Stealth Acquisitions and Product Market Competition

Stealth Acquisitions and Product Market Competition

John D. Kepler†

[email protected]

Graduate School of Business

Stanford University

Vic Naiker

[email protected]

University of Melbourne

Christopher R. Stewart

[email protected]

Booth School of Business

University of Chicago

Draft: November 19, 2020

†Corresponding author. We thank Chris Armstrong, Henk Berkman, Ian Gow, Joe Harrington, Bob

Holthausen, Steve Kaplan, Dave Larcker, Mihir Mehta, Mike Minnis, Micah Officer, Eric Posner,

Shawn Shi, Kevin Smith, Dave Tsui, Thomas Wollmann, Luigi Zingales, an M&A antitrust partner

at Skadden, Arps, Slate, Meagher & Flom LLP and Affiliates, several current Department of

Justice and Federal Trade Commission staff members, as well as workshop participants at Boston

College, Deakin University, University of Amsterdam, and the University of Chicago for helpful

comments and suggestions. We thank Janelle Nelson, Nicholas Scott-Hearn, and the Stanford GSB

Data, Analytics, and Research Computing team for outstanding research assistance. We gratefully

acknowledge financial support from the University of Chicago Booth School of Business, the

University of Melbourne, and the Stanford GSB. Researcher(s) own analyses calculated (or

derived) based in part on data from The Nielsen Company (US), LLC and marketing databases

provided through the Nielsen Datasets at the Kilts Center for Marketing Data Center at The

University of Chicago Booth School of Business. The conclusions drawn from the Nielsen data

are those of the researchers and do not reflect the views of Nielsen. Nielsen is not responsible for,

had no role in, and was not involved in analyzing and preparing the results reported herein.

mailto:[email protected]



Stealth Acquisitions and Product Market Competition

Abstract: This study examines the extent to which firms structure their merger and acquisition

(M&A) deals to avoid scrutiny from antitrust regulators in order to better understand how certain

corporate deals alter firms’ competitive landscapes. We find that an abnormal number of M&A

deals are structured to narrowly avoid antitrust scrutiny, and that these “stealth acquisitions” are

driven by acquisitions of private targets that entail contractual terms with lower deal premiums

that facilitate avoidance of antitrust review, payoff functions that allow for more discretion in

assigning deal values, and additional compensation for managers of target firms (e.g., via post-

acquisition employment). Finally, we find several patterns of evidence consistent with stealth

acquisitions reducing product market competition, as the equity values of acquiring firms’

competitors increase following stealth acquisitions, and detailed micro-level product pricing data

reveals increased product prices following a stealth acquisition by rivals. Our results suggest that

firms can successfully manipulate M&A deals to avoid antitrust scrutiny, thereby leading to

anticompetitive behavior.

JEL classification: G34; G38; K21; L12; L41

Keywords: Mergers and Acquisitions; Antitrust; Rivals; Product Market Competition

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1. Introduction

A core mission of regulators in most countries is to prevent anticompetitive practices that

harm consumers. In order to carry out this regulatory objective in the United States, the Department

of Justice (DOJ) and Federal Trade Commission (FTC) conduct extensive reviews to evaluate the

potential impact of corporate mergers and acquisitions (M&A) on firms’ competitive practices.

Concerns about the increasing incidence of anticompetitive M&A deals motivated the adoption of

the Hart-Scott-Rodino (HSR) Antitrust Improvements Act of 1976, which established a premerger

notification program that requires parties to notify the DOJ and FTC of their intent to merge.

However, since these regulatory agencies operate with limited resources, not all prospective M&A

deals are reviewed. In addition, every year the FTC announces an updated—and increasingly

large—deal size threshold that warrants review of a proposed M&A deal and there has been a

significant increase in the deal size threshold that triggers review over time.1 Consequently, in

recent years, the vast majority of M&A deals do not undergo antitrust review (e.g., Wollmann,

2019).2 Against this institutional backdrop, in this paper we study the extent to which firms

proactively engage in “stealth acquisitions”—whereby a merger or acquisition deal value falls just

below the threshold required for premerger review notification—and examine the effects of stealth

acquisitions on outcomes that are indicative of anticompetitive behavior.

While M&A deals are often triggered when separate firms determine that their combined

value is worth more than the sum of their individual values (e.g., to achieve economies of scale or

synergies), a large body of industrial organization theory suggests that M&As also provide a way

for competitors to “soften” product market competition. For example, Stigler (1964) argues that

1 As of 2020, at least forty-one countries, including all ten of the world’s largest economies, have in place some

form of threshold for the purpose of premerger notification (Thomson Reuters Practical Law, 2020). 2 In our full sample from 2001 through 2019, we find that almost two-thirds of M&A deals, collectively representing

$240 billion in aggregate deal value, fall below the applicable filing threshold.

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competitors are often mutually better off by merging their organizations in an effort to

simultaneously reduce monitoring costs and dampen their incentives to defect from a collusive

agreement in order to raise prices (see also, e.g., Green and Porter, 1984; Ivaldi, Julien, Rey,

Seabright, and Tirole, 2003). Thus, in the face of potential scrutiny from competition authorities,

firms can benefit from stealth acquisitions that avoid regulatory oversight and review.

Reflecting concerns that firms’ stealth acquisitions that avoid regulatory review can be

used to facilitate anticompetitive behavior, several recent legal cases highlight some of the

potential anticompetitive issues that can result from non-reportable M&A transactions, which

comprise the vast majority of all M&A deals in recent years.3 For example, in early 2020, the FTC

launched antitrust investigations into all non-reportable acquisitions made since 2010 by four of

the leading U.S. technology firms—namely Amazon, Apple, Facebook, Google, and Microsoft—

due to concerns that many of these firms’ non-reportable acquisitions had anticompetitive

consequences. Moreover, subsequent FTC statements have questioned the efficacy of the HSR

Act’s premerger notification process in technology and other industries. The FTC has also called

for similar inspections across more industries to better understand the competitive effects of non-

reportable mergers (Wilson and Chopra, 2020). These and other regulatory pronouncements

highlight how, despite the economic importance of and regulatory concerns about the use of stealth

acquisitions to reduce product market competition, little is known about the extent to which these

deals are common in practice and their competitive effects.

3 In 2014, DOJ Deputy Attorney General Overton noted that potential harm to consumers cannot be measured by the

size of the transaction or merging parties (DOJ, 2014). She elaborated on how non-reportable transactions could give

rise to antitrust concerns, including harm to consumers in regional markets, adversely affecting the market for a key

input to a downstream product, and reducing competition in a narrow product market that still creates broader or

national issues (e.g., impair the quality of voting equipment systems).

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Using data on all M&A transactions for U.S. firms between 2001 and 2019, we first show

in tests of discontinuity around the FTC’s premerger notification threshold that an abnormal

number of M&A deals are structured so that the deal value falls just below the threshold that would

trigger antitrust review. We also find that this discontinuity shifts each year and closely follows

the increase in the deal value threshold as it changes, strongly suggesting that many of these deals

are deliberately structured to avoid antitrust scrutiny. Moreover, this discontinuity in M&A deal

frequency around the premerger review threshold does not appear when we (i) focus exclusively

on acquisitions announced for deals in industries that are always-exempt from the FTC’s

premerger notification program (i.e., hotels and real estate), or (ii) assign the current year

notification threshold to the previous year. The presence of significant M&A activity just below

the threshold, coupled with the absence of such a discontinuity in these falsification tests, suggests

that a detectable mass of firms actively manage the size of their deals to avoid antitrust review.4

To better understand the characteristics of these stealth acquisitions, we next conduct a

litany of additional tests. First, we find that stealth acquisitions are predominantly driven by public

firms acquiring smaller private firms, and that the contracts that govern these stealth acquisitions

are more likely to incorporate earnouts, which can allow managers of acquiring firms to use

discretion in valuation methods to assign deal values that falls just below the premerger

notification threshold. We also find that acquirers in stealth acquisitions are more likely to (i)

extend the directors and officers insurance coverage of private target firms and (ii) agree to higher

post-acquisition breach-of-terms deductible thresholds, which can allow acquirers to negotiate

4 We conduct several tests to rule our alternative explanations for this discontinuity. For example, in untabulated

analyses, we do not find any significant difference between the propensity to disclose deals in 8-K or other SEC filings

for just-below- and just-above-threshold deals, suggesting that our results are not driven by incentives to evade the

notice of competitors. Moreover, as discussed later in the paper, our results are unlikely to be explained by compliance-

cost avoidance.

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lower deal values below the notification threshold to offset the additional costs stemming from

these contracting features. Consistent with these lower deal values being actively managed to avoid

antitrust review, we find that public acquirers pay lower deal premiums for private targets in

acquisitions that occur just below the threshold. Finally, to provide additional evidence on why

managers of target firms are willing to accept deal values that are manipulated to fall just below

the notification threshold, we show that such deals are more likely to involve less-risky payoffs

that involve all cash rather than equity payments and employ target CEOs following the

acquisition, resulting in an increased likelihood of earnouts paying off. These findings suggest that

acquirers compensate target firms and their managers for any lower deal values with lower-risk

payoffs and both implicit and explicit compensation to ensure that the deals fall below the review

threshold.

Next, we explore the economic mechanisms that drive the discontinuity in deal values

around the premerger notification threshold. We expect that the firms that complete deals that fall

just below the premerger review threshold are more likely to have incentives to coordinate with

the targets they acquire. Consistent with this expectation, we find that the discontinuity is largely

attributable to acquirers with the strongest incentives to coordinate with their targets—i.e., rivals

from the same industry. We also find that this relation is even more pronounced for deals that

involve firms in the same geographic area and deals in more concentrated industries. Collectively,

these findings suggest that deals that are more likely to have anticompetitive effects are more likely

to be structured as stealth acquisitions that narrowly avoid antitrust review.

Finally, we conduct two sets of tests to examine whether stealth acquisitions are followed

by patterns in the acquiring firms’ product markets that are consistent with reduced product market

competition. First, prior studies argue that certain patterns in the stock market returns of industry

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rivals around a merger can be symptomatic of reduced product market competition. This approach

is based on the intuition that mergers that only provide synergistic benefits to the acquirer should

not propagate to rival firms, but that any benefits from an expected increase in product market

prices should (e.g., Eckbo, 1983; Stillman, 1983). Consistent with the anticompetitive nature of

stealth acquisitions, we find that their public announcements are associated with higher abnormal

returns for rival firms when such acquisitions are horizontal in nature—i.e., between direct

competitors operating in the same industry.

Second, as a more direct test of the impact of stealth acquisitions on product market

competition, we narrow our focus to a stealth acquisition by a horizontal rival in the retail industry

during our sample period, and obtain detailed product level prices for all rivals that share common

products with those of the target involved in the stealth acquisition. This approach is based on the

intuition that product pricing patterns of industry rivals following industry events that reduce

product market competition can be symptomatic of such behavior (e.g., Azar, Schmalz, and Tecu,

2018). Further consistent with the collusive nature of stealth acquisitions, we find a pronounced

spike in average monthly product prices for product market rivals’ common products following

our stealth acquisition of interest.

Our study makes three distinct contributions to the literature that studies the role of M&As

in product market competition. First, our study provides novel insight into a specific economic

mechanism through which M&As can affect product market competition (e.g., Eckbo, 1983, 1992;

Sheen, 2014; Wollmann, 2020). Prior literature finds that horizontal mergers have anticompetitive

effects but does not explain how these deals are able to occur and not run afoul of regulators, or

how these deals tend to be structured (i.e., the specific contractual provisions). For instance, while

Wollmann (2020) finds that M&A deals that are not subject to premerger review in the kidney

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dialysis industry have anticompetitive effects, he does not study whether firms can manipulate the

size of their M&A deals to avoid premerger review in the first place. Relatedly, Cunningham,

Ederer, and Ma (2020) shows that incumbent pharmaceutical firms acquire innovative targets

solely to “kill” their innovative projects, and that some of these deals fall just below the threshold

for premerger review, but they do not examine how these deals are structured to avoid antitrust

scrutiny. In these regards, our study provides novel insights into the motivation behind M&A deals

that reduce product market competition, and is the first to documents the proactive use of stealth

acquisitions to do so.

Second, our study is the first to provide evidence consistent with firms strategically

manipulating the size of their M&A deals to avoid regulatory scrutiny, and that the resulting stealth

acquisitions can have deleterious effects on consumers via reduced product market competition.

Thus, we show how firms organize their economic activity to avoid antitrust scrutiny by exploiting

regulators’ resource constraints at the expense of consumers. While threshold analyses have been

used in other literatures to provide evidence of strategic or manipulative behavior (e.g., Burgstahler

and Dichev, 1997; Peasnell, Pope, and Young, 2005; Garmaise, 2015), ours is the first to apply

this technique to an M&A setting to document a discontinuity around the threshold for premerger

review notification, providing evidence of deal manipulation to avoid antitrust scrutiny. Given that

nearly all countries have regulatory thresholds that trigger premerger notification, our findings

have broader implications for antitrust enforcement around the globe.

