 -10.309 [14.603, -6.0147] * AveRankingDownload 1,244 0.00 0.0178 0.000 -3.093479 [-4.348194, -1.839764]

Similar to the regression results pertaining to totalstreams, the regression results for total downloads yielded a p-value greater than .05. In order to investigate total downloads, I adopted an approach identical to the simple regression on total streams.

With a new variable for above average downloads, the model produced a relationship with an R^2 of 0.0791 and a coefficient of -4,684,396. This coefficient suggests that big four artists are downloaded 4,684,396 times less than other artists in the industry. Similarly, regression results for both average ranking and number of chart appearances both had negative coefficients, as well as similar R^2 values (.016 for number of chart appearances, .0178 for average ranking). Before analyzing these results, “the big four’s” impact on social media plays needs to be investigated.

DATA ANALYSIS: SOCIAL MEDIA PLAYS

31

Source: Gaon Chart Data (2019)

As previously mentioned, the “Big Three” notoriously took advantage of YouTube marketing and utilized their first-mover advantage to spread K-pop internationally. Looking at the figure above, there is an indication that advantage has worn off. Although “The Big

Three” has monopoly power in the market for music in South Korea, they behave as competitive firms on the YouTube market. Issuing takedown notices on copyrighted material is one privilege to their music market status, yet when it comes to publishing new content on YouTube, the market place is competitive. There are zero barriers to distribute content on YouTube and there are hundreds of thousands of “firms”, or users, uploading videos daily. Like any competitive market, if a firm is collecting economic rents with low barriers to entry, other firms will enter the market and bring economic rents back to long- run equilibrium. Given the steady decline in market share of social media plays, it’s reasonable to assume that the “Big Three’s” first mover advantage has diminished as more and more people have decided to upload content to YouTube.

32 ANNUAL HISTOGRAM OF LNPLAYS: INDUSTRY VS. “BIG FOUR”

Source: Gaon Chart Data (2019)

Annual histograms for social media plays reveal something unlike the histograms for streams or downloads: a normal distribution. This could be attributed to a number of things, but most likely it is because the data was compiled weekly rather than monthly.

The two distributions for the industry and “the big four” tend to mirror each other, with slight variation in the bottom and upper-tails. As expected, based on the histograms from streaming and download data, there is more variation on the upper-tail within “the big four”.

33 AGGREGATE HISTOGRAM OF PLAYS: INDUSTRY VS. “BIG FOUR”

Source: Gaon Chart Data (2019) Looking at aggregate histograms for social media plays, the data provides some of the most extreme upper-tails within “the big four”. Within the histogram for plays and total appearances, there are observations over 30% within the upper-tail. Granted, the industry also has extreme occurrences on the upper-tail, but these observations are closer to the middle than what is observed in “the big four” and do not surpass 30%. Perhaps what is most notable from these histograms is not the upper-tail, but actually the bottom-tail. In

“the big four’s” side of average social ranking, the bottom observation hovers around 3.5.

For the industry at large, the start of the bottom-tail begins earlier, at around 3.25. In the

34 histograms for appearances and plays, the bottom-tail observations also begin slightly closer to the middle. Given the original “Big Three’s” first mover advantage, these firms most likely gained a large number of subscribers to their channels when they arrived early to the marketplace. By having more subscribers, it’s probable that the minimum value of plays is much higher for “big four” artists than the average industry artist.

SIMPLE LINEAR REGRESSION ANALYSIS: PLAYS

Dependent Variables Individually 95% Regressed Against N Prob > F R2 P > | t | Coef. Conf. BigFourSocial * LnTotalPlays 1,244 0.0450 0.0033 0.045 -0.1669639 [0.3302117, 0.0037161] * LnAppearanceSocial 1,244 0.0348 0.0036 0.035 -0.1755074 [0.3384727, -0.012542

Regression results indicate a relationship between plays and “the big four” with a p-value of .045 and a R^2 of .0033. Additionally, the model found that “big four” artists receive less plays than industry artist by the value of -.1669 on a natural log scale. Similar results were found in the number of social chart appearances. Results for average ranking could not be modeled due to heteroskedasticity issues that could not be repaired by natural log, robust regression, or creating an interaction variable.

