Portfolio Effects in Conglomerate Mergers: The Empirical
Evidence of Leverage Effects in Korean Liquor Market*
Jinhwa Chung
Research Associate
Sogang Research Institute
of Market Economy
Seonghoon Jeon
Professor
School of Economics
Sogang University
jhchung76@sogang.ac.kr
jeonsh@sogang.ac.kr
June 2013
Preliminary
This work was supported by the National Research Foundation of Korea Grant funded
by the Korean Government (NRF-2010-330-B00091).
*
Abstract
In the paper, we implement an empirical test on the portfolio effects of conglomerate
mergers, using the data of Korean liquor market during the period of 1990~2008 in
which there have been several important conglomerate mergers between beer and soju
companies. We find that the combined company could take the advantage of regional
market dominance in the beer market in expanding regional market shares in the soju
market. Such leverage effects are differentiated from the efficiency enhancing
portfolio effects which result in the combined company’s expanding shares over all
regional soju markets regardless of the presence of dominance in the beer market. The
common distribution channels of liquor wholesalers seem to play a pivotal role in the
combined firm’s expansion of dominance in one market into another. Furthermore, we
implement separate empirical tests for two subsamples of regionally dominant and
non-dominant soju companies in order to differentiate the leverage effects of
foreclosure from those of toehold. The empirical results show the evidence of leverage
effects only for a sample of non-dominant soju companies. This implies that the
leverage effects of conglomerate mergers between beer and soju companies in Korea
had pro-competitive effects in that the combined firm could compete more effectively
with regionally dominant companies with the leverage of dominance in the beer market
as toehold.
Keywords: Portfolio Effect, Leverage Effect, Conglomerate Merger, Korean
Liquor Market
JEL Classification Number: L4, L6
1. Introduction
Portfolio effects, even though there may not be a standard definition of them, have
usually referred to anti-competitive concerns that arise from conglomerate mergers
between firms producing weakly substitutable products.
European Commission and
National Regulatory Agencies have often raised such concerns in several cases of
conglomerate
mergers
such
as
Coca/Amalgamated,
Coca-Carlsberg,
and
Guinnes/Grand Metropolitan in late 1990’s. The U.S. antitrust authorities criticized that
the abuse of portfolio effects theory would lead to block or deter pro-competitive
mergers based on economies of scale and scope. According to DOJ(2001), the theory of
portfolio effects tends to be focused too much on contingent sales as a way to leverage
market power through bundling, tying or full-line forcing, assuming away possible
efficiency-enhancing effects.
The divergence between the European and the U.S.
competition authorities in regard to the stance on the portfolio effects of conglomerate
mergers culminated in the well-known case of GE/Honeywell in 2001.
Portfolio effects may stem from two sources according to Neven(2005): 1)
economies of scale and scope that make competitors difficult to match with; 2)
conditional sales such as bundling, tying, and full-line forcing. Economies of scale and
scope come from either supply-side or demand-side. Combined firms can reduce costs
by integrating production, distribution, marketing or R&D, while consumers may
reduce transaction costs through one stop shopping of products portfolio.
Also,
according to Stigler(1968) and Adams-Yellen(1976), tying or bundling may be adopted
as means of price discrimination when consumers’ preferences are heterogeneous, and
resulting in market enlargement.
Moreover, Evans-Salinger (2005) suggests that
conglomerate mergers may facilitate market participation of potential entrants by
1
reducing fixed costs of production and distribution. Consequent efficiency gains and
price decreases are pro-competitive, enhancing consumer and social welfare.
The second source of portfolio effects, i.e., the possibility of conditional sales, have
been the basis of leverage effects, through which combined firms can exploit market
power in one market to expand market share in another. The consequent results may be
either anti-competitive or competitive.1
The anti-competitive effects come out when
the merged company may build up market power or foreclose new entry in another
market.
Although the traditional views of anti-competitive leverage effects have been
challenged by the so-called “single monopoly profit theorem” of Chicago School, they
have now rigorous theoretical foundations. Whinston(1990) shows that the monopolist
can induce exits or deter entries in cases that tied markets are product-differentiated
and oligopolistic. Afterwards there have been developed many versions of dynamic
leverage theory, e.g., Nalebuff(2000, 2004), Carlton-Waldman(2002), and ChoiStefanadis(2001), which confirm that a monopolist has tying incentives to protect the
market power in tying market or to enlarge the market power into tied market.
Moreover, Vergé(2007) applied such dynamic ideas in the context of full line forcing
onto distribution channels.
On the other hand, leverage effects may enhance
competition, when the merged company in a weak market position can erode
dominance of the incumbent firm with a toehold of leverage [Campbell-Shepherd(1968),
Esterbrook(1972), Kaplan(1980), Load(1982), DOJ(1997), Ponsoldt and David(2007)2].
In Korea, portfolio effects were an important issue in evaluating the competitive
effects of a conglomerate merger between two liquor companies, Hite/Jinro, in 2005;
Hite and Jinro were a dominant firm in beer and soju(Korean popular spirits) markets,
Notice that we use the term of “leverage” neutrally even though it has been read negatively in most previous literature.
Ponsoldt and David (2007) remarks that: “A second business justification that renders tying permissible involves a smaller new
entrant attempting to a toehold in a market occupied by powerful, larger competitors. Courts may justify casting a blind eye toward
otherwise per se illegal tie-ins by evaluating the overall pro-competitive effects of more diverse market.”
1
2
2
respectively.
Korea Fair Trade Commission was concerned of ant-competitive effects
of the merger since all liquor companies used common distribution channels, through
which the merged company might make an abuse of its dominant position in one
market in strengthening its market power in another.3 In this regard, Park(2009) adopts
a discrete choice model to evaluate several conglomerate mergers between beer and
soju companies during the period of 1994-2003, and finds no evidence of portfolio
effects empirically significant.
Especially, he argues that the combined company could
not enlarge its market shares of either beer or soju by pushing its products through
common distribution channels.
However, Park’s analysis has some limitations in terms of data. First of all, his
data cover only the period of 1994-2003, which was after the most important merger
during 1990’s of OB beer/DS(DooSan) soju; OB beer acquired DS soju in 1994, and sold
it out in 1999.
