Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
What Determines Horizontal Merger Antitrust Case Selection?
Ning Gao, Ni Peng and Norman Strong*
The U.S. antitrust agencies claim their mission of antitrust enforcement
is to protect consumers, promote fair competition, and maintain
efficiency. Are antitrust challenges consistent with this claim? If not,
what determines the U.S. antitrust intervention? We explore these
questions by examining horizontal merger case selection in the U.S.
manufacturing sector during 1980–2009 and find no evidence
supporting the consumer protection claim. Overall, our findings suggest
that foreign import competition, the market concentration hurdle
criterion, and certain industry rivals, rather than consumer protection,
determine antitrust agencies’ horizontal merger case selection.
JEL Codes: G34 G38
1. Introduction
Antitrust enforcement has played an important role in the United States for decades.
The Department of Justice (DOJ) and the Federal Trade Commission (FTC) are
authorised to file antitrust complaints against mergers when they believe the mergers
would substantially lessen competition and violate antitrust laws.1 To avoid duplication,
they coordinate their activities and separate their enforcement areas. Both the DOJ and
the FTC may bring civil antitrust cases that violate the Clayton Act, but only the DOJ may
bring criminal lawsuits that violate the Sherman Act as felonies. Before taking any
preliminary investigation against a merger, each requests clearance from the other
(Bruner 2004, p.745). In practice, most business combination complaints are filed
against horizontal mergers.2 Despite a considerable literature exploring antitrust
regulation efficiency and the determinants of antitrust activity intensity in financial
economics, political economy, and law (Long, Schramm, and Tollison, 1973; Ellert,
1976; Stillman, 1983; Wier, 1983; Eckbo and Wier, 1985; Johnson and Parkman, 1991;
Eckbo, 1992; Wood and Anderson, 1993; Bittlingmayer and Hazlett, 2000; Ghosal and
Gallo, 2001; Aktas, deBodt, and Roll, 2004 and 2007; Feinberg and Reynolds, 2010;
Ghosal, 2011), it remains unclear what determines horizontal merger antitrust case
selection by the U.S. antitrust agencies at the deal level.3
The main goal of this paper is to model antitrust case selection and examine the
determinants of antitrust challenges against horizontal mergers. We draw from the
economic theory of regulation and the literature on horizontal merger motives to derive
four hypotheses that explain the likelihood that a horizontal merger faces antitrust
intervention. First, public interest regulation theory suggests that government
intervention corrects market failure and maximises social welfare (Pigou, 1932). It
assumes government intervention is active rather than passive, and based on
*
Accounting and Finance Group, Manchester Business School, University of Manchester, United
Kingdom. Email: Ning Gao, ning.gao@mbs.ac.uk;
Ni Peng, ni.peng@postgrad.mbs.ac.uk;
Norman Strong, norman.strong@mbs.ac.uk.
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Proceedings of 9th Annual London Business Research Conference
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identifying market failure. In the context of antitrust regulation, the theory implies that
antitrust intervention is based on identifying anticompetitive deals that harm consumers.
Therefore, challenges should be more likely for mergers resulting in higher purchasing
prices for downstream corporate customers that are likely to pass these on in the form
of higher product selling prices (Eckbo, 1983; Fee and Thomas, 2004). We label this
rationale the consumer protection hypothesis.
Second, the presence of foreign competition increases the supply elasticity of a
domestic industry and makes it more difficult to monopolise. Katics and Petersen (1994)
show that strong import competition squeezes profit margins and induces domestic
companies to merge in order to compete on improved efficiency. Mitchell and Mulherin
(1996) find that a significant increase in import competition promotes merger waves in
the domestic market for efficiency purposes. Therefore we hypothesise that, with strong
foreign competition, it is more difficult for merging firms to exercise market power and
the authorities are less likely to challenge. We call this the foreign competition
hypothesis.
Third, Stigler (1964) notes that horizontal mergers enable merged firms to collude
more easily with rivals. Monopoly rents, however, differ across industries. For decades,
antitrust agencies have implemented the market concentration doctrine, which posits
that the degree of industry concentration proxies for the size of monopoly rents that
merging firms can extract (Eckbo, 1988). In addition, Stigler (1968) suggests that
monitoring costs of maintaining a collusive agreement are lower in a concentrated
market due to higher visibility. Motivated by larger monopoly rents and less free-riding,
firms in concentrated industries may be more likely to merge for market power reasons
than firms in fragmented industries. As a framework for assessing the potential
anticompetitive effects of a merger, the antitrust agencies use the market concentration
hurdle, which divides industries into categories by market concentration thresholds. 4
They claim they pay more attention to deals that would result in high concentration and
that would substantially increase concentration. Therefore, the market concentration
hurdle hypothesis predicts a higher likelihood of antitrust intervention in deals that hit
the concentration hurdle criterion.
These three hypotheses assume that antitrust agencies act benignly on behalf of
society and make dispassionate decisions. In contrast, Stigler‟s (1971) economic theory
of regulation assumes that government agencies choose cases to pursue by trading off
the costs and benefits of their actions. In other words, other forces can influence
antitrust decisions. Regarding the possible forces, previous literature documents an
active role for industry rivals in regulation. Baron (1998) documents that firms can affect
the stringency of regulation in their favour when a proposed regulation would affect cost
structures among industry players differently. Baumol and Ordover (1985) refer to an
antitrust strategy to constrain competition. Faced with efficient mergers, to avoid a
competitive disadvantage from increasingly efficient peers, industry rivals lobby or
pressure antitrust agencies to intervene. Since the lobbying is not cheap, we presume
that only the most affected rivals have the incentive to lobby: for instance, local rivals
that would lose from sharing a geographical market and less specialised producers that
would face a substitution effect. Therefore, if these industry rivals suffer from the deal,
the likelihood of antitrust intervention increases. We label the fourth hypothesis the rival
influence hypothesis.
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Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
To examine the determinants of U.S. antitrust case selection, we use a sample of
393 public horizontal deals in the U.S. manufacturing sector between 1980 and 2009.
We hand-collect data from the FTC and DOJ‟s joint “Annual Report to Congress
Pursuant to Subsection (j) of Section 7A of the Clayton Act Hart-Scott-Rodino Antitrust
Improvements Act of 1976”, and identify 35 deals that the FTC and the DOJ challenged
in the sector during the period. We model the antitrust agencies‟ case selection with
probit regressions including customer and rival wealth effects, foreign import
competition, industry structure measures, and a range of deal-specific and industryspecific control variables.
A univariate analysis shows that unchallenged deals are associated with positive
abnormal returns to rivals over a (−2, 2) day window surrounding the initial merger
announcement. In contrast, in challenged deals, the price reaction of rivals is not
different from zero. This price reaction pattern contradicts the common understanding
that antitrust agencies intervene in anticompetitive mergers, from which industry rivals
expect to gain monopoly rents. More perversely, challenged deals promise value
creation to corporate customers at the initial announcement. These results confirm the
conclusion of Ellert (1976) and Eckbo and Wier (1985) that challenged mergers would
have been efficient, value-creating, and not anticompetitive.
In a multivariate analysis, we find no evidence supporting the consumer protection
hypothesis. The stock market reaction of corporate customers to merger
announcements does not have a systematic impact on antitrust case selection. Nor do
the antitrust agencies consider local customers and customers of the downstream
industry that purchase the most from the merging industry, who presumably are affected
most by the merger. Instead, we find that the likelihood of antitrust intervention is
negatively related to foreign import competition. A 10% increase in foreign import
competition reduces the likelihood of an antitrust challenge by about 7.2%. This is in line
with Katics and Petersen (1994), who suggest that increasing import competition
threatens the domestic industry and constrains market power. It also echoes Mitchell
and Mulherin‟s (1996) argument that higher foreign competition reduces the need for
antitrust enforcement, and less antitrust intervention increases activity in the market for
corporate control.
Our findings also suggest that the market concentration hurdle criterion predicts
antitrust intervention likelihood. Horizontal mergers that hit the market concentration
hurdle criterion are 15.4% more likely to face antitrust intervention. This confirms the
previous perception that the antitrust agencies stick closely to the market concentration
doctrine.
We also find evidence consistent with the rival influence hypothesis. The likelihood
of a merger being challenged is negatively related to the wealth effect at the merger
announcement of local rivals. A 10% lower wealth effect to local competitors increases
the likelihood of an antitrust challenge by 14.5%. Similarly, a 10% lower wealth effect to
less specialised industry producers leads to a 10.7% higher likelihood of an antitrust
challenge. These results shed light on the use of antitrust to subvert competition
(Baumol and Ordover, 1985) and antitrust agencies‟ deviation from their mission of
promoting social welfare (Bittlingmayer and Hazlett, 2000). Mechanisms that align the
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interests of particular industry rivals and antitrust agencies include lobbying, campaign
contributions (McChesney, 1997), and quid pro quo deals (Tahoun, 2011). Overall, our
findings suggest that foreign import competition, the market concentration hurdle
criterion, and certain industry rivals, rather than consumer protection, influence antitrust
agencies‟ horizontal merger antitrust case selection.
Our study makes the following contributions. First, to our knowledge, this paper is
the first to model government decision determinants in regulating business
combinations at deal level. Previous literature on government antitrust enforcement
considers either the aggregate intensity level (Long, Schramm, and Tollison, 1973;
Wood and Anderson, 1993; Feinberg and Reynolds, 2010; Ghosal, 2011) or particular
cases (Bittlingmayer and Hazlett, 2000). Second, this paper is the first to show that the
efficiency of antitrust enforcement is mixed. Past studies of antitrust policy generally
conclude that antitrust regulation is inefficient, e.g., Stillman (1983), Eckbo (1983, 1988,
1992), Eckbo and Wier (1985) on U.S. antitrust regulation, Atkas, deBodt, and Roll
(2004, 2007) on EU antitrust regulation. We find evidence both of efficiency and
inefficiency. The effect of foreign import competition is efficient and, to some extent,
using the market concentration hurdle criterion is justifiable (though there are many
criticisms of the flawed theoretical grounds of the market concentration doctrine).
However, failing to respond to consumer welfare losses and being influenced by interest
groups is inefficient. Third, we provide direct evidence against the consumer protection
claim, which complements the indirect evidence on benign merger motives from
previous studies (e.g., Stillman, 1983; Eckbo, 1983, 1988, 1992; Eckbo and Wier, 1985).
We also provide quantitative estimates of the impact of foreign import competition and
the widely-applied market concentration doctrine on antitrust case selection. Fourth, our
results shed light on which interest groups influence antitrust decisions, enriching our
understanding of interest group theories, and adding to the literature on the source of
the demand for regulation.
The remainder of the paper continues as follows. Section 2 reviews the relevant
literature and develops the hypotheses. Section 3 describes the sample and
construction of variables. Section 4 reports univariate and multivariate results. Section 5
summarises and concludes.
2. Literature review and hypothesis development
2.1 The consumer protection hypothesis
Antitrust agencies generally claim that the aim of antitrust intervention is to protect
consumers. FTC chairman T.J. Muris claims, “the Federal Trade Commission (FTC)
works to ensure that the nation‟s markets are vigorous, efficient and free of restrictions
that harm consumers”.5 This claim is consistent with public interest regulation theories
starting from Pigou (1932), which suggest that the government should act on behalf of
society, and intervene in the market when market failure happens.
Previous empirical research, however, contradicts this view with three pieces of
evidence. First, efficiency improvements rather than anticompetitive practices drive
horizontal mergers; even challenged deals would have been benign (Ellert, 1976; Eckbo,
1983; Stillman, 1983; Eckbo and Wier, 1985). The HSR Act of 1976 enabled the
antitrust agencies to obtain more information about a deal and more time to analyse it
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before taking legal action,6 and was supposed to strengthen their ability to choose
competitively harmful mergers (Eckbo and Wier, 1985). However, examining a sample
of antitrust cases filed after 1978, Eckbo and Wier (1985) fail to find any improvement in
antitrust case selection. In recent years, Fee and Thomas (2004) and Shahrur (2005)
investigate the upstream and downstream product-market effects of horizontal mergers,
and report non-negative stock price reactions of downstream corporate customers to
deal announcements, concluding that efficiency considerations drive horizontal mergers.
