ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No.E17-3

Vertical Integration and Common Agency:
An Empirical Analysis of the U.S. CarbonatedSoft Drink Industry
byTakanori Adachi

July 2017

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Vertical Integration and Common Agency: An Empirical
Analysis of the U.S. Carbonated Soft Drink Industry
Takanori AdachiyJuly 7, 2017

Abstract
This paper empirically examines the price and share e¤ects of vertical integration withemphasis on the role of common agency in vertical relationships: a downstream distributor
may also distribute its upstream rival’s products. By using the unique feature of the U.S.
carbonated soft drink industry, I …nd that PepsiCo’s 2010 vertical merger lowered its retail prices by 4.4%. More importantly, these price e¤ects are stronger in the markets with
Coca-Cola’s common agency than in the markets with PepsiCo’s common agency: I …nd a
price reduction of 2.5% for the markets where neither Coca-Cola’s nor PepsiCo’s bottler is
a common agent for Dr Pepper. PepsiCo’s prices are additionally lowered by 2.3–2.5% if
Coca-Cola’s bottler is a common agent for Dr Pepper, or by 1.1–1.3% if PepsiCo’s bottler
is a common agent. It is also shown that the price e¤ects of PepsiCo’s vertical merger on
Dr Pepper’s products are weaker in the markets with PepsiCo’s common agency, whereas
the price e¤ects on Coca-Cola’s products are stronger in these markets, suggesting thatthe welfare e¤ects of PepsiCo’s vertical integration di¤er across the mode of common
agency in an important way.

Keywords: Vertical Integration; Common Agency; Di¤erence-in-Di¤erences Estimation.
JEL Classi…cation: L13; L49; L66.

I am grateful to Victor Aguirregabria, Andrew Ching, Yao Luo, Naoki Wakamori, and Weina Zhou forhelpful discussions. 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 Inamori Foundation. Special thanks are due toMike Kruger for answering my inquiries about the structure of the IRI Academic Data Set. All remaining errors
are mine. In particular, all empirical results from SymphonyIRI Group’s data are based on my own work, notof SymphonyIRI Group. I declare no con‡ict of interest with regard 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

Firms merge because they expect their joint surplus to become higher. This e¢ ciency-based
view is simple yet powerful, and it has been the dominating doctrine at least since Coase(1937) when one studies various aspects of organizations such as the boundary of a …rm. Morespeci…cally, since Spengler (1950), it has been widely held that vertical mergers are generallye¢ ciency improving not only for …rms but for consumers as well. Notably, it has been stressedthat vertical mergers eliminate so-called double marginalization, which arises due to externalities
in pricing decisions by upstream and downstream …rms (Bork, 1978). In addition, verticalmergers may entail cost savings via synergies and bring better discipline vis-a-vis …rm-level
corporate governance. As a result, the …nal price decreases, sales rise, and consumers bene…t
as a consequence of e¢ ciency gains. In contrast to horizontal mergers, these bene…ts are noto¤set by upward pricing pressures as a vertical merger is not associated with a change incompetition at the horizontal level. In this respect, it is well recognized that vertical mergers
are fundamentally di¤erent from horizontal mergers in evaluating their e¤ects on consumers.
However, one may still wonder whether these positive e¤ects are monotonously passed
through across the integrated …rm and other rival …rms. Does the mode of competition matter? Are there any losers from a vertical merger? This paper empirically studies how thee¤ects of a vertical merger are in‡uenced by the mode of competition, a topic which has beensomewhat overlooked, but should be important in predicting and evaluating the e¤ects of avertical merger as a whole. More speci…cally, in determining the e¤ects of vertical integrationof the supply-chain, I examine the role of common agency, where a downstream supply-chain
distributor may also distribute its upstream rival’s products in some areas, For this purpose,
I focus on the unique feature of the U.S. carbonated soft drink industry: in about 80% of themarkets (population-weighted) in my sample, either Coca-Cola or PepsiCo bottler is a common
agent because they also distribute Dr Pepper Snapple’s products. As I show below, this institutional feature appears to play a di¤erent role with respect to the e¤ects of PepsiCo’s vertical
integration in 2010, depending on whether a PepsiCo or a Coca-Cola bottler is a common agent.
In this paper, I make use of the fact that PepsiCo acquired its two biggest bottlers (Pepsi
Bottling Group and PepsiAmericas) in 2010, but other bottlers remained independent. Thus, inthe post-merger period, there are e¤ectively two separate groups: treatment markets, where the1integrated subsidiary of PepsiCo distributed its products, and control markets. This is because
the 2010 PepsiCo merger was not nationwide. This feature is advantageous as compared tomany other studies that use the di¤erence-in-di¤erences (DID) methodology where, for example,
a control group consists of private-brand products which, supposedly unrelated to a merger ofa major brand, may possibly be a¤ected by such a merger through competition. At the sametime, in PepsiCo’s vertical merger, no speci…c local markets were targeted to reorganize its chain
bottlers and then integrate such geographically divided bottling parts into a newly establishedsubsidiary. If this were the case, PepsiCo’s choice could have been in‡uenced by geographic
factors in a way unobservable to the researcher. Fortunately, this did not happen.
Overcoming these endogeneity concerns, I conduct an DID analysis to examine the priceand share e¤ects of PepsiCo’s 2010 vertical merger. If competitive pressure is higher for the
integrated entity, then the price e¤ects may be larger because the vertical merger triggers
rivals’ further aggressiveness, and thus additionally lowers the integrated …rm’s prices. At
the same time, however, the price e¤ects may be smaller because the downward pressure isalready lower before the vertical merger. As I argue in Subsection 4.3 below, competitivenessand common agency are found to be closely associated from the observation that PepsiCo’s
prices are lower when a Coca-Cola bottler is a common agent (Table 10): Coca-Cola’s common
agency induces higher competitive pressure on PepsiCo. Since theoretical predictions on therelationship between the price e¤ects of a vertical merger and the intensity of competition would
be ambiguous,1 this issue should be examined as an empirical question.
Empirical analysis based on DID estimation reveals that the answer to the question above
is “larger.” That is, the downward price e¤ects are stronger in the markets with Coca-Cola’scommon agency than in the markets with PepsiCo’s common agency. More speci…cally, while
PepciCo’s vertical integration on average lowered its retail prices by 4.4%, these e¤ects aredi¤erent across the mode of common agency: although the price reduction is 2.5% for themarkets where neither Coca-Cola’s nor PepsiCo’s bottler is a common agent for Dr Pepper,
PepsiCo’s prices are additionally lowered by 2.3–2.5% if Coca-Cola’s bottler is a common agent
for Dr Pepper, and by 1.1–1.3% if PepsiCo’s bottler is a common agent. The price reduction in1

In the context of entry deterrence, it is well known that a …rm’s investment can make itself either severeor lenient to a new entry under price competition (Fudenberg and Tirole, 1984; and Bulow, Geanakoplos,and Klemperer, 1985). Here, PepsiCo’s vertical merger could be interpreted as such a precommitment by anincumbent.

2Coca-Cola’s and Dr Pepper’s products is weaker in the markets with PepsiCo’s or Coca-Cola’scommon agency. Thus, the welfare e¤ects of PepsiCo’s vertical integration would di¤er acrossthe mode of common agency. To fully investigate this issue, a structural approach is required;this is left for future research.
Common agency is particularly common in vertical relationships: a retail chain usuallysells products of competing brands (e.g., McGuire and Staelin, 1983; Choi, 1991; Choi, 1996;
and Sudhir, 2001). Herein, a Coca-Cola or PepsiCo bottler may transact not only with its
own concentrate seller but also with Dr Pepper Snapple. Another example from the context ofvertical relationships is where a regional car dealer sells automobiles of rival companies.2 Suchoccurrences are becoming more common because in many countries increasing consolidation ofsupermarket chains has concomitantly increased common agency (see, e.g., Allain, Chambolle,
Turolla, and Villas-Boas, 2017).3 However, in vertical relationships in many industries such as
the grocery industry, upstream and downstream …rms have various ties. Systems of operationand distribution are complex in many industries, and thus not readily observable to outsiders.
In this respect, as Muris, Sche¤man, and Spiller (1992) point out, the U.S. carbonatedsoft drink (CSD) industry has a relatively simple structure, in terms of both operation anddistribution, which provides an ideal opportunity to study the e¤ects of vertical integration
across the modes of competition. First, the manufacturing process consists principally of two
discrete parts: syrup production by upstream concentrate sellers and bottling and packaging bydownstream regional bottlers. Second, owing to the 1980 Soft Drink Interbrand Competition
Act (SDICA), there are no other competing bottlers of the same concentrate seller’s products
in a regional territory. Since I focus on the big three concentrate sellers (Coca-Cola, PepsiCo,and Dr Pepper Snapple), this means that there are no more than three distributors in thegeographical market.
This paper is mainly related to extant empirical studies of vertical relationships: how institutional settings prior to …nal sales a¤ect retail competition and their anti-trust consequences.
2

However, in terms of car dealers, it may be the case that there are no common agents: each car manufacturertransacts exclusively with one dealer in a market, so-called exclusive dealing. See, e.g., Bernheim and Whinston(1997) and Nurski and Verboven (2016).
3
Other examples in economic and political contexts where a common agent has substantial responsibility
on behalf of competing principals include …nancial contracting where multiple lenders transact with a single
borrower (e.g., Parlour and Rajan, 2001; Tirole, 2003; and Khalil, Martimort, and Parigi, 2007) and politicallobbying where multiple groups decide how much they contribute to the government (e.g., Grossman andHelpman, 1994; Dixit, Grossman, and Helpman, 1997; and Baron and Hirsch, 2012).