CRITIQUES

Before attempting to discussing these results in a cohesive narrative, it’s important to highlight certain limitations which future studies should take into account. The most notable hinderance to this research was the lack of available data prior to 2009. Gaon charts was established in 2011, and therefore I was unable to find any data prior to 2011.

Data from various eras of copyright policy would have been ideal for this analysis. Next, uniform label information is also of vital importance. I was unable to compare downloads to streams or to plays, or any combination of the three, because different artists appeared at different times in different charts. If label data were not limited, I could have filtered

35 each data sheet by an artist’s name and label and matched each artist across each sheet.

This limitation segregated the data and limited my regression analysis to simple linear regressions.

Other issues with the data include a lack of micro-level variables. Gender, age, length of contract, current year in contract, and genre would be excellent tools for a regression analysis. With the addition of these hypothetical variables, the ability to compare one artist across different sheets, and data from various eras in copyright policy, a researcher could potentially create an accurate model for a multiple least squares regression.

INTERPRETATION

For the purposes of this analysis, I believe it is reasonable to disregard the coefficient results from the simple linear regression analyses. It is not that the coefficients are incorrect, it is the possibility that other important variables are missing from the model that discounts their findings. What is important is that each regression found a relationship between the two variables. Despite small R2 values, the indication that there is a relationship between “the big four” and the various dependent variables is important to keep in mind.

If social media plays are settling back to market equilibrium following a first-mover advantage, then there are potential implications for the increases in streaming and download market shares for “the big four”. If copyright policy has not changed since 2009, and “the big four” are expanding their influence over the market, is this the product of monopolistic influence on output? The consistency of bizarre upper-tail events across streams, downloads, and plays cannot be a coincidence. Rayna and Striukova argue that copyrights provide an immense back-catalogue for major labels, which allows them to push harder to market new acts while mitigating risk (4). This could explain why bottom- tail observations rarely appear before the lowest bottom-tail industry observations. 36 Additionally, this could explain why “the big four” consistently land megahits in the upper- tail, due to their constant cash inflows available for marketing.

CONCLUSION

Analyses to confirm that changes in copyright law have strengthened the “the big four’s” oligopoly are inconclusive. Although there are trends in the data that are encouraging for future research, more historical data needs to become available before attempting further analyses. That is not to say that policy makers should not turn a blind eye to the issue until then. There is potential evidence to suggest that “the big four” have advantages rooted in cashflows from back-catalogue royalties. Should these advantages continue unchecked, consumer welfare will decrease as independent or “indie label” acts are pushed out from the marketplace.

37 Works Cited

Aguiar, L. (2017). Let the music play? Free streaming and its effects on digital music

consumption. Information Economics and Policy, 41, 1-14.

“Annual KOMCA Report - 2017.” Annual KOMCA Report - 2017, 2017,

indd.adobe.com/view/21aa2b70-7769-4bd0-b17b-94808b670074.

Besen, S. M., and Raskind, L. J. (1991). An introduction to the law and economics of intellectual

property. Journal of economic perspectives, 5(1), 3-27.

Boldrin, M., and Levine, D. K. (2009). Market size and intellectual property protection.

International Economic Review, 50(3), 855-881.

Boldrin, M., and Levine, D. (2002). The case against intellectual property. American Economic

=Review, 92(2), 209-212.

Boldrin, M., and Levine, D. K. (2008). Against intellectual monopoly (Vol. 8). Cambridge:

Cambridge University Press.

Cihak, L. (n.d.). “Korean Artists Just Got a Pay Raise from Music Streaming Services.” Digital

Music News, 26 June 2018,www.digitalmusicnews.com/2018/06/25/korean-

musicstreaming-royalties/.

Sanchez, D. (2019, January 3). Mobile Music Services Face Higher Royalties in South Korea.

Retrieved March 11, 2019, from https://www.digitalmusicnews.com/2019/01/02/south-

korea-mobile-music-services-more-royalties/Retrieved December 2, 2018,

Einhorn, M. A. (2000). Intellectual property and antitrust: Music performing rights in

broadcasting. Colum.-VLA JL and Arts, 24, 349.