Second, the deterrence effect of antitrust regulation, if it exists at all, is limited.7 To
evaluate the deterrence effect, Eckbo (1992) examines Canadian evidence before 1985,
and reports that horizontal mergers are efficient even when there is no threat of antitrust
intervention.8 Third, some antitrust regulators pursue protectionism, which harms
consumers. For instance, EU regulators use antitrust policy to prevent deals by foreign
bidders and protect domestic firms (Aktas, deBodt, and Roll, 2007).
Though suggestive, the above literature does not directly answer whether consumer
protection is a major concern in antitrust intervention. In addition, some of these studies
report average customer abnormal returns in response to deal announcements, which
does not preclude the possibility that individual cases are anticompetitive. Public
interest theories assume that active government intervention is based on correctly
identifying market failures and government always “behaves like a benevolent and
omniscient dictator on behalf of society as a whole” (Boehm 2007, p.1). In the context of
antitrust regulation, this implies that the antitrust agencies can identify truly
anticompetitive deals that harm consumers. To discriminate between collusion and
efficiency as merger motives, Eckbo (1983) suggests examining the abnormal stock
returns to merger related firms.9 For instance, corporate customers would lose to a
collusive deal announcement resulting in higher product selling prices. Applying this
approach, we examine the relation between the antitrust agencies‟ case selection and
the downstream corporate customer‟s stock market reaction to the deal announcement
to provide direct evidence on consumer protection.
The consumer protection hypothesis predicts that antitrust agencies are more likely
to challenge deals that harm downstream corporate customers.
Hypothesis 1: A horizontal merger is less likely to face a challenge the higher the
announcement wealth effect on corporate customers.
2.2 The foreign competition hypothesis
Katics and Petersen (1994) show that changes in foreign competition have a sizable
impact on price–cost margins and challenge domestic industry efficiency. Mitchell and
Mulherin (1996) suggest that mergers are often the most cost-effective means for
industries to respond to changes caused by economic shocks such as changes in
foreign competition. In countries with anti-protectionist foreign trade policies, increasing
import penetration prompts domestic industries to consolidate to streamline operations
and improve efficiency. Therefore, when an industry faces greater foreign competition, it
is more likely to be efficiency improvements than monopolistic rents that drive firms to
merge. This gives rise to the foreign competition hypothesis, which predicts that
antitrust agencies are less likely to challenge deals in industries with higher import
penetration.
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Hypothesis 2: A horizontal merger is less likely to face a challenge the higher the level
of foreign competition in the industry.
2.3 The market concentration hurdle hypothesis
For decades, antitrust agencies have retained a focus on increased market
concentration when investigating anticompetitive behaviour.10 Since the DOJ‟s 1982
Merger Guidelines, the agencies have used the Herfindahl Index (HHI) to measure
market structure.11 The 1992 DOJ/FTC Horizontal Merger Guidelines classify several
thresholds of industry concentration and changes in industry concentration induced by a
merger.12 The guidelines divide industries into three categories by concentration
thresholds: unconcentrated (Herfindahl–Hirschman Index (HHI) less than 1000),
moderately concentrated (HHI between 1000 and 1800), and concentrated (HHI greater
than 1800).13 They pay more attention if a) firms merge in a concentrated industry or b)
firms merge in a moderately concentrated industry and this would cause a change in
HHI larger than 100. These thresholds remained unchanged for 18 years until a revision
in 2010.14 The focus on increased market concentration is an application of the market
concentration doctrine.
As Eckbo (1988) explains, the market concentration doctrine rests on oligopoly
models originating with Cournot ([1838] 1927) and Nash (1950), which posit that
industry concentration levels relate to the market power that merging firms can achieve,
thus proxying for potential monopoly rents. Furthermore, due to the higher visibility of
each firm‟s actions and lower monitoring costs, concentrated industries usually suffer
less from free-riding on collusive agreements than fragmented industries (Stigler, 1968).
These arguments together probably explain the position of antitrust agencies: motivated
by larger monopoly rents and less free-riding, firms in concentrated industries are more
likely than firms in fragmented industries to merge for market power reasons.
There are many criticisms of the antitrust implementation of the market
concentration doctrine on theoretical and empirical grounds. Academics believe the
authorities should not be so concerned about market concentration when judging an
industry‟s market power. As Eckbo (1988) explains, the oligopoly framework that
underlies the doctrine rules out possible explanations of healthy competition. For
instance, competition from potential entrants serves to police firms‟ behaviour in
concentrated industries; therefore high concentration does not necessarily lead to
market power. Increased industry concentration may result from markets reallocating
resources to respond to new investment opportunities (Eckbo, 1988). Competition
forces firms to increasingly specialize in production, which in turn increases industry
concentration. Aktas, Bodt, and Roll (2007) also cast doubt on antitrust enforcement‟s
heavy reliance on increases in industry concentration, stating that “The problem, of
course, is that the theory behind the ex ante approach is deeply flawed. As is well
known, one can generate a positive relationship between industry concentration and
profitability either via a competitive scale-economies argument, or via a monopoly
argument” (Aktas, Bodt, and Roll 2007, p.1118).15
However, since it is difficult to observe the anticompetitive effect of a horizontal
merger (especially before deal completion), to precisely identify harmful cases requires
investigation, assumption, and analysis. Wood and Anderson (1993) survey previous
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studies addressing resource constraints that shape enforcement, e.g., limited budgets
(Lewis-Beck, 1979), economists or attorney professionals (Weaver, 1977). Given the
difficulty of measuring anticompetitive effects and antitrust resource constraints,
antitrust agencies rely on clear-cut criteria for case selection and to justify their
decisions. This probably explains why, despite being heavily criticized for its
oversimplification, the market concentration doctrine remains central to antitrust
enforcement. In the 1992 DOJ/FTC Horizontal Merger Guidelines, the agencies apply a
five-step process to assess whether a merger creates market power for the merging
firms.16 Compared with other criteria in the guidelines, e.g., whether the merger
forestalls entry, whether there are alternative ways to obtain the merger efficiency gains,
and whether the merger includes a “failing firm”, the market concentration hurdle
criterion is clear-cut and straightforward.
Some point out that the antitrust agencies have not always adhered to the
concentration criterion.17 They may balance multiple concerns such as social welfare,
product innovation, and the variety of product or service choice. Given the
measurement complexity, we believe these concerns would weigh more in the later
remedy stage (e.g., in consent decree negotiation or litigation process) than in the early
investigation and challenge stage. Therefore, we presume that the concentration hurdle
criterion contributes in streamlining workload, and has a substantial impact on antitrust
case selection in practice.
Hypothesis 3: A horizontal merger is more likely to face a challenge if the deal hits the
market concentration hurdle.
2.4 The rival influence hypothesis
Stigler (1971) proposes an economic theory in which regulation is governed by the
laws of supply and demand. The supply side is the legislature or a regulatory agency
delegated by the legislature, while the demand side is an interest group that anticipates
benefits from the regulatory activity.18 The Stiglerian economic theory of regulation
implies that government agencies choose which cases to pursue based on a trade-off
between the costs and benefits of their actions.
The theory implies that the regulated firms can influence the legislation or regulation.
As Stigler (1971) points out, “regulation is acquired by the industry and is designed and
operated primarily for its benefits” (Stigler 1971, p.3). Previous studies provide empirical
evidence that regulated firms collectively utilise industry regulation to shield them from
external entry (e.g., Slovin, Sushka, and Hudson, 1991). Legislation or regulation also
can be used to change competition dynamics; firms may actively influence legislation or
regulation in their favour to handicap their competitors. Baron (1998) documents that
when firms believe proposed legislation affects cost structures among competing firms
differently, they adopt nonmarket means (e.g., by providing support to legislators) to
affect the stringency of the regulation in their favour.
In the context of antitrust regulation, Baumol and Ordover (1985) explain why
antitrust can subvert rather than enhance competition. They point out that industry rivals
have incentives to lobby the antitrust agencies to block efficient industry mergers. By
seeking protection by regulators, affected rivals avoid a competitive disadvantage from
efficient merged peers. Antitrust guidelines sometimes enable such protectionism. For
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example, the measures to differentiate legitimate competitive behaviour from harmful
monopolistic behaviour are murky, allowing firms to claim that “almost any successful
programme by a rival is „anticompetitive‟ and that it constitutes monopolization” (Baumol
and Ordover 1985, p.252).
Empirical evidence also supports the rival influence argument. Eckbo (1990)
documents that Chrysler experienced negative abnormal returns on GM–Toyota‟s joint
venture announcement in 1983, which explains why Chrysler lobbied the FTC to stop
the deal. In their study of the Microsoft antitrust case, Bittlingmayer and Hazlett (2000)
find that Microsoft‟s competitors, Netscape, Sun Microsystems, Oracle, Novell, etc.,
actively promoted antitrust investigations against Microsoft. 19 Eckbo (1983) and Eckbo
and Wier (1985) show that rivals realize gains on news releases of peer mergers being
challenged, which is in line with the incentive of rivals to influence antitrust intervention.
Merger-induced impacts on industry rivals differ, however, since rivals are usually
heterogeneous in terms of geographic market occupancy and product or service
provision. Furthermore, defending their interests requires investment that is not cheap.
A rival is motivated to influence the antitrust agencies only if the expected benefits
outweigh the costs. Only the most affected rivals have the incentive to invest in such
private use of antitrust. If a horizontal merger proceeds, local rivals lose advantages in
the geographical market of the merging firms or existing positions in the local supply
chain, whereas distant rivals are affected less. This is in line with Bittlingmayer and
Hazlett‟s (2000) observation that the 20 state suits that accompanied the 1998 DOJ suit
against Microsoft consistently suggested pressure from local computer companies to
state authorities. Therefore, we conjecture that local rivals are more likely to influence
antitrust decisions in their favour.
Hypothesis 4a: A horizontal merger is more likely to face a challenge when local rivals
have worse announcement returns.
Similarly, efficient industry mergers put rivals producing less specialised products in
a worse position due to a substitution effect, while specialised producers are affected
less. Sutton (1991) shows that firms invest in R&D and advertising to attract qualitysensitive consumers, build endogenous entry barriers, and become less vulnerable to
competition. Hoberg and Phillips (2011) provide empirical evidence that firms producing
more specialised products, proxied by increased spending in R&D and advertising, face
reduced competition. These studies suggest a greater vulnerability to a substitution
effect from industry consolidation for less specialised producers. Therefore, we predict
less specialised rivals are more likely to influence antitrust decisions in their favour.
Hypothesis 4b: A horizontal merger is more likely to face a challenge when less
specialised rivals have worse announcement returns.
3. Data
3.1 Selection of announced mergers and challenged cases
3.1.1 Horizontal merger sample selection
We extract all mergers and acquisitions announced between January 1, 1980 and
December 31, 2009 from the Securities Data Corporation (SDC) Mergers and
Acquisitions database. We apply the following screening criteria to form our initial
sample of horizontal mergers. First, the bidder does not own a majority stake in the
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target before the transaction and is seeking to obtain a majority control through the
transaction. Including transactions with a major holding of target shares can bias results
against the consumer protection hypothesis, while including transactions seeking minor
control can bias results against the rival influence hypothesis. 20 Second, both the bidder
and target are publicly listed firms and have data available from the Centre for Research
in Security Prices (CRSP) to calculate abnormal returns surrounding transaction
announcement. Third, the bidder and target have data available from Compustat at both
the firm and segment levels, and they have at least one four-digit segment SIC code in
common. Using four-digit SIC codes to define horizontal mergers is in line with previous
research on horizontal mergers (e.g., Fee and Thomas, 2004; Shahrur, 2005;
Bhattacharyya and Nain, 2011).21 Fourth, the deal value is no less than $10 million (in
1980 dollars). The above criteria are largely consistent with Fee and Thomas (2004).