Finally, our study contributes to the broader corporate finance and industrial organization

literatures that study the evolution and regulation of industry competition (e.g., Shahrur, 2005;

Azar, Schmalz, and Tecu, 2018; Wollmann, 2019; De Loecker, Eeckhout, and Unger, 2020). Given

competition regulators’ practice of evidence-based policymaking, our results suggest that a more

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subjective and contextual approach to allocate scarce resources to monitor the antitrust

implications of corporate M&A deals may be more effective than the current, objective approach

that is subject to manipulation. Our evidence also suggests that both academics’ and regulators’

concerns that sharp premerger review guidelines may be of limited efficacy are warranted (e.g.,

Rose and Sallet, 2020), as a detectable mass of firms appear to strategically manipulate the size of

their deals to circumvent regulatory review. Moreover, while our focus on M&A deals around this

particular notification threshold is motivated by a clean identification setting (e.g., via the

observable deal size threshold that triggers premerger review), we believe that our findings extend

to M&A activity around the other premerger filing thresholds and regulations implemented by

antitrust authorities, and thus our results likely underestimate the frequency and impact of M&A

antitrust avoidance.

The remainder of this paper proceeds as follows. Section 2 discusses institutional features

of antitrust regulation for M&A deals and related academic literature. Section 3 describes our

sample and key variables. Section 4 describes our research design and presents results on the

existence of and contracting for stealth acquisitions. Section 5 describes our research design and

presents results on the effects of stealth acquisition on product market competition. Section 6

provides concluding remarks.

2. Institutional Background and Related Literature

2.1. Antitrust Regulation and M&A

Competition law in the United States places strict limits on the ability of M&A deals to

impact industry competition. For instance, Section 7 of the Clayton Act prohibits M&As “in any

line of commerce or in any activity affecting commerce in any section of the country, [where] the

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effect of such acquisition may be substantially to lessen competition or tend to create a monopoly.”

Moreover, Section 5 of the FTC Act prohibits “unfair” methods of competition. To enforce these

regulatory objectives, the antitrust division of the DOJ and the FTC rely on the Hart-Scott-Rodino

(HSR) Antitrust Improvements Act of 1976 to review potential anticompetitive effects of M&A

deals before they take place. The filing of the premerger notification report allows regulators up

to 30 days to perform a review of whether the proposed transaction will adversely affect U.S.

commerce under antitrust laws.5 Figure 1 illustrates the FTC’s typical premerger review

notification process and potential outcomes of this process on an M&A deal’s ability to proceed.

While the HSR act initially required notification filings on all transactions that exceeded a

threshold of $15 million, this size-of-transaction threshold was significantly amended by Congress

in 2000 to apply only to transactions with a deal value above $50 million.6 The rationale for

exempting deals with a value below $50 million was that such transactions were unlikely to raise

substantive antitrust concerns. Hence, regulators deem that requiring notifications in such smaller

deals may impose an unnecessary burden on firms and/or weaken regulators’ monitoring capacity,

which would precipitate costs that exceed the social welfare or efficiency benefits from identifying

any competitive issues from small deals.7

The adjustment to the size of transaction threshold had a dramatic effect on premerger

notifications. Notably, while the number of annual notifications increased by around 33% in the

5 The 30-day period is expected to allow regulatory agencies to request additional information, extend the waiting

period by another 30 days, and determine whether it will file a challenge based on antitrust regulations to block a deal.

Notwithstanding a request for additional information by the regulators, the parties must wait 30 days after filing (15

days in the case of a cash tender offer) or until the agencies grant early termination of the waiting period before they

can close the transaction. 6 As its name implies, the size-of-transaction refers to the value of the assets, voting securities and non-corporate

interests that are being acquired. Beginning after September 30, 2004, the size-of-transaction filing threshold has been

further adjusted every year based on the change in gross national product and applies to deals valued at $94 million

or more effective as of January 21, 2020. 7 Fines for failing to file a transaction that meets the requirements for premerger notification are $41,484 per day fine

as of January 23, 2018, which is typically enforced.

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three-year period leading up to the 2000 size-of-transaction threshold amendment, notifications

fell by 79% in the three years immediately following the amendment (DOJ and FTC, 2004). Recent

evidence suggests that the increase in the size of transaction threshold has escalated horizontal

mergers between firms in the same industry. For instance, Wollmann (2019) estimates that the

decade following the threshold increase witnessed up to 324 additional horizontal mergers per year

that collectively involved the acquisition of targets worth $53 billion in annual revenue. Further,

while the general authority granted to FTC and DOJ under Sections 6 (b), 9 and 20 of the FTC Act

and Section 1312 of the United States Code on Commerce and Trade permits them to

retrospectively investigate non-reportable transactions, Wollmann (2019) shows that mergers that

are newly-exempted from the premerger notification requirement are less likely to be subject to

regulatory investigation after the M&A deal is executed. Collectively, these observations give rise

to concerns that smaller non-reportable M&A deals can also raise competitive issues that violate

antitrust statutes.

Concerns over antitrust risk from small deals are further supported by anecdotal evidence

of higher financial gains that acquirers realize from such deals. For example, a study conducted by

McKinsey & Company revealed that firms that adopted a systematic approach to M&As through

the use of an increased number of small deals were able to accrue more market capitalization

relative to peers that focussed on larger deals and selective acquisitions (Rudnicki, Siegel, and

West, 2019). These concerns have led antitrust regulators to question the potential anticompetitive

effects arising from non-reportable M&A transactions. For instance, in a 2014 speech, DOJ Deputy

Assistant Attorney General Overton noted that potential harm to consumers is unlikely to be

captured by the size of the transaction or merging party market values (DOJ, 2014), and elaborated

on how non-reportable transactions could give rise to antitrust concerns, including harm to

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consumers in regional markets, adversely affecting the market for a key input to a downstream

product, and reducing competition in a narrow product market that still creates broader or national

issues (e.g., impair the quality of voting equipment systems).

Consistent with these regulatory concerns, antitrust challenges against non-reportable

transactions have increased drastically (e.g., Mason and Johnson, 2016).8 Moreover, in February

2020, the FTC issued an order under Section 6(b) of the FTC Act to formally launch its own

antitrust investigations into every non-reportable acquisition made by tech giants including

Google, Amazon, Apple, Microsoft, and Facebook dating as far back as 2010. 9 The FTC said its

probe would help it understand “whether large tech companies are making potentially

anticompetitive acquisitions of nascent or potential competitors that fall below HSR filing

thresholds” and reform its policies to promote competition and protect consumers. Subsequent

statements released by FTC commissioners Rohit Chopra and Christine Wilson questioned the

sufficiency of the HSR notification process in other industries and urged the need for studies across

a broader range of industries to gain a better understanding of the competitive effects of non-

reportable mergers (Wilson and Chopra, 2020).

2.2. Related Literature

Although it is illegal for firms to engage in business practices that harm competition under

Section 7 of the Clayton Act, a large body of industrial organization research studies firms’

incentives to engage in anticompetitive behavior (e.g., Stigler, 1964; Green and Porter, 1984;

Ivaldi, Julien, Rey, Seabright, and Tirole, 2003; Levenstein and Suslow, 2006; Harrington and

8 Heltzer and Peterson (2018) point out that antitrust challenges of non-reportable transactions more than doubled in

the year following the 2016 United States Presidential Election under the Trump administration, compared to the

annual rate of such challenges during the entire second term of the Obama administration. 9 For example, Facebook made more than 80 acquisitions in its time, of which dozens involved small deals that were

not reportable under the HSR Act (Cox, 2020). Taken together, however, these smaller deals can add up to a

formidable competitive advantage.

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Skrzypacz, 2011). Within this literature, several studies examine how lax M&A antitrust

enforcement in the pharmaceutical industry leads to an increase in anticompetitive mergers and

worse product market outcomes for consumers in terms of higher product prices (e.g., Eliason,

Heebish, McDevitt, and Roberts, 2019; Wollmann, 2019). Furthermore, Cunningham, Erderer, and

Ma (2020) provide evidence that this result is partially because large acquirers amass firms with

similar research projects and terminate overlapping innovation—i.e., risky pharmaceutical drug

projects—of targets. These studies examine whether certain M&A deals have anticompetitive

effects, but do not examine the specific source of how firms achieve such anticompetitive M&A

deals in the first place.

Building off of this literature, a burgeoning corporate finance literature explores the

broader relation between anticompetitive behavior and corporate finance practices. For example,

Eckbo (1992) and Shahrur (2005) find that horizontal mergers tend to be bad for consumers and

that rents accrue to all firms in an industry following horizontal M&A deals, consistent with recent

evidence on firms’ efforts to more easily navigate the FTC’s antitrust review process (e.g., Mehta,

Srinivasan, and Zhao, 2020). More recently, Dasgupta and Žaldokas (2019) find that increases in

the cost of explicit collusion leads to more M&A activity and equity issuances, and Azar, Schmalz,

and Tecu (2018) provide evidence from the U.S. airline industry that common ownership structure

can lead to anticompetitive pricing strategies.

Our study contributes to these growing corporate finance and economics literatures by

identifying (i) the extent to which firms manipulate the size of their M&A deals in order to avoid

antitrust scrutiny as a novel channel via which firms avoid regulatory scrutiny, (ii) the contracting

characteristics of these stealth acquisition deals that facilitate this regulatory avoidance, (iii)

heterogeneous industry and market conditions that incentivize firms to participate in stealth

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acquisitions, and (iv) the impact of these stealth acquisitions on competition among firms’ product

market rivals. In these regards, our study is the first to examine firms’ avoidance of antitrust

regulation by manipulating the size of their deals, and offers novel evidence that such avoidance

is detrimental to consumers.

3. Data and Descriptive Statistics

3.1. Data Sources and Key Variable Measurement

Our initial sample is drawn from all completed and terminated U.S. M&As involving

public and private targets and acquirers announced from January 2001 through February 2020 on

the Thomson Securities Data Company (SDC) Mergers and Acquisitions database. Following prior

literature (e.g., Moeller, Schlingemann, and Stulz, 2005), we exclude deals below $1 million. We

also discard all deals involving targets that are financial firms (SICs 6000 to 6999) or regulated

utilities (SICs 4900 to 4999), as M&As of these types are subject to industry-specific merger

regulation that is unrelated to our analysis. Finally, we exclude deals involving the acquisition of

hotels and motels (SIC 7011), as these acquisitions are always exempt from premerger review.

This selection process yields a final sample of 19,886 deals with non-missing acquirer and target

firm data for the key variables used in our analyses.10

We use our sample of the universe of all deals to test for a discontinuity in M&As around

the premerger review threshold. We also examine the contracting terms, incentives, and product-

level prices for deals that fall just below the premerger notification threshold. While we rely on

CRSP and SDC for data to construct many of our key variables, data to capture other deal

10 While prior literature typically focuses exclusively on deals that involve public targets, we also examine ones with

privately-held targets, given regulators’ recent heightened concerns over smaller deals (mostly involving private

firms), in addition to practitioner evidence of acquirers accruing significant value from smaller deals (e.g., Rudnicki,

Siegel, and West, 2019).

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provisions important to our study (e.g., earnouts, deal premiums for private targets, provisions for

extended D&O coverage and post-closing deductible thresholds) are collected by reading through

merger-related contract disclosures found in EDGAR 8-K, 10-Q, and 10-K public filings.11 For

these tests that require extensive hand collection, we restrict our test sample to comprise deals that

fall just below and above the premerger notification thresholds. Our data collection process is

detailed in the Internet Appendix.

3.2. Descriptive Statistics

Panel A of Table 1 shows that the top ten industries represented in our full sample account

for almost 70% of M&A deals, with the largest number of deals (around 30% of all deals)

completed by acquirers in the business services industry. Panel B of Table 1 presents the

comparable distributions for the top ten industries in two subsamples representing deals with

transaction values that are within 10% above or 10% below the annual FTC notification threshold,

respectively. We find that the top ten industries in these two subsamples are made up of the same

industries in Panel A, with the exception of the personal services industry and the construction

industry, indicating that the mix of deals that occur near the premerger notification threshold is

similar to the mix across all M&A deals. Further, the two subsamples are similar to each other and

the full sample on the distribution of observations across the industries (e.g., business services

accounts for 33% to 34% of observations). The absence of any industry being overrepresented in

the subsample of deals that are 10% below the threshold suggests that the scrutiny of deals by

DOJ/FTC does not appear to vary significantly across any industries in a manner that produces a

greater concentration of below threshold deals in certain industries. The fact that the behavior we

11 In mergers that involve a foreign-domiciled public acquirer without filings on EDGAR, we read through and obtain

data from the acquirers’ annual reports, which we locate on their corporate websites.

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seek to understand is unlikely to be idiosyncratic to a particular industry enhances the

generalizability of our findings to the broader population of M&A deals.

Panel A of Table 2 presents the descriptive statistics on the variables used in our main

empirical analyses. We find that the average deal value of acquisitions in our full sample is around

$400 million (DealValue) and that roughly 77% of deals involve a private target (PrivateTarget).

Panel B of Table 2 illustrates how the acquirer, target, and deal attributes vary across the

two restricted subsamples with deals within 10% above or 10% below the annual FTC notification

threshold (hereafter, just-above-threshold and just-below-threshold deals, respectively). While the

statistical difference between the values of deals (DealValue) involving just-above- and just-

below-threshold targets is expected, the mean difference amounts to only $7.31 million, which is

small relative to the general variance of deal values in our full sample (standard deviation = $2.6

billion). This implies that deals just above and just below the threshold are in essence

fundamentally similar, as suggested by the insignificant differences in the values of nearly all of

the other variables across the two subsamples of firms. However, we do find some evidence on

greater use of earnouts (Earnouts) and cash and other non-stock payments (AllCashandStock) as

well as lower use of all-stock financing (AllStock) and lower acquirer termination fees

(AcqTermFeePercent) in just-below-threshold deals. Moreover, acquirers in deals that are just-

below the threshold are more likely to have a future economic tie with a target CEO

(EconomicTie).