Erickson, Kristofer and Kretschmer, Martin, 'This Video is Unavailable': Analyzing Copyright

Takedown of User-Generated Content on YouTube (March 20, 2018). (2018) Journal of

Intellectual Property, Information Technology and E- Commerce Law (JIPITEC), 9(1).

Available at SSRN: https://ssrn.com/abstract=3144329

IFPI. (2011). South Korea.Representing the recording industry worldwide. Retrieved March 11,

2019, from https://www.ifpi.org/south-korea.php

38 Kanel, Marchand. “Pop Cultures: A Comparative Analysis of the American and South Korean

Record Industries.” Florida Atlantic University Digital Library, 2017,

fau.digital.flvc.org/islandora/object/fau%3A34567.

Kim, J. (n.d.). “The Other Side of K-Pop and Korean Music: Labor Abuse.” KOREA EXPOSÉ, 5

July 2018, www.koreaexpose.com/k-pop-korean-music-musicians-labor-abuse/. Retrieved

December 2, 2018,

Kim, Jiwhan and Nam, Changi and Ho Ryu, Min. (2017). What do consumers prefer for music

streaming services? : A comparative study between Korea and US. Telecommunications

Policy. 41. 10.1016/j.telpol.2017.01.008.

Landes, W. M., and Posner, R. A. (2009). The economic structure of intellectual property law.

Harvard University Press.

Lie, J. (2012). What is the K in K-pop? South Korean popular music, the culture industry, and

national identity. Korea Observer, 43(3), 339-363. (NaN, ). “(LEAD) Earnings in Show

Business Show Heavy Imbalance between Celebrity Level, Gender.” Yonhap News

Agency, 16 Jan. Retrieved December 2, 2018.

Long, D. (2018). Copyright Reform in the 21st Century: Adding Privacy Considerations into the

Normative Mix. In CORBETT S. and LAI J. (Eds.), Making Copyright Work for the Asian

Pacific: Juxtaposing Harmonisation with Flexibility (pp. 97-132). Australia: ANU Press.

Retrieved from http://www.jstor.org/stable/j.ctv8bt2xn.9

Messerlin, Patrick and Shin, Wonkyu. (2017). The Success of K-pop: How Big and Why so Fast?.

Asian Journal of Social Science. 45. 409-439. 10.1163/15685314-04504003.

Nguyen, G., Dejean, S., and Moreau, F. (2014). On the complementarity between online and

offline music consumption: The case of free streaming. Journal of Cultural

Economics, 38(4), 315-330. Retrieved from http://www.jstor.org/stable/44289548

Oh, Ingyu. (2013). The Globalization of K-pop: Korea's Place in the Global Music Industry. Korea

Observer. 44. 389-409.

39 Parc, J., Messerlin, P., and Moon, H. C. (2017). The secret to the success of K-pop: The benefits

of well-balanced copyrights. Corporate Espionage, Geopolitics, and Diplomacy Issues in

International Business (pp. 130-148). IGI Global

Rayna, T., and Striukova, L. (2009). Monometapoly or the Economics of the Music Industry.

Prometheus, 27(3), 211-222.

Romer, Paul. 2002. "When Should We Use Intellectual Property Rights? ." American Economic

Review, 92 (2): 213-216.

Statista Market Forecast. (2018). Retrieved March 11, 2019, from

https://www.statista.com/outlook/208/100/music-downloads/worldwide#market-revenue

Statista. (2018). Music industry in South Korea. Retrieved March 11, 2019, from

https://www.statista.com/study/58370/music-industry-in-south-korea/

Takeishi, A., and Lee, K. (2005). Mobile music business in Japan and Korea: Copyright

management institutions as a reverse salient. J. Strategic Inf. Sys., 14, 291-306.

Tsai, P. (2013). Discovering the full potential of the 360 deal: an analysis of the Korean pop

industry, seven-year statute, and Talent Agencies Act of California. UCLA Ent. L. Rev.,

20, 323.

Wall Street Journal. (2019). 130960.KR Annual Income Statement - CJ ENM Co. Ltd. Retrieved

March 11, 2019, from

https://quotes.wsj.com/KR/XKOS/130960/financials/annual/income-statement

40