Fifth, we include only transactions in the manufacturing sector (SIC codes 2000–3999)
as consistent market concentration data over our sample period from the BEA census
are only available for this sector.
There are 393 proposed horizontal mergers during 1980–2009 that meet these
criteria. The three Fama–French industries (Fama and French, 1997) with the most
merger activity are electronic equipment, pharmaceutical products, and medical
equipment, accounting for 51% of the mergers in our sample. The average ratio of
target firm to bidder firm market value of equity is 38%. There are 321 deals (82%) that
were eventually completed.
3.1.2 Challenged case selection
We define a deal as challenged if the DOJ or the FTC alleges it violates antitrust
laws, and documents it in their joint “Annual Report to Congress Pursuant to Subsection
(j) of the Clayton Act Hart-Scott-Rodino Antitrust Improvements Act of 1976”.22 We
manually check the joint reports for fiscal years 1980 (4th report) to 2010 (33th report).23
We include the 2010 annual report because investigation decisions are sometimes
documented in the year following the deal announcement. In these reports, the antitrust
agencies document their antitrust interventions, e.g., issued complaints, the outcome of
antitrust investigation or litigation. Most cases end up with one of the following
outcomes: a) firms abandon the merger following the complaint; b) firms voluntarily
restructure the deal; c) firms enter into a consent decree or order (usually requiring
divestiture, grant patent licensing, or other remedies) to satisfy regulatory demands.
Table 1, panel A reports the distribution of antitrust horizontal merger cases over
the sample period.24 Among the 393 proposed horizontal mergers, the antitrust
agencies challenge 35 (9%). This includes 12 by the DOJ and 23 by the FTC.
Considerable heterogeneity is evident in the frequency by year for challenged deals.
The Clinton administration years 1993–2001 include 54% of the challenged deal
sample.25 In panels B and C, we aggregate challenged deals into four-digit SIC
industries and into broader Fama–French industries. The three most frequently
challenged Fama–French industries are pharmaceutical products, chemicals, and food
products, accounting for 51% of the challenged cases in our sample. Appendix 1 lists
the challenged deals.
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For robustness, we relax the fifth screening criterion to include horizontal mergers in
all industry sectors (except financial or regulated firms: SIC codes 6000–6999, 4000–
4099, 4500–4599, and 4800–4999) during 1980–2009, and derive an expanded sample
of 884 mergers, 704 (80%) of which are eventually completed. From this expanded
sample, we identify 53 challenged deals, 19 by the DOJ and 34 by the FTC.
As explained earlier, we restrict our sample to the manufacturing sector mainly
because of the requirement for consistent market concentration data availability from
the BEA census. Ali, Klasa, and Yeung (2008) show that concentration measures
calculated from Compustat data poorly capture real industry concentration, because
Compustat covers only public firms. BEA-based measures, based on all public and
private firms in an industry, are a better choice. However, consistent BEA-based
concentration measures that cover our entire sample period are only available for the
manufacturing sector. Given the importance of concentration measure quality in testing
the market concentration hurdle hypothesis, we trade-off sample size against the
concentration measure quality, and adopt the BEA-based concentration measures for
reported test results.
3.2 Identification of corporate customers and rivals
3.2.1 Corporate customer identification
To measure the downstream impact of a merger, we need to identify corporate
customers of the merging industry. To account for any contemporaneous crosscorrelation among individual customer returns, we construct a customer portfolio for
each deal. Following Shahrur (2005), in the construction of the customer portfolio, we
consider only single-segment firms covered by CRSP and Compustat. Largely in line
with Shahrur (2005), we restrict our customer analysis to single-segment firms for two
reasons. First, this increases test power since many diversified downstream customer
firms have segments that are unaffected by the merger. Second, this ensures that we
exclude firms with segments operating in the merging industry in the customer portfolio.
According to Song and Walkling (2000), merger announcements release industry
information, which affects any firm that operates in the merger industry. Including firms
that also operate in the merging industry in our customer portfolio would not give a
clean downstream effect of the merger.
Following Shahrur (2005), we use the Use table from the BEA Benchmark Input–
Output (IO) accounts to identify corporate customers. The Use table is a matrix showing
estimates of the dollar value of an upstream industry‟s output used by its downstream
industries as input for any pair of downstream–upstream industries.26 For each
customer–merging industry pair, we calculate a Customer Input Coefficient (CIC), i.e.,
the merging industry‟s output value sold to the customer industry divided by the
customer industry‟s total output value. To account for the low dependence of some
downstream industries on the merging industry, we restrict a corporate customer to be
any single-segment firm that operates in the downstream industries with an industry CIC
no less than 1%.27 The 1% cut-off results in an average of 357 (median of 98) corporate
customers in 393 customer portfolios for the deals in our sample.
We also follow Shahrur (2005) in separating local from distant customers and reliant
from non-reliant customers. Dependence on local supply chains causes local customers
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to be affected more by upstream mergers than distant customers. To account for this
asymmetric effect of mergers on customers, we construct a local customer portfolio that
includes only local firms with an industry CIC exceeding 1%. To define local customers,
we divide the U.S. domestic market into six regions: Northeast, Southeast, Southwest,
Mideast, Midwest, and West, and assign a firm to a production region according to its
headquarter region. Since we do not have information on production location for each
firm, we follow Shahrur (2005) and use the headquarter location.28 A corporate
customer is “local” if the customer‟s headquarter is located in either the target‟s or
bidder‟s headquarter region. This restriction results in an average of 120 (median of 33)
local customers in 374 local customer portfolios for our sample deals. For robustness,
we replace the region-based by a state-based geographic classification, i.e., we define
a customer as local if its headquarters is in either the target or bidder headquarter state,
and find results that are qualitatively similar to those reported.29
Similarly, a greater reliance on input purchases from the merging industry implies a
greater effect from upstream mergers. To account for this effect, we construct a reliantcustomer portfolio for each merger. A corporate customer is “reliant” if it operates in the
downstream industry with the highest CIC. This results in an average of 24 (median of 6)
reliant customers in 284 reliant customer portfolios for our sample. If the antitrust
agencies factor customer wealth effects into their decisions, we expect them to pay
more attention to these two types of customers, who are most likely to be affected by
anti-competitive mergers. By design, tests using announcement returns to local and
reliant customers are biased towards finding a negative relation between customer
returns and the likelihood of challenge.
The SDC and Compustat databases and the Use tables adopt different industry
classification systems, i.e., the SDC and Compustat use four-digit SIC codes, whereas
the Use table use six-digit IO codes. For IO–SIC matching, we adopt different
approaches. For the 1982, 1987, and 1992 Use tables, we largely follow Shahrur (2005)
and use the conversion tables of Fan and Lang (2000) to directly convert IO to SIC
codes. We include an industry only if its SIC code can be unambiguously matched to a
unique IO code. But an IO code may correspond to more than one SIC code. For the
1997 and 2002 Use tables, since there is no direct IO–SIC mapping available, we adopt
the conversion strategy suggested by Bhattacharyya and Nain (2011). First, we use the
IO–North American Industrial Classification System (NAICS) conversion tables provided
by the BEA to convert IO codes to NAICS codes.30 Then we use correspondence tables
provided by the U.S. Census Bureau to convert NAICS to SIC codes. 31 Finally, we
match all 1982, 1987, 1992, 1997, and 2002 Use tables data to merger sample data
using SIC codes provided by the SDC.
Given that product market relations may change over time, we use the 1982, 1987,
1992, 1997 and 2002 Use tables for proposed deals during 1980–1986, 1987–1991,
1992–1996, 1997–2001, and 2002–2009. We find similar results repeating our analysis
using the 1982, 1987, 1992, 1997 and 2002 Use tables for deals occurring during 1980–
1984, 1985–1989, 1990–1994, 1995–1999, and 2000–2009. Our reported results are
based on the first matching set of Use tables since this involves no hindsight bias.
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3.2.2 Industry rival identification
Following Fee and Thomas (2004), we define a firm as a rival if it reports at least
one segment in the year before the merger announcement in the four-digit SIC industry
in which the bidder and target overlap. For each merger, we construct a general rival
portfolio to account for the correlation of abnormal returns among individual rivals. This
approach means the rival portfolio includes both single- and multi-segment competitors,
and represents a more complete picture of the competitive environment, which is central
to our rival influence hypothesis test. Including only single-segment rivals would rule out
many big competitors, leaving mainly small firms in the rival portfolio, which might be
less capable of exerting credible pressure on regulation. On average there are 60 rivals
(median of 42) identified for 393 deals.32
To avoid confounding problems, we construct an alternative rival portfolio including
only single-segment firms for each bidder–target pair (e.g., Shahrur, 2005). On average
this identifies 41 rivals (median of 23) for 385 deals. We find that all results are
qualitatively similar. Therefore we report our empirical results based on the first
approach to constructing rivals portfolios.
Abnormal returns of a general rival portfolio only represent an average price
reaction of rivals to proposed deals. As section 2.4 explains, when a merger
differentially affects competitive positions of industry rivals, their incentives to influence
the antitrust agencies differ. Therefore, to better capture this difference in incentives and
to test the rival influence hypothesis, we further categorize general rivals by their
location and product differentiation. To account for the asymmetric impact of a merger
on geographic markets, we construct a local and a distant rival portfolio for each deal
and focus on local rivals‟ reactions. Consistent with Shahrur (2005), we define a rival as
“local” if its headquarters is located in either the bidder or target headquarter region,
otherwise “distant”. On average, we identify 42 local rivals (median of 17) and 63 distant
rivals (median of 33) for each deal.33 For robustness, we replace the region-based
geographic classification with the state-based one and find results are qualitatively
similar to those based on regional classification.34
To account for the asymmetric substitution effect of a merger on rivals, we construct
portfolios for rivals producing differentiated products and a homogeneous product
separately. Previous literature suggests that firms use R&D and advertising to
differentiate themselves from their competitors, to build entry barriers and to reduce
competition (e.g., Sutton, 1991; Shaked and Sutton, 1987; Hoberg and Phillips, 2011).
Firms invest in R&D to improve product appeal. They rely on advertising to increase
brand recognition and attract customers to their differentiated products (Ivanov, Joseph,
and Wintoki, 2012). Therefore, we follow Valta (2012) and Klasa, Ortiz-Molina, Serfling,
and Srinivasan (2013) and proxy product differentiation by R&D and advertising
intensity, which is calculated as R&D and advertising expenditures divided by total sales
in the year before the deal announcement.35 A rival is a “differentiated product producer”
if its R&D and advertising intensity resides in the top quartile of its four-digit industry,
otherwise it is a “homogeneous product producer”. On average, there are 39 rivals
producing differentiated products (median of 16) and 73 producing homogeneous
products (median of 38) for each deal.
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3.3 Calculating announcement period abnormal returns
We calculate abnormal returns using
̂ ̂
,
where
is the CRSP equally-weighted index return on day t,
is firm i‟s actual
̂
return on day t, and ̂ and are parameters estimated using the market model. We
estimate market model parameters over 250 trading days starting from day −300 before
the announcement date. We require a firm to have at least 100 daily returns available
during the estimation period, otherwise we omit the firm.
To calculate the combined wealth effect of merging firms (Combined CAR), we
value-weight the (−2, 2) day cumulative abnormal returns (CAR) to the bidder and target.
