ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No.E17-4

On the Possibility of an Unsuccessful Merger:
Implications from Stock Market andRetail Scanner DatabyTakanori Adachi

July 2017

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

On the Possibility of an Unsuccessful Merger:
Implications from Stock Market andRetail Scanner Data
Takanori AdachiyJune 27, 2017

Abstract
Industrial economists and competition policymakers have traditionally assumed thata horizontal or vertical merger raises involved parties’joint pro…ts, whereas whether consumers bene…t or lose should be judged on a case-by-case basis. However, if a completed
merger is not as successful as expected, its observed e¤ects on the retail market may
not necessarily be a result of the integrated …rm’s anti-competitive conduct. How can
one assess whether a merger has achieved its initial objectives? This paper proposes to
use two di¤erent data sources to empirically argue the possibility of such an unsuccessful
merger. First, I use stock market data to provide an event study analysis, and compare
PepsiCo’s vertical integraton of two of the its chain bottlers (February 2010) and CocaCola’s acquistion of its biggest bottler (October 2010). I argue that the stock market may
not have perceived Coca-Cola’s vertical merger as promising as PepsiCo’s vertical merger.
Furthermore, the former may have been perceived as helping Dr Pepper Snapple rather
than Coca-Cola itself. Secondly, I use retail scanner data to present evidence, based on adi¤erence-in-di¤erences estimation, which shows that Coca-Cola’s retail prices rose by 5%
after its vertical merger, suggesting that internal con‡icts may have been passed throughto its …nal prices.
JEL Classi…cation: L4; K21; G34.
Keywords: Mergers; Vertical Integration; Event Study Analysis; Di¤erence-in-Di¤erences
Estimation.
I am grateful to Hiroshi Gunji and Hidenori Takahashi for helpful comments. I also thank a Grant-in-Aid
for Scienti…c Research (C) (15K03425) from the Japan Society for the Promotion of Science and a …nancial
support from the Seimeikai Foundation. All empirical results from SymphonyIRI Group’s data are based on my
own work, not of SymphonyIRI Group. Any remaining errors are mine. I declare no con‡ict of interest withregard to the research presented in this paper.
ySchool of Economics, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan. E-mail:adachi.t@soec.nagoya-u.ac.jp

1

Introduction

One presumption under merger evaluation is that a merger must raise joint pro…ts of both parties. Salop and Culley’s (2016) recent proposal for a revision of the Vertical Merger Guidelines
is, for example, no exception. However, at least ex post, the completed merger can turn out
to be less successful than expected, or even harmful to the integrated entity. As recognizedby Bresnahan and Levin (2012), the industrial organization literature tends to assume away
the possibility of organizational con‡icts when a horizontal or vertical merger is considered.
However, obviously, any merger is associated with organizational change.
This paper empirically presents evidence for such an “unsuccessful” merger by using twodi¤erent data sources in a constructive way. Speci…cally, I study the two biggest vertical mergers
in the U.S. carbonated soft drink industry: PepsiCo’s merger with two bottlers (February 2010)
and Coka-Cola’s merger with the biggest bottler in its supply chain (October 2010). First,
from an event study analysis using stock market data, the contrast between PepsiCo’s andCoca-Cola’s vertical mergers is revealed and explored. Indeed, it suggests that Coca-Cola’s
vertical merger was not perceived to be as promising as PepsiCo’s vertical merger. In addition,
the stock market may have perceived that Coca-Cola’s vertical merger would help Dr Pepper
Snapple (hereafter, Dr Pepper) rather than Coca-Cola itself. Secondly, other evidence fromretail scanner data suggests that Coca-Cola’s vertical merger raised its own retail prices by
about 5%, with statistical signi…cance. It is thus inferred that Coca-Cola’s vertical integration
may have caused transactional con‡icts between upstream and downstream …rms which werepassed through to its retail prices.
The rest of the paper is organized as follows. First, in Section 2, I further describe the twovertical mergers in the U.S. carbonated soft drink industry, which form the focus of this study.
Then, Section 3 presents empirical results from an event study analysis. After a robustness
analysis is conducted in Section 4, I provide a supporting analysis that shows Coca-Cola’s
vertical merger raised its own retail prices in Section 5. Lastly, Section 6 concludes the paperwith remarks on implications for competition policy based on the foregoing empirical results.