4. Contracting for Stealth Acquisitions

This section examines three features of stealth acquisitions. First, using statistical

techniques that are commonly applied in the literature to show evidence of manipulation around

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thresholds, we examine the existence and prominence of stealth acquisitions by assessing the

frequency of deals occurring just below relative to just above the premerger review notification

threshold. The advantage of this approach is that it focuses on a narrow subset of deals in close

proximity to the threshold, in which merger activity and attributes of the parties involved should

be similar. Second, we examine differences in deal and contract characteristics for M&A deals

occurring just below relative to just above the premerger review notification threshold. These tests

allow us to identify the types of deals most often falling just below as well as the explicit and

implicit contracting mechanisms that facilitate acquirers’ ability to manipulate deal values to

below-notification levels. Finally, we investigate whether stealth acquisitions are more likely to

consist of deals that can lead to anticompetitive outcomes. Collectively, these analyses allow us to

understand the incentives that drive firms to intentionally structure deals to avoid antitrust scrutiny.

4.1. Existence of Stealth Acquisitions

We first examine whether firms seeking to avoid antitrust review structure contracts such

that deal sizes cluster just below the premerger review dollar-based threshold, generating a

discontinuity in the number of M&As occurring in close proximity to the threshold. While

circumventing the review process significantly reduces potential regulatory costs, such as forced

asset divestitures or even blocking of the merger, anticompetitive behavior following the

acquisitions of existing or nascent competitors can still attract complaints from market participants

such as customers and competitors in the aftermath of acquisitions. This can prompt regulators to

conduct post-acquisition reviews and issue enforcement actions aimed at deals that were not

subject to premerger notification—even years after deals are completed. The significance of this

threat is underlined by the fact that remedies sought by antitrust regulators in such cases can be

harsher than in deals with premerger notification (Heltzer and Peterson, 2018). This is because the

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unwinding of transactions to restore competition to premerger levels can require the closure of

business units, divesture of acquired assets at fire-sale prices, and other costly interventions.12 Such

potential costs can disincentivize acquirers and targets from colluding to manipulate transaction

sizes to avoid notification. As such, it is not clear whether and to what extent a discontinuity exists

in the number of M&As surrounding the notification thresholds.

4.1.1. Research design

To first document evidence of the existence of stealth acquisitions—i.e., a discontinuity in

the number of M&A deals around the premerger notification threshold—we take advantage of two

notable features of the HSR Act: (i) annual adjustments to the dollar-based threshold for requiring

premerger notifications, and (ii) the pegging of these adjustments to the U.S. gross national income

growth rate. In combination, these policies result in a time-varying threshold that grows (or

shrinks) by unequal dollar amounts annually, which we exploit to examine near-threshold deal size

activity.

We begin this process by calculating the distance, in dollars, that each deal’s value is from

the threshold in a given year. We define this measure as:

Distance-from-Thresholdi,t = DealValuei,t – Thresholdt, (1)

where i represents a unique deal and t represents time.13 Our distance measure acts to standardize

the time-varying threshold, allowing us to plot our data around a single threshold centered on zero.

12 For example, in 2017 the FTC challenged the acquisition of Synacthen Depot by Mallinckrodt subsidiary Questcor

Pharmaceuticals, Inc. that was not subject to the premerger notification requirement. The allegations were settled

through Mallinckrodt agreeing to disgorge $100 million in obtained profits as well as divesting part of the acquired

assets. 13 For example, a deal of size $85 ($95 million) in 2018 (2019) when the premerger notification threshold was $84.4

million ($90 million) would be assigned a Distance-from-Threshold value of $85 – $84.4 = $0.6 million ($95 – $90 =

$5 million).

17

In our first set of tests, we use these distances and employ McCrary’s (2008) test for a

discontinuity at the threshold (e.g., Jäger, Shoeder, and Heining, 2020). The null hypothesis of the

McCrary test in our setting is that the discontinuity around the premerger notification threshold is

zero. In other words, absent a manipulation of deal sizes, a significant difference in the number of

deals occurring just below relative to just above the threshold should be unobservable. In a first

stage, McCrary’s test obtains a finely graded histogram and then smooths the histogram on either

side of the threshold using local linear regression techniques. In a second stage, the McCrary test

evaluates the difference in the density heights just below and just above the threshold. A finding

of a significant difference in these heights would be indicative of a discontinuity.

4.1.2. Results

Figure 2 presents the graph of the McCrary (2008) test of continuity in the density function

around the premerger review threshold. The solid lines, which depict the density function around

the review threshold along with the 95% confidence interval (i.e., dotted lines), provide visual

evidence of a discontinuity; and a Wald test, reported in Panel A of Table 3, confirms this by

rejecting the null of continuity of the density function at the threshold (p-value < 0.001). In

combination, these results provide compelling evidence that is consistent with the manipulation of

deals by acquirers and targets to avoid premerger antitrust reviews. Nonetheless, we conduct

several additional tests to further support this inference.

First, given that the McCrary (2008) method automatically selects optimal bin widths, we

construct our own histogram—based on bin widths of $2.5 million around the premerger review

threshold—and compare the frequency of deals. The histogram, presented in Figure 3, shows that

deal frequencies generally increase as deal values decrease. However, upon closer inspection, we

find a noticeable jump in the number of deals occurring in the bin to the immediate left of the

18

threshold as compared to the bin to the immediate right.14 To test whether there is a significant

difference in these bin heights relative to what we would expect, we employ a commonly-used

statistical approach for testing for discontinuities. This approach entails comparing the actual

frequency of deals in each bin with the expected deal frequency, computed as the mean of deal

frequencies in the two adjacent bins. The results, reported in Panel B of Table 3, show that the

actual number of deals in the left (right) bin is around 45% (30%) higher (lower) than the expected

number (p-value < 0.01).15 Our findings from these additional tests reinforce our inference about

the manipulation of deals to circumvent premerger notification obligations.

Second, we conduct a falsification test to help alleviate the concern that other salient

features of deals explain the phenomenon we observe around the threshold. Specifically, we

exploit a sample of real estate and hotel deals that are, by law, always exempt from premerger

review and repeat the McCrary analysis. If our earlier findings are not driven by incentives to avoid

premerger review then we’d expect to find a similar discontinuity around the threshold for these

always-exempt deals. However, the McCrary graph (presented in Figure 4) reveals no detectable

discontinuity around the threshold (p-value of 0.445 reported in Panel A of Table 3).16 This result

14 An alternative explanation of these findings is that fewer deals are initiated above the notification threshold because

of concerns that such deals may not pass antitrust scrutiny. However, this explanation cannot explain our finding of a

significant increase in deals just-below the notification threshold, which is more consistent with our manipulation of

deals explanation. Our evidence is analogous to that in a large class of corporate performance manipulation studies

that rely on measures of abnormal accruals as symptoms of—and to draw inferences about—managers’ inherently

unobservable earnings management decisions (e.g., Graham, Harvey, Rajgopal, 2005). 15 Our inferences from this method are unaffected when we draw our comparisons based on bin widths of $5 million

around the premerger review threshold. 16 We conduct another McCrary test where we first assign the threshold from year t+1 to year t. To the extent that the

discontinuity we observe in Figure 2 is a natural phenomenon, we should observe a similar discontinuity after the

reassignment of the thresholds. However, our results from this extension, presented in Figure IA.1 in the Internet

Appendix, reveal no such discontinuity around the threshold.

19

further corroborates our assertion that the manipulation of deals to avoid premerger reviews likely

explains the discontinuity of deals documented in our earlier analyses.17

4.2. Contracting for Stealth Acquisitions

Having provided evidence consistent with firms engaging in acquisitions to avoid

premerger reviews, we next turn our attention to examining whether deals falling just below the

threshold have contract characteristics that differ systematically from (1) all other deals, and from

(2) deals just above the threshold, in subsequent near-threshold tests.

Motivated by recent regulatory inquiries into non-reportable deals completed by large

public acquirers, we first focus on examining the types of firms participating in below-threshold

deals. We follow this with an examination of a set of deal terms that legal practitioners suggest

could be used to placate or incentivize target managers to structure deals that avoid premerger

reviews. Specifically, we look at (1) the form of payment (i.e., cash vs. stock), (2) the level of deal

premiums, (3) the use of contingency payments such as earnouts, (4) the extension of D&O

insurance for target managers and directors, and (5) the level of the deductible that acquirers are

willing to pay before demanding breach-of-terms damages from the target.


17 An alternative explanation, which is also consistent with our findings, is that acquirers and targets structure deals to

fall just below the threshold to avoid paying legal fees and other compliance costs, particularly fees incurred if the

merger is delayed or blocked by the regulator. While compliance costs are unlikely to be consequential to our sample

of acquirers, whose average size is $6.3 billion ($6.5 billion) in deals just below (just above) the threshold, compliance

costs are likely to be important to targets. To address this possibility, we examine the use of acquirer (or “reverse”)

termination fees in deals around the threshold. Such fees are paid by acquirers to targets in the event that a deal is

terminated, including in the case when a deal fails to receive regulatory approval, and are intended to compensate the

target for business disruption during the premerger review process. However, if targets are able to transfer regulatory

risk to acquirers, in the form of higher termination fees, then it is less likely that targets will have incentives to avoid

compliance costs. In untabulated analysis, we find evidence that is consistent with targets transferring risk in deals

that are just above the threshold. Specifically, we find that acquirer termination fees are roughly $0.7 million higher

in deals just above relative to just below the threshold, suggesting that acquirers are willing to compensate targets for

regulatory risk. Given that the average acquirer termination fee is 4.50% (3.87%) of the deal value or $3.07 ($2.41)

million for deals just above (just below) the threshold, we believe it is unlikely that acquirers’ incentives to avoid

paying the roughly $0.7 million in additional compliance costs is driving our results.

20

To investigate how these aforementioned deal characteristics relate to just-below-threshold

M&As, we estimate the following OLS model:

JustBelowThresholdi,t = α + β Xi,t + θ Controlsi,t + εi,t, (2)

where i represents a unique deal announced in year t. Our dependent variable,

JustBelowThresholdi,t, is designed to capture deals just below the premerger threshold and is

measured as an indicator term that assumes the value of 1 if the deal size is both within 10% of

and below the threshold, and zero otherwise.18 Xi,t represents one of several test variables (e.g.,

type of firm, form of payment, deal premium, usage of earnout provisions) and Controlsi,t is a

vector of firm- and deal-level variables whose inclusion we describe in table notes. Other controls

include industry and year fixed effects. In all specifications of this model, we address concerns of

serial and cross-sectional correlation by clustering standard errors at the industry and year of deal

announcement level. Note that our choice to estimate an OLS model in (2), despite

JustBelowThreshold being binary in nature, follows a similar empirical approach to that used in

Garmaise (2015) and Card, Dobkin, and Maestas (2008), and addresses the incidental parameters

problem in non-linear maximum likelihood estimation introduced by our inclusion of multiple

fixed effects (Abrevaya, 1997).

For our first set of tests, to provide evidence that JustBelowThreshold deals differ in

systematic ways that are unrelated to other deals occurring below but not proximate to the

threshold, we estimate (2) using our full sample of M&As. We follow this up with additional tests

using only our sample of near-threshold deals.

18 Our choice of a ten percent bandwidth allows us to increase the power of our tests without sacrificing identification,

as it is likely that manipulated deal sizes fall within a band equal to several million dollars. To put the magnitude of

our bandwidth into perspective, in 2001, our JustBelowThreshold variable includes deals between $45 and $50 million

(when the threshold is set at $50 million), and in 2019, it includes deals between $81 and $90 million (when the

threshold is set at $90 million).

21

4.2.2. Results

To determine the types of firms that are likely to be involved in stealth acquisitions, we

estimate Equation (2) using indicator variables that capture the ownership status of targets and

acquirers (i.e., public versus private), in addition to an indicator for whether the acquiring firm is

a subsidiary of a larger parent. Our results, reported in Table 4, Panel A, indicate that just-below-

threshold deals are (1) less likely to involve public acquirers buying other publicly listed firms

(Column 3), and are (2) less likely to involve public targets regardless of the type of acquirer

(Column 2). We also find that just-below-threshold acquisitions are less likely to be deals where

the acquirer and target are both privately held firms (Column 5). Notably, in Column 4, we find

that just-below-threshold deals are more likely to involve takeovers of privately-held firms by

publicly-listed ones, aligning with recent concerns of regulators on non-reportable deals

undertaken by large public firms.19

Next, we evaluate the prevalence of cash financed deals, which, by way of providing lower-

risk payoffs, might entice targets to accept a lower offer price, helping acquirers to avoid premerger

reviews. We investigate this after estimating Equation 2 using indicator variables to represent

different payment methods used in deals. The results from this analysis, presented in Table 4 Panel

B, indicate that just-below-threshold deals are indeed more likely to include all-cash payments

(Column 1) or payments methods that use a combination of cash and other non-stock consideration

(Column 4). Conversely, we find that all-stock financed acquisitions (Column 2) and deals

19 It is important to note that this last finding is unlikely to be explained by below-threshold deals naturally involving

smaller targets that are likely to be private firms. This is because the majority of acquisitions coded zero for our

dependent variable are deals that are smaller in comparison to the just-below-threshold deals. Hence, the completion

of deals involving public acquirers and private targets is systematically higher in just-below-threshold acquisitions

compared to the entire population of deals, including many smaller deals. In untabulated analyses, we confirm this

using a sample of deals that fall further below the threshold (i.e., just-just-below) and compare this group of deals to

all other deals and find no systematic difference of the types of firms involved. We also repeat this analysis using only

the just-just-below and the just-above and again find no systematic differences.