The weights are the relative equity market values of the bidder and target before the
merger, excluding the value of any pre-merger holdings in the target by the bidder (e.g.,
Bradley, Desai, and Kim, 1988; Fee and Thomas, 2004). Following previous literature
(Eckbo, 1983; Song and Walkling, 2000; Fee and Thomas, 2004; Shahrur, 2005) we
estimate the CARs to corporate customers and rivals by forming equally-weighted
portfolios. For robustness, we replace equally-weighted CARs with value-weighted
CARs, and find qualitatively similar results.
3.4 Industry-level variables
3.4.1 Foreign competition
Consistent with Mitchell and Mulherin (1996) and Shahrur (2005), we measure
foreign competition as the merging industry‟s total imports divided by its total domestic
supply (Import Ratio). Raw data for constructing the foreign competition variable are
from the 1982, 1987, 1992, 1997, and 2002 Use tables of the BEA. Following
Streitwieser (2010), we build an import matrix from each Use table and calculate the
domestic supply of each industry, where domestic supply is commodity output minus
imports, exports, change in private inventories, and sales of scrap and used goods.
Following Giroud and Mueller (2010) and Valva (2012) and using industry penetration
(calculated as total value of imports divided by total value of imports plus domestic
production) as an alternative measure of foreign competition leaves our conclusions
about the impact of foreign competition intact.
3.4.2 Market structure
The sales-based concentration ratio HHI is a widely adopted measure of market
structure. As mentioned earlier, Ali, Klasa, and Yeung (2008) find that concentration
measures calculated from Compustat data have only 13% correlations with the
corresponding measures from censuses conducted by the U.S. Census Bureau. This is
because Compustat only covers public firms, whereas the U.S. Census Bureau census
covers public and private firms in an industry. Klasa and Young‟s findings suggest that
using Compustat-based industry concentration measures to conduct product market
research may be inappropriate.36 Therefore, we define the Census Herfindahl Index as
the census concentration estimate (census HHI obtained from the national census at
the four-digit SIC level conducted by the U.S. Census Bureau) divided by 10000; 37
∆Census Herfindahl Index is 2 × target market share × bidder market share in the year
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before the merger announcement, where the bidder and target market shares equal
their Compustat segment sales divided by census-based industry sales.
We define Census Hurdle Dummy according to the criteria outlined in the 1992
DOJ/FTC Horizontal Merger Guidelines. Census Hurdle Dummy equals one if a deal
reaches the following market concentration thresholds: a) Census Herfindahl Index
exceeds 0.18, or b) Census Herfindahl Index is between 0.1 and 0.18 and ΔCensus
Herfindahl Index is no less than 0.01, otherwise zero.
In the 1982, 1987, and 1992 censuses, the U.S. Census Bureau conducted
concentration data surveys only for the manufacturing sector. From 1997 onwards, the
census data cover the business service sector. For consistency, we examine only
manufacturing sector deals during the sample period in this paper.
4. Empirical results
4.1 Univariate analysis of antitrust case selection
Table 2 presents results of a univariate analysis of the factors that may influence
the antitrust agencies‟ challenge decisions. We winsorize all variables at the 1 st and 99th
percentiles to avoid distortion by outliers. Comparing with unchallenged deals,
challenged deals induce higher changes in industry concentration (∆Census Herfindahl
Index), face lower import pressure from foreign competition (Import Ratio), have a
higher deal value relative to bidder size (Relative Deal Size), and are initiated by a
bidder with a larger equity size (Bidder Size). These differences are statistically
significant in the hypothesized direction.
Table 3 reports (−2, 2)-day CARs to merging firms, rivals, and corporate customers.
On average, combined firms have significant positive abnormal returns of 2.02% for the
proposed deal sample. Bidder firms experience significantly negative abnormal returns
of −2.59%, while targets have a positive abnormal return of 23.14%. Examining the
challenged and unchallenged subsamples, these wealth effect patterns hold
qualitatively. For the challenged deal subsample, the positive target five-day abnormal
return (18.40%) is qualitatively similar to the three-day abnormal returns of 10.5% that
Eckbo and Wier (1985) report for their horizontal challenged merger sample during
1963–1981. However, we find bidders experience a negative five-day abnormal return
(−3.85%) in the subsample of challenged deals, whereas Eckbo and Wier (1985) report
a small positive three-day abnormal returns of 0.8% for bidders.
Our general rival sample has a significant five-day CAR of 0.39%, which suggests
that rivals benefit from merger announcements on average. This is consistent with past
literature. Eckbo (1985) finds that rivals earn a significant positive CAR of 0.58% over a
seven-day window. Song and Walkling (2000) report a 0.56% CAR over an eleven-day
window. Fee and Thomas report a 0.24% CAR over a three-day window and Shahrur
(2005) reports a 0.39% CAR over a five-day window. Local rivals and distant rivals
realize 0.55% and 0.38% CARs, while less specialised rivals and specialized rivals
realize 0.35% and 0.44% CARs.38
Separately scrutinising challenged and unchallenged subsamples reveals
interesting wealth effect patterns: for deals that antitrust agencies challenge, most rivals
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experience abnormal returns not different from zero, 39 whereas for unchallenged deals
rivals experience positive wealth effects. While a test for a difference between these two
subsamples lacks power, this pattern is not consistent with the common understanding
that antitrust agencies intervene in monopolistic mergers that benefit industry peers. 40
Our customer sample shows a positive impact of proposed upstream consolidation.
Over a five-day window, the customer CAR is a significant 0.24%, which suggests that
merger announcements are good news on average for corporate customers; upstream
industry consolidation disseminates positive information to downstream industries in
general. Local customers and reliant customers realize insignificant CARs. These
results are in line with previous studies. Shahrur (2005) reports that general customers
in main and dependent downstream industries experience a significant 0.30% and an
insignificant CAR, and local customers in these two downstream industries on average
are unaffected. Fee and Thomas (2004) find an insignificant CAR to corporate
customers in their sample.
Our univariate analysis provides two interesting observations. First, in the
challenged deal subsample, customers on average are not harmed by merger news.
Second, rivals experience positive abnormal returns in unchallenged deals. These
results cast reasonable doubt on the consumer protection hypothesis, and suggest
support for the rival influence hypothesis. However, because challenge decisions may
be affected by various non-mutually exclusive factors, we conduct cross-sectional tests
to disentangle the various factors at play.
4.2 Probit model
We use a Probit model to test the consumer protection, foreign competition, market
concentration hurdle, and rival influence hypotheses. Our model is,
Pr(Antitrust Challenge = 1) = Φ(X′β),
where Antitrust Challenge equals one if the DOJ or the FTC challenges the proposed
deal, X is a vector of explanatory variables, β is a vector of parameters, and Φ is the
cumulative normal distribution function estimated by maximum likelihood. Customer
CAR, Import Ratio, Census Hurdle Dummy, and Local Rival CAR and Homogeneous
Product Provider CAR are the key explanatory variables for the four tested hypotheses.
The HSR Act requires combinations above certain size thresholds to submit
information to the DOJ and the FTC in advance of consummating a deal. For example,
it requires premerger notification if the buyer‟s assets or sales exceed $100 million, and
the target‟s assets or sales exceed $10 million. The information that the agencies collect
from pre-merger notification includes data on SIC-based revenues and internal market
shares.41 These requirements indicate the agencies‟ concerns. To address the sizerelated concern, we control for Relative Deal Size, Bidder Size, and Census Bidder
Market Share for each deal, where Relative Deal Size is the SDC reported deal value
relative to the bidder equity market value,42 Bidder Size is the logarithm of the bidder‟
equity market value, and Census Bidder Market Share is bidder sales in the merging
sector divided by the census-based merging industry‟s total sales. We measure all firmspecific characteristics at the fiscal year-end before the merger announcement. In
addition, as Eckbo, Maksimovic, and Williams (1990) and Aktas, deBodt, and Roll (2004
and 2007) point out, antitrust agencies learn from the stock market reaction to proposed
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deals and see it as an indication of the magnitude of potential wealth creation.
Therefore, we include Combined CAR to control for any efficiency or anticompetitive
effects due to the merger.
Previous studies suggest a role for economic conditions in regulatory enforcement
activity, though they reach mixed conclusions. Amacher, Higgins, Shughart, and
Tollison (1985) examine FTC enforcement of the Robinson-Patman Act and report that
it decreases during business contractions and increases during periods of expansion.
Their findings suggest that the FTC cushions producers against losses during hard
times, and transfers wealth to consumers during good times. In contrast, Ghosal and
Gallo (2001) find that antitrust litigation by the DOJ is countercyclical. They explain that
this is because antitrust violations increase during business contractions as firms face
pressure to maintain profits. To account for any concern over industry cycles in antitrust
enforcement, we control for Industry Growth, calculated as the ratio of merging industry
median firm sales in the year before the deal announcement to industry median firm
sales three years before the deal. We use median values to control for skewness and
the influence of outliers (Loughran and Ritter, 1997). As a robustness check we replace
the ratio of the merging industry median firm sales with the ratio of the merging industry
aggregate sales, without qualitatively affecting our reported results.
Previous studies suggest other factors that shape antitrust enforcement, e.g.,
economic and unemployment cycles (Ghosal and Gallo, 2001), the economist–attorney
ratio in the Antitrust Division (Eisner and Meier, 1990), the budget of the Antitrust
Division (Lewis-Beck, 1979), interaction among the president, Congress, and the courts
(Wood and Anderson, 1993), and presidency administration and regime shift (Ghosal,
2011). We control for year effects to account for these factors. Appendix 2 defines all
variables. Table 4 displays a correlation matrix for the variables we use in our
hypothesis tests. Most correlations are small and do not exceed 0.5, with a few
exceptions: (1) a 0.68 correlation between Customer CAR (local) and Customer CAR
(general), (2) correlation coefficients between Rival CAR (general) and sub-rival
portfolio CARs, and (3) some correlation coefficients between Rival CAR (less
specialised or specialised) and Rival CAR (local or distant). For (1) and (2), we expect
high correlations as these variables are alternative measures of wealth effects to
downstream and industry competitors at merger announcement. For (3), since local vs.
distant rival portfolios and less specialised vs. specialised rival portfolios are formed
separately from two distinct classifications to split all rivals, the cross-classification
correlations do not affect our regression analysis of rival influence based on one
particular rival classification at a time. Overall, we conclude that multicollinearity is
unlikely to be an issue. Table 5 reports the results of testing the consumer protection,
foreign competition, and market concentration hurdle hypotheses. Table 6 reports the
rival influence hypothesis test. For convenience of interpretation, all probit analysis
results are presented as average marginal effects.
4.3 Testing the consumer protection hypothesis
Table 5, regression 1 regresses antitrust case selection on customer CAR (based
on general customer portfolios), import pressure and the market concentration hurdle
dummy, controlling for deal- and industry-specific variables and year effects.43
Regressions 2 and 3 replace the general customer portfolio-based customer CAR with
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local and reliant customer portfolio-based customer CARs. The consistent findings of
models 1–3 suggest that our results are robust.
The consumer protection hypothesis predicts a negative relation between the
likelihood of an antitrust challenge and the customer wealth effect of a horizontal merger.
Surprisingly, we find no evidence of a negative relation across the three regressions. In
regressions with general customer wealth effects we even find a positive point estimate,
though not significant at conventional levels. In regressions with local and reliant
customers, the Customer CAR coefficient remains insignificant. These results suggest
that antitrust cases do not systematically relate to downstream corporate customer
reactions. We reject the consumer protection hypothesis.