1

2

The Two Vertical Mergers in the U.S. Carbonated Soft
Drink Industry

PepsiCo’s acquisition of its two biggest bottlers (Pepsi Bottling Group and PepsiAmericas)
in 2010 and Coca-Cola’s acquisition of Coca-Cola Enterprises (CCE) in the same year are
considered as the two biggest vertical mergers in the industry’s history. First, PepsiCo reacheda deal to acquire Pepsi Bottling Group (PBG) and PepsiAmericas (PAS) on August 4, 2009.
At the time of this announcement, PepsiCo owned about 40% and 43% of the shares of PBG
and PAS, respectively (Federal Trade Commission’s (FTC) press-release on February 26, 2010),
and it completed the merger process on February 26, 2010. Chairwoman Indra Nooyi statedthat the main reason for the vertical merger was to respond to changes in consumers’tastes ina faster way (PepsiCo’s press release on August 4, 2009), although bottlers had been reluctant
to adjust to these new trends (Zhou and Wan, 2017). It seemed that the news of this verticalmerger was received favorably by the stock market. There was a 5.1% increase in the valueof PepsiCo’s shares on the same day of PepsiCo’s announcement of the merger plan; and thenumbers were even higher for the two bottlers: 8.5% for PBG and 9% for PAS (Kilibarda,
2010).
At the time of The Wall Street Journal’s article about PepsiCo’s attempt to acquire the twobottlers (on April 20, 2009), The Coca-Cola Company and CCE established a new organization
to for coordination and cost reduction in their distribution system (The Wall Street Journal,
April 21, 2009). Chairman Muhtar Kent said (to The Wall Street Journal, April 22, 2009) thatthe organization would simplify “the payment system for concentrate and syrup.” As Isdell
and Beasley (2011, p.195) reveal, Coca-Cola had a plan to acquire CCE in the spring of 2006
“in order to lower costs and increase pro…ts.” Then, on February 25, 2010, Coca-Cola …nallyabandoned its own franchise system of bottling and distribution. At the time, CCE was thelargest Coca-Coca bottler in North America, operating in 46 states and Washington, D.C.,
accounting for about 80% of Coca-Cola’s sales volume in the U.S. According to FTC’s pressrelease document issued on September 27, 2010, Coca-Cola, at the time of the announcementof the agreement, “owned about 34 percent of Coca-Cola Enterprises.” Interestingly, on the
day of the announcement, The Coca-Cola Company’s shares dropped by 3.7%, although CCE’sshares rose sharply by 31% (Kilibarda, 2010). Coca-Cola completed its acquisition process on

2

October 1, 2010.
However, two years after the Coca-Cola’s acquisition of CCE, The Coca-Coca Company wasreportedly (on December 12. 2013 according to The Wall Street Journal, “Coca-Cola Shakes
Up Americas Management”) initiating an “organizational shakeup” to “refranchise much ofits U.S. distribution” (emphasis added) over the next several years. More recently, The WallStreet Journal, March 22, 2016, also reported, Coca-Cola was “refranchising nearly half of its
distribution territory.”These movements suggest that Coca-Cola’s vertical integration was not
successful in achieving its obejectives. In contrast, PepsiCo does not appear to have changed
its distribution system following its own merger activities.