Hence, it cannot capture after-the-merger changes properly in
comparison with before-the-merger situations.
Table 1 shows that DS’s soju market shares have increased much more in Region 1,
2, and 3 where OB was dominant in terms of beer market shares than in Region 8, 9, and
10 where OB was not.4
KFTC final decision (2006) was to allow the merger with some behavioral remedies attached. The remedies included a price cap of
RPI+5% on Hite and Jinro’s beer and soju, the division of marketing workforce and organization of Hite and Jinro for 5 years, and
the provision of some arrangements by Hite itself that would ensure it not to commit exclusionary practices in the future.
4 Region 1 to 10 refers to Seoul capital city, Gyeonggi province, Ganngwon province, Chungbuk province, Chungnam province,
Jeonbuk province, Jeonnam province, Gyeongbuk, Gyeongnam province, Busan broad city, respectively. Appendix shows the map of
South Korea and the location of each region. Notice that we do not include Jeju Island which is the farthest from Seoul in the
analysis because the beer sales information is not available.
3
3
Table 1: DS’s M/S Changes in each Soju Market after OB/DS Merger (1994-1998)
DS’s
Soju M/S
1991
1992
1993
1994(combine)
1995
1996
1997
1998
1999(divesture)
2000
2001
2002
Beer M/S in 1994
OB
Hite
OB’s Dominant Market
Region1
Region2
Region3
0.008
0.049
0.682
0.012
0.052
0.680
0.033
0.041
0.664
0.082
0.135
0.718
0.150
0.236
0.854
0.188
0.276
0.830
0.221
0.292
0.831
0.232
0.321
0.880
0.201
0.300
0.842
0.050
0.074
0.576
0.079
0.073
0.523
0.085
0.065
0.586
0.548
0.334
0.621
0.286
0.558
0.340
Hite’s Dominant Market
Region8
Region9
Region10
0.000
0.005
0.000
0.000
0.006
0.000
0.000
0.005
0.000
0.007
0.030
0.000
0.037
0.049
0.002
0.020
0.048
0.023
0.006
0.031
0.004
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000
0.000
0.391
0.495
0.302
0.647
0.349
0.595
Sources: Korean Alcohol and Liquor Industry Association and AC Nielsen Korea 5
Moreover, Park(2009) does not take into account the merger between Hite beer and
Jinro soju in 2005, which is the most important conglomerate merger in regards to
portfolio effects. Hite beer had strong market positions in Region 8, 9, and 10, while it
did not in other regions. On the other hand, Jinro soju was dominant in Region 1, 2,
and 3, while it was not in other regions. Hence, the merger between Hite beer and
Jinro soju might be an optimal combination which could take an advantage of
leveraging one’s strength into another’s weakness. Table 2 shows that over all Jinro’s
soju market shares have increased much more in Region 3, 8, 9, and 10 where Hite had
strong market positions in terms of beer market shares than in Region 1 and 2 where
Hite did not.6
5
AC Nielson data of beer cover only retail sales. On the other hand, KALIA(Korea Alcohol and Liquor Industry Association)
data comprehend all sales including consumption in bars and restaurants as well. In general, market shares in retail sales are not
much different from those in sales through bars and restaurants.
6
Comparing Table 1 with Table 2, we can observe it that OB was dominant in 1990’s, while Hite acquired
dominance in 2000’s in Region 3.
4
Table 2: Jinro’s M/S Changes in each Soju Market after Hite/Jinro Merger (2005-Present)
Jinro’s
Soju M/S
2001
2002
2003
2004
2005(combine)
2006
2007
2008
2009
2010
Beer M/S in 2005
OB
Hite
OB’s Dominant Market
Region1
Region2
0.893
0.920
0.891
0.930
0.910
0.945
0.911
0.948
0.910
0.951
0.808
0.874
0.789
0.842
0.781
0.842
0.748
0.814
0.727
0.788
0.592
0.407
Region3
0.475
0.414
0.434
0.458
0.484
0.523
0.523
0.590
0.578
0.594
0.523
0.477
Hite’s Dominant Market
Region8
Region9
0.034
0.043
0.040
0.037
0.040
0.039
0.041
0.054
0.046
0.061
0.052
0.077
0.057
0.104
0.068
0.127
0.071
0.138
0.073
0.155
0.395
0.604
0.146
0.853
0.056
0.943
Region10
0.066
0.067
0.053
0.054
0.055
0.055
0.051
0.058
0.064
0.065
0.102
0.897
The same sources as above
The main purpose of this paper is to review the empirical evidence of portfolio
effects based on comprehensive experiences of conglomerate mergers in Korean liquor
market. Especially, we try to confirm econometrically that changes in regional market
shares of combined firms in soju market after beer/soju mergers were closely related
with regional variances in the market power of combined firms in beer market, to
contend that the main source of such changes were the portfolio effects via common
distribution channels, i.e., a dominant beer producer’s enforcing tying or full-line
forcing onto wholesalers, rather than efficiency enhancement due to synergies, and to
identify their competitive ramifications, i.e., to differentiate between foreclosure and
toehold effects.
The paper is organized as follows.
Section 2 introduces the structure
and characteristics of Korean liquor markets as a background.
used, and empirical framework adopted, in the paper.
Section 3 describes data
Section 4 implements the
empirical test for portfolio effects of beer/soju mergers using Korean liquor market data
5
during the period of 1990~2008 and presents results for alternative specifications and
tests of robustness.
Section 5 concludes with a summary of the results and their
implications for competition policies.
2. Beer and Soju Markets in Korea
Beer and soju7 take an absolute portion in Korean liquor market.8 Beer market is a
duopoly where Hite and OB compete fiercely. Two companies divide the national
market almost evenly,
9
but they show different strengths across regions. Roughly
saying, Hite is dominant in southern regions while OB in middle regions in Korean
peninsula.10
On the other hand, soju market is regionally segmented by 9 companies. As Table
3 shows, only Jinro shows national presence, while all other companies have their own
regional bases.