4.4 Testing the foreign competition hypothesis
The foreign competition hypothesis predicts a negative impact of import pressure on
antitrust challenge. Table 5 shows a negative impact of import pressure from foreign
competition on the likelihood of a horizontal deal being challenged across the three
regressions. This supports the foreign competition hypothesis. The impact of foreign
competition on antitrust case selection is also economically significant. The average
marginal effect analysis shows that a 10% increase in import pressure from foreign
competition on the merger industry reduces the likelihood of an antitrust challenge by
about 5%. Holding all other independent variables at their mean values, the effect of
changing foreign competition from the 10th percentile to the 90th percentile reduces the
antitrust intervention probability from 16.6% to 0.5%. A one standard deviation increase
in foreign competition leads to a 6% decrease in antitrust intervention probability.
4.5 Tests of the market concentration hurdle hypothesis
The market concentration hurdle hypothesis predicts a positive effect of market
concentration thresholds on antitrust agencies‟ propensity to challenge a horizontal
merger. Table 5 shows a positive coefficient on Census Hurdle Dummy across the three
regressions, indicating that antitrust agencies are more likely to challenge deals that hit
the market concentration thresholds they define. This hurdle effect is economically large.
Regression 1 shows that, on average, hitting the market structure criterion hurdle leads
to a 15% increase in the probability of antitrust intervention. This evidence supports the
market concentration hurdle hypothesis, confirming that the market structure hurdle
remains an indicator of antitrust challenge.
The coefficients on other control variables are largely consistent with the univariate
results. Relative Deal Size and Bidder Size have the predicted effects, though their
marginal effects are smaller in magnitude. Larger deals are more likely to be challenged,
which indicates that the antitrust agencies pay more attention to larger deals and larger
bidders, which they may believe are either more likely to have a greater impact on the
public interest or will attract more public attention. The coefficients on Industry Growth,
Combined CAR and Census Bidder Market Share are insignificant, indicating a limited
impact of these factors on antitrust case selection. The coefficient on Rival CAR is also
insignificant, which suggests a limited impact of industry competitors as a whole, but
does not rule out an influence from certain industry rivals on antitrust case selection. We
explore this in more detail in section 4.6.
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4.6 Tests of the rival influence hypothesis
There are two contrasting effects of a deal on rival shareholder wealth. First, rivals
benefit from an increased product price if there are monopoly rents; second, rivals may
lose market share to the aggressive expansion of merging firms. For rivals facing a
merger-induced competitive disadvantage, our rival influence hypothesis predicts a
negative impact from these rivals on the likelihood of antitrust challenge. For rivals
whose competitive positions are less affected, a positive wealth effect on these rivals is
more likely to indicate expected anticompetitive gains from the merger. The prediction
here is therefore a positive effect of rival abnormal return surrounding the merger
announcement on the likelihood of antitrust challenge.
Table 6, regression 1 regresses antitrust case selection on Local Rival CAR and
Distant Rival CAR, and other determinants and control variables, controlling for year
effects. Regression 2 examines the impact of Homogeneous Product Provider CAR and
Differentiated Product Provider CAR, on antitrust challenge decisions.
Regression 1 gives a negative coefficient on local rivals‟ wealth effect, suggesting
that the agencies are more likely to challenge a deal if it harms the local competitors of
merging firms. A 10% decrease in local rivals‟ wealth at the deal announcement leads to
a 14.5% increase in the probability of antitrust intervention. Holding all other
independent variables at their mean values, the effect of changing the local rivals‟
wealth effect from the 10th to the 90th percentile decreases the antitrust intervention
probability from 9.3% to 1.8%. A one standard deviation increase in the local rivals‟
wealth effect leads to a 3% decrease in antitrust intervention probability. In contrast,
there is a positive relation between the propensity for antitrust intervention and distant
rival wealth effects, consistent with antitrust agencies taking into account less affected
rivals to gauge the market power that a merger may bring about.
Regression 2 shows a negative relation between the wealth effect of less
specialised product producers and the likelihood of antitrust intervention, suggesting
that antitrust agencies are more likely to challenge a deal if it harms less specialised
competitors. Although the coefficient is significant only at 10% in this regression, the
economic significance is large: a 10% decrease in rivals‟ wealth at the deal
announcement leads to a 10.7% increase in the likelihood of a case being challenged.
Holding all other independent variables at their mean values, the effect of changing the
less specialised rivals‟ wealth effect from the 10th to the 90th percentile reduces the
antitrust intervention probability from 7.1% to 2.7%. A one standard deviation increase
in less specialised rivals‟ wealth effect leads to a 3% decrease in antitrust intervention
probability. The specialised producer wealth effect has an insignificant coefficient in this
regression. The weak test power is likely due to the small sample size.
It is difficult to reconcile this pattern with anything other than the rival influence
hypothesis. As discussed earlier, there are various ways that harmed rivals can
influence the antitrust agencies. For example, local rivals can form a local trade
association to exert direct pressure on antitrust agencies, lobby against the deal, or
exert indirect influence through campaign contributions (McChesney, 1997) or quid pro
quo deals (Tahoun, 2011).44 Our evidence is consistent with Bittlingmayer and Hazlett‟s
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(2000) observation on the Microsoft antitrust case that, “The May 1998 suit filed by the
DOJ was accompanied by a suit filed by 20 states. As has been widely observed in the
press, these states appear to have been the subject of intense lobbying pressure from
locally based computer companies”. Our evidence also echoes Posner (1969), who
asserts that the FTC‟s antitrust activities face pressure from Congress, and their
investigations are initiated “at the behest of corporations, trade associations, and trade
unions whose motivation is at best to shift the costs of their private litigation to the
taxpayer and at worst to harass competitors” (Posner 1969, p. 88).
4.7 Robustness issues
To check whether our findings are generalizable to the whole economy, we test the
impact of antitrust intervention determinants using an expanded sample that covers all
industry sectors excluding the financial and regulated sectors. In these robustness tests,
since consistent BEA-based market concentration data are only available for the
manufacturing sector, we use the Compustat-based HHI instead. This analysis shows
that the conclusions of this paper are robust. In particular, we find a negative coefficient
(−1.199) on the less specialised rival wealth effect, significant at 5%. This finding
strengthens our conclusion on the rival influence hypothesis.45
5. Summary and concluding remarks
What are the determinants of antitrust intervention in horizontal mergers? In this
paper, we systematically model the decision process of the antitrust agencies. In
particular, we formulate four hypotheses on the determinants of antitrust case selection
based on economic theories. We test these hypotheses using a sample of U.S.
horizontal mergers in the manufacturing sector between 1980 and 2009.
Consistent with the consumer protection hypothesis, we find antitrust agencies do
not systematically respond to customer wealth effects when selecting antitrust cases.
Antitrust enforcement practice is inconsistent with the stated aim of consumer protection.
Consistent with the foreign competition hypothesis, we find that the likelihood of antitrust
intervention decreases with foreign import competition. The presence of strong imports
increases the supply elasticity of the domestic industry, promoting mergers for efficiency
purposes and making it more difficult to gain market power in an industry. As a
consequence, the antitrust agencies are less likely to challenge when imports are
greater relative to industrial demand. We also find evidence for the market
concentration hurdle hypothesis. In particular, the authorities are more likely to
challenge a merger when the deal is in a highly concentrated industry (HerfindahlHirschman Index above 1800) or the deal is in a moderately concentrated industry and
would increase the Herfindahl-Hirschman Index by more than 100. Consistent with the
rival influence hypothesis, the regulatory authorities are more likely to challenge a deal
when local rivals or specialist rivals have worse announcement returns. This finding
supports the argument of several previous studies that rivals can influence antitrust
decisions through lobbying or other activities (Bittlingmayer and Hazlett, 2000; Duso,
2005; McChesney, 1997; Tahoun, 2011).
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End Notes
1
Wood and Anderson (1993) review U.S. antitrust policy and process. U.S. antitrust laws include the
Sherman Act of 1890, the Clayton Act of 1914, the Federal Trade Commission Act of 1914, the RobinsonPatman Act of 1936, the Celler-Kefauver Act of 1950, the Antitrust Procedures and Penalties Act of 1974,
the Hart-Scott-Rodino Antitrust Improvements Act (The HSR Act) of 1976, and other minor modifications
that strengthen the Clayton Act. Despite minor modifications, the core of U.S. antitrust legislation has
remained since the early 1900s, with Section 7 of the Clayton Act being the principal antitrust law
regulating business combinations.
2
Eckbo (1988) documents that “since 1950, the DOJ and the FTC have filed more than 500 antitrust
complaints against firms involved in mergers, on the grounds that these mergers would violate Section 7 of
the Clayton Act. Approximately 85 percent of the complaints were filed against horizontal combinations,
and most resulted in divestiture or cancellation of the merger”.
3
In the rest of the paper, “antitrust agencies” refers to the DOJ and the FTC, if not otherwise specified.
4
E.g., The 1992 DOJ/FTC Horizontal Merger Guidelines, available at
www.usdoj.gov/atr/public/guidelines/horiz_book/hmg1.html. Except for minor modifications in 1997, the
guidelines remained largely unchanged until a major revision in 2010.
5
A Guide to the Federal Trade Commission, Jan 2002, available on the FTC website, www.ftc.gov.
6
Eckbo and Wier (1985) summarise two ways in which the HSR Act enhanced the antitrust agencies‟
antitrust enforcement capability. First, it empowered the DOJ to issue Civil Investigative Demands to the
merging firms and other parties before issuing complaint. Second, it required premerger notifications to
firms with sales or assets in excess of certain thresholds before they complete the deal.
7
In one exception, Block and Feinstein (1986) find support for the effective deterrence argument. They
examine data on highway construction contracts and the DOJ‟s actions in this industry over 1975−1982,
and find that increases in DOJ‟s intervention against bid-rigging reduce subsequent anticompetitive
behaviour in this industry.
8
Until 1985, Canada has a relatively unconstrained legal environment for mergers.
9
Eckbo (1983) and Stillman (1983) first developed the approach of using stock price reactions to proxy
market expectations about future gains or losses from monopolistic wealth transfers.
10
For the evolution of U.S. horizontal merger guidelines, see Pittman‟s presentation in 2012, available at
http://www.consiliulconcurentei.ro/en/docs/178/7457/mr-russell-pittman_presentation_the-evolution-of-theus-horizontal-merger-guidelines.html.
11
Previous guidelines (Merger Guidelines of 1968) used market shares (i.e., the four-firm concentration
ratio, CR4) to measure market structure. For instance, a market is “highly concentrated” if the shares of the
four largest firms amount to approximately 75% or more. The Merger Guidelines of 1968 are available at
www.justice.gov/atr/hmerger/11247.pdf.
12
The 1992 DOJ/FTC Horizontal Merger Guidelines are available at
www.usdoj.gov/atr/public/guidelines/horiz_book/hmg1.html.
13
The unit of HHI measurement in the official guidelines is consistent with U.S economic census data
surveyed by the Bureau of Economic Analysis (BEA). The census-based HHI is the normal HHI (between 0
and 1) multiplied by 10,000.
14
In the 2010 DOJ/FTC Horizontal Merger Guidelines, antitrust agencies still use HHI to measure market
structure. However, they raise the concentration thresholds for harmful mergers from the 1992 levels to
unconcentrated (HHI less than 1500), moderately concentrated (HHI between 1500 and 2500), and
concentrated (HHI greater than 2500). The 2010 DOJ/FTC Horizontal Merger Guidelines is available at
http://www.justice.gov/atr/public/guidelines/hmg-2010.html. For a comparison between the 1992 and 2010
guidelines, see Weber (2010), available at
http://www.americanbar.org/newsletter/publications/aba_health_esource_home/weber.html.
15
Eckbo (1992) documents that “the evidence systematically rejects the antitrust doctrine even for values
of industry concentration and market shares which, over the past four decades, have been considered
crucial in determining the probability that a horizontal merger will have anticompetitive effects” (Eckbo 1992,
p.1028).