22

employing a combination of stock and other non-cash consideration are less prevalent in just-

below-threshold acquisitions. The greater use of cash rather than stock payments in the just-below-

threshold deals is consistent with targets accepting the most liquid and least risky form of payments

in exchange for a lower deal price.20

In concert with, or as an alternative to, offering lower-risk payoffs to targets, legal

practitioners highlight other options available to acquirers that would facilitate the manipulation

of deal values to avoid premerger notifications. First, we consider the use of earnouts, i.e., deferred

payments that are contingent on a target's ability to meet or exceed certain milestones, in just-

below-threshold deals. An important nuance of the premerger regulations is that the inclusion of

contingent payment options such as earnouts necessitates the assessment of the fair value of the

acquisition rather than using the face value of the deal to determine whether the deal meets the

size-of-transaction test for filing premerger notifications. The FTC expects that the fair value

determinations in such cases will be performed in good faith and

on a commercially reasonable basis by the acquirer’s board of directors. However, the use of an

earnout could be a means of utilizing accounting and valuation methods to generate a fair valuation

that falls below the premerger notification thresholds.21 We investigate this based on a restricted

sample of deals with values falling within a +/-10% window centered around the FTC threshold

20 All-stock deals are considered riskier because a drop in the acquirer’s stock price in the period between the

announcement and effective date can directly impact the value that target shareholders receive in fixed-share deals,

for example. In the Internet Appendix, we consider the use of stock-price collars, which are employed to protect the

target (acquirer) from a large fall (run up) in the acquirer’s stock price between when contract terms are determined

and the deal is closed. Interestingly, we find (in Table IA.2) that collars are more likely to be used when in deals fall

just below the threshold, suggesting that not only are just-below-threshold deals less likely to be financed with stock,

but just-below-threshold deals that do include stock are more likely to include negotiated collars as an additional

mechanism to protect targets. 21 E-mail discussions between the legal representatives of acquirers and regulators, which are publicly disclosed on

the FTC website, reveal that acquirers actively consider the impact of contingency payments on deal price. These

emails frequently request confirmation from the FTC on acquirers’ ability to unilaterally choose discount rates and

probabilities of payoff, for example, when estimating the fair market value of earnouts, and whether acquirers’

valuation methods would exempt a deal from premerger review.

23

for this and the remaining analyses after hand-collecting the relevant granular data—e.g., use of

extended D&O insurance—on acquisitions, which would only serve to increase the power of these

tests if collected for the full sample.22

Table 5 presents results. Consistent with the notion that earnouts allow greater discretion

in assigning deal values, results reported in Column 1 of Table 5 indicate greater use of earnouts

in the just-below- (p-value < 0.05) relative to just-above-threshold deals. We also consider whether

earnouts amount to a larger fraction of transaction value in the just-below-threshold deals,

conditional on deals using earnouts. However, this analysis does not produce significant results

(Column 2 of Table 5), arguably due to the fact that the use of valuation methods to generate deal

values below notification thresholds is triggered by the existence rather than the size of earnouts.

Next, we explore the inclusion of contracting provisions by acquirers to lower deal prices

to below the premerger notification thresholds. In the context of our setting, for instance, the

agreement to extend, and pay for, the D&O coverage for private target firms can serve as another

mechanism for acquirers to manipulate deal values to fall just below the threshold. Note that the

cost to the acquirer of extending D&O coverage is not trivial, with combined premiums often

exceeding $1 million, and is a likely to be weighed against the total deal price. To assess this

possibility, we focus on private targets, which comprise the vast majority of firms involved in just-

below-threshold deals. Whether such a provision is included in the contract requires us to conduct

a rigorous data collection process, which we detail in the Internet Appendix.23 Table 6 presents

22 In the other tests that follow, we focus on this subsample of 640 near-threshold mergers. Variation in sample sizes

is attributed to tests that (i) use only private targets; or (ii) use only public acquirers; or (iii) data limitations due to a

lack of disclosure. 23 Although targets provide D&O coverage during the course of the acquisition, such coverage ceases after the

transaction closes. However, because target-firm executives and directors can still be held liable for their firms’ pre-

acquisition activities even after the deal closes, targets and acquirers typically negotiate run-off policies that extend

this insurance coverage well beyond the effective date of the deal. In private discussions, M&A lawyers suggest that

24

results. In Column (1), we find that stealth acquisitions are associated with a higher likelihood of

extending D&O coverage for the former directors and officers.

In Column (2), we conduct another test that looks at the level of the deductible an acquirer

is willing to accept before demanding post-acquisition breach-of-terms damages from the target.

Deductible levels in a merger contract are analogous to deductibles in other settings (i.e. car or

health insurance), in that a higher deductible should be associated with a lower premium. Thus,

the existence of higher deductibles in just-below-threshold deals would be consistent with

acquirers willing to accept higher post-closing risk in exchange for a lower deal price. Our results

are consistent with this: we find that stealth acquisitions are more likely to involve higher

deductible thresholds (p-value < 0.05 in both columns). Collectively, the results from Table 6

indicate that stealth acquisitions are more likely to accommodate contracting terms that implicitly

compensate targets with greater legal protections that are typically met with lower deal prices.

To the extent that firms employ contractual provisions to manipulate deal values to avoid

antitrust review, we expect to find lower average deal premiums for firms just below as compared

to just above the cutoff. However, an empirical challenge with assessing deal values of private

targets is that private firms do not have observable market values with which we can calculate the

premium paid by the acquirer firm. To overcome this issue, we take advantage of reporting rules

required by the U.S. Securities and Exchange Commission (SEC), which require publicly traded

firms to report in their filings the amount paid for the target that is above the fair value of assets

and liabilities (i.e., the goodwill portion of the deal). Because goodwill reflects the premium paid

extended D&O premiums are economically meaningful to the acquirer, and can be used as leverage to negotiate a

lower upfront deal price. Similarly, escrow arrangements, which facilitate post-closing clawbacks of the deal price

should the target be sued for events that occurred pre-merger, are subject to a deductible threshold. Higher thresholds

are more desirable to targets but allocate higher risk to acquirers (i.e., inability to recover losses below the deductible

threshold). In exchange for accepting a high deductible threshold, M&A lawyers suggest that acquirers can negotiate

a lower upfront deal price.

25

above the fair value of the target’s assets, it is analogous to the market premiums paid for private

targets (e.g., Lys and Yehuda, 2016). We hand collect from public SEC filings the reported

goodwill amounts for all deals involving public acquirers and private targets around the threshold

over our entire sample period and calculate the proportion of the deal value that is recognized as

goodwill. Table 7 presents results. Column 1 indicates a negative relation between just-below-

threshold deals and premiums paid for private targets (p-value < 0.10). This result suggests that as

the deal premium increases, the deal is significantly less likely to fall below the threshold. Column

2 presents results for deal premiums for public targets.24 Consistent with our previous findings that

deals involving public targets are not any more likely to fall just below the premerger notification

threshold, we find no difference between the deal premium paid for public targets in deals falling

just above or just below the threshold. Taken together, this evidence is consistent with deal

values—particularly for private targets—being manipulated to land below the notification

threshold.

Finally, we consider the use of continued economic ties and easier earnout targets as

implicit compensation for lower deal values. We manually collect data on post-merger

employment between the target CEOs and the acquiring firms in near-threshold deals (e.g., from

executives’ LinkedIn, Bloomberg, and public proxy statement profiles). To the extent that

acquirers exploit such economic incentives to reduce the purchase price to below the premerger

notification thresholds, we expect a greater representation of deals with target CEOs being retained

by or offered an economic interest in an acquiring firm for deals that are just-below the threshold.

Such private benefits can also act to persuade target CEOs to accept deal terms that contain

24 Our findings also help to rule out the possibility that the unusually high level of merger activity just below the

threshold is being driven by acquisitions involving better targets that are already below the threshold. Such targets

would likely command higher deal premiums--we find the opposite for deals just below the threshold, which is further

consistent with a detectable mass of stealth acquisitions involving manipulated deal values.

26

contingency payments, since earnout negotiations are typically shaped by target executives that

know how to maximize the probability of meeting the earnout targets. As such, we also examine

whether economically-connected executives are more likely to achieve post-acquisition earnout

payoffs. Table 8 presents results from this analysis. In Panel A, we find a positive relation between

target CEOs with post-merger economic ties with acquirers and just-below-threshold deals (p-

value < 0.05). Panel B of Table 8 indicates a positive and significant (p-value < 0.01) interactive

effect between CEOs with post-merger economic ties in acquirers and earnout payoffs in just-

below-thresholds deals. Collectively, these results are consistent with the use of discretion to

employ target executives and with greater earnout payoffs as a means to implicitly compensate

such executives for lower deal premiums.

4.3. Heterogeneity in Stealth Acquisitions: Incentives to Coordinate

We next examine whether horizontal M&A deals (i.e., targets and acquirers operating in

the same industry), deals between geographically-proximate targets and acquirers, and deals in

concentrated industries—all of which represent M&A deals that are theoretically more likely to

lead to anticompetitive outcomes—have a higher likelihood of falling just below the threshold.

These considerations are also prompted by the increased focus by the FTC and DOJ on the

anticompetitive effects of horizontal mergers, prevalence of forced divestitures due to geographic

market concentration, and evidence of price gouging and poor quality services in concentrated

industries.


To explore the prevalence and nature of horizontal mergers in just-below-threshold deals,

we employ Equation (2) and also estimate the following OLS model:

JustBelowThresholdi,t = α + β Zi,t × Wi,t + Zi,t + Wi,t + θ Controlsi,t + εi,t, (3)

27

where i represents a unique deal announced in year t. Our dependent variable,

JustBelowThresholdi,t, is designed to capture deals just below the premerger threshold and is

measured as an indicator term that assumes the value of one if the deal size is both within 10% of

and below the threshold, and zero otherwise. Zi,t × Wi,t is a binary interaction term that assumes

the value of 1 if (i) the deal is horizontal and involves firms that share the same state of operations

(in Column 3 of Panel A in Table 9); or, if (ii) the deal is horizontal and the industry is highly

concentrated (in Columns 2 and 5 of Panel B in Table 9); or, if (iii) the deals involves firms that

share the same state of operations and the industry is highly concentrated (in Columns 3 and 6 of

Panel B in Table 9), and 0 otherwise. Finally, Controlsi,t is a vector of firm- and deal-level variables

whose inclusion we describe in the table notes. Other controls include industry and year fixed

effects. In all specifications of this model, we address concerns of serial and cross-sectional

correlation by clustering standard errors at the industry and year of deal announcement level.

4.3.2. Results

Prior to estimating Equation (3), we once again employ McCrary’s (2008) test to examine

the discontinuity around the threshold after restricting our attention to horizontal mergers and show

this result in Figure 2 of the Internet Appendix. We find a noticeable jump in the number of

horizontal deals just to the left of the threshold, which we verify with a Wald test (p-value < 0.01).

It is important to note that, while prior research suggests that changing the notification threshold

level is associated with more horizontal mergers at all levels of deal size below the threshold

(Wollmann 2019), our findings in this section suggest an abnormally high occurrence of such

mergers in deals that are just below the threshold.

Motivated by this preliminary evidence, we estimate Equation (3) within the sample of

deals that fall immediately below and above the premerger notification threshold (since deals that

28

are expected to be more homogeneous to each other along other deal characteristics). The results

reported in Column (1) of Table 9, Panel A, confirm a greater likelihood of just-below-threshold

deals including horizontal mergers (p-value < 0.05). We also consider whether just-below-

threshold deals are more likely to include targets and acquirers that share the same state of

operations, which can enable acquirers to realize significant gains in geographically confined

markets. We do not find higher rates of such intrastate acquisitions in the just-below-threshold

deals (Column 2 of Panel A). However, when we expand this analysis to include the interactive

effects of horizontal mergers and intrastate deals in Column (3) of Panel A, we document a

significant result for the interactive effect, indicating that our findings for horizontal mergers in

Column (1) are driven by intrastate horizontal mergers (p-value < 0.01).

To further explore factors that motivate the implementation of horizontal acquisitions in

just-below-threshold deals, we also consider the role of industry concentration, given the evidence

on increased market power in concentrated industries (e.g., hospitals and dialysis centers)

adversely affecting not only prices but quality of services (Gowrisankaran, Nevo, and Town, 2015;

Eliason, Heebsh, McDevitt, and Roberts, 2018; Wollmann, 2019). Stigler (1971) also suggests that

acquisitions in concentrated industries are likely to be a concern to regulators and oligopoly

markets, particularly in the case of horizontal mergers in such industries. We investigate this by

considering the interaction between horizontal mergers and two measures of concentrated

industries, namely the Hoberg and Phillips (2010) measure and a concentration measure estimated

using net sales by four-digit SIC code. The results from this analysis are reported in Panel B of

Table 9. While we do not find a higher frequency of concentrated industry deals just below the

threshold (Columns 1 and 4), we find strong evidence (p-value < 0.05) of just-below-threshold

deals including horizontal mergers in concentrated industries (Columns 2 and 5). This result is

29

consistent with the concerns expressed over antitrust practices in concentrated industries.