3

Event Study Analysis

To study the di¤erences in how Coca-Cola’s and PepsiCo’s vertical mergers were conceived in
the …nancial market, I conduct an event study analysis. In an event study analysis, one isinterested in knowing whether and how much the value of a public company is a¤ected by aparticular event. To study how the stock market evaluated the acquiring …rm and rival …rms
when a merger proposal was announced. I follow Hosken and Simpson (2001); Simpson (2001);
Duso, Gugler, and Yurtoglu (2010); Kwoka and Gu (2015); and Günster and van Dijk (2016),
among others, to consider the standard market model: Rjt =j +j Rmt +

j Dt + jt , where

Rit is the rate of return to …rm j on day t, Rmt is the rate of return on the value-weighted
market index at the New York Stock Exchange, Dt is a dummy variable which takes one if day
t belongs to the event window (see below), and zero otherwise, and jt is a serially uncorrelated
random shock that is independent of Rmt and Dt .1
Herein, an event is an announcement of a soft drink seller’s acquisition of a bottler. Let
t = 1; 2; :::; t0 ; :::; T be the time index for the entire data period. The time period is daily, and1

One could also, as Filson, Olfati, and Radoniqi (2015) do, consider a more sophisticated version of themarket model such as Fama and French’s (1993) three-factor model:
Rjt

rt =

j +

j (Rmtrt ) +

j Dt +

j1 SM Bt +j2 HM Lt + jt ,where rt is the risk-free return rate on day t, and SM Bt and HM Lt measure the excess returnsof small and low-growth company stocks, respectively (“the Fama-French factors”). For the purpose
here, however, it would su¢ ce to consider the standard model, although the estimation is readily conducted because historical data on rt , SM Bt , and HM Lt are available via Kenneth French’s webpage(http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).

3

date t0 is the day when the event occurs. For Coca-Cola’s vertical merger, it is February 24,
2010, and for PepsiCo’s vertical merger, it is August 4, 2009. Then, the coe¢ cientj captures…rm j’s abnormal return due to the event. For each merger, the data period starts at 200
trading days before the event date, and ends 10 trading days after the event, which means that
there are 211 days included in each of the two data sets. For each merger, the equation aboveis estimated using the entire period.
Data on Rjt and Rmt are taken from the Center for Research in Security Prices’s (CRSP) US
Stock Database.2 I obtain information on daily returns for The Coca-Cola Company, PepsiCo,and Dr Pepper Snapple, Inc. I am also able to include Jones Soda Co, which is listed on NYSE,
as a rival …rm.3 Information on daily returns for CCE and PBG is also available.4 Admittedly,
an announcement of a merger may not be a real shock not least because some information
about the proposed merger may have been leaked prior to the announcement. In addition, themarket may not quickly respond to the announcement. To mitigate these issues, I follow Kwoka
and Gu (2015) and consider the following …ve cases for the estimation window: [0], [-1,1], [-3,3],[-10,5], and [-20,10], where, for example, [-3,3] is the event window that includes, the event day,
3 trading days before the event, and 3 trading days after the event.
Table 1 shows the estimation results for abnormal returns to the acquiring …rm (The CocaCola Company), its rival …rms, and the target (CCE). The cumulative abnormal return (CAR)
to each …rm is obtained by multiplying the estimated bj by the total number of days in theevent window (for example, it is 7 for the [-3,3] window). Table 2, on the other hand, shows theestimation results for abnormal returns due to the announcement of PepsiCo’s vertical merger.
As explained above, I focus on event windows [-1,1] and [-3,3] more than on [0].
First, Coca-Coca’s own abnormal return is negative for either speci…cation [-1,1] or [-3,3] inthe announcement of its merger plan (albeit not statistically signi…cant). On the other hand,2

Two pieces of information are available for Rmt ; value-weighted market return that includes distributions,and an alternative that excludes dividends. I use the former and have veri…ed that there are no signi…cantchanges if the latter is used.
3
The listing of Jones Soda Co ended on September 19, 2012. The fourth largest company in terms of unit
sales in the IRI Academic Data Set is National Beverage Corporation, which cannot be included because its
listing on NYSE ended on June 11, 2007. It is now listed on NASDAQ.
4
The other bottler, PepsiAmericas Inc ended its listing on June 9, 2004. While PBG’s listing ended on
February 26, 2010, which I take as the day when PepsiCo consummated its vertical merger, CCE did not wholly
terminate listing on NYSE: its listing as CCE ended on October 3, 2010, but “Coca-Cola Enterprises New”waslisted starting from October 4, 2010.