16
These five-steps are outlined in section 1.51, „General Standards‟, of The 1992 DOJ/FTC Horizontal
Merger Guidelines, available at www.usdoj.gov/atr/public/guidelines/horiz_book/hmg1.html.
17
Eckbo (1988) cites Rogowsky‟s 1982 report as evidence that antitrust agencies sometimes intervened in
deals that were below guideline thresholds: 20 percent of challenged mergers fell below the 1968 Merger
Guidelines’ four-firm market concentration ratio thresholds, and one-third of these violated Section 7 of the
Clayton Act.
18
Stigler‟s (1971) work on the economic theory of regulation was further developed by Peltzman (1976,
1989), Posner (1970, 1971, 1974, 1975), and Becker (1983). Stigler (1971) focuses on the role of industry
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firms in capturing regulation, whereas later contributions extend demand side to include other interest
groups, e.g., banks, donors, trade unions, politicians, foreign governments.
19
See Bittlingmayer and Hazlett (2000 p.350) for further details.
20
A bidder with a major holding in a target is more likely to pursue the deal to realize full efficiency. In this
case antitrust agencies are more likely to intervene due to considerations other than consumer protection.
Similarly, a bidder seeking minority control is more likely to be driven by collusion, which benefits existing
rivals. Therefore, rivals are less likely to lobby against the deal.
21
The definition of horizontal mergers based on four-digit segment SICs is similar to Fee and Thomas
(2004). Shahrur (2005) uses historical primary four-digit SICs to define horizontal mergers. Bhattacharyya
and Nain (2011) use primary four-digit SICs to define horizontal mergers. Using their approach to define
horizontal mergers leaves our reported results unchanged.
22
Interaction with the antitrust agencies takes place in several ways. Some deals receive information
requests from the antitrust agencies, and some receive second requests for information, both of which
suggest concern by the agencies. If this concern is resolved in the information analysis stage, or the deals
are not documented as an antitrust violation in the FTC and DOJ‟s joint annual report, we do not count the
deal as challenged.
23
There are 30 reports covering the 31-year period, 1980–2010. The 10th annual report covers 1986–1987.
These reports are available on the FTC website, www.ftc.gov.
24
Our count of challenged horizontal deals gives different totals to those in the DOJ and FTC reports,
mainly for two reasons. First, we use four-digit SIC to gauge product market scope, consistent with Fee and
Thomas (2004) and Shahrur (2005), whereas the antitrust agencies use “relevant market”, the outer
boundary of which is based on cross elasticity of demand. Unfortunately, the components of cross elasticity
of demand, e.g., price and demand quantity, are not publicly available. Second, we focus on deals between
listed firms, whereas the agencies count deals in both the public and private sectors.
25
This is in line with Ghosal (2011) who finds that the Democrats initiated more civil cases than the
Republicans after the antitrust regime shift of U.S. antitrust enforcement in the mid-to-late 1970s, which
largely overlaps with our sample period.
26
The 1982, 1987, 1992, 1997 and 2002 Use tables are available at
http://www.bea.gov/industry/io_benchmark.htm.
27
The 1% threshold follows the literature, e.g., Shahrur (2005), Kale and Shahrur (2007). We repeat our
analysis for 3% and 5% cutoffs and find qualitatively similar results.
28
Another choice is to use a firm‟s registration location. As Shahrur (2005) explains, using the headquarter
location is a better choice because firms may choose their registration location for considerations other
than production reasons such as taxation strategy.
29
The following states are in each of the regions. Northeast: Connecticut, Delaware, District of Colombia,
Massachusetts, Maine, Maryland, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island,
and Vermont. Southeast: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina,
Tennessee, and Virginia. Southwest: Arizona, Arkansas, Louisiana, New Mexico, Oklahoma, and Texas.
Mideast: Illinois, Indiana, Michigan, Ohio, West Virginia, and Wisconsin. Midwest: Iowa, Kansas, Minnesota,
Missouri, Nebraska, North Dakota, and South Dakota. West: Alaska, California, Colorado, Hawaii, Idaho,
Montana, Nevada, Oregon, Utah, Washington, and Wyoming.
30
The IO–NAICS concordance for 1997 is available in Appendix A of “Benchmark Input-Output Accounts of
the United States, 1997”, available at http://www.bea.gov/scb/pdf/2002/12December/1202I-OAccounts2.pdf.
The IO–NAICS concordance for 2002 is available in Appendix A of “U.S. Benchmark Input-Output
Accounts, 2002”, available at http://www.bea.gov/scb/pdf/2007/10%20October/1007_benchmark_io.pdf.
31
The 1997 and 2002 NAICS–SIC concordance tables are available at
http://www.census.gov/eos/www/naics/concordances/concordances.html. For robustness, we include only
industries that have unique IO–NAICS matching codes and unique NAICS–SIC codes in order to retain a
clean matching pattern for the 1997 and 2002 Use tables, and find that this restriction does not qualitatively
change our conclusions. However, this restriction substantially reduces the number of identified 1997 and
2002 upstream–downstream industry pairs. Therefore, we do not apply this restriction.
32
SIC-based rivals do not necessarily overlap with those identified by the antitrust agencies, which is based
on the “relevant market”. As Eckbo and Wier (1985) note, the number of rivals identified by agencies
ranges from 0 to 7. However, previous studies generally record a larger rival portfolio. For example, Song
and Walkling (2000) adopt the Value Line classification to identify rivals and derive an average rival
portfolio size of 15. Fee and Thomas (2004) use four-digit SIC codes and obtain an average rival portfolio
size of 76. Shahrur (2005) also uses four-digit SIC codes and obtains a mean (median) rival portfolio size
of 43 (19). Aktas, deBodt, and Roll (2007) use SIC codes and geography zone classification and get an
average rival portfolio size of 38 for proposed deals with European targets.
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33
If we include only single-segment rival firms, we identify 20 local rivals (median of 8) and 25 distant rivals
(median of 15) for each deal. Our region-based local rival portfolio size is similar to the one reported in
Shahrur (2005). He obtains a mean (median) region-based local rival portfolio size of 19 (7).
34
We identify 26 local rivals (median of 9) and 80 distant rivals (median of 43) for each deal. If we include
only single-segment rival firms, we identify 12 state-based local rivals (median of 5) and 32 distant rivals
(median of 20) for each deal. Again, this state-based local rival portfolio size is fairly close to the one
reported in Shahrur (2005). He reports a mean (median) state-based local rival portfolio size of 13 (4).
35
Following Ivanov, Joseph, and Wintoki (2012), we set advertising expense to zero if it is missing. We
similarly set R&D expenditure to zero if it is missing or not reported.
36
Recent studies of mergers and acquisitions have started to use the U.S. Census Bureau census-based
concentration measures (e.g., Bhattacharyya and Nain, 2011; Ahern, 2012).
37
Since the census is conducted every five years, it is impossible to derive an accurate concentration
measure for each year. We use the 1982, 1987, and 1992 census concentration data, and adjusted 1997
and 2002 census concentration data for deals during 1980–1986, 1987–1991, 1992–1996, and 1997–2001,
and 2002–2009. For the 1997 and 2002 censuses, U.S. Census Bureau data are provided on an NAICS
basis instead of an SIC basis. Since one SIC code may correspond to several NAICS codes and the
matching relation changes over time, we follow Ali, Klasa, and Yeung (2008) and construct our adapted
SIC-based Census Herfindahl Index for 1997 and 2002 by summing the sales-weighted HHI for NAICS
industries from 1997 and 2002 published census data. All census data is available at
http://www.census.gov/econ/concentration.html.
38
We cannot assert that market power exists based on these rival CARs, as they are consistent with either
market power or a valuable information dissemination argument (Eckbo, 1988).
39
The insignificant abnormal return to rivals for challenged deals is consistent with Eckbo and Wier (1985).
They report insignificant three-day abnormal returns to rivals identified by five-digit SIC.
40
The lack of statistical power is probably due to the small sample size of challenged deals.
41
For more details of the HSR filing obligation and process, see Bruner (2004), p.753.
42
We also use an alternative measure of deal size, the logarithm of the SDC reported deal value, and find
qualitatively similar results to those we report.
43
Controlling for year effects reduces the number of observation from 372 to 309. This reduction occurs
because there are no challenged cases in our sample in some years. These year dummies predict failure
perfectly and so we drop these year dummies and the corresponding observations.
44
Quid pro quo deals are non-contractible agreements between two parties to exchange benefits. Tahoun
(2011) finds that US Congress members‟ ownership of firms contributing to their election campaigns is
higher than their ownership of non-contributing firms. Politicians invest more (less) in firms favouring their
party (the opposition). Firms with stronger associations between ownership and contributions receive more
government contracts.
45
These robustness test results are available upon request.
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Table 1
Sample description (manufacturing sector)
Challenged and unchallenged horizontal public mergers in the manufacturing sector during 1980–2009. The sample of
mergers is from the SDC, with data available on CRSP and Compustat. A horizontal merger is between two firms with at
least one four-digit SIC segment overlap. A challenged merger is one challenged by the DOJ or FTC, and documented
in the FTC and DOJ‟s joint “Annual Report to Congress Pursuant to Subsection (j) of Section 7A of the Clayton Act HartScott-Rodino Antitrust Improvements Act of 1976”. Panel A reports the distribution by year and by antitrust agencies,
the DOJ and FTC. Panels B and C report the distribution of challenged mergers by four-digit SIC and Fama–French
industries. Appendix 1 lists the challenged deals.
Panel A: Challenged and unchallenged horizontal merger distribution by year
Year
Announced mergers
Challenged mergers
Unchallenged mergers
DOJ
FTC
Total
Freq.
%
Freq.
Freq.
Freq.
%
Freq.
%
1980
2
0.51
0
0
0
0.00
2
0.56
1981
5
1.27
0
0
0
0.00
5
1.40
1982
7
1.78
0
2
2
5.71
5
1.40
1983
3
0.76
0
1
1
2.86
2
0.56
1984
8
2.04
1
0
1
2.86
7
1.96
1985
10
2.54
0
0
0
0.00
10
2.79
1986
8
2.04
0
0
0
0.00
8
2.23
1987
1
0.25
0
0
0
0.00
1
0.28
1988
5
1.27
0
0
0
0.00
5
1.40
1989
10
2.54
0
0
0
0.00
10
2.79
1990
2
0.51
0
0
0
0.00
2
0.56
1991
6
1.53
0
0
0
0.00
6
1.68
1992
1
0.25
0
0
0
0.00
1
0.28
1993
2
0.51
0
0
0
0.00
2
0.56
1994
13
3.31
0
2
2
5.71
11
3.07
1995
20
5.09
1
1
2
5.71
18
5.03
1996
17
4.33
0
1
1
2.86
16
4.47
1997
25
6.36
0
1
1
2.86
24
6.70
1998
33
8.40
1
1
2
5.71
31
8.66
1999
45
11.45
1
5
6
17.14
39
10.89
2000
29
7.38
3
0
3
8.57
26
7.26
2001
19
4.83
0
2
2
5.71
17
4.75
2002
8
2.04
1
1
2
5.71
6
1.68
2003
20
5.09
1
0
1
2.86
19
5.31
2004
14
3.56
0
1
1
2.86
13
3.63
2005
19
4.83
1
2
3
8.57
16
4.47
2006
15
3.82
1
0
1
2.86
14
3.91
2007
20
5.09
1
0
1
2.86
19
5.31
2008
13
3.31
0
0
0
0.00
13
3.63
2009
13
3.31
0
3
3
8.57
10
2.79
Total
393
100
12
23
35
100
358
100
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Table 1 (continued)
Panel B: Challenged horizontal merger distribution by SIC industry
SIC
SIC Industry
2011
Meat Packing Plants
2026
Fluid Milk
2041
Flour and Other Grain Mill Products
2051
Bread and Other Bakery Products, Except Cookies and Crackers
2621
Paper Mills
2834
Pharmaceutical Preparations
2836
Biological Products, Except Diagnostic Substances
2844
Perfumes, Cosmetics, and Other Toilet Preparations
2851
Paints, Varnishes, Lacquers, Enamels, and Allied Products
2869
Industrial Organic Chemicals, Not Elsewhere Classified
2873
Nitrogenous Fertilizers
2899
Chemicals and Chemical Preparations, Not Elsewhere Classified
2911
Petroleum Refining
3312
Steel Works, Blast Furnaces & Rolling Mills (Coke Ovens)
3357
Drawing and Insulating of Nonferrous Wire
3674
Semiconductors and Related Devices
3728
Aircraft Parts and Auxiliary Equipment, Not Elsewhere Classified
3731
Ship Building and Repairing
3812
Search, Detection, Navigation, Guidance, Aeronautical, and Nautical
Systems and Instruments
3841
Surgical and Medical Instruments and Apparatus
3845
Electromedical and Electrotherapeutic Apparatus
Total
Panel C: Challenged horizontal merger distribution by Fama–French industry
Fama–French Industry
Pharmaceutical Products
Chemicals
Food Products
Electronic Equipment
Medical Equipment
Petroleum and Natural Gas
Shipbuilding, Railroad Equipment
Steel Works Etc.