3

To the best of my knowledge, Corts (2001) is the only empirical study of common agency in a
vertical structure context. Using U.S. motion picture industry release-date data, Corts (2001)…nds that common agency, in which …lms created by di¤erent producers are released by thesame distributor, does not achieve the same level of coordination to avoid cannibalization of
two …lms (measured as the di¤erences in the two …lms’release dates) as in the case where two…lms are produced by the same producer. However, Corts (2001) does not study the e¤ects ofvertical integration in the presence of common agency, which is this paper’s focus.
Hastings (2004) also employs a similar empirical strategy to that adopted herein in orderto study the e¤ects of independent market share on retail gasoline prices by invoking the event
whereby a number of independent gasoline stations in Southern California were purchased by abranded chain in 1997. The treatment and control groups consist of areas that did and did notexperience this change, respectively. Hastings (2004) revealed that the loss of an independent
station raised local retail prices by 5 cents per gallon. Then, as also in this paper, Hastings(2004) looks into structural causes behind this result. More speci…cally, gasoline stations in thetreatment group are classi…ed by brand quality, and the negative e¤ects of losing an independent
station are largest for low-quality gasoline stations. This result lends support for the hypothesis
consumers’preferences are heterogeneous so that some consumers prefer non-branded gasoline,
rather than the hypothesis that consumers have similar preferences for quality. If the latter
were the case, entry of a branded chain would lead to …ercer competition with existing stations,
resulting in lower retail prices. Although Hastings (2004) focuses on the demand structure (i.e.,
preference heterogeneity), this paper categorizes treatment e¤ects by the mode of common
agency to examine the consequences of supply-side di¤erences in determining the price and themarket share e¤ects of vertical integration.
The two industries often studied by existing studies are the U.S. healthcare industry, where
hospitals transact with health insurance companies (Gowrisankaran, Nevo, and Town, 2015; andHo and Lee, 2017) or medical device companies (Grennan, 2013), and the U.S. cable televisionindustry, where cable distributors transact with content channels (Crawford and Yurukoglu,
2012; and Crawford, Lee, Whinston, and Yurukoglu, 2015).4 In contrast to these industries,
the U.S. CSD industry exhibits a relatively simple vertical structure mainly due to, as stated
4

Other studies of the U.S. cable television industry include Waterman and Weiss (1996), Chipty (2001), andSuzuki (2009).

4

above, the 1980 SDICA. This research aims to be a starting point for studying a more complex
structure of vertical relationships observed in many other industries.5
One might argue that my focus on the CSD category alone could be too restrictive, assuming away any important substitutions between carbonated soft drinks and other categories ofbeverages such as mineral water that have gained more popularity in recent years. Obviously, it
is important to study how beverage companies are shifting emphasis to non-CSDs, in response
to consumers’increasing health awareness. As The Wall Street Journal, March 27, 2015, “SoftDrinks Hit 10th Year of Decline”reports, “U.S. bottled-water consumption totaled 10.87 billion
gallons in 2014, up 7.3% from 2013, its fastest growth rate since 2006, according to Beverage
Marketing. Soda consumption in the U.S., meanwhile, slid 1% to 12.76 billion gallons, the 10thstraight yearly decline.”This restriction is mainly because the original data set used herein (i.e.,
the IRI Academic Data Set; see Section 3) does not include beverages other than carbonatedsoft drinks. In addition, the information on Coca-Cola’s and PepsiCo’s plants producing beverages other than CSDs were not available. More agents are involved in the production of otherbeverages, and it is less structured than the system that we study here. Of course, it would bepossible that a vertical merger of a soft drink chain might improve consumer welfare in muchnewer categories such as mineral water, while lowering consumer welfare in more traditional
categories such as soda. This issue is left for future research.
More importantly, from a competition policy perspective, it would be more appropriate tode…ne the category in a seemingly narrower way. As White (1994) describes, in the aforementioned challenge by the Coca-Coca Company to acquire the Dr Pepper Company in 1986, theCSD category was deemed as the relevant market by the Federal Trade Commission (FTC)and trial judge. In particular, there were arguably no substitutable products for concentrate,
and thus the usual “Small but Signi…cant and Non-transitory Increase in Price” (SSNIP) test
applies: if concentrate sellers jointly raise their prices by 5%, they still do not su¤er from asigni…cant decline in their sales. Therefore, the CSD category alone is an appropriate market
to study the price e¤ects of a vertical merger and its welfare consequences.
The rest of the paper is organized as follows. The next section provides further details
5

Other structural studies of vertical relationships focus on cases in the U.S. yogurt industry (Villas-Boas,
2007); the U.S. video rental industry (Mortimer, 2008); the U.S. vending industry (Conlon and Mortimer, 2013);
the U.S. beer industry (Chen, 2014; and Asker, 2016); and the Japanese fMRI industry (Onishi, Wakamori,
Bessho, and Hashimoto, 2017).

5

concerning the U.S. carbonated soft drink industry. In particular, I describe the characteristicfeatures of the industry’s distribution system, as well as PepsiCo’s 2010 vertical merger. Afterthe data set for this study is described including some related statistics in Section 3, empiricalresults based on reduced-form equations are presented and interpreted in Section 4. Finally,
Section 5 concludes.

2

Industry BackgroundIn this section, I describe the main characteristics of the U.S. CSD industry’s distribution
system. This draws heavily from case studies at the University of Michigan’s Ross School ofBusiness (Kilibarda, 2010) and Harvard Business School (Yo¢ e and Kim, 2011), complemented
by newspaper articles, FTC’s documents, and other relevant information.

2.1

The Industry’s Distribution System

The U.S. carbonated soft drink industry is probably one of the most important industries in
the 20th century. It is characterized by a unique franchise business model. In its traditional
form, national beverage companies sell and ship concentrate, syrup, and so on, to independent
bottlers that are spread across the states. Then, bottlers produce beverages in their plants
by adding water and sweeteners, and distribute …lled bottles and cans directly to retail storesin their geographical territories.6 They also deliver concentrate and sweetener without water
to local restaurants. Although national concentrate sellers focus on branding and promotionale¤orts across the nation, regional bottlers concentrate on locally targeted marketing activities.
Historically, since the early stage around the late 19th century, CSD companies had granted
exclusive bottling and distribution rights to independently-owned local bottlers in speci…c geographical territories.7 However, as explained below, under the current system, some areas areunder the control of independent bottlers, and other areas are subject to subsidiary bottlers
6This channel is also called the direct store delivery (DSD) system. The bottler’s sales sta¤ regularly visit
stores to maintain stocks. Although other types of distribution channels are also used, especially for othercategories, carbonated soft drinks are usually distributed by the DSD system for Coca-Cola, Pepsi, and Dr
Pepper.
7
According to Butler and Tischler (2015), the origin of the franchise business model is ascribed to BenjaminFranklin Thomas and Joseph Brown Whitehead, two lawyers from Chattanooga, TN, who, in 1899, purchased
the right to bottle and distribute Coca-Cola (the price was one dollar). They then started to sell Coca-Cola’s
secret concentrates to regional bottlers.

6merged by upstream concentrate sellers.
The industry’s exclusive territory system became institutionally supported when President
Carter signed the Soft Drink Interbrand Competition Act (SDICA) in July 1980. This is largely
considered a result of the soft drink companies’ success in persuading Congress, in responseto the Federal Trade Commission’s challenge in 1978 against Coca-Cola and Pepsi with theclaim that their exclusive territory systems violated Section 5 of the FTC Act. According toWhite (1994, p.79), Yo¢ e and Kim (2011), and Salinger and Elbittar (2013), the action was
initiated by the FTC in 1971 with the claim that the industry-wide territory system prevented
intrabrand competition, and the investigation had continued for nearly 10 years. The industry
refuted that interbrand competition was not su¢ ciently weak to invalidate the territory system.
To date, the industry’s territory system has been maintained and no more than one bottler isin charge of one concentrate seller’s product in a territory.
Importantly, SDICA guarantees that an exclusive territory between a concentrate seller
and a bottler is exempted from anti-trust laws such as the Clayton Act, provided that bottlers
are under “substantial and e¤ective”8 competition. It is usually deemed that “substantialand e¤ective” competition is guaranteed if there is more than one brand in a geographicalterritory. Thus, as Salinger and Elbittar (2013) sarcastically point out, this is usually satis…ed
because at least Coca-Cola and Pepsi are sold in almost all the geographical territories. Thus,
the bottler in a certain territory is e¤ectively a monopolistic wholesaler of the brand. In otherwords, the intrabrand competition is absent for local retailers such as supermarkets, convenience
stores, and restaurants. This institutional feature would simplify a structural approach becauseinterlocking relationships between manufacturers and bottlers do not have to be taken into
account.9
The industry is currently dominated by three giants, namely, The Coca-Cola Company(Atlanta, GA), PepsiCo Inc (Purchase, NY), and Dr Pepper Snapple (Plano, TX).10 Until
the mid-1980s, Coca-Cola and PepsiCo had been vigorously acquiring small independent local
8