4

Table 1: Estimates for Abnormal Returns due to the Announcement of Coca-Cola’s Vertical Merger
Event Window

Coca-Cola
PepsiCoDr Pepper
Jones Soda
CCE[-1,1]
Est(bj 102 )-1.378p
0.167-0.0660.754-0.199

3.668

0.226

11.003

2.836

0.262

8.509

10.381

0.264

31.142(0:994)(0:211)(3:023)(2:520)(9:265)

Event Window

Coca-Cola
PepsiCoDr Pepper
Jones Soda
CCE[-3,3]
CAR(%)-4.133Est(bj 102 )-0.838

p
0.088

0.061

0.743

0.425

0.823

0.610

5.763

2.276

0.473

15.929

4.160

0.353

29.121(0:083)(0:185)(1:613)(3:163)(4:468)[-10,5]Est(bj 102 )-0.105

p
0.732

0.309

0.047

4.943

0.726

0.339

11.616

2.197

0.208

35.160

1.742

0.402

27.866(0:154)(0:758)(1:740)(2:075)Est(bj 102 )
0.162

p
0.052

CAR
(%)
0.162

0.025

0.759

0.025

0.179

0.191

0.179

2.258

0.000

2.258

-1.1800.000-1.180(0:083)(0:081)(0:136)(0:502)(0:165)[-20,10]
CAR(%)-1.688(0:307)

[0]
CAR
(%)
-5.865Est(bj 102 )-0.177

p
0.341

CAR
(%)
-5.479

0.092

0.482

2.863

0.491

0.250

15.208

0.533

0.724

16.516

0.718

0.510

22.258(0:185)(0:131)(0:426)(1:505)(1:088)Notes: For each pair of Event Window and Company, the number of observations (trading days) is
211. Robust standard errors are shown in parentheses, and p-values less than 0.1 are emboldend.

5

Table 2: Estimates for Abnormal Returns due to the Announcement of PepsiCo’s Vertical Merger
Event Window[-1,1][-3,3]

Coca-Cola

Est(bj 102 )0.653-0.8220.008

Dr Pepper-1.127

0.000

0.439

0.814

1.316

1.602

0.558

4.806

PepsiCo

Jones Soda
PBG

(1:780)

p
0.714

CAR
(%)
1.958
-2.465Est(bj 102 )0.044(0:306)(0:244)(1:866)(2:730)

Event Window

[0]

p
0.957

CAR
(%)
0.306

-0.3310.257-2.319Est(bj 102 )4.956-3.381-1.0440.000-7.306-1.4970.576-10.482

0.379

0.763

2.653

(0:801)(0:291)(0:277)(2:676)(1:258)[-10,5]Coca-Cola-0.4660.056

Dr Pepper-0.2680.419-4.287

Jones Soda

-0.8340.618-13.352-1.9200.127-59.506

0.027

0.964

0.437

-0.3360.371-10.428

PBG(0:377)

p
0.823

CAR
(%)
-1.351-7.459(0:242)(0:331)(1:670)(0:605)-0.3650.001-0.365-0.8700.000-0.870

0.758

0.225

0.758

8.207

0.000

8.207

(0:099)(0:110)(0:197)(0:523)(0:172)[-20,10]Est(bj 102 )-0.084PepsiCo

p
0.000

CAR
(%)
4.956

Est
(bj 102 )-0.293

p
0.231

CAR
(%)
-9.097-0.276

0.189-8.544

0.202

0.587

6.257

(0:244)(0:209)(0:371)(1:253)(0:375)

Notes: For each pair of Event Window and Company, the number of observations (trading days) is
211. Robust standard errors are shown in parentheses, and p-values less than 0.1 are emboldened.

