THE JOURNAL OF FINANCE • VOL. LXIX, NO. 5 • OCTOBER 2014
Corporate Innovations and Mergers
and Acquisitions
JAN BENA and KAI LI∗
ABSTRACT
Using a large and unique patent-merger data set over the period 1984 to 2006, we
show that companies with large patent portfolios and low R&D expenses are acquirers,
while companies with high R&D expenses and slow growth in patent output are
targets. Further, technological overlap between firm pairs has a positive effect on
transaction incidence, and this effect is reduced for firm pairs that overlap in product
markets. We also show that acquirers with prior technological linkage to their target
firms produce more patents afterwards. We conclude that synergies obtained from
combining innovation capabilities are important drivers of acquisitions.
IT HAS LONG BEEN argued that synergies are key drivers of mergers and acquisitions (M&As),1 and that many M&As occur due to technology reasons.2
However, there is little direct evidence of whether and how synergies in the
∗ Jan Bena and Kai Li are at the Sauder School of Business, University of British Columbia.
We thank Cam Harvey (the Editor), an anonymous referee, an anonymous Associate Editor, Anup
Agrawal, Ken Ahern, Julian Atanassov, Gennaro Bernile, Simon Firestone, Kathleen Hanley, Jarrad Harford, Keith Head, Jerry Hoberg, Marcin Kacperczyk, Jon Karpoff, Ambrus Kecskés, Ron
Masulis, Michael Meloche, Gordon Phillips, Shinichi Sakata, Amit Seru, Anju Seth, Shang-jin Wei,
Shan Zhao, seminar participants at BlackRock, CERGE-EI, Chinese University of Hong Kong, Federal Reserve Board of the Governors, Fudan University, Office of the Comptroller of the Currency,
Shanghai Jiaotong University, Shanghai University of Finance and Economics, University of Alabama, University of Hong Kong, UIBE, Virginia Tech, Xian Jiaotong University, and conference
participants at the Pacific Northwest Finance Conference (Seattle), the Financial Intermediation
Research Society Conference (Sydney), the UBC Summer Finance Conference (Vancouver), the
Northern Finance Association Meetings (Vancouver), the 8th Annual Corporate Finance Conference at Washington University in St. Louis, the 24th Australasian Finance and Banking Conference
(Sydney), and the American Finance Association Meetings (Chicago) for helpful comments. We also
thank Milka Dimitrova, Chang Jie Hu, Yan Jin, Kairong Xiao, and Feng Zhang for research assistance. We acknowledge financial support from the Social Sciences and Humanities Research
Council of Canada (SSHRC). All remaining errors are our own.
1 See the surveys by Andrade, Mitchell, and Stafford (2001) and Betton, Eckbo, and Thorburn
(2008).
2 In the introduction to Kaplan (2000), a collection of merger case studies, editor Steven Kaplan concludes that, “ . . . a general pattern emerges from these studies. It is striking that most
of the mergers and acquisitions were associated with technological or regulatory shocks.” (See
http://www.nber.org/books/kapl00-1.) Notably, almost two-thirds of all public firm mergers in the
United States over our sample period involve firms that pursue technological innovations, as captured by patenting activities. This percentage is large, given that only 30% of firms report R&D
expenses, and less than 10% of firms repeatedly deliver patentable innovation output.
DOI: 10.1111/jofi.12059
1923
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technology space drive individual firms’ decisions to participate in M&As, and
of how they affect merger outcomes.3 In this paper, we first examine the relation between characteristics of corporate innovation activities and whether a
firm becomes an acquirer or a target firm. We then study whether technological overlap between firm pairs affects transaction incidence. Finally, using a
sample of bids withdrawn due to reasons exogenous to innovation as a control
sample, we estimate the effect of a merger on future innovation output when
there is premerger technological overlap between merging firms. Our large
and unique patent-merger data set over the period 1984 to 2006 allows us to
construct targeted measures of innovation output and technological overlap,
extending the analysis of Hoberg and Phillips (2010) in product markets.