Such a market configuration mostly stems from the past government
regulation of the mandatory local soju purchase policy introduced in 1976. It required
the distributors in each regional market to purchase more than 50% of soju from the
designated local company in each market.11
Even though the policy was abolished
finally in 1996, its impact still remains in the regional market segmentation where each
Soju is popular liquor in Korea which is a kind of spirit with alcoholic content of about 20%. There are two kinds of soju – distilled
and diluted. Popular one in Korea is the diluted, which are made by diluting alcohol essence extracted from grains – ethanol made
from rice, barley, corn, etc.
8 Beer and soju together represent 90.5% in sales, and 97.6% in quantities, among total alcohol consumption in Korea in 2008. Sales
of beer and soju in 2008 amount to 3.5 trillion won(about 3.2 billion dollars), and 2.8 trillion won(about 2.5 billion dollars).
9 Currently Hite competes with OB neck and neck. But OB was dominant until early 1990’s..
10 Region 1 to 5 are in the middle area, and region 6 to 10 are in the southern area in Korean peninsula.
11 The match of company and region in the designation policy is as follows: Jinro/Seoul(R1) and Gyeonggi(R2),
Doosan(C3)/Gangwon(R3), Chungbuk(C4)/Chungbuk(R4), Seonyang(C5)/Chungnam (R5), Bobae(C6)/Jeonbuk(R6),
Bohae(C7)/Jeonnnam(R7), Geumbok(C8)/Gyeongbuk(R8), Moohak (C9)/Gyeongnam(R9), and Daesun(C10)/Busan(R 10).
Notice that we mostly use simple numbered notations for companies and regions.
7
6
local company maintains a persistent leadership in each own former designated
region.12
Table 3: Soju Companies’ Regional Market Shares (Average for 1994~2008)
R1
0.83
0.09
0.00
0.01
0.02
0.03
0.02
0.00
0.00
Jinro
C3
C4
C5
C6
C7
C8
C9
C10
R2
0.81
0.12
0.00
0.00
0.03
0.01
0.03
0.01
0.00
R3
0.33
0.66
0.00
0.00
0.00
0.00
0.01
0.00
0.00
R4
0.48
0.03
0.36
0.02
0.03
0.04
0.03
0.01
0.00
R5
0.35
0.02
0.00
0.52
0.03
0.03
0.05
0.00
0.00
R6
0.28
0.02
0.00
0.03
0.59
0.08
0.00
0.00
0.00
R7
0.12
0.00
0.00
0.00
0.02
0.86
0.00
0.00
0.00
R8
0.14
0.01
0.00
0.00
0.00
0.00
0.83
0.01
0.00
R9
0.14
0.00
0.00
0.00
0.00
0.00
0.01
0.71
0.13
R10
0.20
0.00
0.00
0.00
0.00
0.01
0.02
0.05
0.71
Sources: Korean Alcohol and Liquor Industry Association
In this paper, we analyze the competitive effects of conglomerate mergers between
beer and soju companies, presuming that relevant geographic markets are local. First
of all, it is now well received that the geographic market of soju in antitrust enforcement
should be defined as local markets.
Jeon(2004) implemented a SSNIP test to define the
relevant geographic market for evaluating competitive effects of an attempted merger
between C9 and C10 in 2002, and found the the relevant markets were local, i.e., R9 and
R10.
Seoul High Court(2004) concurred with Jeon’s analysis, and concluded that the
merger was anti-competitive.
Table 3 above may ascertain the local nature of soju
market.
Table 4 also hints some localities in beer market. However, we do not have a
rigorous test of the geographic market definition of beer in Korea, and may not argue
strongly that it is local.
Nevertheless, it does not seem to be problematic that we
regard the beer market in Korea as local for the purpose of our analysis, since we are
12
See Hong-Choi-Jeon(2011) more for its history and remnant influence.
7
mainly concerned of whether combined firms could exploit the regional dominance in
beer market for extending their local soju markets, not vice versa.
Table 4: Beer Companies’ Regional Market Shares (Average for 1994~2008)
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
OB
0.50
0.51
0.40
0.50
0.48
0.23
0.45
0.27
0.10
0.14
Jiro/Coors13
0.11
0.08
0.11
0.18
0.09
0.01
0.02
0.04
0.02
0.03
Hite
0.39
0.41
0.49
0.32
0.43
0.76
0.52
0.70
0.88
0.83
Sources: AC Nielsen Korea
Beer and soju are substitutes to a certain degree.
But, it is not controversial that
they constitute separate product markets in antitrust analyses.14
Table 5 summarizes 5
conglomerate mergers between beer and soju companies which occurred during the
period of 1990~2008.
Table 5: Conglomerate Mergers between Beer and Soju Companies
Period
1993~1999
1994~1998
1997~2004
1997~Present
2005~Present
Combined Companies
Cass Beer+Jinro Soju
OB Beer+C3 Soju
Hite Beer+C4 Soju
Hite Beer+C5 Soju
Hite Beer+Jinro Soju
Remark
Jinro launched Cass
OB acquired C3
Hite acquired C4
Hite acquired C5
Hite acquired Jinro
Sources: Annual reports of each Soju company.
Even though there might have been many reasons for those mergers, we focus on
the motive of a combined company exploiting its dominant position in the beer market
for extending the shares in the soju market. The market environments surrounding
liquor distribution make such portfolio effects plausible. According to Korean liquor
13
Jinro/Coors entered with a brand of Cass in 1993, and gained a market share as much as 18% in 1996. But since OB acquired it in
1999, the beer market returned to a duopoly. The competition between two companies has been fierce so as to be called as “beer war”.
14 Jeon-Kim-Park-Yoon (2005) implemented a SSNIP test for the product market definition relevant to Hite/Jinro merger, and
concluded that beer and soju were separate product markets. Accordingly, KFTC approved the conglomerate merger with some
behavioral remedies attached.
8
tax laws, producers are prohibited from acquiring the license to wholesale liquor, and
from making exclusionary contracts with liquor distributors which can deal with all
kinds of liquor. Moreover, liquor producers should supply their products to
distributors with the same wholesale price.
Hence, they do not compete with price in
the wholesale market. But producers can influence distributors in regions where they
have strong bases through tied marketing, for example, combining quantity rationing of
popular products with quantity enforcing of unpopular ones.
3. Data and Empirical Framework
3.1. Data and Descriptive Statistics
In order to test the portfolio effects, we constructed a firm-level panel dataset for
local soju markets, which are composed of 2 metropolitan areas and 8 provinces in
Korea from 1990 to 2008 as shown in Appendix.15
Table 6 provides the mean values of
market characteristics for all regions.