6

PepsiCo’s own abnormal return is positive; it is nearly 5% (and the p-value shows statisticalsigni…cance) if the event window is [0]. Thus, Coca-Cola’s vertical merger was less favorably
perceived around the announcement date. Second, this is also apparent in the abnormal returns
to the rival …rms. Table 2 indicates moderate values for PepsiCo’s abnormal returns, andrelatively high abnormal returns to Dr Pepper and Jones Soda, which implies that the …nancial
market might have perceived that Coca-Cola’s vertical merger would not strengthen its positionin the U.S. carbonated soft drink industry. This reasoning also applies if the event window is[-10,5] or [-20,10]. In contrast, an opposite …nding is observed with regard to the announcementof PepsiCo’s merger plan: in Table 2, Coca-Cola’s and Dr Pepper’s abnormal returns are both
negative in the speci…cations [-1,1], [-3.3], and [0]. In particular, the corresponding p-values
are low, showing statistical signi…cance. In sum, while the …nancial market might have favored
PepsiCo’s vertical merger, it might not have perceived Coca-Cola’s vertical merger in a similarway.

4

Robustness

Now, I consider two robustness checks. First, instead of including a dummy for the event
window, I estimate Rjt =j +j Rmt +

jt , for t = 1; 2; ::; ts < t0 (this period is called the

bjt = b j + bj Rmt for
estimation window). Then, the predicted (or the counterfactual) values, Rt = tu ; :::; tv , are computed, where tu = ts + 1 and tv
event window). I set ts = t0

t0 (now, the period t = tu ; :::; tv is the

1. Below, I consider two possibilities for the event window: [-1,1](tv = 1) and [-3,3] (tv = 3). Then, for each speci…cation, I construct the following t statistic:P
bt )R
t=tu ;:::;tv (Rtp,b = tv tu + 1
where

jt is assumed to be distributed according to iid N (0;and CAR is now de…ned byP

t=tu ;:::;tv (Rt2), and b is the estimate of,5bt ). Table 3 shows that PepsiCo and Dr PepperR

had positive abnormal returns due to Coca-Cola’s announcement. However, Dr Pepper now also
has positive abnormal returns as a result of PepsiCo’s announcement.
5

If there are J …rms, then one can compute the variance for the abnormal byP

j=1;:::;JP

t=tu ;:::;tv [Rjt

J

7

bjt
R

(Rjt

bjt )]2
R.

Table 3: Cumulative Abnormal Returns from the Announcements of Coca-Cola’s and PepsiCo’s
Vertical Mergers

Event Window

Coca-Cola’s merger

Coca-Cola
PepsiCoDr Pepper
Jones Soda
CCE[-1,1]CAR (%)
t-0.740-1.342
1.402
2.218
2.140
1.438
-26.589-1.417-0.377-0.489[-3,3]CAR (%)
t-0.289-0.796
0.342
0.819
5.100
5.201
-13.010-1.0990.038
0.074

PepsiCo’s merger

PepsiCo
Coca-Cola
Dr Pepper
Jones Soda
PBG

-3.139-0.6147.700-32.455-2.472-1.660-0.55410.140-19.160-1.088-2.702-0.4271.761-0.686-0.603-2.161-0.5823.510-0.613-0.401

Note: The number of observations (trading days) for each company in Event Windows [-1,1] and[-3,3] is 199 and 197, respectively. Reported t-statistics are based on robust standard errors.

Table 4: Sample Quantile Tests for Cumulative Abnormal Returns from the Announcements of CocaCola’s and PepsiCo’s Vertical Mergers
Event Window[-1,1][-3,3]

bjt
R

Coca-Cola’s merger

Coca-Cola
PepsiCoDr Pepper
Jones Soda
CCE

Est
(bj 102 )-1.381-0.0603.680
2.716
10.3815%-1.454-1.466-2.123-8.949-1.942

PepsiCo’s merger

PepsiCo
Coca-Cola
Dr Pepper
Jones Soda
PBG

0.636
-0.829-1.0940.274
1.587-2.130-2.448-3.355-15.202-3.588

bjt
R

95%
1.525
1.599
2.842
9.364
1.892

Est
(bj 102 )-0.839
0.062
0.849
2.213
4.161

5%
-1.462-1.463-1.914-8.825-2.087

95%
1.529
1.597
2.796
9.447
1.917

2.463
2.595
3.844
17.9663.233

0.034-0.338-0.998-1.5910.371-2.055-2.451-3.361-15.206-3.578

2.462
2.599
3.838
17.9793.228

Note: The number of observations (trading days) for each company in Event Windows [-1,1] and[-3,3] is 202 and 204, respectively. Reported t-statistics are based on robust standard errors.