See http://uscode.house.gov/view.xhtml?path=/prelim@title15/chapter61&edition=prelim.
See Nocke and Rey (2014) for a theoretical analysis of interlocking vertical relationships.
10
It is reported that as of 2010, there were 151 companies in the U.S. soft drink industry that sold syrup
concentrates (Kopylovsky D. Syrup & Flavoring Production in the US. IBISWorld Industry Report, 31193:
2010; available at: www.ibisworld.com/industry/default.aspx?indid=275). According to Beverage Digest’s Fact
Book 2012 (Table 17, p.36), Coca-Cola, PepsiCo, and Dr Pepper Snapple’s all-channel shares in 2011 were 41.9,
28.5, and 16.7, respectively; far above the fourth largest, Cott Corporation (5.2%).
9

7

bottlers.11 Then, in the late 1980s, Coca-Cola started to spin o¤ its bottling parts, retaining a
non-negligible portion of ownership, to focus on product development and marketing, leaving
logistic decisions to the independent bottlers. In 1986, Coca-Cola Enterprises (CCE) wasestablished as a publicly-owned bottling company; 51% of the shares were sold to the public.
CCE then continued acquiring large and small independent bottlers, including Johnston CocaCola Bottling Company in 1991, and then the second largest independent bottler. On theother hand, PepsiCo, which was established in 1965 as a result of the merger of Pepsi-Cola
Company and Frito-Lay, Inc, started to spin o¤ its bottling part in 1999, creating Pepsi BottlingGroup (PBG), which was then a subsidiary of PepsiCo. In 2000, PepsiAmericas also became aprominent bottler when Whitman Corp purchased the smaller PepsiAmericas.
However, Dr Pepper Snapple, which was established in May, 2008 as a spin-o¤ from Cadbury PLC, pursued its own strategy regarding distribution. Although it owned “independentand in-house distribution” (The Wall Street Journal, June 7, 2010) in some areas,12 its distribution relied on Coke’s or Pepsi’s bottling system in many areas, as I explain below in Section
3. According to Beverage Digest’s Fact Book 2012, per capita consumption of CSDs during
the sample period (2008 to 2012; see Section 3) continued to decline by about 10% annually,re‡ecting consumers’concerns for health. However, the CSD category was still dominant: in
2011, it accounted for 75%, 64%, and 91% of Coca-Cola’s, PepsiCo’s, and Dr Pepper Snapple’ssales volume in the United States.

2.2

PepsiCo’s 2010 Vertical Merger

On April 20, 2009, it was reported by The Wall Street Journal that PepsiCo had revealeda plan for a takeover bid to acquire Pepsi Bottling Group (Somers, NY) and PepsiAmericas(Minneapolis, MN) in order to transfer its bottling and distribution operations to itself. These
represented PepsiCo’s two largest independent bottlers: Pepsi Bottling Group (PBG) distributed 56% of PepsiCo’s sales volume in the United States in the carbonated soft drink category,
11

According to Elmore (2014, p.231), “[t]he number of Coca-Cola bottlers operating in the United States had
dropped dramatically from roughly 1,200 in 1929 to an estimated 500 in 1979, and the total number of bottlers
in the soft drink industry declined from over 4,000 in 1960 to under 3,000 by 1972.” White (1994, p.90) also
provides a similar account. See Muris, Sche¤man, and Spiller (1992) for an explanation based on transaction
cost theory.
12
The name of this distribution system is Dr Pepper Snapple Group Company-Owned Bottling Operations(DPSG COBO).

8and its operations ranged across (all or a part of) 41 states and Washington, D.C., whereas
PepsiAmericas (PAS) accounted for 19%, spanning (all or a part of) 19 states and Washington,
D.C. According to an FTC press-release document issued on February 26, 2010, PepsiCo, when
the agreement was made public, “already owned about 40 percent of Pepsi Bottling Groupand about 43 percent of PepsiAmericas, which together account for” “20 percent of all U.S.
bottler-distributed sales of Dr Pepper Snapple carbonated soft drinks.” On August 4, 2009,
PepsiCo …nally reached a deal to acquire PBG and PAS. Four months later (on December 7,2009), PepsiCo also revealed its plan to take over the production and distribution of Dr Pepper
Snapple’s products that were operated by PBG and PAS.
PepsiCo completed the acquisition process on February 26, 2010. On the same day, as acondition for approving PepsiCo’s acquisition of PBG and PAS, the FTC required PepsiCo to“set up a “…rewall”in response to its concern that Dr Pepper Snapple’s ability to compete with
PepsiCo could be weakened because PepsiCo’s headquarters was now “closer” to Dr Pepper’s
headquarters. Subsequently, on September 27, 2010, Eric A. Croson was appointed by theFederal Trade Commission as the monitor for the order that required limited access by PepsiCo to con…dential business information of Dr Pepper Snapple. Finally, after the period for
public comments, on September 28, 2010, the Federal Trade Commission approved PepsiCo’s
acquisition of PBG and PAS.13 It seems that the FTC’s concerns were not only on the pricing
but also on marketing activities such as promotions for newly introduced products. As alreadystated in the Introduction, this paper focuses on the pricing aspect.
As Beverage Digest’s Fact Book 2012 describes, PepsiCo’s acquisition of its two biggest
bottlers (PBG and PepsiAmericas) in 2010 was one of the biggest vertical mergers in theindustry’s history. This vertical merger would be ascribed to PepsiCo’s recognition of theneed of quickly adjust to changes in consumers’ tastes, as expressed in Chairwoman Indra
Nooyi’s statement released on August 4, 2009 (“PepsiCo Reaches Merger Agreements with
Pepsi Bottling Group and PepsiAmericas”). However, the bottlers may have been reluctant
to work cooperatively with their upstream counterparts to distribute such a wider line of new
products, because, as Zhou and Wan (2017) argue, their assets were customized to bottle
traditional soda, and scale economy e¤ects would be best achieved if they concentrated on
13

Between February 26 and September 28, 2010, PepsiCo purchased the Pepsi-Cola Bottling Company of
Yuba City on April 19, which was a relatively small bottler operating in Yuba City, CA, and surrounding areas.

9

production of a small number of beverages. Under this situation, it is not surprising that
PepsiCo came to recognize the need for vertical integration to increase control over the bottlingand distributing part. Hypothetically, after the integration is fully achieved, the downstream
part will become a subsidiary of the newly established organization, and as economic theory
such as Spengler (1950) supposes, the control rights should be centralized. In this way, a verticalmerger is expected to reduce the costs of management and operations.
Lastly, it should be noted that on February 25, 2010, just a day before the consummationof PepsiCo’s vertical merger, Coca-Cola also announced a plan to acquire its largest bottler,
Coca-Cola Enterprises (Atlanta, GA), which accounted for 80% of Coca-Cola’s sales volumes
in the United States. The acquisition process was completed on October 1, 2010, and it
was approved by the Federal Trade Commission on November 5, 2010. PepsiCo’s and CocaCoca’s vertical mergers were similar in the sense that Coca-Cola Enterprises also distributed
Dr Pepper’s products in a large portion of the territories across the nation, and therefore the
FTC also expressed a similar concern. However, this paper does not study Coca-Cola’s verticalmerger mainly because another analysis by Adachi (2017) suggests that it raised Coca-Cola’s
own retail prices as well as Pepsi’s. This could indicate that Coca-Cola’s 2010 vertical merger
might have been a failure, or something else. This issue could be fruitfully investigated in a
separate study.14

3

Data

3.1

Sample Construction

The sample for this study is constructed from the IRI Academic Data Set (see Bronnenberg,
Kruger, and Mela (2008) for an introduction to this data set), supplemented by various sources
covering demographic and cost information. The IRI Academic Data Set is a store-level scanned
data set that covers a wide range of geographic areas across the United States. Information accrues on a weekly basis, and is collected from participating retailers, who are major supermarket
and drugstore chains, although Walmart is not included. Independent stores of small-scale operation are also not covered. The period covered by the IRI Data Set spans from 2001 through14
Indeed, it was reported by The Wall Street Journal on December 12, 2013, that Coca-Cola initiated actions
to detach some bottling parts and grant them distribution franchise status, which may imply that Coca-Cola’s
vertical integration was not as successful as anticipated. See Adachi (2017) for more on this issue.

102012. I select the …rst week of January 2008 through the last week of December 2012 for the
sample period, so that the event of PepsiCo’s vertical merger lies in the middle. Unfortunately,2012 is the last year covered by the IRI Academic Data Set; hence, this precluded a longer
sample. Throughout the paper, monetary values are expressed in 2008 U.S. dollars.
From the carbonated beverages category in the IRI Academic Data Set, I select six products
from the three biggest concentrate sellers: Coke Classic and Diet Coke from Coca-Cola, Pepsi
and Diet Pepsi from PepsiCo, and Dr Pepper and Diet Dr Pepper from Dr Pepper Snapple.15
The store-level information in this data set records the sales revenue and the units sold for eachscan. Thus, the average price is computed from the revenue divided by the units sold. Products
are categorized Universal Product Codes (UPC). For example, di¤erent packages with the samecontent are considered as di¤erent products.16 To construct my sample, I aggregate the same
product across di¤erent package sizes, and choose 12 ounces (355 ml) as a base unit.
A “market” is de…ned by a combination of a geographical area and a time period. In
this study, I use county for the former, and month for the latter. County is chosen as thegeographical area in order to exploit cost heterogeneity across bottlers therein. As explained
further below, I use the distance between a county and the county where a bottler’s nearest
plant is located to measure that bottler’s cost. Accordingly, a set of relevant county-level
demographic characteristics is utilized in the following empirical analysis. An individual’shousehold income is presumably relevant for his or her demand for CSDs. In addition, an
individual’s age may also matter. When market demands are estimated if a structural approachis combined with the current analysis, annual information from the Public Use Microdata
Sample (PUMS) of the American Community Survey (2008 through 2012) is used to randomly
select 50 individuals from each county (and for each year).17 Thus, an individual’s income
and age are taken into account to allow heterogeneous responses to price and other productcharacteristics (more precisely, whether the product is diet drink). I also use annual projections
15

Other representative products include Sprite and Fanta (Coca-Cola), Mountain Dew and Sierra Mist (Pepsi),and Canada Dry and 7 Up (Dr Pepper Snapple). These products would be considered as di¤erent from thetraditional soda category which is the focus of this study.
16
According to Yo¢ e and Kim (2011), about 98% of all packages of CSDs are made of metal cans (56%) andplastic bottles (42%), while the remaining 2% are glass bottles. It would be possible to compute the ratio ofcans to bottles for each observation (a combination of a market (county month) and a product), so that one
could use this ratio as a weighted index for packaging costs. However, this additional information would not
signi…cantly alter the empirical results presented below.
17
I exclude individuals who are 11 years old or younger, and those who are 90 years old or older. I also
exclude individuals with zero household income.