Table 6: Mean Values of Market Characteristics over 1990~2008
Market
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
15
Designated
Firm
Jinro
Jinro
C3
C4
C5
C6
C7
C8
C9
C10
Market Volume
(1000 liter)
234,536
160,618
44,988
31,000
60,623
91,721
87,421
34,170
72,838
79,163
Real GRDP
(billion Won)
195,731
178,490
21,470
22,045
58,709
23,580
57,299
78,993
87,600
43,968
Unemployment
rate (%)
4.0
3.3
1.8
2.3
2.8
2.6
2.8
2.2
2.5
4.5
We do not include Jesu Island which our data source of beer sales, AC Niesen Retail Index, does not cover.
9
Population
(in 1000)
9,468
8,870
1,400
1,379
3,068
1,760
3,143
2,538
3,820
3,463
We obtain firm-level data in each regional market from various sources such as
Korean Alcohol and Liquor Industry Association (KALIA), AC Nielsen Retail Index,
each firm’s Annual Reports and Statistics Korea.
Table 7 summarizes the main
variables used in our empirical work.
Table 7: Summary Statistics of Variables Used in Regressions
Variables
Mean
Std. Dev.
Min
Max
share
Local market share (%)
11.11
23.42
0.00
96.27
conglo
Conglomerate merger
0.17
0.38
0.00
1.00
dominance
Dominant beer company
0.12
0.32
0.00
1.00
congXdomi
Conglo × Dominance
0.12
0.32
0.00
1.00
wprice
Wholesale price index (log)
2.81
0.11
2.62
3.02
# product
The number of products
165
103
29
533
# newprod
The number of new product
0.91
1.02
0.00
4.00
advertising
Advertising expenditures per capita (log)
10.31
7.12
0.00
16.01
distance
Plant location (log)
3.53
4.76
0.00
6.24
designated
Designated local company
0.11
0.31
0.00
1.00
desiXcong
Designated soju company × Conglo
0.02
0.14
0.00
1.00
grdpc
Gross regional product per capita (log)
2.96
0.26
2.33
3.58
unemp
Unemployment rate (%)
3.09
1.48
0.81
9.08
The table reports summary statistics for the period during 1990~2008. The number of total observations is
1,710.
Local Market Share: Our key dependent variable is the local market share of each
soju company;
represents i (= Jinro, C3,…, C10)’s local market share in region
r (= R1,…., R10) in period t (= month in 1990~2008). The data source of the sales
volume of each firm in a regional market is KALIA; we obtained monthly sales
information after 1994 from its electronic database, and the previous corresponding
data from KALIA(1998).16
16 We compared the sales information of KALIA database with that in its published book for the overlapped period of 1994-1997,
and confirmed their consistency.
10
Conglomerate Merger: The dummy variable for conglomerate merger identifies
the period during which the soju company was combined with a beer company;
𝑐𝑜𝑛𝑔𝑙𝑜𝑚
𝑡
is 1 if the combined company produced both soju and beer, and 0 if the
company produced soju only at the time 𝑡. Table 8 shows the changes in market share
of the dominant firm in each regional soju market during the period of the
conglomerate mergers between beer and soju companies as introduced in Table 5.
The shaded cells in Table 8 show notable changes in each regional market; that is,
when a non-dominant soju company was combined with a dominant beer company, the
market share of the incumbent dominant soju companies decreased in general.
At a
first glance, this observation suggests that the merged firm erodes dominance of the
incumbent local soju company with the leverage of strong position in the regional beer
market.
Dominant Beer Company:
𝑜𝑚 𝑛 𝑛𝑐
The dummy variable for dominant in beer market
is 1 if the beer firm, which merged with soju company i, has the largest
market share in each regional market, or 0 otherwise. Regional sales volumes of all
beer firms were obtained from AC Nielsen Retail Index, which is POS transaction
information from most liquors selling retailers in Korea.
Table 8: Changes of Market Shares of Regional Dominant Soju Companies during the Period of
Conglomerate Mergers (1986~2008)
Market Share
Conglom
No
OB+C3 (1994-1998)
Hite+C4 & C5
Hite+Jinro (2005-2008)
erate
Merger
(1997- 2004)
Merger
(1986Hite+C5 (1994-2008)
Cases
1993)
R1
0.85
0.73*
0.88
0.82+
R2
0.78
0.69*
0.89
0.88+
11
R3
R4
R5
R6
R7
R8
R9
R10
0.82+*
0.5
0.53*
0.66
0.64*
0.66
0.87
0.63
0.69
0.46
0.56
0.63
0.83
0.76
0.92
0.59
0.54+*
0.63+
0.51+*
0.76*
0.90*
0.50*
0.80*
0.83*
0.61*
0.64
0.56*
0.83*
0.95*
0.57*
0.81*
0.85*
The table reports the average market share of the largest firm in each market during the merger period.
* denotes the case in which the beer company in the merger had the largest share in each regional market during the period.
+ denote the case in which the soju company in the merger has the largest share in each regional market during the period.
Wholesale price index:
follows;
𝑜𝑙
𝑙
∑
𝑐
price index per liter of firm
of firm
at time 𝑡,
We construct the firm-level wholesale price index as
𝑜𝑙
where
at time 𝑡,
𝑙
𝑐
is the real
is the sales volume share of soju-brand
per liter at time 𝑡. The data
is the real price of soju-brand
on wholesale prices for each soju company are also obtained from KALIA.
The prices
of all brands for the period 1994~2008 were created by dividing the total amounts of
sales by the total volumes of sales.
The price data before 1994 were acquired from the
prices listed on KALIA quarterly magazines. However, due to regulations in Korean
liquor industries, the price competition in soju market has been very limited.
Number of Products: The number of product controls the possibility of increasing
market share due to product innovation. Variable
whole product portfolio, and
𝑛
𝑜
𝑐𝑡
𝑜
𝑐𝑡
refers to the number of
refers to the number of new product
released by the soju company. Here, the product portfolio is composed of all products
in terms of SKU (Stock Keeping Unit), which are distinguished by brand, ABV (alcohol
by volume), volume, and container. Table 9 shows the mean values of each firm’s
strategic variables.