The merger of Pharmacia & Upjohn and Monsanto, creating Pharmacia Corporation, in 2000 illustrates how synergies in the technology space trigger
transactions. Both companies were active in the market for prescription pharmaceuticals, but in different therapeutic areas; their products were mostly
complementary. A merger motive of both companies was for each to obtain access to the other’s technological assets and skills. Monsanto’s most successful
product (Celebrex) used a novel technological platform known as Cox-2-specific
inhibitors; the merger allowed Pharmacia & Upjohn access to this technology. Similarly, Pharmacia & Upjohn had strong expertise in biotechnology (genomics) based on large biotech proteins, which had not been adopted by Monsanto in its small chemicals prior to the merger. The merger’s most important
technological consequence was the creation of a critical mass allowing for expanded in-house clinical research; the typical scale of R&D projects increased,
while the lead time of research decreased. Other important consequences were
the improvement of existing technological competencies, and the merged company gaining access to a much broader network of research institutes.4
This example highlights a number of key features of merger transactions
that we study. First, merger participants pursue related R&D activities prior
to the acquisition. Second, certain technologies of one party appear valuable
to the other party and vice versa, triggering the transaction. Third, improvement in postmerger innovation output occurs through technological synergy.
To understand whether this example represents a general pattern underlying
M&As, we investigate the following research questions. How are firms’ innovation activities related to transaction incidence? Do merger participants possess
related technologies prior to the transaction? Does the presence of premerger
technological overlap affect postmerger innovation output? Assuming that acquirers and target firms are active in technological innovation, we expect that
parties with interfirm linkages in the technology space are more likely to form
merger pairs. We also conjecture that transactions with premerger overlapping
technologies result in an improvement in innovation output postmerger. The
3
Very little empirical work has shed light on such effects, in large part because measuring
synergy at the firm pair level is difficult. We discuss the relevant literature later in the introduction.
4 See Cassiman and Colombo (2006) for more details on this merger, and for additional examples
on technological synergy-driven M&As.
Corporate Innovations and Mergers and Acquisitions
1925
central idea guiding our analysis is that synergies obtained from combining
corporate innovation activities are an important driver of M&As.
To examine the role of corporate innovations in M&As, we compile an
economy-wide patent-merger data set, and develop measures that capture
firms’ innovation output and potential synergistic gains stemming from technological overlap between merger participants. We first show that both acquirers and target firms are active in technological innovation, but with different
characteristics. Firms with large patent portfolios and low R&D expenses are
more likely to become acquirers, while firms with high R&D expenses and slow
growth in patentable innovation output are more likely to become target firms.
We next find that the presence of technological overlap between two firms’
innovation activities, as captured by the proximity of patent portfolios, shared
knowledge bases, and mutual citations of patent portfolios, has a significant
effect on the probability of a merger pair formation. This finding provides direct
firm pair-level evidence in support of the synergy perspective of M&As.
Furthermore, we show that, when firms overlap in terms of both product
markets and technological innovation, the positive effect of technological overlap on the likelihood of a merger pair formation is reduced for firm pairs that
also overlap in product markets.
Finally, we use a quasi-experiment, involving bids withdrawn due to reasons
exogenous to the innovation activities of either the acquirer or the target firm,
to estimate the treatment effect of a merger on postmerger innovation output.
Following Seru (2014), we argue that the assignment of deals into the treatment
sample (i.e., completed deals) versus the control sample (i.e., bids withdrawn
due to reasons exogenous to innovation) can be treated as random. As such, any
selection concerns are differenced out by comparing firms’ innovation output in
the treatment sample, pre- and postmerger, with that in the control sample. We
show that the presence of premerger technological overlap between merging
firms leads to a significant improvement in the combined firms’ postmerger
innovation output.
In summary, our results highlight the ex ante selection effects of corporate
innovation activities on transaction incidence and merger pairing, as well as
the ex post treatment effect of a merger on firms’ innovation output.5
Our paper differs from prior work, and thus contributes to the M&A literature, in at least three ways. First, using unique data on patents and patent
citations, we develop measures of bilateral technology-specific firm characteristics, and provide evidence of their importance as drivers of merger pairing at an
economy-wide level. Notably, all our technological overlap measures transcend
traditional industry classifications such as SICs or the Fama-French industries. Second, by employing different innovation characteristics—innovation
output (i.e., patents) and R&D expenses—in our analysis, we add to prior
findings by showing that such output and expenses have different implications for M&As. Third, by examining multiple sources of synergy, namely,
5 See Li and Prabhala (2007) for a more detailed discussion on selection effects versus treatment
effects in corporate finance.
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technological and product market overlaps, our paper highlights the importance of interactions between multiple core corporate activities for merger
pairing.