11

from the Census (https://www.census.gov/popest/index.html) for the total population of thecounty in the reduced-form analysis below.
A monthly time-step is used for practical reasons. I plan to consider a bargaining gameover the wholesale price of concentrate. Arguably, concentrate sellers not only sell concentrate
but also provide bottlers with various resources targeted for local marketing. The “wholesale
price” in a structural model would include all these pecuniary expenses, which are inherently
unobservable to researchers. Thus, using a shorter, weekly time-step could incur responsivenessissues. Indeed, a monthly time-step could be associated with such issues, albeit to a lesser
degree; this choice is partly a compromise to ensure a su¢ cient sample size.18 In total, there
are 50,661 observations at the product-county-month-year level.
In the reduced-form analysis below, I use aggregated demographic variables at the countylevel. Annual data on median income and median age are taken from the American Community
Survey (ACS). I also use ACS to calculate the proportion of those with a bachelor degree orhigher as a measure for educational attainment in a county. Income, age, and education, andvariables are expressed in annual terms: they cannot capture monthly variations. Thus, I also
use two monthly variables related to market demands, motivated by Muris, Sche¤man, andSpiller’s (1992) analysis of the determination of soda prices. One is the county-level unemployment rate, which is available from the Local Area Unemployment Statistics (LAUS) provided
by the U.S. Bureau of Labor Statistics (BLS). The second variable is the average temperature
determined using online climate data from the Global Summary of Month maintained by the
U.S. National Oceanic and Atmospheric Administration (NOAA). I take the arithmetic meanof recorded average temperatures of all observation points in a county to represent the averagetemperature for the county.19
To measure a bottler’s cost of distribution as well as production, I use data on the distance
between a county and the county where a bottler’s nearest plant is located, as well as quarterly
data on weekly average wages for the manufacturing sector in each county of the plant (from18

Hosken, O’Brien, Sche¤man, and Vita (2002) point out that weekly purchase data may lead to overestimation of elasticities because consumers tend to purchase large amounts in discount sales, and CSDs are often
sold as “loss leaders.” This e¤ect could be substantial for storable products such as CSDs. Thus, consumption
aggregation would serve to estimate “robust” elasticities that rule out such instantaneous shocks.
19
In exceptional cases, a county has no observation points. In this case, I identify for a neighboring county(not necessarily an IRI county) to impute the mean temperature for the county.

12the BLS’s Quarterly Census of Employment and Wages).20 As in Ashenfelter, Hosken, andWeinberg’s (2015, p.335) study of beer, I assume that beverages are delivered to stores fromthe closest plant. Thus, I calculate the distance between each county (a market) in my sample
and the county where the nearest bottler is located for each of the three brands, with the helpof the National Bureau of Economic Research’s County Distance Database.21 If the plant islocated in the same county, then the “radius”of the county is used as the distance between the
county and plant.22
The principal source used for plant locations is the Beverage Digest’s Bottling System
Books (2008 to 2012).23 Since 2005, Beverage Digest has annually issued The Coke System
Book and The Pepsi System Book. Both System Books list all operating plants for Coca-Cola’s
and PepsiCo’s products. Notably, The Coke System Book and The Pepsi System Book containgraphical information on which territories Dr Pepper’s products are distributed by a Coke or
Pepsi bottler. In about 80% of the markets in my sample (see Table 5 below), Dr Pepper’s
products are produced and distributed by either a Coca-Cola bottler or a PepsiCo bottler.
However, it is observed that in some counties, Dr Pepper’s products are not distributed by a
Dr Pepper bottler.
As Yo¢ e and Kim (2011) describe, the main components of bottlers’ costs, other thanconcentrate, include sweeteners (high fructose corn syrup), packaging, which needs aluminum(for cans), plastic (for bottles), and water, and transportation, which consumes gasoline. In
this respect, I also add monthly data on weekly retail diesel prices for each census division(available from the U.S. Energy Information Administration’s (EIA) website). For indirectelectricity measures, I use the EIA’s annual report on Electric Sales, Revenue, and Average
Price (more speci…cally, Table 8 therein), which provides the average retail price (cents/kWh)for industrial sectors at the utility level. I select the utility that is judged to be the dominant
20
However, the inner process of how concentrate is handled in the integrated chain (e.g., whether it is embedded with transfer pricing) is less clear, and almost no information is available as to syrup manufacturing,
including locations of each concentrate seller’s plant. Although concentrate production requires ca¤eine andcoca-leaf extract, the actual process is usually opaque probably because it is each company’s trade secret.
21
The URL is http://www.nber.org/data/county-distance-database.html. Over the 5 years in the sample, thenumber of closed or newly opened plants is small, but not zero. These events are taken into account when the
distances are calculated.
22
The county’s radius is computed by dividing its land area (available from the 2010 Census Gazetteer Files)
by 3.14 before taking its square root.
23
See Beverage Digest’s website (http://www.beverage-digest.com/systembooks/) for more information on
Bottling System Books.

13

Table 1: Summary Statistics (Retail Prices and Market Shares)

Variable
Retail Price ($ per 12 oz)
Market ShareWithin the 6 products
Outside Good’s Market Share

Mean
0.30780.14850.16670.1088Median0.29950.13130.14780.0909Notes: The observation unit is “productof observations is 54,876.

countyStandard
Deviation
0.05550.1011
0.11300.0377year

Min
0.08010.00080.00090.0909Max
0.99220.75200.82720.4439month.” The number

provider for the county where each plant is located. As for water rates, I use (the inverse of)county-level water use as a proxy variable. The United States Geological Survey’s (USGS)
website publishes Estimated Use of Water in the United States County-Level Data every 5
years. I use its 2010 version to calculate per-capita water use (total of fresh withdrawals;
million gallons per day) for each plant county.
Finally, recall that the six selected products are categorized as either regular-‡avor or thediet alternative. According to Bonnet and Réquillart (2013, p.79), the two main ingredients forregular drinks are “water (approximately 90%) and sweetener (approximately 10%).”Sweeteneris not used in diet drinks, and as a result, “water (99.7%)”is the primary ingredient. Thus, while
sweeteners in regular soda are added by bottlers, non-calori…c sweeteners are contained in theprocess of syrup manufacturing by concentrate sellers. Beverage Digest’s Fact Book (Appendix
E for each year) reveals that for regular drinks, HFCS55 (55% High Fructose Corn Syrup) isused by Coca-Cola and Pepsi, whereas HFCS42 (42% High Fructose Corn Syrup) is used for
Dr Pepper. The U.S. Department of Agriculture, Economic Research Service’s annual Sugarand Sweeteners Yearbook (Table 9 therein) lists monthly wholesale prices (cents per pound)for both HFCS55 and HFCS42. These sweetener prices are common across markets (i.e., allcounties in the same month). I also collect monthly prices of aluminum and WTI (West Texas
Intermediate) crude oil from the Federal Reserve Bank of St. Louis (FRED) website to capture
time variations in the bottling production.

14

Table 2: Average Retail Prices and Market Shares (By Product and Year)

Coke

Diet Coke

Pepsi

Diet Pepsi

Dr Pepper

Diet Dr Pepper

Price ($ per 12 oz)

2008-90.3051

2010
0.3324

2011-120.3332Market share

0.26430.23300.2226

Within the 6 products

0.29170.25750.2571Price ($ per 12 oz)

0.31020.33340.3348Market share

0.17760.16480.1560Within the 6 products

0.19590.18190.1796

Price ($ per 12 oz)

0.28020.28090.2843Market share

0.22810.25060.2402

Within the 6 products

0.2516

0.27670.2759Price ($ per 12 oz)

0.28880.29170.2935Market share

0.12190.13000.1234

Within the 6 products

0.13440.14350.1416

Price ($ per 12 oz)

0.31210.31270.3127

Market share

0.0738(0:0542)0.081(0:0570)0.0820Within the 6 products

0.08140.08980.0950Price ($ per 12 oz)

0.31080.31460.3193Market share

0.04080.04590.0441

Within the 6 products

0.04490.05060.0509(0:0438)(0:0762)(0:0847)(0:0429)(0:0655)(0:0720)(0:0399)(0:0970)(0:1068)(0:0382)(0:0512)(0:0562)(0:0568)(0:0597)(0:0701)(0:0217)(0:0239)(0:0528)(0:0774)(0:0862)(0:0503)(0:0691)(0:0760)(0:0420)(0:1102)(0:1213)(0:0428)(0:0536)(0:0588)(0:0533)(0:0628)(0:0720)(0:0245)(0:0269)(0:0543)(0:0737)(0:0859)(0:0552)(0:0687)(0:0772)(0:0426)(0:1083)(0:1219)(0:0443)(0:0541)(0:0606)(0:0542)(0:0510)(0:0590)(0:0744)(0:0222)(0:0253)

Notes: For each product, the number of observations is 3698 in 2008-2009,
1839 in 2010, and 3609 in 2011-2012. Each product’s market share is relative
to these six products (so that the sum is 100%). Standard deviations areshown in parentheses.