Aircraft
Consumer Goods
Business Supplies
Total
Freq.
1
1
1
1
1
9
1
1
1
1
1
1
3
1
1
1
1
3
2
%
2.86
2.86
2.86
2.86
2.86
25.71
2.86
2.86
2.86
2.86
2.86
2.86
8.57
2.86
2.86
2.86
2.86
8.57
5.71
2
1
35
5.71
2.86
100
Freq.
10
4
4
3
3
3
3
2
1
1
1
35
%
28.57
11.43
11.43
8.57
8.57
8.57
8.57
5.71
2.86
2.86
2.86
100
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Table 2
Descriptive statistics
Summary statistics for independent variables in a study of horizontal mergers in the manufacturing sector (SIC 2000–
3999) during 1980–2009. All data are from the Bureau of Economic Analysis (BEA) IO accounts, SDC and COMPUSTAT
consolidated and industry-segment tapes. Appendix 2 defines all variables. A t-test (Ranksum test) tests mean (median)
differences between challenged and unchallenged deals. A Pearson
test tests dummy variable frequency differences
between challenged and unchallenged deals.
Variable
Mean
Median
Std. Dev.
Obs.
Census Herfindahl Index
Overall
0.077
0.057
0.053
375
Challenged deals (A)
0.080
0.055
0.054
33
Unchallenged deals (B)
0.077
0.057
0.053
342
p-value (A – B = 0)
0.74
0.58
∆Census Herfindahl Index
Overall
Challenged deals (A)
Unchallenged deals (B)
p-value (A – B = 0)
0.072
0.199
0.060
0.00
0.001
0.044
0.001
0.00
0.260
0.398
0.241
375
33
342
Overall
Challenged deals (A)
Unchallenged deals (B)
p-value (A – B = 0)
0.184
0.125
0.190
0.00
0.177
0.136
0.193
0.00
0.114
0.087
0.114
393
35
358
Industry Growth
Overall
Challenged deals (A)
Unchallenged deals (B)
p-value (A – B = 0)
1.274
1.145
1.287
0.32
1.125
0.883
1.128
0.19
0.805
0.768
0.809
389
35
354
Relative Deal Size
Overall
Challenged deals (A)
Unchallenged deals (B)
p-value (A – B = 0)
0.568
0.739
0.551
0.09
0.396
0.610
0.366
0.01
0.622
0.624
0.620
393
35
358
Census Bidder Market Share
Overall
Challenged deals (A)
Unchallenged deals (B)
p-value (A – B = 0)
0.302
0.391
0.293
0.37
0.061
0.208
0.053
0.00
0.593
0.595
0.592
375
33
342
7.641
8.906
7.517
0.00
7.574
8.996
7.438
0.00
2.153
1.712
2.154
393
35
358
Import Ratio
Bidder Size
Overall
Challenged deals (A)
Unchallenged deals (B)
p-value (A – B = 0)
(%)
Census Hurdle Dummy
Overall
Challenged deals (A)
Unchallenged deals (B)
Pearson
test p-value (A – B = 0)
16.00
27.00
15.00
0.06
Obs (Dummy=1)
60
9
51
30
Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
Table 3
Announcement wealth effects to merging firms, rivals, and customers
Abnormal returns (%) to the combined firms, rivals, and customers in challenged and unchallenged horizontal merger
samples during 1984–2009. Appendix 2 defines all variables. Mean Diff. is the difference in mean abnormal returns
between the challenged and unchallenged deal samples. The t-statistics in parentheses are based on a test for the
equality of means. *, **, and *** denote significance at 10%, 5%, and 1%.
Firm Portfolio
Overall Sample
Challenged Deals
Unchallenged
Mean Diff.
Deals
(t-stat)
N
Mean (%)
N
Mean (%)
N
Mean (%)
(t-stat)
(t-stat)
(t-stat)
Merging Firms
Combined firms
393
2.015***
35
1.580***
358
2.057***
0.477
(4.64)
(1.32)
(4.45)
(0.31)
Bidder
393
−2.589***
35
−3.847***
358
−2.463***
1.383
(−5.67)
(−3.12)
(−5.07)
(0.86)
Target
393
23.144***
35
18.395***
358
23.608***
5.213
(18.08)
(4.80)
(17.45)
(1.16)
Rivals (single- and multi-segment)
General rivals
393
Local rivals
383
Distant rivals
392
Less specialised rivals
392
Specialised rivals
393
Customers
General customers
393
Local customers
374
Reliant customers
284
0.392***
(3.48)
0.551***
(3.44)
0.376***
(3.02)
0.353***
(2.92)
0.443***
(2.65)
35
0.244**
(2.16)
−0.098
(−0.69)
0.210
(0.81)
35
34
34
35
35
33
25
0.289
(0.85)
0.038
(0.09)
0.601
(1.60)
0.089
(0.25)
0.810*
(1.84)
358
0.859**
(2.07)
−0.273
(−0.65)
0.018
(0.02)
358
349
358
357
358
341
259
0.402***
(3.37)
0.601***
(3.51)
0.355***
(2.69)
0.379***
(2.96)
0.407**
(2.28)
0.183
(1.57)
−0.081
(−0.54)
0.251
(0.84)
0.113
(0.28)
0.564
(1.00)
−0.246
(−0.56)
0.291
(0.68)
−0.403
(−0.69)
−0.676*
(−1.71)
0.192
(0.38)
0.233
(0.23)
31
Proceedings of 9th Annual London Business Research Conference
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Table 4
Pearson correlation coefficients
Correlation matrix for all variables in our sample of manufacturing sector horizontal mergers (SIC 2000–3999) during 1980–2009. Appendix 2 gives the definition of all variables.
Variable
Cus.
Cus.
Cus.
Rival
Rival
Rival
Rival
Rival
Comb.
Census
Import
Industry Rel.
Census
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
Hurdle
Ratio
Growth
Deal
Bidder
(Gen.) (Local)
(Rel.)
(Gen.)
(Local)
(Dist.)
(Less
(Spec.)
Dummy
Size
Market
Spec.)
Share
Antitrust
0.089 −0.017
−0.014
−0.014
−0.051
0.028
−0.035
0.035
−0.015
0.096
−0.163
−0.050
0.086
0.047
Challenge
Customer CAR
0.679
0.359
0.219
0.247
0.137
0.205
0.123
0.048
−0.068
0.020
0.029
−0.045
0.107
(General)
Customer CAR
0.331
0.208
0.252
0.158
0.182
0.157
−0.004
−0.055
−0.034
−0.042
−0.046
0.041
(Local)
Customer CAR
0.152
0.244
0.058
0.150
0.066
0.120
−0.014
−0.003
−0.062
0.036
0.030
(Reliant)
Rival CAR
0.742
0.853
0.916
0.647
0.292
−0.031
−0.080
0.045
0.042
0.029
(General)
Rival CAR
0.383
0.684
0.498
0.244
−0.097
−0.145
0.012
0.069
0.004
(Local)
Rival CAR
0.768
0.574
0.216
−0.026
−0.059
0.072
0.035
0.061
(Distant)
Rival CAR
0.303
0.305
−0.017
−0.082
0.056
0.034
0.051
(Less Specialised)
Rival CAR
0.109
−0.072
−0.054
0.003
0.015
−0.024
(Specialised)
Combined CAR
−0.070
−0.167
−0.003
0.179
−0.002
Census Hurdle
Dummy
Import Ratio
Industry Growth
Relative Deal Size
Census Bidder
Market Share
0.278
Bidder
Size
0.184
0.028
0.019
−0.053
0.025
−0.017
0.021
0.056
−0.032
−0.120
−0.005
0.025
0.127
0.083
−0.070
−0.098
−0.044
−0.030
−0.032
0.034
−0.028
−0.209
−0.379
0.471
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Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
Table 5
Probit regressions of antitrust case selection on customer wealth effect, foreign competition, and market
concentration hurdle dummy
The table reports average marginal effects (AME) of Probit regressions of antitrust case selection on customer wealth
effect, foreign competition, and market concentration hurdle dummy. The sample includes only manufacturing horizontal
mergers (SIC 2000–3999) during 1980–2009. The dependent variable, Antitrust Challenge, equals one if the DOJ or
FTS challenges a deal and zero otherwise. Appendix 2 defines all variables. Standard errors are corrected for
heteroskedasticity. Z-statistics are in parentheses. *, **, and *** denote significance at 10%, 5%, and 1%.
Dependent Variable: Antitrust Challenge
Relevant customer definition
General customers
Local customers
Reliant customers
Independent Variable
1
2
3
Customer CAR
0.931*
−0.150
0.230
(1.89)
(−0.39)
(0.70)
Import Ratio
−0.716***
−0.754***
−0.809***
(−4.27)
(−4.33)
(−3.70)
Census Hurdle Dummy
0.154***
0.154***
0.132**
(2.74)
(2.68)
(2.05)
Rival CAR (General)
−0.664
−0.500
−0.327
(−1.03)
(−0.72)
(−0.44)
Industry Growth
−0.016
−0.022
−0.040
(−0.76)
(−0.96)
(−1.37)
Combined CAR
−0.196
−0.174
−0.087
(−1.02)
(−0.89)
(−0.37)
Relative Deal Size
0.069***
0.076***
0.062**
(3.10)
(3.38)
(2.21)
Census Bidder Market Share
−0.047
−0.049
−0.020
(−1.13)
(−1.20)
(−0.54)
Bidder Size
0.037***
0.037***
0.035***
(4.13)
(4.02)
(3.07)
Year Effects
Y
Y
Y
Log pseudo-likelihood
Wald
Pseudo
Observations
−75.58
83.75***
0.28
309
−70.58
70.59***
0.38
291
−51.94
56.99***
0.27
197
33
Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
Table 6
Probit regressions of antitrust case selection on local rival and less specialized rival wealth effects
The table reports average marginal effects (AME) of Probit regressions of antitrust case selection on local vs. distant
rival and less specialised vs. specialised rival wealth effects. The sample includes only manufacturing horizontal
mergers (SIC 2000–3999) during 1980–2009. The dependent variable, Antitrust Challenge, equals one if the DOJ or
FTS challenges a deal and zero otherwise. Appendix 2 defines all variables. Standard errors are corrected for
heteroskedasticity. Z-statistics are in parentheses. *, **, and *** denote significance at 10%, 5%, and 1%.