8Second, I use Gelbach, Helland, and Klick’s (2013) sample quantile–“SQ”–test, which is asingle-…rm, single-event method, based on Conley and Taber (2011). By involking the sample
quantile of the estimated abnormal returns, this test rejects the null hypothesis H0 : j = 0
bjt = b j + bj Rmt + bj Dt is no less thanagainst H0 : j < 0 if and only if the -th percentile of R
bj , where the estimates are obtained for t = 1; 2; ::; t0 ; :::; tv , and , which the researcher canfreely choose, corresponds to the Type I error probability. Table 4 compares the estimated bj
bjt for each …rm j in both mergers. The only statisticallyand the 5th and 95th percentiles of R
signi…cant result is Dr Pepper’s abnormally high returns (with event window [-1,1]) associatedwith the announcement of Coca-Cola’s vertical merger. This also implies that the …nancial

market might have perceived that Coca-Cola’s vertical merger would not strengthen its own
position; it would rather bene…t Dr Pepper.

5

Di¤erence-in-Di¤erences Evidence from Retail Scanner Data

The argument so far is also supported by the following evidence that uses another data sets (inparticular, SymphonyIRI’s Academic Data Set (Bronnenberg, Kruger, and Mela (2008)) and
Kantar Media’s Ad$pender); see Adachi (2017) for details about sample construction. Here, thereader should note that an observation unit is a product in each “market”(a pair of county andmonth). The constructed sample covers six products (Coca-Cola, Diet Coke, Pepsi, Diet Pepsi,
Dr Pepper, and Diet Dr Pepper), encompassing the period of January, 2008, through December,2012. October 2010, when The Coca-Cola Company completed its merger procedure, lies in
the middle of this data period.
Table 5 shows the di¤erence-in-di¤erences (DID) estimates for an average treatment e¤ect
in the price equation in Adachi (2017) for the case of Coca-Cola’s vertical merger. As arobustness check, estimates using the re-weighting method are also presented. It is observedthat Coca-Cola’s vertical merger raised its own retail prices by 5% (and the corresponding
p-values are less than 0.1 (10% signi…cance level) under both methods). This result is incontrast to Adachi’s (2017) analysis of PepsiCo’s case where its own retail price decreased by
4%. Both PepsiCo’s and Dr Pepper’s retail prices also rose a result of Coca-Cola’s verticalmerger. However, the estimates are smaller and are not statistically signi…cant, lending less9

Table 5: Average Treatment E¤ects (ATE) of Coca-Cola’s Vertical Merger on the Retail Prices

Dep Var: log pcoke
ATE

R2

Standard DID
Estp

Re-Weighting
Estp0.04880.05390.075(0:0273)

0.028(0:0243)

0.60230.6708

Dep Var: log ppepsi
ATE

R2

0.0354(0:0286)

0.216

0.58440.0044(0:0314)0.889

0.6048

Dep Var: log pdr_p
ATE

R20.0161(0:0218)

0.461

0.64560.0218(0:0171)0.204

0.7417

Notes: The number of observations is 18,292 for each of the six regressions. A “diet drink”
dummy variable is included (results not presented). In addition, County …xed e¤ects, time
…xed e¤ects, dummies for couties that experience PepsiCo’s vertical merger (February 2010),
interacted with monthly dummies for its occurence, market covariates, and cost covariates arealso included. Standard errors, clustered by county, are shown in parentheses, and p-values
less than 0.1 are emboldened.