15

Table 3: Market Concentration

HHI

2008-920102011-12Mean
2009.2
1994.5
1833.7

Median1954.1
1902.41776.2Standard
Deviation
331.3
402.4
432.5

Min
1413.81412.8790.6

Max4616.35736.24863.7

Notes: The observation unit is “county
year
month,” and thenumber of observations is 3698 in 2008-2009, 1839 in 2010, and 3609
in 2011-2012. The HHI is scaled from 0 to 10,000.

3.2

Summary Statistics

Table 1 displays the summary statistics on the retail prices and market shares of the six products. The average price of a 12 ounce soda is 31 cents with a standard deviation of 6 cents.
The average market share is 15% if the outside good is included, or 11% if it is excluded. Table
2 shows the average prices and market shares for each product across the county-month pairs.
The 5-year sample period is divided into (i) the pre-merger years (2008 and 2009) (ii) the yearof the vertical merger (2010), and (iii) the post-merger years (2011 and 2012). All of six products exhibit reasonably stable retail prices. The price of Coke and Diet Coke increased mostmarkedly, by 9% and 8%, respectively, between 2009-9 and 2011-12. Price increases associatedwith the four other products were milder, ranging from 0% to 3%. Between these periods,
Coke and Diet Coke lost 14% and 12% of their market shares, respectively, although within
the six products these numbers are smaller: 12% and 8% respectively. On the other hand,
Pepsi and PepsiCo increased their market shares by 10% and 5% respectively within the six
products (5% and 1% if the outside good is included), and Dr Pepper and Diet Dr Pepper
had the largest within-product share increases of 17% and 13%, respectively. These numbers
are still large if the outside good is included (11% and 8%). To observe market concentration
trends in the CSD industry, I calculate the Her…ndahl–Hirschman Index (HHI) for each market(countymonth), de…ned by the sum of the squares of individual …rms’market shares. Table

3 shows that the median HHI decreased across the sample period from 2000 to 1800, that is,
the industry became less concentrated, re‡ecting the decrease in Coca-Cola’s market shares.
Characteristics of the counties in the sample are shown in Panel (a) of Table 4. These
16

Table 4: Summary Statistics on Market Characteristics(a) Demand Side
Population
Median Income
Median Age
Unemployment Rate (%)
Bachelor’s Degree (%)
Average Temperature (F)Median365,275
52,965
37.1
8.1
18.4
56.9

Standard
Deviation
1,081,084
14,324
3.4
2.5
5.6
16.3

Min
106,30230,016
23.3
2.0
7.0
5.0

Max
9,962,789
101,171
45.6
18.7
34.5
94.5

Mean

MedianStandard
Deviation

Min

Max

54.9
77.8
50.1

34.3
40.1
33.5

59.9
181.5
49.5

4.3
4.4
4.4

479.4
2101.4300.8

1132.31097.01107.91098.01070.31088.0

220.7
222.3
221.8

360.2
496.0
360.2

1921.22653.02653.0Mean
721,81455,674
37.0
8.2
19.2
56.3

(b) Supply Side
Distance from Plant (miles)
Coke
Pepsi
Dr Pepper
Weekly Wage ($)
Coke
Pepsi
Dr Pepper

Notes: For Panel (a), Population, Median Income, Median Age, and Bachelor’s Degree areobserved annually. The observation unit is “county
year,” and the number of observations
is 764. For Unemployment Rate and Average Temperature, which are observed monthly, theobservation unit is “county
year
month,” and the number of observations is 9146. For
Panel (b), the number of observations is 9146. “Coke”, “Pepsi”, and “Dr Pepper” areidentities of bottlers. If Dr Pepper’s products are distributed by a Coke bottler in a county(see Table 5 below), “Coke” is also used for “Dr Pepper”, and the same for “Pepsi”.

17

Table 5: Distribution of Bottlers

Pepsi Bottling Group (PBG)
PepsiAmericas (PAS)
Pepsi Bottling Company (PBC)
Other PepsiCo Bottlers

2008
0.769
0.077
–
0.154

2009
0.766
0.078
–
0.156

2011
–
–
0.842
0.158

2012
–
–
0.846
0.154

Dr Pepper distributed by
Coca-Cola bottler
PepsiCo bottler
Dr Pepper Snapple

0.410
0.391
0.199

0.409
0.390
0.201

0.408
0.401
0.191

0.409
0.396
0.195

Number of counties

156

154

152

149

Note: The year 2010, when PepsiCo’s vertical merger took place, is
excluded.

county-level variables are used as explanatory variables in the di¤erence-in-di¤erences estimation below. They vary across the treatment counties and the control counties as well as overtime. Recall that the unemployment rate and average temperature vary month-by-month.
Other variables, however, change only annually. Next, Panel (b) of Table 4 shows descriptive
statistics for the two main components of bottlers’costs in each county. Note that if a PepsiCo (or Coca-Cola) bottler takes charge of Dr Pepper’s distribution in a county, PepsiCo (orCoca-Cola) and Dr Pepper share the same values for these cost variables. Relative to weekly
wage, the distance between the bottler’s plant and the market varies within and across thebrands. Table 5 shows that 85% of counties had a PepsiCo bottler (Pepsi Bottling Group or
PepsiAmericas) that was vertically integrated in February 2010. In about 80% of counties,
Coca-Cola or PepsiCo took charge of distribution of Dr Pepper’s products.24
In the empirical analysis in the next section, PepsiCo’s vertical merger is considered as atreatment; thus, counties which experience this treatment belong to the treatment group, elseto the control group. Table 6 shows the prices and market shares for the period 2008 through2009 and 2011–2012, where the treated counties are those with PBG or PAS bottlers prior
24

In this sample, 67% of counties had a Coca-Cola bottler (Coca-Cola Enterprises) that was vertically integrated in October 2010. I use this event of another vertical integration as one of the control variables in theempirical analysis below.

18

Table 6: Comparison of Treatment and Control Groups

Coke

Diet Coke

Pepsi

Diet Pepsi

Dr Pepper

Diet Dr Pepper

Retail Price ($ per 12 oz)

Treatment
2008-9 2011-120.3041 0.3305(0:0416)(0:0520)

Control
2008-9 2011-12
0.3103 0.3483(0:0539)(0:0633)

Market Share

0.26460.22380.26250.2156

Within the 6 products

0.29230.2576

0.28880.2544Retail Price ($ per 12 oz)

0.30940.33200.3146

0.3498Market Share

0.18030.16040.16290.1320Within the 6 products

0.19900.18400.17920.1554Retail Price ($ per 12 oz)

0.27990.28200.28210.2972Market Share

0.22580.23720.24080.2564

Within the 6 products

0.24920.27250.26490.2945Retail Price ($ per 12 oz)

0.28850.29080.29030.3080Market Share

0.12400.12640.11040.1070Within the 6 products

0.13680.14490.12150.1233Retail Price ($ per 12 oz)

0.31090.31200.31880.3165Market Share

0.07140.07920.0867

0.0975

Within the 6 products

0.07880.0912

0.09540.1156

Retail Price ($ per 12 oz)

0.30910.31850.31980.3236

Market Share

0.03990.04330.0456

0.04820.04400.04980.0502

0.0568

3122

3047

576

562

Control
2029.91898.0-3.97

t-value
1.63
3.84(0:0757)(0:0409)(0:0656)(0:0721)(0:0374)(0:0940)(0:1035)(0:0367)(0:0519)(0:0570)(0:0541)(0:0545)(0:0600)(0:0712)(0:0211)Within the 6 products
Observations

HHI (2008-9)
HHI (2011-12)
t-value(0:0843)

Treatment
2005.41821.9-21.07(0:0232)(0:0721)(0:0829)(0:0512)(0:0695)(0:0779)(0:0394)(0:1003)(0:1136)(0:0416)(0:0535)(0:0601)(0:0528)(0:0502)(0:0576)(0:0756)(0:0217)(0:0245)(0:0791)(0:0870)(0:0525)(0:0630)(0:0693)(0:0511)(0:1112)(0:1223)(0:0458)(0:0451)(0:0496)(0:0692)(0:0510)(0:0561)(0:0635)(0:0242)(0:0267)(0:0816)(0:1006)(0:0714)(0:0584)(0:0686)(0:0548)(0:1433)(0:1582)(0:0546)(0:0546)(0:0603)(0:0611)(0:0524)(0:0622)(0:0676)(0:0243)(0:0287)

Notes: The observation unit is “county
month.” The numbers of observations are: 3122
(treatment, 2008-9); 576 (control, 2008-9); 3047 (treatment, 2011-12); and 562 (control,
2011-12).