Advertising expenditures per capita: Advertising data were obtained from the
Nielsen Media Research in Korea. The data contains information about total advertising
12
expenditures for all major media including TV, radio, newspapers and magazines from
1990 to 2008. However, in case of soju industry, companies are prohibited from running
TV and radio commercials, which are the most powerful advertising channel among all
the media. Consequently, the advertising expenditure of each company is fairly small
and concentrating on the newspaper channel that is issued nationally. We divide this
variable by the number of regional population.
Table 9: Mean Values of Firm’s Strategic Variables
Firm
C12
C3
C4
C5
C6
C7
C8
C9
C10
The Number of
Products
287
230
84
131
111
178
135
154
86
The Number of New
Products
1.1
0.7
0.3
0.7
0.9
0.9
1.0
1.3
0.8
Total Advertising
(million Won)
6,128
3,635
88
251
95
1,115
1,525
530
418
Average
AVB
24.3
25.0
24.2
25.6
25.6
24.9
24.7
24.9
25.1
The table reports the average value of each variables for local firms during 1990~2008.
Plant location: It is widely speculated that the factory location is associated with
brand loyalty in the region. We also measure the distance from a factory to each
market,
𝑡 𝑛𝑐
, in order to capture brand loyalty for each soju company. The
information about factory location was obtained from each company’s Annual Report.
Local Identity Effects: In soju market, we believe that there exists strong brand
loyalty which is closely related to local identity.
Using regional soju market data
during the period of 1994~ 2008, Hong-Choi-Jeon (2011) empirically investigates that
the local identity of soju products was a main factor for local soju producers to keep
persistent leadership in their regional markets, even after the mandatory local soju
purchase regulation was abolished in 1996.
13
In order to consider such local identity
𝑔𝑛 𝑡
effects, we introduce a dummy variable,
firm
is designated local company in region
, which is an indicator of whether
.
However, we may guess that a conglomerate merger between a national beer
producer and a local soju company might mitigate the local identity attached to local
soju. For example, when OB beer with nation-wide basis merged with C3, a local soju
in R3, some regional consumers in R3 might regard the combined firm’s product no
longer as their local one.
For this reason, we introduce
𝑔𝑛 𝑡
𝑐𝑜𝑛𝑔𝑙𝑜 , which
is to check those loyalty dynamics caused by conglomerate mergers.
Regional Macroeconomic Shocks: Gross regional product per capita, 𝑔
𝑛 𝑚
,
may capture macroeconomic shocks in each region.
𝑐
and
The data on such local
market characteristics are obtained from Statistics Korea.
3.2. Empirical Framework
The main purpose of this paper is to identify the portfolio effects of conglomerate mergers
between beer and soju companies in Korean liquor market. In particular, we focus on the
possibility that the combined company (i.e., a local soju company integrated by a major beer
company) may enforce local wholesalers to sell its soju products.
Therefore, the main
difficulty in this work is to distinguish these two kinds of mechanisms using the existing
merger cases in Korean liquor market. We strongly believe that the unique structure of soju
industry provides a rare chance to investigate the mechanisms underlying the portfolio effects
and to decompose them into the leverage effect and their efficiency-enhancing aspects.
At first, it is worth mentioning that conglomerate mergers between soju and beer
14
companies would lead to different dominance combinations in each local market.
For
example, as Figure 1 displays, if there are soju company A and beer company B, which have
different dominance status, H (high) or L (low) in each local market, the integration of these
firms would give us opportunity to compare with 4 different merger cases (HH, HL, LH, LL)
in each local market.
Figure 1. Identification of the Portfolio Effects
soju company A’s M/S
H
Region 1
Region 3
beer company B’s M/S
H
Region 2
Region 4
L
H
Conglomerate
Merger
L
Region 1
Region 3
H
L
Region 2
Region 4
L
Second, we use the following characteristics to distinguish two aspects of the portfolio
effects. As for the efficiency-enhancing effects, they will exist regardless of the combined
market power of beer and soju companies. Because we use the term ‘soju company’ to just
mean the manufacturing sector of the soju industry, main possible source of increased sales
after the merger is the economies of scale and scope by the joint production. However, soju
and beer are produced and managed separately and share a small portion of inputs, and thus
economies of scope can be limited [Park (2009)].
Moreover, even though such efficiency-
enhancing effects exist, the effects would spread out across the country due to the regulations
which Figure 2 is summarizing.
15
Figure 2. Characteristics of Efficiency Enhancing Effects
Re g io n 1
d is t r ib u t o r s
s o ju p r o d u c e r s
soju and beer are produced in a
separate facility and share a small
portion of inputs (park 2009).
p ro d u c e r ’
s c o s t s a v in g
a re lim it e d
manufacturer is prohibited from
• operating in distribution channel.
• making exclusionary contracts.
• setting different wholesale price
for same product by each market
• running TV advertisements and
offering bundled discounts
distributors are
• operating in regional market
• dealing with all kinds of liquors
Re g io n 2
d is t r ib u t o r s
p ro d u c e r ’
s c o s t s a v in g a n d it s e ff e c t
co u ld n o t b e c o n c e n t ra t e d o n s o m e lo c a l d is t r ib u t o r s
On the other hand, the leverage effects would be related to regional variances in the market
power of the combined company in beer market. However, the leverage effects of
conglomerate mergers may have different implications on competition in regional soju
markets, depending on the market power of combined company in soju market as
summarized in Table 11.
If the combined company has dominance in both beer and soju markets, as
Nalebuff(2004) and Vergé(2007) suggest, it may deter potential entrants or exclude
current competitors from the market by tying or bundling, and result in foreclosure. On
the other hand, when the integrated company has dominance only in the beer market, it
can implement aggressive push strategies in local soju markets, and take the advantage
of market power in the beer market as a toehold for more effective competition with
regionally dominant soju companies.