Collectively, our findings in Table 9 are consistent with more pervasive manipulation of deals to

avoid premerger reviews in horizontal mergers, especially those that occur between firms that

conduct business in the same state as well as those in concentrated industries.

5. Product Market Competition following Stealth Acquisitions

In this section, we examine whether stealth acquisitions are associated with patterns

consistent with reduced product market competition. In particular, our analyses in this section

examine whether successful antitrust avoidance benefits colluding firms and their horizontal rivals.

5.1. Effects on Product Market Competition: Industry Rival Returns

Our results in the previous section identify salient attributes of firms with the strongest

incentives to avoid antitrust scrutiny by engaging in deals that fall just below the notification

threshold. Given that antitrust laws seek to ensure competition within industries, our findings on

the prevalence of horizontal mergers in just-below-threshold deals is particularly striking. As such,

we next examine whether successful premerger notification avoidance via successful stealth

acquisitions has product market consequences in horizontal mergers. It is well-known that mergers

or acquisitions amongst industry competitors can reduce the monitoring costs of industry rivals

within that industry, and can facilitate collusive behavior (Stigler, 1964). That is, as industry

concentration increases, the actions of each member become more visible, which in turn increases

the likelihood that cheating within any collusive equilibrium—e.g., by increasing output or cutting

prices—is detected. As the likelihood of detection increases, the collusive equilibrium becomes

more stable—and consequently, more profitable—because the expected gains from cheating

decrease (Levenstein and Suslow, 2006).

30

Following Eckbo (1983) and Stillman (1983), one way to formally test for this is by

examining the abnormal returns of industry rivals around the announcement date of horizontal

mergers. The intuition for this test is that if these mergers are collusive in nature, monopoly rents

should accrue for merging firms. Rents should also accrue for industry rivals, since these firms

can free ride on higher product prices. Assuming markets are efficient, stock prices—including

those of horizontal rivals—should reflect these rents soon after the collusive merger is announced,

because the combined effect of all expected future cash flows should be impounded into price

relatively quickly.


We test for a discontinuity in the announcement-date abnormal returns of horizontal rivals

around the threshold by estimating the following OLS model:

yi,[-1,1] = α + β JustBelowThresholdi,t + θ Controlsi,t + εi,t, (4)

where yi,[-1,1] is, for deal i, the 3-day, market-adjusted portfolio returns (centered on the

announcement date) of all of the horizontal rivals of the acquiring firm. To be consistent with

Eckbo (1983) and Stillman (1983), we construct equal-weighted portfolios. JustBelowThresholdi,t

is an indicator for whether the deal falls just below and within ten percent of the threshold, and

Controlsi,t is a vector of firm- and deal-level variables. Other controls include industry and year

fixed effects. We cluster standard errors at the industry and year level.

While our estimate of Equation (4) resembles that of a standard regression discontinuity

design (RDD), we acknowledge that the motivation and empirical approach in our study differs in

notable ways. Unlike the standard RDD approach, which, in order for the researcher to draw causal

inferences about a treatment or policy, relies on the strong assumption that firms cannot

endogenously determine whether they are above or below a specified threshold, we study whether

31

firms manipulate deal values to avoid antitrust scrutiny in mergers, and then examine product

market effects. In our setting, if firms can manipulate deal values, then it could be that those firms

that choose to be just below the threshold are somewhat different from those that choose to be just

above, which would then invalidate the standard RDD approach (Lee and Lemieux, 2010).

Nevertheless, evidence of discontinuous product market effects around the threshold can shed new

light on the impact of systematic regulatory avoidance in the M&A market, which is what the

techniques of RDD are designed to describe (Garmaise, 2015), but we note that the interpretation

of our results is different from those of a standard RDD.

5.1.2. Results

Column (1) of Table 10 presents the results from the estimation of Equation (4), which

considers the impact of just-below-threshold deals on the abnormal returns of industry rivals.

While this analysis does not yield significant results, when we extend this analysis after

considering the interaction between just-below-threshold deals and horizontal mergers in Column

2, we find strong evidence of such deals generating more positive abnormal returns for rivals in

horizontal mergers (p-value < 0.05). These findings are consistent with economic theories

suggesting that industry rivals can also accrue significant gains from horizontal mergers as a result

of such deals increasing overall prices in the market (e.g., Stigler, 1964; Salant, Switzer, and

Reynolds, 1983; Deneckere and Davidson, 1985).

5.2. Effects on Product Market Competition: Product Prices

Our final set of tests provide direct evidence on the product market competition effects of

horizontal stealth acquisitions by considering how such acquisitions impact product prices of

industry rivals that share common products. In particular, product price increases by rivals

following events that reduce product market competition can be symptomatic of such behavior

32

(e.g., Azar, Schmalz, and Tecu, 2018). Conducting this analysis requires data on detailed micro-

level product pricing for shared common products of industry rivals over time evaluate changes in

product prices after deal completion. Although such data is scarce, we are able to narrow the focus

of our analysis to a single horizontal merger located just below the premerger notification threshold

for which we can identify (1) common products of the acquirer’s rivals through conducting an

exhaustive examination of product groupings in online advertising and in retail stores, and (2)

retail scanner pricing data for the rivals’ products using Universal Product Codes (UPCs) provided

by Nielsen Company. Agreements with data vendors preclude us from disclosing information that

could identify the industry, firms and products employed in this analysis. This produces a sample

consisting of approximately 78 million observations of rival retail scanner observations relating to

all products that were sold during a two-year period surrounding the closing of the focal merger.

To the extent that stealth acquisitions reduce product market competition, we expect an increase

in the prices of rivals’ common products following the closing, relative to other products.25


To assess changes in the pricing of rivals’ common products following the stealth

acquisition, we estimate the following differences-in-difference specification:

yi,t = α + β1 CommonProduct×Post + β1 CommonProduct + β1 Post

+ θ Controlsi,t + εi,t, (5)

where yi,t is the average weekly prices for the products of rivals, CommonProduct is an indicator

for whether the products sold by the target of the stealth acquisition are also sold by the rival firm,

and Post is an indicator for after the completion of the stealth acquisition, and Controls includes a

25 We are not able to investigate for changes in the pricing of the focal products of acquirers because of the

unavailability of product pricing data for the private target’s products during the premerger period of the stealth

acquisition we examine.

33

measure of time trends and various dimensions of fixed effects, depending on the specification.26

We cluster standard errors at the product and week levels.

5.2.2. Results

Figure 5 illustrates that the monthly average prices for rivals’ common products are stable

during the months leading up to the focal stealth acquisition, and that monthly average prices

increase sharply following the completion of the acquisition. Furthermore, Table 11 presents

results from estimating Equation (5) using five specifications with various fixed effect structures

to control for time trends in product prices and local economic shocks (i.e., using week and

geographic region fixed effects).27 Across all specifications, we find evidence of a positive and

statistically significant (p-value < 0.01 in all columns) coefficient on the interaction between

CommonProduct and Post, indicating an upward shift in the pricing of the rivals’ common

products following the completion of the focal stealth acquisition, relative to price changes for

their other products. These findings are consistent with the notion that stealth acquisitions between

horizontal rivals can reduce product market competition and be deleterious for consumers through

increasing product prices.

5. Conclusion

We examine the economic forces that motivate firms to structure their M&A deals to avoid

antitrust scrutiny from regulators. We first show that an abnormal number of M&A deals are

structured to narrowly avoid antitrust scrutiny, and that these “stealth acquisitions” are driven by

acquisitions of private targets that entail contractual terms with lower deal premiums, and payoff

26 We follow Sheen (2014) and define a product as “common” based on interviews with marketing experts and

common product discussions in firms’ public SEC filings, which we explain further in Internet Appendix IA1. 27 We define geographic fixed effects using Nielsen’s Designated Market Area (DMA) codes, which represent

standardized regions for local television marketing.

34

functions that allow for more discretion in allocating deal values. We then explore the economic

mechanisms driving the discontinuity in acquisitions around the premerger notification threshold,

and find that the discontinuity in stealth acquisitions around the premerger notification threshold

is driven by firms with greatest incentives to coordinate, indicative of stealth acquisitions occurring

in settings more likely to have anticompetitive effects. Consistent with this, we find that both

acquiring firms and their industry rivals benefit from stealth acquisitions, indicating reduced

product market competition that limits output and raises prices.

Collectively, our results highlight that firms can successfully manage the contractual

features of their M&A deals to avoid antitrust scrutiny from regulators, and such regulatory

avoidance can have deleterious effects on consumers. Furthermore, our study suggests a more

nuanced view of government resource allocation in monitoring the antitrust implications of

corporate M&A deals: implementing arbitrary thresholds can have real effects on industrial

organizational behavior to the extent that firms have discretion to manipulate the criteria used to

screen for corporate transactions that warrant regulatory scrutiny. In this regard, our study provides

evidence suggesting that regulatory concern about the limitations set by premerger review

thresholds are plausibly warranted as firms appear to systemically manipulate the size of their

deals to circumvent regulatory review.

35

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38

Appendix A. Variable Definitions

This appendix provides definitions for the key variables used in our tests.

Variable Description Source

Dependent variables

JustBelowThreshold Indicator variable that equals one if a merger's deal value is within ≥ -10% and ≤ 0%

of the threshold, calculated as [(deal value − threshold)/threshold]; and zero otherwise.

SDC

RivalRet Equal-weighted portfolio return of horizontal rivals, measured over the three-day

window [-1, 1] centered on the announcement date.

CRSP

Price Average weekly product prices, by UPC code. Nielsen

Explanatory variables

PublicAcquirer Indicator variable that equals one if the acquirer is a publicly traded company, and

zero otherwise.

SDC

PrivateAcquirer Indicator variable that equals one if the acquirer is a private company, and zero

otherwise.

SDC

SubsidAcquirer Indicator variable that equals one if the acquirer is a subsidiary, and zero otherwise. SDC

PublicTarget Indicator variable that equals one if the target firm is a publicly traded company, and

zero otherwise.

SDC

PrivateTarget Indicator variable that equals one if the target firm is a private company, and zero

otherwise.

SDC

Public-Public Indicator variable that equals one if both the acquirer and the target firm are publicly

traded companies, and zero otherwise.

SDC

Public-Private Indicator variable that equals one if the acquirer is a publicy traded company and the

target firm is a private company, and zero otherwise.

SDC

Private-Public Indicator variable that equals one if the acquirer is a private company and the target

firm is a publicly traded company, and zero otherwise.

SDC

Private-Private Indicator variable that equals one if both the acquirer and the target firm are private

companies, and zero otherwise.

SDC

Subsid.-Public Indicator variable that equals one if the acquirer is a subsidiary and the target firm is

a publicly traded company, and zero otherwise.

SDC

Subsid.-Private Indicator variable that equals one if the acquirer is a subsidiary and the target firm is

a private company, and zero otherwise.

SDC

AllCash Indicator variable that equals one if the payment terms include 100% cash, and zero

otherwise.

SDC

AllStock Indicator variable that equals one if the payment terms include 100% stock, and zero

otherwise.

SDC

AllOther Indicator variable that equals one if the payment terms include 100% non-cash and

non-stock consideration (e.g., debt, earnouts, etc.), and zero otherwise.

SDC

39

Appendix A. Variable Definitions (cont’d) AllCashandOther Indicator variable that equals one if the payment terms include 100% of cash and other

non-stock and non-cash consideration (e.g., debt, earnouts, etc.), and zero otherwise.

SDC

AllCashandStock Indicator variable that equals one if the payment terms include 100% cash and stock,

and zero otherwise.

SDC

AllStockandOther Indicator variable that equals one if the payment terms include 100% stock and other

non-cash and non-stock consideration (e.g., debt, earnouts, etc.), and zero otherwise.

SDC

Horizontal Indicator variable that equals one if the acquirer and target share the same 4-digit SIC

code, and zero otherwise.

SDC

Intrastate Indicator variable that equals one if the headquarters of the acquirer and target are in

the same state, and zero otherwise.

SDC

HighConc Indicator variable that equals one if the industry is above the median concentration,

and zero otherwise. We calculate industry concentration using (i) Hoberg and Phillips

(2010), and using (ii) net sales (by four-digit SIC code).

Hoberg

and

Phillips

website.

Earnouts Indicator variable that equals one if earnouts are included in the payment terms, and

zero otherwise.

SDC

EarnoutPerc Percent of the deal value that consists of earnouts. SDC

Collar Indicator variable that equals one if the deal includes a collar, and zero otherwise. SDC

AcqTermFeePercent Continuous variable that measures acquirer termination fee as a proportion of the total

deal value.

SDC

PrivateTargetDealPrem Premium paid for a private target. Measured as the proportion of goodwill relative to

the total deal value. Calculated using the amount of goodwill recognized in the first

available 10-K SEC filing for publicly traded acquirers.

SEC

EDGAR

EconomicTie Indicator variable that equals one if the target CEO is retained by and/or holds equity

in the acquiring firm, and zero otherwise.

Various

online

sources

PublicTargetDealPremium Premium paid for a publicly traded target firm. Measured as the deal price divided by

the target firm's stock price (four weeks prior to the announcement date) minus 1

multiplied by 100.

SDC

EarnoutPayoff Indicator variable that equals one if an earnout is achieved (and paid out), and zero

otherwise.

SEC

EDGAR

ExtendedLiabilityCoverage Indicator variable that equals one if the acquiring firm agrees to extend and pay for

D&O liability insurance for directors and officers of the target firm, and zero

otherwise.