Independent Variable
Dependent Variable: Antitrust Challenge
1
2
Rival CAR (Local)
−1.446***
(−2.85)
Rival CAR (Distant)
1.360**
(2.20)
Rival CAR (Less Specialised)
−1.065*
(−1.65)
Rival CAR (Specialised)
0.407
(0.92)
Customer CAR (General)
0.777*
0.783
(1.73)
(1.54)
Import Ratio
−0.728***
−0.713***
(−3.97)
(−4.32)
Census Hurdle Dummy
0.133**
0.157**
(2.17)
(2.84)
Industry Growth
−0.019
−0.015
(−0.87)
(−0.72)
Combined CAR
−0.196
−0.129
(−1.11)
(−0.66)
Relative Deal Size
0.076***
0.067***
(3.15)
(3.00)
Census Bidder Market Share
−0.062
−0.051
(−1.53)
(−1.31)
Bidder Size
0.037***
0.038***
(4.09)
(4.31)
Year Effects
Y
Y
Log pseudo-likelihood
Wald
Pseudo
Observations
−70.86
77.50***
0.29
300
−74.82
82.69***
0.29
308
34
Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
Appendix 1: Summary of challenged deal details (manufacturing sector)
Challenged cases used in this study. It lists the bidder name, target name, their overlapping operating segment, the antitrust agency that files the challenge and the complaint
issuance documented in the FTC and DOJ‟s joint “Annual Report to Congress Pursuant to Subsection (j) of Section 7A of the Clayton Act Hart-Scott-Rodino Antitrust
Improvements Act of 1976”.
No.
Announcement
Bidder Name
Target Name
SIC
FF_IND
Agency
Record from Agency Report
Year
1
1982
ConAgra Inc
Peavey Co
2041 FOOD
FTC
FTC Consent agreement accepted July 19, 1982.
Documented in 1982 annual report.
2
1982
Gulf Oil Corp
Cities Service Co
2911 OIL
FTC
Civil Action No. 82-2131 (D.D.C. filed July 29, 1982).
3
1983
LTV Corp
3312
STEEL
DOJ
Cv. No. 85-0884 (D.D.C. filed March 21, 1984).
Texaco Inc
Republic Steel Corp(LTV
Corp)
Getty Oil Co
4
1984
2911
OIL
FTC
1994
IVAX Corp
Zenith Laboratories Inc
2834
DRUGS
FTC
FTC Consent agreement accepted July 10, 1984.
Documented in 1984 annual report.
Docket No. C-3565, issued March 27, 1995.
5
6
1994
Boston Scientific Corp
SciMed Life Systems Inc
3841
MEDEQ
FTC
Docket No. C-3573, issued April 28, 1995.
7
1995
Interstate Bakeries Corp
2051
FOOD
DOJ
Cv. No. 95C4194 (N.D. Ill. filed July 20, 1995).
8
1995
Kimberly-Clark Corp
Ralston-Continental Baking
Company
Scott Paper Co
2621
PAPER
FTC
Cv. No. 3:95CV3055-P (N.D. Tex. Filed December 12,
1995).
9
1996
Lockheed Martin Corp
Loral Corp
3812
CHIPS
FTC
Docket No. C-3685, issued September 19, 1996.
10
1997
Lockheed Martin Corp
Northrop Grumman Corp
3812
CHIPS
DOJ
11
1998
Suiza Foods Corp
Broughton Foods Co
2026
FOOD
DOJ
DOJ complaint issued March 23, 1998. Documented in
1998 annual report.
99-CV-130, DOJ complaint issued March 18, 1999.
12
1998
Medtronic Inc
3845
MEDEQ
FTC
Docket No. C-3879, issued June 3, 1999.
13
1999
Rhone-Poulenc SA
Physio-Control International
Corp
Hoechst AG
2834
DRUGS
FTC
Docket No. C-3919, issued January 28, 2000.
14
1999
Pfizer Inc
Warner-Lambert Co
2834
DRUGS
FTC
Docket No. C-3957, issued July 28, 2000.
15
1999
Dow Chemical Co
Union Carbide Corp
2869
CHEM
FTC
Docket No. C-3999, issued March 16, 2001.
16
1999
Rohm & Haas Co
Morton International Inc
2899
CHEM
FTC
Docket No. C-3883, issued July 13, 1999.
17
1999
General Dynamics Corp
3731
SHIPS
DOJ
Civil No: 1:01CV02200, filed October 23, 2001.
18
1999
Litton Industries Inc
Newport News Shipbuilding
Inc
Newport News Shipbuilding
3731
SHIPS
DOJ
DOJ complaint issued July 9, 1999. Documented in 1999
35
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Inc
annual report.
36
Proceedings of 9th Annual London Business Research Conference
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Appendix 1 (continued)
No.
Announcement
Bidder Name
Year
19
2000
Smithfield Foods Inc
Target Name
SIC
FF_IND
Agency
IBP Inc
2011
FOOD
DOJ
Agency Report
Civil Action No. 1:03CV00434 (HHK), filed 28 February,
2003.
Docket No. C-3995, issued January 26, 2001.
20
2000
Valspar Corp
Lilly Industries Inc
2851
CHEM
FTC
21
2000
JDS Uniphase Corp
3674
CHIPS
DOJ
22
2001
Valero Energy Corp
E-Tek Dynamics Inc(Summit
Partners LP)
Huntway Refining Co
2911
OIL
FTC
C.V. No. C 00 2227 (THE) (N.D. Cal. Filed June 22,
2000).
Docket No. C-4031, issued February 19, 2002.
23
2001
General Dynamics Corp
3731
SHIPS
DOJ
No. 1:01CV02200 (D.D.C. Oct.23, 2001).
24
2002
Pfizer Inc
Newport News Shipbuilding
Inc
Pharmacia Corp
2834
DRUGS
FTC
Docket No. C- 4075, issued May 27, 1999.
25
2002
Northrop Grumman Corp
TRW Inc
3728
AERO
DOJ
No. 1:02CV02432 (D.D.C. filed Dec.11, 2002).
26
2003
Cephalon Inc
CIMA Labs Inc
2834
DRUGS
FTC
Docket No. C-4121, issued September 20, 2004.
27
2004
Genzyme Corp
ILEX Oncology Inc
2836
DRUGS
FTC
Docket No. C-4128, issued December 20, 2004.
28
2005
IVAX Corp
2834
DRUGS
FTC
Docket No. C-4155, issued January 20, 2006.
29
2005
Teva Pharmaceutical
Industries Ltd
Procter & Gamble Co
Gillette Co
2844
HSHLD
FTC
Docket No. C-4151, issued September 29, 2005.
30
2005
Boston Scientific Corp
Guidant Corp
3841
MEDEQ
FTC
Docket No. C-4164, issued July 21, 2006.
31
2006
Watson Pharmaceuticals
Inc
Andrx Corp
2834
DRUGS
FTC
Docket No. C-4172, issued October 31, 2006.
32
2007
CommScope Inc
Andrew Corp
3357
STEEL
DOJ
No. 1:07-CV-02200 (D.D.C. filed Dec.6, 2007).
33
2009
Pfizer Inc
Wyeth
2834
DRUGS
FTC
Docket No. C-4267, proposed order issued Oct.14, 2009.
34
2009
Merck & Co Inc
Schering-Plough Corp
2834
DRUGS
FTC
Docket No. C-4268, proposed order issued Oct.29, 2009.
35
2009
Agrium Inc
CF Industries Holdings Inc
2873
CHEM
FTC
Docket No. C-4277, proposed order issued Dec.23, 2009.
37
Proceedings of 9th Annual London Business Research Conference
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Appendix 2: Variable descriptions
Definitions of variables. Variables are in alphabetical order. All variables are measured at the end of the fiscal
year before the merger announcement, unless noted otherwise.
Variable
Definition
Antitrust Challenge
Equals one if a merger is challenged by the DOJ or FTC, and documented in the
FTC and DOJ‟s joint “Annual Report to Congress Pursuant to Subsection (j) of
Section 7A of the Clayton Act Hart-Scott-Rodino Antitrust Improvements Act of
1976”, zero otherwise. These reports are available on the FTC website,
www.ftc.gov.
Bidder Size
The logarithm of the bidder equity market value in $millions.
Census Bidder Market
Bidder sales in the merging sector divided by census-based total sales of the
Share
merging industry. Census data is available at
http://www.census.gov/econ/concentration.html.
Census Herfindahl Index
The U.S. Census Bureau estimate of the industry Herfindahl index divided by
10000. Census data is available at http://www.census.gov/econ/concentration.html.
∆Census Herfindahl Index
Equals 2 × percentage of bidder sales in the merging sector over the merging
industry‟s census-based total sales × percentage of target sales in the merging
sector over the merging industry‟s census-based total sales. Census data is
available at http://www.census.gov/econ/concentration.html.
Census Hurdle Dummy
Equals one if a) the Census Herfindahl Index exceeds 0.18, or b) the Census
Herfindahl Index is between 0.1 and 0.18, and ∆Census Herfindahl Index is no less
than 0.1, zero otherwise.
Combined CAR
A value-weighted market-adjusted returns in a (−2, 2) day window around the
announcement date of the bidder and target.
Customer CAR (General)
An equally weighted market-adjusted returns over a (−2, 2) day window around the
announcement date of all corporate customer portfolio. A general corporate
customer is defined as any Compustat single-segment firm in an industry whose
production depends on the takeover industry‟s output with the proportion of its
inputs from the merging industry greater than 1%. The input purchase relation from
upstream industries is derived from the 1982, 1987, 1992, 1997, and 2002 Use
tables of the BEA, available at http://www.bea.gov/industry/io_benchmark.htm.
Customer CAR (Local)
An equally weighted market-adjusted returns over a (−2, 2) day window around the
announcement date of local corporate customer portfolio. A customer firm is a local
customer if it is located in the same region as the bidder or target.
Customer CAR (Reliant)
An equally weighted market-adjusted returns over a (−2, 2) day window around the
announcement date of reliant corporate customer portfolio. A customer firm is a
reliant customer if it operates in the downstream industry with most dependence on
merging industry‟s product as input.
Import Ratio
The takeover industry‟s total imports divided by its total domestic supply. Total
domestic supply is commodity output minus imports, exports, change in private
inventories, and sales of scrap and used goods (Streitwieser, 2010). Raw data for
import ratio construction is from the 1982, 1987, 1992, 1997, and 2002 Use tables
of the BEA, available at http://www.bea.gov/industry/io_benchmark.htm.
Industry Growth
The ratio of industry median firm sales in the year before the merger to industry
median firm sales three years before the merger.
Relative Deal Size
The ratio of deal value to the bidder market value of equity.
Rival CAR (General)
An equally weighted market-adjusted returns over a (−2, 2) day window around the
announcement date of general rival portfolio. A general rival is defined as any
Compustat firm operating in the same four-digit SIC as the merging firms‟
overlapping segment in the year before the deal announcement.
Rival CAR (Distant)
An equally weighted market-adjusted returns over a (−2, 2) day window around the
announcement date of distant rival portfolio. A rival firm is a distant rival if it is not
located in the same region as the bidder or target.
Rival CAR (Local)
An equally weighted market-adjusted returns over a (−2, 2) day window around the
announcement date of local rival portfolio. A rival firm is a local rival if it is located in
the same region as the bidder or target.
Rival CAR (Less
An equally weighted market-adjusted returns over a (−2, 2) day window around the
Specialised)
announcement date of less specialised rival portfolio. A rival firm is a less
specialised rival if its R&D and advertisement expenditure in the fiscal year before
th
the deal announcement is below the 75 percentile of the sample.
Rival CAR (Specialised)
An equally weighted market-adjusted returns over a (−2, 2) day window around the
announcement date of specialised rival portfolio. A rival firm is a specialised rival if
its R&D and advertisement expenditure in the fiscal year before the deal
th
announcement is above the 75 percentile of the sample.
38