16
Table 11: Implications of Portfolio Effects on Local Soju Markets
Soju
High M/S
Low M/S
Efficiency-enhancing Effects
Efficiency-enhancing Effects
+
+
Foreclosure effects
Toehold effects
Beer
High M/S
Low M/S
Efficiency-enhancing Effects
Efficiency-enhancing Effects
We propose a fixed-effect model of estimation as follows (1990~2008):
𝑦
𝛽 𝑐𝑜𝑛𝑔𝑙𝑜𝑚
𝑦
≡ ln
where sh re
𝑡
+ 𝛽2 𝑐𝑜𝑛𝑔𝑙𝑜
𝑜𝑚 𝑛 𝑐
+𝛾 𝑋
+𝜃 +𝛼 +𝛿 +𝜀
ℎ𝑎 𝑒
(1)
− ℎ𝑎 𝑒
is the local soju market shares, 𝑐𝑜𝑛𝑔𝑙𝑜
and
𝑜𝑚 𝑛 𝑐
are dummy
variables for conglomerate mergers and their dominance in beer markets, respectively.
𝑋
.
is the vector of other explanatory variables of soju company
in time 𝑡 in market
𝜃 , 𝛼 , and 𝛿 capture time-specific fixed effect, firm-specific fixed effect, and
regional-specific fixed effect, respectively. 𝜀
is the idiosyncratic error term.
In our main results, the dependent variable is the logistic transformation of the
local market share:
i.e., 𝑦
ln
ℎ𝑎 𝑒
− ℎ𝑎 𝑒
.
17
Since the logistic transformation usually
results in heteroskedastic disturbances, we adopt the two-step FGLS estimation method
[Cameron and Trivedi (2010)]. 𝑋
includes several control variables that may
influence local market shares such as wholesale prices, number of products, distance,
advertising, local brand loyalty, and income. 𝜃 controls common shocks in the given
17 The
logistic transformation excludes zero market shares. However, in alternative specifications for robustness checks, we include
them by adopting the dependent variable of simple market share.
17
year, such as change in regulations to promote competitions or trends in soju
products. 18 𝛼 represents time invariant firm-specific characteristics.
Similarly, 𝛿
controls time invariant market-specific characteristics.
In the model, 𝛽 represents the efficiency effects which exist regardless of
market dominance of the beer company, while 𝛽2 represents the leverage effects of
using dominance in beer markets and extending market shares in soju markets.
If 𝛽
is positive and statistically significant, it implies that the merger effect of increasing
market share, which stems from the economies of scale and scope, is realized
nationwide. And, if 𝛽2 is positive and significant, it means that there exist additional
increases in market shares due to leverage effects.
In addition, we divide the data into two groups according to the market
dominance of soju companies in 1986~1989. 𝑜𝑗
high market power, whereas 𝑜𝑗
𝐿
𝐻
is the group of soju companies with
is that of soju companies with low market power.
Then, we estimate equation (1) for two subsamples, in order to see whether the leverage
effects of conglomerate mergers are different between the two groups.
shows, 𝛽2 > 0 for 𝑜𝑗
𝐻
As Table 10
may be the evidence for foreclosure, while 𝛽2 > 0 for 𝑜𝑗
𝐿
may be the evidence of toehold effect.
18 For
example, the mandatory local purchase regulation was abolished in 1993, revived in 1995, and finally abolished again in 1996
due to a constitutional petition. Afterwards, free competition was introduced such as price and product differentiations. Also, there
were the changes in regulations for additive or alcohol tax. Thus, to control these changes, we should consider time-specific fixed
effects.
18
4. Results
4.1.
Identification of Portfolio Effects
According to the estimates from our baseline specifications in Table 11, we can find
the evidence that combined firms increased their local soju market shares over all
regions by means of efficiency enhancement or other sources.
estimate of 𝛽
without 𝑐𝑜𝑛𝑔𝑙𝑜
𝑜𝑚 𝑛 𝑛𝑐
Table 11 shows that the
, which captures the general
conglomerate merger effects over all markets, is statistically significant in Column (1)
and (3).
However, it is difficult to argue that conglomerate companies had a general
advantage in efficiency-based competition, compared with other single firms.
Column (2) and (4) show, the estimate of 𝛽 with 𝑐𝑜𝑛𝑔𝑙𝑜
but is not significant at the 5% level.
is positive,
On the other hand, we obtain positive and
statistically significant estimates for coefficient 𝛽2 on 𝑐𝑜𝑛𝑔𝑙𝑜
specifications in Column (2) and (4).
𝑜𝑚 𝑛 𝑛𝑐
As
𝑜𝑚 𝑛 𝑛𝑐
for all
These results mean that if the merged beer
company has dominance in regional beer markets, its soju counterpart can increase
regional soju market shares at 1% significance level.
19
Table 11: The Estimation Results of the Fixed-effect Models
VARIABLES
(1)
Conglomerate
0.360**
(0.163)
Dependent Variables: Logistic transformation
OLS
FGLS
(2)
(3)
0.032
(0.180)
0.508***
(0.186)
-1.290
(1.108)
0.003***
(0.001)
0.060
(0.041)
0.018***
(0.007)
-0.166***
(0.022)
4.474***
(0.350)
-2.014***
(0.309)
-0.085
(0.112)
-0.145
(0.958)
Yes
Yes
Yes
1,042
Conglo Dominance (in Beer Market)
Wholesale price index
# Products
# New products
Advertising
Distance
Designated
Designated
Conglomerate
Unemployment rate
GRDP per capita
Year Fixed-Effects
Firm Fixed-Effects
Regional Fixed-Effects
Observations
-1.459
(1.109)
0.003***
(0.001)
0.051
(0.041)
0.020***
(0.007)
-0.165***
(0.022)
4.484***
(0.346)
-2.112***
(0.318)
-0.099
(0.112)
-0.071
(0.956)
Yes
Yes
Yes
1,042
0.384***
(0.056)
-1.448***
(0.355)
0.003***
(0.000)
0.009
(0.019)
0.018***
(0.003)
-0.156***
(0.016)
4.004***
(0.253)
-1.955***
(0.096)
-0.096***
(0.022)
0.061
(0.207)
Yes
Yes
Yes
1,042
(4)
0.116
(0.061)
0.427***
(0.064)
-1.468***
(0.413)
0.003***
(0.000)
0.018
(0.019)
0.014***
(0.003)
-0.137***
(0.020)
4.214***
(0.392)
-1.920***
(0.094)
-0.050
(0.036)
0.334
(0.287)
Yes
Yes
Yes
1,042
Note: The table displays OLS and FGLS estimates. All columns were estimated using a panel data of 9 firms in 10
regional markets over the period 1990 to 2008. All estimates are corrected for heteroskedasticity and contemporaneous
correlation. Robust standard errors are in parenthesis; ** and *** indicate significant at the 5% and 1% levels,
respectively.