SEC

EDGAR

DeductibleThreshold Continuous variable that measures the dollar-based threshold above which the

acquirer can clawback a portion of the purchase price to defend and pay damages

associated with post-closing breaches of representations and warranties made by the

target.

SEC

EDGAR

CommonProduct × Post Indicator variable that equals one if an acquirer’s rival’s product overlaps with a

product of the target and retail purchase occurs after the effective date of the merger,

and zero otherwise.

Nielsen

40

Appendix A. Variable definitions (cont’d) Control Variables

DealValue Value of the merger in US$ millions. SDC

TargetTermFee Indicator variable that equals one if the deal includes a termination fee payable by the

target firm, and zero otherwise.

SDC

TenderOffer Indicator variable that equals one if the deal is structured as a tender offer, and zero

otherwise.

SDC

NumRivals Number of horizontal rivals of the acquirer. Calculated using the number of publicly

traded companies that share the same four-digit SIC code as the acquirer.

CRSP

LowNumRivals Indicator variable that equals 1 if the number of public rivals is below the median for

all industries, and zero otherwise. We use the number of publicly traded companies

that share the same four-digit SIC code as the acquirer to calculate the number of

rivals.

CRSP

TargetTermFeePercent Continuous variable that measures target termination fee as a proportion of the total

deal value.

SDC

RepsSurvive Indicator variable that equals one if the representations and warranties made by the

target and contained in the merger agreement survive beyond the effective date of the

deal, and zero otherwise. Data is hand collected from merger agreements located on

EDGAR.

SEC

EDGAR

SurvivalPeriod Continuous variable that measures the amount of time the representations and

warranties made by the target in the merger agreement have been extended. Data is

hand collected from merger agreements located on EDGAR.

SEC

EDGAR

Escrow Indicator variable that equals one if the purchase price holdback is kept in third-party

escrow, and zero otherwise.

SEC

EDGAR

TimeTrend Continuous variable that measures, for each observation, the number of days after the

effective date of the deal. Observations prior to the effective date assume a negative

value.

Nielsen

41

Figure 1. Premerger Notification and Review Process

The Hart-Scott-Rodino Act established the federal premerger notification program, which provides the Federal Trade Commission and the Department of Justice

with information about mergers and acquisitions before they become effective. This figure depicts the premerger notification and review process from start to

completion. Arrows indicate the flow of the process. Positive symbols indicate the merger closed successfully; whereas, a negative symbol indicates the agency

seeks to prevent the merger from closing.

Annual Premerger Notification Threshold from 2001 to 2019 (in $ millions)

Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Threshold 50.0 50.0 50.0 50.0 53.1 56.7 59.8 63.1 65.2 63.4 66.0 68.2 70.9 75.9 76.3 78.2 80.8 84.4 90.0

42

Figure 2. Density of Public and Private M&As around FTC Premerger Review Threshold

This figure depicts the estimated kernel densities of the distance (in $ millions) of deal values from the FTC premerger

review threshold. Confidence bands (at the 95% level) are depicted by the thin lines. The circles, which describe scaled

frequencies, are analogous to histogram bins. For ease of interpretation, we restrict our kernel density estimations to

distances within +/- $30 million of zero; where zero represents the FTC threshold. Estimation procedure follows the

method in McCrary (2008).

43

Figure 3. Histogram of M&A Deals around FTC Premerger Review Threshold

This figure depicts the frequency of public and private deals around the FTC premerger review threshold. For ease of

interpretation, we restrict our histogram to deals within +/- $25 million of zero, where zero represents the FTC

threshold. Bin widths are set to $2.5 million and constructed such that when the distance from the threshold equals

zero, the deal is included in the bin to the left of the threshold (i.e., [-2.5, 0)), indicating the deal would be exempt

from premerger review, which is consistent with the regulation.

44

Figure 4. Falsification Test: Density of Public and Private Always-Exempt M&As


review threshold for deals involving real estate and hotels, which are always exempt from the premerger notification

and review process. Confidence bands (at the 95% level) are depicted by the thin lines. The circles, which describe

scaled frequencies, are analogous to histogram bins. For ease of interpretation, we restrict our kernel density

estimations to distances within +/- $30 million of zero; where zero represents the FTC threshold. Estimation procedure

follows the method in McCrary (2008).

45

Figure 5. Product-level Pricing Before and After Below-Threshold Mergers

This figure plots monthly averages of 410,000 retail scanner observations collected before the announcement date and

after the effective date of a below-threshold horizontal merger. All data are for comparable rivals’ products. Products

are defined as comparable if they compete in the same market segment. We provide, in the Data Collection Process

section of the Internet Appendix, details on the method used to define comparable products.

46

Table 1. M&A Descriptive Statistics

This table presents the distribution of deals from Securities Data Company (SDC) Mergers and Acquisition database

announced between February 1, 2001 and February 27, 2020. Panel A presents the top ten industries in our full sample

of 20,511 deals, using Fama-French 48 industry classifications. Panel B presents the top ten industries for deals with

transaction values that are within 10% above or 10% below the annual FTC threshold. Panel B presents the top ten

industries separately for below- and above-threshold deals.

Panel A. Industry Distribution (Top Ten industries)

Fama-French 48 Industry Groups

Number

of deals

% of all

deals

Top 10 Business services 6,417 32.27

Pharmaceutical products 1,305 6.56

Healthcare 1,012 5.09

Electronic equipment 991 4.98

Wholesale 776 3.90

Retail 753 3.79

Medical equipment 716 3.60

Communication 665 3.34

Computers 570 2.87

Transportation 503 2.53

Total (Top 10) 13,708 66.40

Panel B. Industry Distribution (within +/- 10% of annual threshold)

10% Below

Threshold

10% Above

Threshold

Fama-French 48 Industry

Groups

Number

of deals

% of

deals

“below”

Number

of deals

% of

deals

“above”

Top 10 Business services 123 33.61 93 33.94

Pharmaceutical products 22 6.01 17 6.20

Electronic equipment 22 6.01 11 4.01

Medical equipment 20 5.46 13 4.74

Retail 18 4.92 15 5.47

Healthcare 13 3.55 16 5.84

Wholesale 13 3.55 7 2.55

Personal services 12 3.28 7 2.55

Computers 12 3.28 13 4.74

Construction 10 2.73 3 1.09

Total (Top 10) 265 72.40 195 71.13

47

Table 2. Descriptive Statistics

This table presents the distribution of key variables used in our analysis. All variables are defined in Appendix A.

Panel A presents descriptive statistics for all variables for both the pooled and near-threshold analysis, and Panel B

presents descriptive statistics for key variables in the near-threshold analysis (split by below versus above the

premerger review threshold).

Panel A. Key Variables (Pooled and Near-Threshold Analysis)

Variable N Mean Std. 25th Median 75th

Deal analysis PublicAcquirer 19,886 0.69 0.46 0.00 1.00 1.00

PrivateAcquirer 19,886 0.15 0.36 0.00 0.00 0.00

SubsidAcquirer 19,886 0.14 0.35 0.00 0.00 0.00

PublicTarget 19,886 0.23 0.42 0.00 0.00 0.00

Public-Public 19,886 0.12 0.33 0.00 0.00 0.00

Public-Private 19,886 0.57 0.49 0.00 1.00 1.00

Private-Private 19,886 0.09 0.29 0.00 0.00 0.00

Private-Public 19,886 0.06 0.24 0.00 0.00 0.00

Subsid-Public 19,886 0.04 0.20 0.00 0.00 0.00

Subsid-Private 19,886 0.10 0.30 0.00 0.00 0.00

AllCash 19,886 0.32 0.47 0.00 0.00 1.00

AllStock 19,886 0.08 0.28 0.00 0.00 0.00

AllOther 19,886 0.12 0.32 0.00 0.00 0.00

AllCashandOther 19,886 0.78 0.41 1.00 1.00 1.00

AllCashandStock 19,886 0.47 0.50 0.00 0.00 1.00

AllStockandOther 19,886 0.48 0.50 0.00 0.00 1.00

Horizontal 19,886 0.28 0.45 0.00 0.00 1.00

Intrastate 19,886 0.19 0.39 0.00 0.00 0.00

Earnouts 19,886 0.10 0.30 0.00 0.00 0.00

EarnoutPerc 19,886 3.60 13.11 0.00 0.00 0.00

Collar 19,886 0.01 0.07 0.00 0.00 0.00

AcqTermFeePercent 19,886 0.00 0.02 0.00 0.00 0.00

PublicTargetDealPremium 3,642 64.58 456.29 14.34 31.46 54.28

EconomicTie 603 0.75 0.43 0.00 1.00 1.00

PrivateTargetDealPremium 222 55.74 22.84 40.63 57.21 73.40

EarnoutPayoff 45 0.69 0.47 0.00 1.00 1.00

Contract analysis

ExtendedLiabilityCoverage 234 0.53 0.50 0.00 1.00 1.00

DeductibleThreshold 192 0.33 0.47 0.00 0.19 0.50

Return analysis RivalRet 594 0.00 0.03 –0.01 0.00 0.01

Product pricing analysis

Price 77,835,861 6.46 3.31 3.99 5.67 8.09

CommonProduct 77,835,861 0.01 0.07 0.00 0.00 0.00

Post 77,835,861 0.46 0.50 0.00 0.00 1.00

48

Table 2. Descriptive Statistics (cont’d)

Panel A: Key Variables (cont’d)

Controls N Mean Std. 25th Median 75th

DealValue 19,886 400.33 2,625.67 7.50 26.00 120.00

TenderOffer 19,886 0.04 0.19 0.00 0.00 0.00

PrivateTarget 19,886 0.77 0.42 1.00 1.00 1.00

NumRivals 594 40.54 55.77 4.00 14.00 49.00

RepsSurvive 232 0.59 0.49 0.00 1.00 1.00

SurvivalPeriod 222 0.85 0.95 0.00 1.00 1.50

TargetTermFeePercent 19,886 0.01 0.03 0.00 0.00 0.00

49

Table 2. Descriptive Statistics (cont’d)

Panel B. Near-Threshold Sample (+/-10% of threshold)

Just-Below-Threshold Just-Above-Threshold

Diff. in

means

Diff. in

medians Variable N Mean Median N Mean Median

Deal analysis

PublicAcquirer 366 0.73 1.00 274 0.73 1.00 0.00 0.00

PrivateAcquirer 366 0.10 0.00 274 0.13 0.00 –0.03 0.00

SubsidAcquirer 366 0.17 0.00 274 0.12 0.00 0.05 0.00

PublicTarget 366 0.19 0.00 274 0.23 0.00 –0.04 0.00

Public-Public 366 0.08 0.00 274 0.12 0.00 –0.04* 0.00*

Public-Private 366 0.65 1.00 274 0.61 1.00 0.04 0.00

Private-Private 366 0.05 0.00 274 0.07 0.00 –0.02 0.00

Private-Public 366 0.05 0.00 274 0.07 0.00 –0.02 0.00

Subsid-Public 366 0.06 0.00 274 0.03 0.00 0.03* 0.00*

Subsid-Private 366 0.11 0.00 274 0.09 0.00 0.00 0.00

AllCash 366 0.36 0.00 274 0.33 0.00 0.02 0.00

AllStock 366 0.04 0.00 274 0.09 0.00 –0.05** 0.00***

AllOther 366 0.12 0.00 274 0.12 0.00 0.00 0.00

AllCashandOther 366 0.85 1.00 274 0.76 1.00 0.09** 0.00***

AllCashandStock 366 0.46 0.00 274 0.51 1.00 –0.05 –1.00

AllStockandOther 366 0.42 0.00 274 0.47 0.00 –0.05 0.00

Horizontal 366 0.28 0.00 274 0.26 0.00 0.02 0.00

Intrastate 366 0.17 0.00 274 0.20 0.00 –0.03 0.00

Earnouts 366 0.16 0.00 274 0.11 0.00 0.05* 0.00*

EarnoutPerc 366 4.62 0.00 274 3.57 0.00 1.05 0.00

Collar 366 0.00 0.00 274 0.01 0.00 –0.01 0.00

AcqTermFeePercent 366 0.00 0.00 274 0.00 0.00 0.00** -0.00**

PublicTargetDealPremium 50 93.46 47.36 51 65.62 49.05 27.84 –1.69

PrivateTargetDealPrem 142 60.19 57.77 94 59.88 59.34 0.31 –1.57

EconomicTie 343 0.80 1.00 260 0.68 1.00 0.12*** 0.00***

EarnoutPayoff 28 0.64 1.00 17 0.76 1.00 –0.12 0.00

Contract analysis

ExtendedLiabilityCoverage 123 0.54 1.00 111 0.51 1.00 0.03 0.00

DeductibleThreshold 105 0.39 0.24 87 0.27 0.06 0.12** 0.18

Return analysis

RivalRet 344 0.00 0.00 250 0.00 0.00 0.00 0.00

Controls

DealValue 366 59.35 58.27 274 66.66 65.13 –7.31*** –7.86***

TenderOffer 366 0.03 0.00 274 0.03 0.00 0.00 0.00

RepsSurvive 122 0.61 1.00 110 0.56 1.00 0.05 0.00

SurvivalPeriod 116 0.87 1.00 106 0.84 1.00 0.03 0.00

TargetTermFeePct 366 0.00 0.00 274 0.01 0.00 0.01 0.00*

NumRivals 344 39.66 14.00 250 41.76 13.50 –2.10 0.50

50

Table 3. Tests of the Difference between the Frequency of Below-Threshold and Above-

Threshold Deals

This table presents the results of tests of the statistical difference between the frequency of just-below-threshold deals

and just-above-threshold deals. In Panel A, we show results for the difference in density heights around the threshold

related to Figures 2 and 4. The log difference in heights is from the perspective of the bin just to the right of the

threshold (i.e., a negative sign indicates the right bin is lower than the left bin). In Panel B, we show results for the

difference between actual and estimated frequencies of deals occurring in the bins just to the left and just to the right

of zero related to Figure 3. *, **, *** represent significance at the 10%, 5%, and 1% level, respectively. Estimation

procedure follows the methods in McCrary (2008) in Panel A and Burgstahler and Dichev (1997) in Panel B.