10

support to the claim that Coca-Cola’s vertical merger induced collusive behavior in the industry.6
To discuss the validity of the parallel trend assumption, I draw Figure 1 to check whether it
reasonably holds before Coca-Cola’s vertical merger (see Adachi (2017)). The plots should be all
zero prior to the merger if the parallel trend assumption strictly holds. Panel (a) shows for mostof the pre-merger period, the 95% con…dence interval includes zero. However, it is observed
from Panel (b) that the treatment group had a negative trend on Coca-Cola’s market shares
before the merger. This would be because the treatment group of Coca-Cola’s vertical merger
consists of populated and urbanized counties: consumers in these counties may have been more
conscious about their health, being against the traditional icon of soda: Coca-Cola’s products.
While the pre-merger market share trend became nearly zero as the time for the …nalization of
Coca-Cola’s merger approached, this movement seems to bounce back. The estimated positivee¤ects of Coca-Coca’s vertical merger on its own market share may be in‡uenced by the negativevalues of the trend in the earlier months of the pre-merger period. In contrast, Panel (a) showsa more moderate trend, suggesting validity for the di¤erence-in-di¤erences estimation of theprice e¤ects of Coca-Cola’s vertical merger.

6

Concluding Remarks

This paper has demonstrated how one can use both stock market and retail scanner data toargue the possibility of an unsuccessful merger. Both data sources from the U.S. carbonatedsoft drink industry suggest that the 2010 Coca-Cola vertical merger might not have achieved
its initial objectives: (i) the stock market might have correctly anticipated its outcome, and (ii)Coca-Cola’s retail price increase after its vertical merger may be a result from the pass-throughof organizational con‡icts, not a result from collusive behavior in the industry induced by thevertical merger. Although the newspaper articles cited in Section 2 assist the empirical results
above, more direct evidence suggesting such internal issues would be further helpful.
I conclude this paper by suggesting that the empirical analysis herein has the following
implications for competition policy. In most cases, competition policy authorities form policy
recommendations by implicitly assuming that …rms’ activities are mutually bene…cial to the6See, e.g., Nocke and White (2007) and Normann (2009) for theoretical studies of the relationship between
vertical integration and upstream collusion.

11

Figure 1: Price and Share Trends in the Treatment Group of Coca-Cola’s Integration
Notes: The coe¢ cient estimates forin Equation (1) in Adachi (2017) are plotted for eachmonth . The dependent variable is log of Coca-Cola’s own retail prices for Panel (a) and log
of Coca-Cola’s own market shares for Panel (b). The dashed lines show 95% con…dence
intervals, calculated by robust standard errors clustered by county.

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involved parties. However, the e¤ect of …rms’conduct on consumers may not be a result fromthis one simple story where …rms always succeed. Rather, …rms are surrounded by uncertainty, and a proposed plan may not proceed as originally intended. This paper suggests that,
although it is unknown how much the competition authority should take into account suchorganizational and internal issues, merger evaluation would bene…t from recognizing that proand anti-competitive e¤ects of mergers can be a¤ected by the degree of internal coordination.
When conducting an ex-post evaluation, competition policy authorities should have in mind
that …rms can take various courses, and that the consequences observed in the market are notnecessarily a result of …rms’ successes. In accordance with Hosken, Miller, and Weinberg’s(2017) emphasis on the importance of ex-post evaluation of antitrust policy to improve model
predictions, the results of this paper would suggest that when evaluating antitrust policies, notnecessarily limited to merger policies, competition authorities may need to take a moment toconsider the possibility that the market outcomes at hand have realized not because the verticalmerger is successful to the integrated entity but because it is unintentionally unsuccessful.
At the same time, when formulating an ex-ante prediction, competition policy authorities
should prepare for an unintended result, notwithstanding the inherent di¢ culties in arriving atrobust predictions. If competition authorities mistakenly judge that the resulting market outcomes will be harmful to consumers because they assumed a given merger would be successful,when in fact the merger has not paid o¤ to the integrated entity, this raises important andinteresting questions about the assumptions used in policy processes. In essence, competition
policy formation should strive to be open-minded in terms of potential outcomes, and thus
potential distributions of costs and bene…ts, as a result of changes such as mergers.

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