19

Table 7: Mean Comparison of Treatment and Control Groups(a) Demand Side
Population
Median Income
Median Age
Unemployment Rate (%)
Bachelor’s Degree (%)
Average Temperature (F)Treatment
748,945
56,869
36.9
8.2
19.5
55.8

Control
574,761
49,195
37.3
8.5
17.6
58.9

t-value
-1.62
-5.47
1.28
5.01
-3.31
6.60

Treatment

Controlt-value

49.8
71.0
44.2

63.3
97.3
60.3

2.20
1.45
3.13

1167.01137.91136.01020.8
927.5
972.3

-5.91
-8.55
-6.82

(b) Supply Side
Distance from Plant (miles)
Coke
Pepsi
Dr Pepper
Weekly Wage ($)
Coke
Pepsi
Dr Pepper

Notes: The number of observations is: for the treatment group, 7720
(Unemployment Rate and Average Temperature) and 645 (all othervariables); for the control group 1426 (Unemployment Rate and
Average Temperature) and 119 (all other variables).

20

to PepsiCo’s vertical merger. Pepsi’s price increase is lower in the treatment group (0.7%)
than in the control group (5%). However, both groups experienced similar increases in market
share (5% for the treatment group and 6% for the control group). Table 6 also shows that thedegree of market concentration in the treatment group is similar to that in the control group(the t-value is 1.63). However, the treated counties became less concentrated after PepsiCo’s
merger than the controlled counties, and the di¤erence is statistically signi…cant. Finally, Table
7 compares the treatment and control groups in terms of demographic characteristics as wellas the supply side. Median income and the proportion of those with a bachelor’s degree are
signi…cantly higher in the treatment group, re‡ecting the fact that Pepsi Bottling Group andPepsiAmericas mainly operated in metropolitan areas. The temperature comparison shows that
control counties are located in lower latitude counties. Di¤erence in terms of weekly wage andelectricity price indicate that the treated counties were mainly located in metropolitan areas.
The comparison in terms of distance between the county and its nearest plant indicates thatplants are located in areas close to the center of the populated areas.

4

Empirical Analysis

In this section, a di¤erence-in-di¤erences (DID) analysis is employed to quantify the reducedform e¤ects of PepsiCo’s 2010 vertical merger, in part to motivate structural analysis for future
research. I take advantage of the fact that this vertical merger was not nationwide: if that werethe case, one would need to consider “control products” (products of the merged chain) and“treatment products”(products of other chains).25 In the following analysis, a treatment groupconsists of the counties where a bottler was a target of the vertical integration, and a control
group consists of the counties where bottlers remained independent. Note that in the following
DID analysis, the average treatment e¤ect (ATE) is measured, that is, the average change (i.e.,the di¤erence) in the treatment group is compared to the average change in the control group.
Heterogeneous e¤ects are considered as interactive terms with the treatment variable. I also
consider a modi…cation of this standard DID analysis, by which each observation is re-weighted
using the propensity score to better account for observational di¤erences between the treatment
25

However, since both product types coexist in the same market, it might be the case that the treatment
also has some e¤ects on the control group. See, e.g., Ashenfelter and Hosken (2010) and Green…eld (2015) for
alternative methods for choosing a control group in the case of nationwide mergers.

21and control counties.
To control for possible advertisement e¤ects, I use Kantar Media’s Ad$pender Databasefrom 2008 through 2012 to compute monthly per-capita advertisement expenditures for eachproduct. It covers information on the Coca-Cola Company, PepsiCo, and Dr Pepper Snapple’smonthly expenditures for advertising in representative metropolitan areas,26 including TV,
radio, local newspapers and magazines, and on-site advertisements. This is because the verticalmergers might have been associated with targeted advertising, which supposedly a¤ected the
local demand for soda products after PepsiCo’s vertical merger. If these advertising e¤ects
are not controlled for, the e¤ects of the vertical merger on retail prices and market sharesare not adequately estimated due to these confounding factors. Some advertisement units aretargeted for either the regular or the diet product, although many of them are non-speci…c.
When aggregating advertisement units within a month-MSA (Metropolitan Statistical Area)pair, I distinguish between the regular and the diet categories whenever possible. Finally, eachcounty from the IRI Academic Data Set is connected to the closest MSA in Kantar Media’s
Ad$pender. Fortunately, it appears that advertising e¤ects are not important. Even if theanalysis is not supplemented by Ad$pender data, the empirical results shown below do notchange signi…cantly.

4.1

Di¤erence-in-Di¤erences Estimation

Now, I consider PepsiCo’s vertical merger in 2010 as a treatment. A county is categorized into
the treatment group if it has a bottler that was integrated after the merger, otherwise, it iscategorized into the control group.27 First, j indexes for product, r for county, and t is a timeindex. Let Post-merger t be a dummy variable that takes 1 if t is a post-merger month and 0
if it is a pre-merger month. Similarly, de…ne Tr as a dummy variable that takes 1 it r belongs
to the treatment group, and 0 if it belongs to the control group. Then, I consider the following
two speci…cations:log pj;(r;t) =p0+

p1 fPost-merger t

Tr g

26
The geographical unit of Ad$pender is MSA (Metropolitan Statistical Area), and the total number of MSAs
covered is 101.
27
In a similar spirit, Allain, Chambolle, Turolla, and Villas-Boas (2017) make use of the fact the some markets
were not served by either of the merging supermarket chains. This enables them to use these markets as a control
group in their DID analysis to study the price e¤ects of a horizontal supermarket merger in France.

22

+ p2 Post-merger t +p0
3 Tr + z(r;t)

p+ c0j;(r;t)

p

+

r +

p
t + "j;(r;t)

andlog pj;(r;t) =p0+

p
11 fPost-merger t+ p12 fPost-merger t+ p13 fPost-merger t+ p2 Post-merger t +
where

r

Tr g

Tr

1(In county r, Dr P is distributed by PepsiCo)g

Tr

1(In county r, Dr P is distributed by Coca-Cola)gp0
3 Tr + z(r;t)

captures county …xed e¤ects, andp+ c0j;(r;t)

p

+

r +

t time …xed e¤ects.

pt + "j;(r;t) ,In addition, I also include

time-varying demographic and cost variables, z(r;t) and cj;(r;t) , which are already explained in
Section 3. In my sample, pj;(r;t) is a quantity-adjusted price. The second speci…cation is used to
explore whether common agency (i.e., whether Dr Pepper’s products are distributed by PepsiCoor Coca-Cola) matters to the price e¤ects.
I also conduct a DID analysis for market shares by considering:log sj;(r;t) =s
0+s
1 fPost-merger t+ s2 Post-merger t +

Tr g

s
0
3 Tr + z(r;t)

s

+

r +

s
t + "j;(r;t)

andlog sj;(r;t) =s
0+s
11 fPost-merger t

Tr g+ s12 fPost-merger t

Tr

1(In county r, Dr P is distributed by PepsiCo)g+ s12 fPost-merger t

Tr

1(In county r, Dr P is distributed by Coca-Cola)g+ s2 Post-merger t +s0
3 Tr + z(r;t)

s

+

r +

s
t + "j;(r;t) ,where sj;(r;t) is the market share of product j in market (r; t). Note that market shares, rather
than quantities sold, are used to capture the e¤ects of rivalry, and they are computed withinclusion of the market share of outside goods.
The ordinary least squares (OLS) estimation for

p
1 and

s
1 provides an estimate for the

ATEs of vertical integration on the retail prices and market shares, respectively. The additionaldi¤erences from common agency are captured byp
12 andp
13 for the retail prices, ands
12 ands
13 for the market shares. To take into account possible heteroskedasticity across counties andpossible correlation of the error terms within counties, Huber-White robust standard errors,
clustered by county, are shown in all results below.
23

4.2

Discussions on the Parallel Trend Assumption

Before presenting the empirical results, I brie‡y argue their validity. The fundamental identifying assumption in a DID analysis is the parallel trend assumption: treatment and control groups
had experienced similar trends prior to a vertical merger, and absent the treatment similar patterns would have been maintained across the treatment and control groups. If the treatment andcontrol groups had di¤erent time trends in an unobserved way, the ATEs would be estimatedwith bias. It is expected that PepsiCo’s 2010 vertical merger in this study is less susceptible toendogeneity concerns than other smaller mergers because PepsiCo did not particularly target
bottlers with poor performance in an unobservable way. However, the treated counties may
have experienced unobserved exogenous changes di¤erently from the control counties.
To consider this issue, I follow Kaplan, Taylor, and Villas-Boas (2016) to estimate thefollowing equation:log pj;(r;t) or log sj;(r;t)
=

0+

t
X

(Tr

D ) + z0(r;t) +r +t + "j;(r;t) ,

(1)

=1

where t denotes the last period covered in the data (December 2012) and Dt is a dummy variable
that takes one if period is t and zero otherwise, to determine whether there is a systematic
discrepancy in the unobservable trends between the treatment and the control groups in the
pre-merger period for PepsCo’s products. The term c0j;(r;t)

is included in the right hand side

if the dependent variable is log pj;(r;t) . Figure 1 plots f

g, and the dashed lines show theand the control groups prior to the vertical merger, then,

95% con…dence intervals. If there are no unobservable trend di¤erences between the treatment= 1; :::; t01, where t0 is theperiod when PepsiCo’s vertical merger was completed (February 2010), should be close to zero.
Fortunately, Figure 1 shows that,

= 1; :::; t0lies in the 95% con…dence interval in most of the periods:

1 are not systematically di¤erent from zero. Thus, although the treatmentgroup exhibits a slightly negative retail price trend (Panel (a)) and a positive trend for market
share (Panel (b)), these small trends should not lead to signi…cant bias.28
28

In contrast, a similar analysis of Coca-Cola’s vertical merger reveals less validity of the parallel trend
assumption (see Adachi, 2017). Thus, and together with the reasoning in Section 2, I have decided to solely
study the e¤ects of PepsiCo’s vertical integration.