Panel A: Difference in Density Heights

Log Diff. in Heights t-stat

Difference around threshold (Figure 2) –0.680*** –5.019

Falsification test (Figure 4) 0.129 0.445

Panel B: Difference in Estimated and Actual Bin Heights (Figure 3)

Bin: Frequency (Actual) Frequency (Estimated) Difference t-stat

JustBelowThreshold 176 121 55*** 4.046

JustAboveThreshold 96 141 –45*** –3.103

51

Table 4. Acquirer-Target Characteristics and Below-Threshold M&As

This table presents results from ordinary least squares (OLS) regressions of M&As on acquirer-target characteristics. In Panels A and B, the dependent variable,

JustBelow, is an indicator variable that assumes the value of one if a deal’s transaction value is within a 10% window below the FTC annual premerger review

threshold, and zero otherwise. In Panel A, the main variables of interest in Columns (1) and (2) are indicator variables that assume the value of 1 if the acquirer or

target is a publicly traded company, and zero otherwise. The main variables of interest in Columns (3) to (8) are indicator variables that assume the value of 1 based

on combined acquirer-target characteristics, and zero otherwise. In Panel B, the main variables of interest in Columns (1) to (6) are indicator variables that take the

value of 1 if the deal includes payment terms such that is structured as 100% cash, 100% stock, 100% other (i.e., debt, and earnouts), 100% cash and other, 100%

stock and other, or 100% cash and stock. All variables are defined in Appendix A. All columns include target-firm industry fixed effects (using Fama-French 48

industry classifications) and year fixed effects. Robust t-statistics are reported in parentheses and calculated using standard errors clustered at the target-firm

industry and year levels. *, **, *** represent significance at the 10%, 5%, and 1% level, respectively.

Panel A: Acquirer-Target Characteristics (1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES JustBelow JustBelow JustBelow JustBelow JustBelow JustBelow JustBelow JustBelow

PublicAcquirer 0.003

(1.13)

PublicTarget –0.004*

(–1.89)

Public-Public –0.007**

(–2.64)

Public-Private 0.005**

(2.71)

Private-Private –0.010***

(–3.40)

Private-Public –0.004

(–1.57)

Subsid-Public 0.008*

(1.73)

Subsid-Private 0.002

(0.40)

DealValue –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000***

(–5.10) (–4.49) (–3.82) (–5.96) (–5.08) (–5.60) (–5.04) (–5.08)

Constant 0.017*** 0.020*** 0.019*** 0.016*** 0.020*** 0.019*** 0.018*** 0.019***

(10.38) (119.74) (231.01) (17.09) (238.40) (362.25) (162.81) (41.35)

Observations 19,886 19,886 19,886 19,886 19,886 19,886 19,886 19,886

Adjusted R2 0.001 0.001 0.001 0.002 0.002 0.001 0.001 0.001

Year Fixed Effects yes yes yes yes yes yes yes yes

Industry Fixed Effects yes yes yes yes yes yes yes yes

52

Table 4. Acquirer-Target Characteristics and Below-Threshold M&As (cont’d)

Panel B: Form of Payment Characteristics (1) (2) (3) (4) (5) (6)

VARIABLES JustBelow JustBelow JustBelow JustBelow JustBelow JustBelow

AllCash 0.005*

(2.01)

AllStock –0.013***

(–12.16)

AllOther 0.003

(0.59)

AllCashandOther 0.008***

(4.01)

AllStockandOther –0.006*

(–1.95)

AllCashandStock –0.000

(–0.16)

DealValue –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000***

(–4.22) (–4.49) (–4.39) (–3.47) (–4.47) (–4.45)

PublicTarget –0.005** –0.004* –0.003* –0.004** –0.005** –0.004

(–2.20) (–1.83) (–1.73) (–2.11) (–2.37) (–1.53)

Constant 0.018*** 0.021*** 0.019*** 0.013*** 0.023*** 0.020***

(51.04) (132.57) (36.97) (11.17) (16.50) (40.47)

Observations 19,886 19,886 19,886 19,886 19,886 19,886

Adjusted R2 0.002 0.002 0.001 0.002 0.002 0.001

Year Fixed Effects yes yes yes yes yes yes

Industry Fixed Effects yes yes yes yes yes yes

53

Table 5. Contract Characteristics and Below-Threshold M&As

This table presents results from OLS regressions of M&As on contract characteristics. The sample is restricted to

observations where the deal value falls within a +/-10% window centered around the FTC threshold. The dependent

variable, JustBelowThreshold, is an indicator variable that assumes the value of one if a deal’s transaction value is

within a 10% window below the FTC annual premerger review threshold, and zero otherwise. The main variable of

interest in Column (1) is an indicator variable that assumes the value of 1 if the contract includes a provision for

earnouts, and zero otherwise. Results presented in Column (2) are conditional upon the inclusion of an earnout

provision, where the main variable of interest is a continuous variable that measures the percent of the transaction

value represented by earnouts. All variables are defined in Appendix A. All columns include target-firm industry fixed

effects (using Fama-French 48 industry classification) and year fixed effects. Robust t-statistics are reported in

parentheses and calculated using standard errors clustered at the target-firm industry and year levels. *, **, ***

represent significance at the 10%, 5%, and 1% level, respectively.

(1) (2)

VARIABLES JustBelow JustBelow

Earnouts 0.062**

(2.42)

EarnoutPerc 0.001

(0.66)

DealValue –0.071*** –0.108***

(–11.01) (–6.53)

Constant 5.022*** 7.295***

(12.60) (7.25)

Observations 637 79

Adjusted R2 0.440 0.530

Year Fixed Effects yes yes

Industry Fixed Effects yes yes

54

Table 6. Merger Agreement Terms and Below-Threshold M&As

This table presents results from OLS regressions of M&As on contract terms. The dependent variable,

JustBelowThreshold, is an indicator variable that assumes the value of one if a deal’s transaction value is within a

10% window below the FTC annual premerger review threshold, and zero otherwise. The main variable of interest in

column (1), ExtendedLiabilityCoverage, is an indicator variable that assumes the value of 1 if the acquirer extends

D&O coverage for the former directors and officers of the target, and zero otherwise. The main variable of interest in

column (2), DeductibleThreshold, is a continuous variable that measures the threshold (as a percent of the total deal

value) above which the target is responsible for post-acquisition claims against the acquirer. We also include controls

for whether the representations and warranties survive beyond the effective date (RepsSurvive), the length of time of

the survival period (SurvivalPeriod), and whether a condition that the holdback funds are held in escrow or otherwise

(Escrow). All variables are defined in Appendix A. All columns include target-firm industry fixed effects (using Fama-

French 48 industry classification) and year fixed effects. Robust t-statistics are reported in parentheses and calculated

using standard errors clustered at the target-firm industry and year levels. *, **, *** represent significance at the 10%,

5%, and 1% level, respectively.

(1) (2)


ExtendedLiabilityCoverage 0.131**

(2.16)

DeductibleThreshold 18.705**

(2.19)

DealValue –0.108*** –0.095***

(–19.99) (–14.36)

Constant 7.125*** 6.020***

(19.16) (11.56)

Observations 122 99

Adjusted R2 0.603 0.609

Controls yes yes



55

Table 7. Private-Target Deal Premiums and Below-Threshold M&As

This table presents the results from OLS regressions of M&As on private-target deal premiums. The dependent


within a 10% window below the FTC annual premerger review threshold, and zero otherwise. The main variables of

interest in Column (1), PrivateTargetDealPrem, is a continuous variable that measures the premium paid for private

targets. The main variable of interest in Column (2), PublicTargetDealPrem, is a continuous variable that measures

premium acquirers pay for publicly traded target firms. All variables are defined in Appendix A. All columns include

target-firm industry fixed effects (using Fama-French 48 industry classification) and year fixed effects. Robust t-

statistics are reported in parentheses and calculated using standard errors clustered at the target-firm industry and year

levels. *, **, *** represent significance at the 10%, 5%, and 1% level, respectively.

(1) (2)


PrivateTargetDealPrem –0.199*

(–1.96)

PublicTargetDealPrem –0.000

(–0.25)

DealValue –0.069*** –0.060**

(–7.54) (–2.80)

Constant 4.966*** 4.030***

(9.57) (3.19)

Observations 219 84

Adjusted R2 0.449 0.373



56

Table 8. Acquirer-Target CEO Economic Ties and Below-Threshold M&As

This table presents results from OLS regressions of M&As on of acquirer-target CEO economic ties. The dependent


within a 10% window below the FTC annual premerger review threshold, and zero otherwise. In Panel A, the main

variable of interest, EconomicTie, is an indicator variable that takes the value of 1 if the target CEO is retained by the

acquiring firm and/or has an economic interest in the surviving firm. We also control for whether the acquirer is public

(PublicAcquirer) and for whether the merger is horizontal (Horizontal). In Panel B, column (1), the main variable of

interest is EarnoutPayoff, and in column (2) the main variable of interest in the interaction term EconomicTie ×

EarnoutPayoff. All variables are defined in Appendix A. All columns include target-firm industry fixed effects (using

Fama-French 48 industry classification) and year fixed effects. Robust t-statistics are reported in parentheses and

calculated using standard errors clustered at the target-firm industry and year levels. *, **, *** represent significance

at the 10%, 5%, and 1% level, respectively.

Panel A: Economic Ties and Below-Threshold M&As

(1)

VARIABLES JustBelow

EconomicTie 0.084**

(2.15)

DealValue –0.078***

(–8.67)

Constant 5.341***

(9.66)

Observations 423

Adjusted R2 0.466

Controls yes

Year Fixed Effects yes

Industry Fixed Effects yes

57

Table 8. Acquirer-Target CEO Economic Ties and Below-Threshold M&As (cont’d)

Panel B: Economic Ties and Earnout Payoffs

(1) (2)


EarnoutPayoff 0.059 –0.386

(0.49) (–1.78)

EconomicTie –0.198

(–1.31)

EconomicTie × EarnoutPayoff 0.590***

(9.04)

DealValue –0.144*** –0.151***

(–10.12) (–6.06)

Constant 9.190*** 9.682***

(12.11) (6.84)

Observations 39 29

Adjusted R2 0.753 0.505



58

Table 9. Acquirer-Target Industry and Location and Below-Threshold M&As

This table presents results from OLS regressions of M&As on acquirer-target industry and location characteristics.

The sample is restricted to observations where the deal value falls within a +/-10% window centered around the FTC

threshold. In Panels A and B, the dependent variable, JustBelowThreshold, is an indicator variable that assumes the

value of one if a deal’s transaction value is within the 10% window below the FTC annual premerger review threshold,

and zero otherwise. In Panel A, the main variables of interest in Columns (1) and (2) are indicator variables that

assume the value of 1 based on whether the target and acquirer share the same four-digit SIC code, i.e., horizontal

merger, or share the same state of operations, i.e., intrastate. In Column (3), the main variable of interest is the

interaction term, Horizontal × Intrastate, which takes the value of 1 if the merger is both horizontal and intrastate, and

zero otherwise. All variables are defined in Appendix A. In Panel B, Columns (1) and (4), the main variable of interest,

HighConc, is an indicator that assumes the value of 1 if the target firm’s industry is above the median concentration,

and zero otherwise. In Columns (2 and 5) and (3 and 6), the main variables of interest is interaction terms, Horizontal

× HighConc, and Intrastate × HighConc, which assume the value of 1 when the target firm’s industry is above the

median concentration and the acquirer and target share the same four-digit SIC code (in Columns (2) and (5)), or share

the same state of operations (in Columns (3) and (6)), and zero otherwise. In Columns (1) to (3) industry concentration

is estimated using the methodology in Hoberg and Phillips (2010). In Columns (4) to (6), industry concentration is

estimated using net sales by four-digit SIC code, by year. All variables are defined in Appendix A. All columns in

Panel A include target-firm industry fixed effects (using Fama-French 48 industry classifications) and year fixed

effects, while all columns in Panel B include industry fixed effects. Robust t-statistics are reported in parentheses and

calculated using standard errors clustered at the target-firm industry and year levels. *, **, *** represent significance

at the 10%, 5%, and 1% level, respectively.