It can be taken as the empirical evidence to show that there are leverage effects on
common distribution channels of the conglomerate firm.
That is, the combined soju
company can increase its regional soju market shares by about 1.5 times. [Column (4)],
compared to the other integrated firms with non-dominant beer company, by tying or
20
full-line forcing on the common distribution channel in the region where its beer
partner is dominant.19
Taking into account the general effects of mergers, the gains (𝛽 + 𝛽2 ) in soju
market shares of the combined company with beer dominance amount to about 1.7
times [Column (4)] increases in share. The magnitude of market share increases seems
unreasonably high.
But as we will see soon in the following subsection 4.2, such
effects mostly occurs in the regions where regional soju market shares are low, and
hence are not so realistic as in the first glance.the coefficients of other explanatory
variables such as price (
𝑜𝑙
𝑙
𝑐
advertising expenditures per capita(
identity (
𝑔𝑛 𝑡
), the number of all products (
𝑣 𝑡
𝑛𝑔 ), plant location(
) and local identity shock (
𝑔𝑛 𝑡
𝑜
𝑐𝑡 ) and
𝑡 𝑛𝑐
), local
𝑐𝑜𝑛𝑔𝑙𝑜 ) show
expected signs, and are statistically significant at 5% significant level at least. On the
other hand, explanatory variables such as the number of newly released
products (𝑛
GRDP(𝑔
4.2.
𝑜
𝑐𝑡 ) ,
unemployment
rate
(
𝑛 𝑚
)
and
per
capita
𝑐 ) are not statistically significant.
Robustness Checks
In order to check the robustness of our results, we estimate alternative specifications.
Table 12 reports estimates from FE models using soju market share as the dependent
variable in Column (1)~(4). It shows that the results are similar to the previous ones.
19
In the logistic models, the rate of increase approximately equals to exp{b} - 1 where b is the estimated coefficient.
21
We consistently obtain positive and significant estimates for coefficient β2 for both
specifications in Column (2) and (4). In particular, the combined soju company can
increase its regional soju market shares by about 1.8~2.7% points when its beer partner
is dominant.
Table 12: Using Market Shares as Dependent Variables
Dependent Variables:
Share>0
Share≥0
VARIABLES
(1)
(2)
(3)
(4)
Conglomerate
2.321***
(0.347)
1.485***
(0.402)
1.290***
(0.412)
-0.749
(1.497)
0.010***
(0.002)
-0.030
(0.052)
0.005
(0.010)
-0.457***
(0.057)
58.839***
(3.178)
-20.334***
(0.610)
-0.165
(0.113)
0.177
(1.600)
Yes
Yes
Yes
1,710
1.000
(0.538)
0.334
(0.586)
1.495**
(0.605)
-4.008
(4.138)
0.003
(0.003)
0.056
(0.135)
0.049
(0.035)
-0.493***
(0.093)
62.040***
(1.376)
-17.122***
(1.924)
0.082
(0.244)
-1.506
(2.254)
Yes
Yes
Yes
1,042
Conglo Dominance (in Beer Market)
Wholesale price index
# Products
# New products
Advertising
Distance
Designated
Designated
Conglomerate
Unemployment rate
GRDP per capita
Year Fixed-Effects
Firm Fixed-Effects
Regional Fixed-Effects
Observations
-1.039
(1.441)
0.010***
(0.001)
-0.062
(0.050)
0.010
(0.009)
-0.446***
(0.060)
58.922***
(3.283)
-20.922***
(0.547)
-0.235**
(0.110)
0.023
(1.510)
Yes
Yes
Yes
1,710
-3.384
(4.161)
0.004
(0.003)
0.087
(0.138)
0.052
(0.036)
-0.493***
(0.092)
62.103***
(1.367)
-17.280***
(1.894)
0.095
(0.252)
-1.286
(2.338)
Yes
Yes
Yes
1,042
Note: The table displays FGLS estimates. All columns are estimated using a panel data of 9 firms in 10 regional markets
over the period 1990 to 2008. The number of total observations is 1,710. In case of (3), (4), the observations that
market share is zero, were excluded from the data set. All estimates are corrected for heteroskedasticity and
contemporaneous correlation. Robust standard errors are in parenthesis; ** and *** indicate significant at the 5% and 1%
levels, respectively.
22
4.3.
Differentiating the Leverage Effects into Foreclosure and Toehold
We now try to differentiate the leverage effects into two, i.e., foreclosure effects and
toehold effects, depending on the combined firm’s dominance of soju markets.
this purpose, we divide our samples into two groups of 𝑜𝑗
𝐻
and 𝑜𝑗
𝐿
For
, based on the
average market share between 1986 and 1989, and estimate our main fixed effect model
in (1) separately. For the criterion for 𝑜𝑗
𝐻
, we try three classifications: i) “the largest
firm in the regional market”, ii) MS>40%, and iii) MS≥70% . If the combined company
belongs to 𝑜𝑗
𝐻
, i.e., if it is already dominant in the soju market, β2 > 0 may be
interpreted as the evidence of foreclosure effects.
in 𝑜𝑗
𝐿
On the other hand, if the company is
, and increases its market share by leveraging market dominance in the beer
market, it will be interpreted as the evidence of toehold effects.
These two possibilities
have very different implications for consumer welfare and competition policy.
Table
14 shows our empirical results for two subsamples.