24

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

25

4.3

Estimation Results

Tables 8 and 9 show the estimation results of the e¤ects of PepsiCo’s vertical merger on retailprices and market shares, respectively. I employ the two estimation methods: one is thestandard DID by which the equations above are estimated by OLS, and the other is a reweighing method motivated by, among others, Hastings, Kane, Staiger, and Weinstein (2007),
Suzuki (2009), and Allain, Chambolle, Turolla, and Villas-Boas (2017). This method utilizes
propensity score matching in a way proposed by Barsky, Bound, Charles, and Lupton (2002),
Hirano, Imbens and Ridder (2003), and Imbens (2004) to re-weight the observations in thecontrol group to match the covariates in the treatment group as closely as possible. Notably,in contrast to the standard methods using matching, all observations are used in estimation
with this re-weighting method. I …rst compute propensity scores using a Probit model where adummy for counties that experienced a vertical merger is regressed on county covariates as well
as month dummies and year dummies. Then, in a second step, I conduct a DID estimation using
a re-weight P S=(1P S) for observations from control counties, where P S is the corresponding

propensity score. In this way, whether a county experiences a treatment is not dealt as purely arandom weight. This method is particularly appealing in this paper’s context because treatedcounties belong to relatively large metropolitan areas (see Table 7 (a) above).
For each table, I …rst show the ATEs ( p1 ands
1 above) for each brand , and the estimation

results using the re-weighting method are also shown. Then, I show the results interacting thetreatment term with whether Dr Pepper’s products are distributed by PepsiCo or Coca-Cola( p12 andp
13 , ands
12 ands
13 above).

The estimation results using the re-weighting method

are also shown for this speci…cation as well. First, it is observed from Table 8 that PepsiCo’s
vertical merger lowered its own prices by 4.4%, which is statistically di¤erent from zero at the 5%
signi…cance level for both methods. This observation is consistent with the e¢ ciency-based viewof vertical integration: vertical integration, ceteris paribus, mitigates double marginalization.29
However, Table 8 also shows that these treatment e¤ects are not homogeneous with respect tothe mode of common agency. Speci…cally, the e¤ects of PepsiCo’s vertical merger on its own
prices are the largest in the markets where Coca-Cola also distributes Dr Pepper’s products.
Although the price reduction is 2.5% in the markets where neither Coca-Cola’s nor PepsiCo’s
29

A similar …nding is also obtained by Muris, Sche¤man, and Spiller (1992) who show that metropolitan areas
where PepsiCo acquired the ownership of bottlers in 1987 experienced a 4% reduction in the bottler’s prices.

26

Table 8: Treatment E¤ects of PepsiCo’s Vertical Merger on Retail Prices
Average E¤ects
Standard DID
Re-Weighting
Est
p-val
Est
p-val

Interacted with Common Agency
Standard DID
Re-Weighting
Est
p-val
Est
p-val

-0.04350.011-0.02500.205-0.0257

0.174

Pepsi distributes Dr P–

Coke distributes Dr P–

Dep Var: log ppepsi
Post Merger

TR2-0.0437

0.004

–

–

–

-0.01130.312-0.0133

0.290

–

–

–

-0.0251

0.049-0.0228

0.093(0:0169)(0:0151)

0.5856

Dep Var: log pcoke
Post MergerT(0:0196)0.6064(0:0112)(0:0127)(0:0188)(0:0125)(0:0135)

0.58650.6067-0.01100.596-0.0166

0.405-0.0192

0.443-0.02770.243

Pepsi distributes Dr P

–

–

–

–

0.00220.890

0.0065

0.688

Coke distributes Dr P

–

–

–

–

0.01350.388

0.01570.315

R2(0:0207)(0:0199)

0.60290.6693(0:0250)(0:0161)(0:0156)

0.6032(0:0236)(0:0161)(0:0156)

0.6694

Dep Var: log pdr_p
Post Merger

T

0.0031

0.871-0.0034

0.861-0.0105

0.638-0.0191

0.393

Pepsi distributes Dr P

–

–

–

–

0.01510.340

0.0178

0.270

Coke distributes Dr P

–

–

–

–

0.01370.331

0.01550.302(0:0192)(0:0192)(0:0222)(0:0158)(0:0141)(0:0222)(0:0161)(0:0150)R20.64550.74150.65470.7415Time FECounty FE
CCE!CCR counties,
and its interaction with
the Coca-Cola merger
Market Covariates
Cost Covariates

X
X

X
X

X
X

X
X

X

X

X

X

X
X

X
X

X
X

X
X

Notes: For each regression, the number of observations is 18,292. A dummy variable for dietdrinks is also included (the estimate is not presented). Standard errors, clustered by county,
are shown in parentheses, and p-values less than 0.1 are emboldened.

27

Table 9: Treatment E¤ects of PepsiCo’s Vertical Merger on Market Shares
Average E¤ects
Standard DID
Re-Weighting
Est
p-val
Est
p-val

Interacted with Common Agency
Standard DID
Re-Weighting
Est
p-val
Est
p-val

0.06590.093

Pepsi distributes Dr P–

Coke distributes Dr P–

Dep Var: log spepsi
Post Merger

TR2-0.0350

0.511

0.02180.623-0.0692

0.210

–

–

–

0.03520.178

0.0280

0.294

–

–

–

0.05370.007

0.0411

0.044(0:0390)(0:0531)0.8001

Dep Var: log scoke
Post MergerT

0.7521(0:0442)(0:0260)(0:0195)(0:0549)(0:0266)(0:0202)0.80030.75210.01000

0.785

0.04190.306

0.04530.276

0.07690.096

Pepsi distributes Dr P

–

–

–

–

-0.0423

0.104-0.0417

0.111

Coke distributes Dr P

–

–

–

–

-0.03140.149-0.03130.154

R2(0:0366)(0:0407)0.69490.6452(0:0415)(0:0259)(0:0216)0.6951(0:0460)(0:0260)(0:0219)0.6454

Dep Var: log sdr_p
Post Merger

T

0.00850.805-0.0372

0.329

0.00460.911-0.0376

0.390

Pepsi distributes Dr P

–

–

–

–

0.00450.858

0.0025

0.924

Coke distributes Dr P

–

–

–

–

0.00370.886-0.0012

0.966(0:0345)(0:0379)(0:0411)(0:0249)(0:0257)(0:0437)(0:0259)(0:0275)

R20.83040.80610.83040.8061Time FECounty FE
CCE!CCR counties,
and its interaction with
the Coca-Cola merger
Market Covariates

X
X

X
X

X
X

X
X

X

X

X

X

X

X

X

X

Notes: For each regression, the number of observations is 18,292. A dummy variable for dietdrinks is also included (the estimate is not presented). Standard errors, clustered by county,
are shown in parentheses, and p-values less than 0.1 are emboldened.

28

bottler is a common agent for Dr Pepper, PepsiCo’s prices are additionally lowered by 2.3–
2.5% if Coca-Cola’s bottler is a common agent for Dr Pepper, and by 1.1–1.3% if PepsiCo’sbottler is a common agent. In addition, the coe¢ cient for Coke’s common agency is statisticallysigni…cant under both the standard DID and the re-weighting methods, although the F statistic
for

p
12 =

p
13 (with 1 numerator and 156 denominator degrees of freedom) is 1.33 under the

standard DID and 0.48 under the re-weighing method, which implies that the signi…cance levelis 24.5% and 48.8%, respectively; the hypothesis cannot be rejected at a reasonable level ofstatistical signi…cance.
As for the e¤ects on PepsiCo’s own market shares, the results are less clear (see Table 9).
Although the standard DID indicates a share increase of 6.6%, the re-weighting method suggeststhat PepsiCo’s vertical integration lowers its own market share by 3.5%. These mixed results
might imply that PepsiCo’s vertical merger triggered the “price war”against Coca-Cola. Recall
that the treatment counties are relatively populated and urbanized areas whose population
presumably favors Pepsi rather than Coke (see Panel (a) of Table 7). If these demographic
characteristics are properly weighted as per the re-weighting method employed herein, thenCoca-Cola’s market share increase becomes more apparent, as shown in Table 9. Tables 8 and9 also show that PepsiCo’s vertical merger had a similar impact on Coca-Cola’s products: itlowers Coca-Cola’s prices by 1.1–1.7%, while it raises Coca-Cola’s market shares by 1.0–4.1%.
It is, thus, inferred that the reduction of PepsiCo’s prices caused by its vertical merger led to…ercer competition between PepsiCo and Coca-Cola. However, these results are not statisticallysigni…cant. This is also the case for Dr Pepper’s products in terms of both price and market
share. In particular, the price changes are almost negligible for both methods, less than 0.1%.
Thus, it could be inferred that Dr Pepper is less a¤ected than Pepsi presumably because Dr
Pepper is not as strong a substitute for Coca-Cola compared to Pepsi. This conjecture could
be supported through estimation of substitution patterns using a structural analysis. However,
the estimation results in Table 9 also show that the mode of common agency also matters here.
The negative e¤ects are indeed o¤set by the price increases in the markets with PepsiCo’s or
Coca-Cola’s common agency. With regard to the e¤ects on Dr Pepper’s market shares, lessclear results are obtained.
The empirical …nding that the price e¤ect is weaker in the markets with Pepsi’s common
agency than in the markets with Coke’s common agency would suggest that the bottling part of29

Table 10: The Means of Pepsi’s Retail Prices and Market Shares Before and After PepsiCo’s Vertical
Merger
Before (in 2008 and 2009)(A) Regular Pepsi
No Common Agency
Pepsi distributes Dr Pepper
Coke distributes Dr Pepper

Price ($)
0.27540.29370.2697

MarketShare (%)
18.47
22.43
25.30

Number of
Observations
744
1440
1514

Price ($)
0.2796
0.29940.2831MarketShare (%)
12.02
12.57
11.91

Number of
Observations
744
1440
1514

(B) Diet Pepsi
No Common Agency
Pepsi distributes Dr Pepper
Coke distributes Dr Pepper

After (in 2011 and 2012)(A) Regular Pepsi
No Common Agency
Pepsi distributes Dr Pepper
Coke distributes Dr Pepper

Price ($)
0.28170.2987
0.2717

MarketShare (%)
19.25
23.09
27.18

Number of
Observations
696
1437
1476

Price ($)
0.28700.30300.2872MarketShare (%)
11.46
12.65
12.46

Number of
Observations
696
1437
1476

(B) Diet Pepsi
No Common Agency
Pepsi distributes Dr Pepper
Coke distributes Dr Pepper

Note: The observation unit is “product30

countyyear

month.”