Panel A. Horizontal, and Intrastate M&As (1) (2) (3)

VARIABLES JustBelow JustBelow JustBelow

Horizontal 0.066** 0.028

(2.61) (1.43)

Intrastate –0.061 –1.26*

(–1.31) (–1.92)

Horizontal × Intrastate 0.194***

(2.91)

DealValue –0.072*** –0.072*** –0.072***

(–10.81) (–11.06) (–11.45)

Constant 5.048*** 5.064*** 5.076***

(12.35) (12.66) (13.17)

Observations 637 637 637

Adjusted R2 0.442 0.440 0.447

Year Fixed Effects yes yes yes

Industry Fixed Effects yes yes yes

59

Table 9. Acquirer-Target Industry and Location and Below-Threshold M&As (cont’d)

Panel B. Highly-Concentrated Industry M&As (1) (2) (3) (4) (5) (6)

VARIABLES JustBelow JustBelow JustBelow JustBelow JustBelow JustBelow

HighConc 0.011 –0.014 0.050 –0.003 –0.024 –0.009

(0.28) (–0.27) (1.30) (–0.12) (–1.15) (–0.36)

Horizontal 0.013 0.005

(0.55) (0.25)

Horizontal x HighConc 0.133** 0.100**

(2.27) (2.24)

Intrastate 0.036 –0.105*

(0.45) (–1.80)

Intrastate x HighConc –0.223 0.051

(–1.62) (0.60)

DealValue –0.074*** –0.074*** –0.075*** –0.071*** –0.072*** –0.072***

(–10.45) (–10.64) (–11.59) (–11.11) (–11.32) (–11.47)

Constant 5.111*** 5.110*** 5.173*** 5.035*** 5.055*** 5.069***

(11.86) (12.18) (12.96) (12.53) (12.76) (13.01)

Observations 501 501 501 640 640 640

Adjusted R2 0.460 0.464 0.467 0.444 0.447 0.446

Year Fixed Effects yes yes yes yes yes yes

Industry Fixed Effects no no no no no no

60

Table 10. Horizontal Rivals’ Announcement Returns and Below-Threshold M&As

This table presents results from OLS regressions of announcement returns on M&As. The dependent variable,

RivalRet, is a continuous variable that represents the equal-weighted (three-day) market-adjusted portfolio returns of

horizontal rivals of acquirers. The main variable of interest in Column (1), JustBelowThreshold, is an indicator variable

that assumes the value of 1 if a deal’s transaction value is within a 10% window below the FTC annual premerger

review threshold, and zero otherwise. In Column (2), we present results for the association between announcement

returns and an interaction term, Horizontal × LeftBin, which assumes the value of 1 if the deal is below the threshold

and the acquirer and target share the same four-digit SIC code, and zero otherwise. The regressions also include as

controls DealValue, PublicAcquirer, PublicTarget, and LowNumRivals. All variables are defined in Appendix A. All

columns include acquirer industry fixed effects (using Fama-French 48 industry classification) and year fixed effects.

Robust t-statistics are reported in parentheses and calculated using standard errors clustered at the acquirer industry

and year levels. *, **, *** represent significance at the 10%, 5%, and 1% level, respectively.

(1) (2)

VARIABLES RivalRet RivalRet

JustBelowThreshold 0.005 0.002

(1.05) (0.49)

Horizontal –0.006*

(–1.79)

Horizontal × JustBelowThreshold 0.009*

(1.78)

Constant –0.064*** –0.066***

(–2.92) (–2.91)

Observations 543 543

Adjusted R2 0.115 0.115

Controls yes yes



61

Table 11. Product-Level Prices for Below-Threshold M&As

This table presents results from Difference-in-Differences OLS regressions of product prices on below-threshold mergers. The dependent variable, Price, is a

continuous variable that represents the average weekly prices for products of rivals. The main variable of interest, CommonProduct × Post, is an interaction term

that takes the value of 1 if the product is Common and the week falls after the effective date of the merger; and zero otherwise. All variables are defined in Appendix

A. We vary the inclusion of week and geographic fixed effects across columns such that column (5) represents our fully specified model. Robust t-statistics are

reported in parentheses and calculated using standard errors clustered at the product and week levels. *, **, *** represent significance at the 10%, 5%, and 1%

level, respectively.

(1) (2) (3) (4) (5)

VARIABLES Price Price Price Price Price

CommonProduct 4.953*** 4.947*** 4.924*** 4.944*** 4.921***

(10.45) (10.32) (10.29) (10.41) (10.39)

Post –0.145*** 0.116* 0.118*

(–3.01) (1.78) (1.82)

CommonProduct × Post 0.625*** 0.636*** 0.616*** 0.646*** 0.626***

(9.59) (8.69) (8.38) (14.61) (14.19)

TimeTrend –0.001***

(–17.93)

Constant 6.498*** 6.363*** 6.363*** 6.432*** 6.43***

(13.67) (13.47) (13.56) (12.99) (13.07)

Observations 77,835,861 77,835,861 77,835,861 77,835,861 77,835,861

Adjusted R2 0.014 0.014 0.021 0.018 0.024

Week Fixed Effects no no no yes yes

Geographic Fixed Effects no no yes no yes

i

Internet Appendix for “Stealth Acquisitions and Product Market

Competition”

This appendix contains additional analyses referenced in our paper, and is organized as follows

• Data Collection Process for Manually-Collected Data

• Falsification using One-Year Ahead Threshold Level (Figure IA.1)

• Falsification using Always-Exempt M&As (Figure IA.2)

• Falsification using Deals further Below the Threshold (Table IA.1)

• Additional Analysis using Stock-Price Collars (Table IA.2)

• Acquirer Termination Fees (Table IA.3)

ii

Appendix IA1. Data Collection Process

This section details the data collection process for key variables in the paper.

Goodwill to construct private target deal premiums

For calculating our private target deal premium measure, we hand collect data on the amount of

goodwill recorded in the quarterly or annual financial statements (e.g., 10-Q or 10-K) of public

acquirers located on EDGAR (at www.SEC.gov). The amount of goodwill for a given M&A

transaction is located in the notes to the financial statements; e.g., the note for the ‘Acquisition of

(target name)’ provides information about the acquisition, including the allocation of the purchase

price to various balance sheet accounts such as the Goodwill account. We exclude observations

when we cannot locate the goodwill number or when the acquirer has record an aggregated amount

of goodwill, such as when the acquirer has made multiple acquisitions in the same year.

Target-CEO career outcomes

We begin our search for the career outcomes of target CEOs by first identifying their name, using

EDGAR filings (in the case when the target is publicly traded), press releases, mentions in the

media, or other sources (e.g., trade associations; or conference attendee lists). We code a CEO as

having an economic tie with the acquirer, if we can confirm that the CEO has been retained in an

executive position and/or has an ownership interest in the acquirer. To identify whether the CEO

holds a post-acquisition executive role in the acquiring firm, we use public filings (in the case

when the acquirer is publicly traded), Google searches, LinkedIn profiles, and media mentions.

We code a CEO as not having been retained if we have definitive evidence that they do not hold a

post-acquisition executive position with the acquirer, or if the position is disclosed as being

transitionary (e.g., short-term consulting agreement). We exclude target CEOs from our analysis

when we cannot code them as being retained or not retained.

Earnout payoffs

We rely on EDGAR (at www.SEC.gov) to locate company filings for public acquirers to hand

collect data on post-acquisition earnout results. We begin by collecting data (from the merger

agreement or the acquirer’s 10-K) on the number of years over which contingency payments to the

target are to be paid. Next, we search the acquirer’s financial statements in subsequent years for

information on whether performance was achieved and an earnout was paid.

D&O insurance and deductible threshold

We read publicly available merger agreements located on EDGAR (at www. SEC.gov) to hand

collect data on (1) whether the acquirer extends (and pays for) liability insurance for the former

director and officers of the target firm; and, (2) to collect the dollar-based deductible threshold, in

the case when the acquirer can only clawback a portion of the purchase price when the aggregate

claims against the target exceed the deductible level.

Common products

To conduct our product-level pricing analysis, we begin by matching product-level barcodes to

firms. We do so by merging barcodes from our Nielsen RMS dataset to a library of barcodes we

obtain from GS1 US, the single official supplier of barcodes to US companies. This merge allows

http://www.sec.gov/

http://www.sec.gov/

iii

us to observe, at the firm level, a firm’s portfolio of products at any point in time during our sample

period.

Our identification of rivals’ products that are common to the acquirer by following the advice of

marketing experts, who suggest that we examine product similarities by attributes, prices,

advertising, sales channels (e.g., online, in-store), and whether rivals publicly disclose rivals’

comparable products in their SEC filings.

For our regression analysis, we aggregate prices at the product-week level, and use the average

price in a week as our main measure.

iv

Figure IA.1. Falsification: Density of Public and Private Horizontal M&As around FTC

Premerger Review Threshold (Using One-Year Ahead Threshold Level)


review threshold for when we substitute the one-year ahead threshold level for this year’s threshold level. Confidence

bands (at the 95% level) are depicted by the thin lines. The circles, which describe scaled frequencies, are analogous

to histogram bins. For ease of interpretation, we restrict our kernel density estimations to distances within +/- $30

million of zero; where zero represents the FTC threshold. Estimation procedure follows the method in McCrary

(2008).

v

Figure IA.2. Density of Public and Private Horizontal M&As around FTC Premerger

Review Threshold


review threshold for horizontal mergers. Confidence bands (at the 95% level) are depicted by the thin lines. The

circles, which describe scaled frequencies, are analogous to histogram bins. For ease of interpretation, we restrict our

kernel density estimations to distances within +/- $30 million of zero; where zero represents the FTC threshold.

Estimation procedure follows the method in McCrary (2008).

vi

Table IA.1. Falsification Test: Acquirer-Target Characteristics for M&As Occurring Further Below the Threshold

This table presents results from OLS regressions of M&As on In Panels A and B, the dependent variable, FurtherBelow, is an indicator variable that assumes the

value of one if a deal’s transaction value is within a 20% to 10% window below the FTC annual premerger review threshold, and zero otherwise. The main variables

of interest in Columns (1) and (2) are indicator variables that assume the value of 1 if the acquirer or target is a publicly traded company, and zero otherwise. The

main variables of interest in Columns (3) to (8) are indicator variables that assume the value of 1 based on combined acquirer-target characteristics, and zero

otherwise. The sample includes FurtherBelow deals and deals in the 10% window just above the threshold. All variables are defined in Appendix A. All columns

include target-firm industry fixed effects (using Fama-French 48 industry classifications) and year fixed effects. Robust t-statistics are reported in parentheses and

calculated using standard errors clustered at the target-firm industry and year levels. *, **, *** represent significance at the 10%, 5%, and 1% level, respectively.

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES FurtherBelow FurtherBelow FurtherBelow FurtherBelow FurtherBelow FurtherBelow FurtherBelow FurtherBelow

PublicAcquirer 0.010

(0.59)

PublicTarget –0.052

(–1.66)

Public-Public –0.041

(–1.06)

Public-Private 0.025

(1.34)

Private-Private 0.044

(1.09)

Private-Public –0.037

(–0.87)

Subsid-Public 0.043

(1.39)

Subsid-Private 0.022

(0.90)

DealValue –0.063*** –0.063*** –0.063*** –0.063*** –0.063*** –0.063*** –0.063*** –0.063***

(–24.88) (–24.66) (–24.29) (–24.97) (–24.85) (–24.82) (–24.98) (–24.84)

Constant 4.300*** 4.320*** 4.301*** 4.292*** 4.301*** 4.312*** 4.300*** 4.302***

(29.42) (29.58) (29.09) (28.72) (29.21) (29.32) (29.58) (29.42)

Observations 569 569 569 569 569 569 569 569

Adjusted R2 0.749 0.750 0.749 0.749 0.749 0.749 0.749 0.749

Year Fixed Effects yes yes yes yes yes yes yes yes

Industry Fixed Effects yes yes yes yes yes yes yes yes

vii

Table IA.2. Stock-Price Collars and Below-Threshold M&As

This table presents results from OLS regressions of M&As on stock-price collars. The sample is restricted to

observations where the deal value falls within a +/-10% window centered on the FTC threshold and the form of

payment includes the acquirer’s stock. The dependent variable, JustBelowThreshold, is an indicator variable that

assumes the value of one if a deal’s transaction value is within a 10% window below the FTC annual premerger review

threshold, and zero otherwise. The main variable of interest, Collar, is an indicator variable that assumes the value of

one if the merger agreement includes a stock-price collar, and zero otherwise. All variables are defined in Appendix

A. All columns include acquirer-firm industry fixed effects (using Fama-French 48 industry classification) and year

fixed effects. Robust t-statistics are reported in parentheses and calculated using standard errors clustered at the

acquirer-firm industry and year levels. *, **, *** represent significance at the 10%, 5%, and 1% level, respectively

(1)

VARIABLES JustBelow

Collar 0.102*

(1.77)

DealValue –0.109***

(–12.39)

Constant 7.077***

(13.53)

Observations 113

Adjusted R2 0.648



viii

Table IA.3. Acquirer Termination Fees and Below-Threshold M&As

This table presents results from OLS regressions of M&As on acquirer termination fees. The sample is restricted to

observations where the deal value falls within a +/-10% window centered on the FTC threshold. The dependent

variable, AcqTermFeePercent, is a continuous variable that measures the termination fee as proportion of the deal

value. The main variable of interest, JustBelowThreshold, is an indicator variable that assumes the value of one if a

deal’s transaction value is within a 10% window below the FTC annual premerger review threshold, and zero

otherwise. All variables are defined in Appendix A. All columns include acquirer-firm industry fixed effects (using

Fama-French 48 industry classification) and year fixed effects. Robust t-statistics are reported in parentheses and

calculated using standard errors clustered at the acquirer-firm industry and year levels. *, **, *** represent

significance at the 10%, 5%, and 1% level, respectively.

(1)

VARIABLES AcqTermFeePct

JustBelowThreshold –0.002*

(–1.78)

TargetTermFeePercent 0.109

(1.47)

DealValue 0.000

(0.45)

Constant –0.002

(–0.05)

Observations 634

Adjusted R2 0.110



Stealth Acquisitions and Product Market Competition

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