Table 14: Estimates for Subsamples based on the Soju Market Dominance
VARIABLES
Conglomerate
Conglo Dominance (in Beer Market)
Wholesale price index
Dependent Variables: : Logistic transformation
Dominant Soju Companies
Non Dominant Soju Companies
(1)No.1 (2)M/S>4 (3)M/S>7 (4)Others (5)MS<40 (6)MS<70
0
0
-0.550***
(0.082)
-0.161
(0.110)
0.637
-0.367**
(0.165)
-0.472**
(0.216)
1.284
23
-0.237
(0.202)
-0.665**
(0.266)
-2.425
-0.181***
(0.050)
0.539***
(0.046)
-1.638***
-0.175***
(0.063)
0.550***
(0.061)
-1.505***
-0.186***
(0.060)
0.513***
(0.050)
-1.756***
# Products
# New products
Advertising
Distance
Unemployment rate
GRDP per capita
Year Fixed-Effects
Firm Fixed-Effects
Regional Fixed-Effects
Observations
(0.481)
-0.002***
(0.000)
-0.041**
(0.018)
0.006*
(0.003)
-0.045***
(0.014)
-0.047
(0.049)
-0.783**
(0.305)
(0.675)
-0.003***
(0.000)
-0.031
(0.024)
0.003
(0.007)
-0.065***
(0.019)
-0.044
(0.056)
-0.663
(0.377)
(1.412)
-0.003***
(0.001)
-0.065
(0.046)
0.019
(0.016)
0.089
(0.049)
-0.137
(0.143)
-4.022**
(2.018)
(0.416)
0.003***
(0.000)
0.042**
(0.017)
0.015***
(0.003)
-0.171***
(0.015)
-0.067***
(0.025)
-0.518**
(0.219)
(0.552)
0.003***
(0.000)
0.043
(0.024)
0.010***
(0.004)
-0.248***
(0.017)
-0.066**
(0.030)
-0.280
(0.266)
(0.415)
0.002***
(0.000)
0.040**
(0.016)
0.014***
(0.003)
-0.309***
(0.023)
-0.043
(0.029)
-0.497**
(0.214)
Yes
Yes
Yes
190
Yes
Yes
Yes
171
Yes
Yes
Yes
114
Yes
Yes
Yes
1,520
Yes
Yes
Yes
1,539
Yes
Yes
Yes
1,596
Note: The table displays FGLS estimates. All columns are estimated using a panel data of 9 firms in 10 regional markets
over the period 1990 to 2008. All estimates are corrected for heteroskedasticity and contemporaneous correlation.
Robust standard errors are in parenthesis; ** and *** indicate significant at the 5% and 1% levels, respectively.
According to the estimation results in Table 14, a non-dominant soju company in
𝑜𝑗
𝐿
can increase its market share considerably by merging with a dominant beer
company. The implied toehold effects are very strong both in terms of magnitude and
significance. On the other hand, we cannot find comparable results for the dominant
soju company. The estimates of the coefficient estimates on 𝑐𝑜𝑛𝑔𝑙𝑜
𝑜𝑚 𝑛 𝑛𝑐
are not statistically meaningful even at 10% significance level in Column (1). Moreover,
in Column (2) and (3), the estimates of 𝛽2 is significant at the 5% level, but is negative.
These results hold true no matter how dominance is defined.
This means that the
leverage effects of conglomerate mergers in our study are mainly due to toehold effects
in the regions where the combined soju company is not dominant.
The non-dominant
soju company can increase its regional soju market shares by about 1.7 times by tying or
full-line forcing on common distribution channels in the region where its beer partner is
24
dominant.
Taking into account the general negative effects of mergers, the gains in
soju market shares of the merged company with beer dominance amount to about 1.4
times. The magnitude of market share increases here seems to be reasonable, since the
non-dominant soju firm’s market shares were initially low.
5. Concluding Remarks
There have been many controversies in EU and US about the portfolio effects of
conglomerate mergers and their implications on merger regulation.
The combination
of weakly substitutable products may provide the merged firm with competitive
advantages which stem either from economies of scale and scope in production and
distribution or from the leverage of market power through tying and full-line forcing.
The possibilities of economies of scale and scope due to the combination of a wide
portfolio of products are the basis of a more prudent approach toward conglomerate
mergers in US competition agencies.
On the other hand, the concerns about the
leverage effects and consequent anti-competitive foreclosure have led EU competition
agencies to take a more aggressive stance in enforcing the regulation of conglomerate
mergers.
Despite abundant theoretical discussions, we cannot find many empirical works to
test the portfolio effects of conglomerate mergers, to identify their sources, and to
explore their implications on competition policy.
In this paper, we provided an
empirical evidence of the leverage effects of conglomerate mergers between beer and
25
soju companies in Korea. We used data of Korean liquor market during the period of
1990~2008 in which there have been several important conglomerate mergers between
beer and soju companies.
We identified the leverage effects of the combined
company’s taking the advantage of regional market dominance in the beer market in
expanding regional market shares in the soju market.
Those effects are differentiated from the efficiency-enhancing portfolio effects which
must result in the combined company’s expanding shares over all regional soju markets
regardless of the presence of dominance in the beer market. In fact, our empirical results
did not show the evidence of such efficiency effects in the studied mergers. There may
be many mechanisms of leveraging the market power in one market into another. In the
context of Korean liquor market, we speculate that the common distribution channels of
liquor wholesalers play a pivotal role in the combined firm’s expansion of dominance in
one market into another, since wholesalers deal with all kinds of liquors and
manufacturers have a strong leverage on closely tied wholesalers.
Furthermore, we implemented separate empirical tests for two subsamples of
regionally dominant and non-dominant soju companies in order to differentiate the
leverage effects into two, one resulting in foreclosure and another providing with toehold.
The empirical results showed the evidence of leverage effects only for a sample of nondominant soju companies.
This implies that the leverage effects of conglomerate
mergers between beer and soju companines in Korea had pro-competitive effects in that
the combined firm could compete more effectively with regionally dominant companies
26
by the leverage of dominance in the beer market. On the other hand, we could not find
the evidence of anti-competitive foreclosure effects where the combined firm had
dominance in both beer and soju markets. This finding suggests that we should pay
more attentions to the market conditions in merged markets in evaluating the
competitive effects of conglomerate mergers which raise the concerns of leverage effects.
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Appendix: Regional Map of South Korea
Region3(Gangwon)
Region2(Gyeonggi)
Region1(Seoul)
Region4(Chungbuk)
Region5(Chungnam)
Region8(Gyeongbuk)
Region6(Jeonbuk)
Region9(Gyeongnam)
Region10(Busan))
Region7(Jeonnam)
Jeju Island
31