PepsiCo’s chain may have stronger price control over its own prices because it also controls Dr
Pepper’s products, or it might simply indicate that its wholesale price may be higher, perhaps
re‡ecting the bottler’s stronger position in the transaction with PepsiCo’s headquarters due to
its transactional relationship with Dr Pepper’s headquarters. The top panel of Table 10 showsthat before PepsiCo’s vertical merger, the average price is highest in the markets with PepsiCo’s
common agency in both the Regular and Diet categories. Mean di¤erences are all statisticallysigni…cant at the 1% level. This also applies after the vertical merger (see the bottom panel ofTable 10), excepting that “No Common Agency” and “Coke’s Common Agency” in the Diet
category are no longer di¤erent to a reasonable level of statistical signi…cance. The results interms of mean market shares are less clear, though. Both before and after PepsiCo’s verticalmerger, Pepsi’s market share is lower in the markets with Pepsi’s common agency than in themarkets with Coke’s common agency in the Regular category, whereas the opposite is true forthe Diet category. This similarity between the pre- and post-merger periods may suggest that
the FTC’s concerns are partly validated (see Subsection 2.2 above) in the sense that if PepsiCobottlers transact with Dr Pepper, then its retail prices increase, hampering the bene…ts fromvertical integration. However, this does not arise uniquely from vertical merger per se, as the
top panel of 10 shows. Thus, other inherent and structural reasons may be more important.
Then, what is the di¤erence between PepsiCo’s common agency and the other two modes
when demand factors are controlled for? The welfare e¤ects of PepsiCo’s vertical merger would
probably be di¤erent across the mode of common agency. At this point, one would expect thatthe welfare change is higher in the markets with Coke’s common agency than in the markets
with Pepsi’s common agency. It becomes possible to quantify these welfare di¤erences if one
estimates a structural model. In addition, it may also be associated with cost savings in thebottling process, although it is unclear solely from these results. The ambiguity in the results
above provide a rationale for disentangling the managerial e¤ects on margins from the e¤ects ofcost savings, using a structural model of vertical relationships with a bargaining process. This
analysis is left for future research.
In sum, it is observed that PepsiCo’s vertical merger lowered its retail prices. Whether
it raised its market shares is less clear. More importantly, it appears that this e¤ect depends
on the structure of common agency in the market: PepsiCo lowered its own prices and raised
its market shares greatest in the markets where Coca-Cola bottlers were common agents for31

Table 11: Treatment E¤ects of PepsiCo’s Vertical Merger on Retail Prices and Market Shares (13Months around the Completion of the Merger are Excluded)
Retail Prices
AverageCommon Agency
Est
p-val
Est
p-val

Market Shares
AverageCommon Agency
Est
p-val
Est
p-val

Dep Var: log spepsi
Post MergerT-0.0578

0.003

Pepsi distributes Dr P–

Coke distributes Dr P–

R2

-0.0378

0.116-0.0280

0.631-0.06880.260–-0.0122

0.437

–

–

0.0321

0.331–-0.0273

0.096

–

–

0.04990.033(0:0195)(0:0239)(0:0156)(0:0163)

0.6064

Dep Var: log scoke
Post MergerT

0.6068(0:0582)

0.7656(0:0608)(0:0329)(0:0231)0.7657-0.0271

0.269-0.03900.172

0.04340.362

0.07700.161

Pepsi distributes Dr P––

0.00466

0.808

–

–

-0.0414

0.194

Coke distributes Dr P––

0.0188

0.322

–

–

-0.02880.266

R2(0:0244)(0:0284)(0:0191)(0:0194)

0.67830.6785(0:0474)

0.6525(0:0546)(0:0317)(0:0258)

0.6526

Dep Var: log sdr_p
Post Merger

T-0.00667

0.781-0.0297

0.279-0.05230.235-0.05420.299

Pepsi distributes Dr P––

0.0294

0.138

–

–

-0.00408

0.895

Coke distributes Dr P––

0.0209

0.272

–

–

0.00689

0.832(0:0239)(0:0273)(0:0197)(0:0190)(0:0438)(0:0520)(0:0309)(0:0324)R2

0.74740.74750.80560.8056Time FECounty FE
CCE!CCR counties,
and its interaction with
the Coca-Cola merger
Market Covariates

X
X

X
X

X
X

X
X

X

X

X

X

X

X

X

X

Notes: For each regression, the number of observations is 14,300. A dummy variable for dietdrinks is included (the estimate is not presented). The estimation method is the re-weighting
method. Standard errors, clustered by county, are shown in parentheses, and p-values less than
0.1 are emboldened.

32

Dr Pepper. With regard to the e¤ects on Dr Pepper’s retail prices, common agency has thee¤ects of raising them after the vertical merger, although these changes are relatively small
and the p-values are relatively high, implying that Dr Pepper is less a¤ected by PepsiCo’s
vertical integration. It also seems that whether a PepsiCo or a Coca-Cola bottler distributes
Dr Pepper’s products is less important to these results regarding Dr Pepper’s retail prices andmarket shares.
The results herein on the price e¤ects of PepsiCo’s vertical merger on its own products
are in line with Gil (2015), who also conducts a DID analysis of vertical integration from dataon movie theaters’ prices, revenues, and ownership in 26 U.S. cities for the period 1945 to
1955. Gil (2015, p.162) …nds that “vertically integrated theaters charged lower prices and soldmore admission tickets than nonintegrated theaters.”Then, Gil (2015) spells out three possible
channels through which vertical integration a¤ects …nal retail prices: (i) double-marginalization,(ii) downstream competition, and (iii) productivity, which may be attained through better scaleand scope economies in production and distribution. Again, these issues could be investigatedwith a structural model in consideration of the structure of common agency.
Finally, Table 11 explores the robustness of the empirical results. They do not change
signi…cantly even if 13 months around the completion of PepsiCo’s vertical merger (February
2010, and 6 months before and after) are removed. The estimation is implemented by the reweighting method. Unfortunately, statistical signi…cance disappears for the market share e¤ect
on Coca-Cola in the markets where neither a PepsiCo nor a Coca-Cola distributes Dr Pepper’s
products. However, the price and share e¤ects of Coca-Cola’s common agency on PepsiCo’s
products remain statistically signi…cant.

5

Concluding Remarks

In this paper, I analyze the price and market share e¤ects of one of the biggest vertical mergers
in the U.S. carbonated soft drink (CSD) industry. In particular, in determining the e¤ects ofvertical integration, this paper focuses on the unique feature of the U.S. CSD industry to study
the role of common agency, where a downstream distributor may also distribute its upstream
rival’s products in some areas, Empirical results suggest that PepsiCola’s vertical merger isconsistent with the e¢ ciency-based view: it lowered its retail prices by 4.4%. More importantly,
33

these downward price e¤ects are stronger in the markets with Coca-Cola’s common agency thanin the markets with PepsiCo’s common agency. The price e¤ects of PepsiCo’s vertical merger on
Dr Pepper’s products are also weaker in the markets with PepsiCo’s common agency. However,the price e¤ects on Coca-Cola’s products are stronger in these markets. Obviously, the welfaree¤ects of PepsiCo’s vertical integration would di¤er across the mode of common agency, whichshould be investigated by a structural approach. More speci…cally, these empirical results
suggest a structural analysis of a vertical merger in a bargaining model with an emphasis on how
trade with multiple sellers a¤ects the bargaining outcome, and hence consumer welfare. Then,it becomes possible to evaluate vertical as well as horizontal mergers with vertical relationships
explicitly taken into account.
In this paper, I have not considered non-price e¤ects of vertical integration such as thee¤ects on introduction of new products, mainly due to the fact that the IRI Academic Data Set
unfortunately does not include the non-CSD category. In this respect, Zhou and Wan (2017)provide an interesting study examining the e¤ects of a vertical merger on product quality by using product-level data, including carbonated non-carbonated soft drinks, from 264 distribution
centers of one (anonymous) major bottling company that owns 50 plants in the U.S. Zhou andWan (2017) empirically establish that vertical integration (again, the identity of the integrated
entity is kept anonymous) was bene…cial to the integrated entity’s coordination (in terms of,for example, the frequency of stockout that a product experiences) for their own products,
although it worked negatively for rival products. In contrast, I directly study price and sharee¤ects, holding the quality dimension …xed. I thus leave the quality e¤ects of vertical mergers
to future research.

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