Our paper is closely related to Hoberg and Phillips (2010), who are the first
to pinpoint product market synergy as important drivers of M&As, and contributes to a broader literature on asset complementarity and mergers (RhodesKropf and Robinson (2008)). Fan and Goyal (2006) demonstrate the importance
of vertical relatedness of firms’ industries for merger outcomes. Ahern and Harford (2014) argue that both intra- and interindustry merger waves are driven
by customer-supplier relations between industries. Our paper also adds to a
new and growing literature that examines interactions between innovation
and M&As. Phillips and Zhdanov (2013) posit that small firms are R&D intensive because they want to be acquired by large firms, while large firms
optimally may decide to purchase smaller innovative firms and conduct less
R&D themselves. Sevilir and Tian (2012) find a positive association between
M&As and acquirers’ postmerger innovation outcomes, particularly if target
firms are innovative premerger.
The paper proceeds as follows. In Section I, we develop our hypotheses. We
describe our empirical methodology and construction of key variables, and
provide a sample overview in Section II. We examine the ex ante selection
effects of corporate innovation activities on transaction incidence and merger
pairing in Section III. In Section IV, we explore the ex post treatment effect of
a merger on firms’ innovation output. We conclude in Section V.
I. Hypothesis Development
A. Innovation Characteristics and Transaction Incidence
Interactions between firms in innovation have been a major topic in the
growth, productivity, and industrial organization literatures for many decades
(see the seminal work by Spence (1984) and Aghion and Howitt (1992), and the
empirical evidence in Jaffe (1986)). Innovation as a key driver of firm value is
well established (Bloom and Van Reenen (2002), Nicholas (2008), Pástor and
Veronesi (2009)). Both innovation and R&D activities result in informationsensitive assets, making them distinct from other tangible assets.
Prior literature has mainly examined how financing, ownership, and governance provide incentives for in-house (internal) innovation, and how firms
source innovation externally.6 Holmström and Roberts (1998) argue that many
M&A transactions are made to source innovation. Buying innovation is generally not a feasible alternative to M&As, since establishing an innovation’s
6 Bhattacharya and Ritter (1983) develop a model in which firms may compromise their ability to
innovate if they disclose details of their R&D projects to capital markets in order to raise financing.
For this reason, financing innovation externally may be more costly compared to financing capital
investment; see the survey by Hall (2009). Incorporating similar trade-offs, Maksimovic and Pichler
(2001) show how both technological and competitive risks affect the timing of private and initial
public offerings in new industries, and Ferreira, Manso, and Silva (2014) and Bernstein (2014)
show that public ownership discourages innovation.
Corporate Innovations and Mergers and Acquisitions
1927
value requires disclosure and a potential buyer has no incentive to pay once
such information has been revealed. Hart and Holström (2010) further show
that, when two firms’ production functions exhibit externalities—for example,
when they need to coordinate their technologies—a merger facilitates coordination that cannot otherwise be achieved. These arguments lead to our first
hypothesis: the likelihood of a firm to participate in M&As increases in its level
of innovation activities. In our empirical investigation, we capture the level of
such activities using both patents and R&D expenses.
B. Technological Overlap and Merger Pairing
We next ask how acquirers identify prospective target firms. We conjecture
that interactions between firms in innovation may lead to merger-pairing decisions through the following channels.
First, technological overlap can help overcome information asymmetry in
acquisitions. Intellectual property and technological knowhow, by their very
nature, are more difficult to evaluate than tangible assets. One concern for
an acquirer is its ability to accurately value a target firm. If the acquirer and
the target firm are familiar with each other’s technologies, then information
asymmetry between merger participants is mitigated (Kaplan (2000), Higgins
and Rodriguez (2006)). Second, technological overlap can lead to economies
of scale and scope in innovation—for example, through reduction in duplicate R&D efforts—and hence can trigger mergers (Henderson and Cockburn
(1996)). Finally, as highlighted in the introduction’s motivating example, one
merger partner’s technology may fill gaps in the other’s patent portfolio, resulting in the postmerger firm experiencing strengthened innovation prowess
or more competitive positioning (Cassiman and Colombo (2006), Cassiman and
Veugelers (2006)).
As such, we expect acquirers to pursue target firms with which they have
overlapping innovation activities or similar levels of technological competency.
Our second hypothesis is thus: mergers are more likely to occur between firms
with technological overlap.
C. Interaction between Technological and Product Market Overlaps
Using a text-based analysis of firms’ product descriptions in their 10-K reports, Hoberg and Phillips (2010) show that, ceteris paribus, firms with broad
product market similarities to all firms in the economy are more likely to merge,
while firms with highly similar rivals in the product space are less likely to do
so. Their results suggest that, when close rivals compete for growth opportunities and market share, such competition lessens the likelihood of a merger
pair formation. Moreover, when firms are too similar, antitrust concerns might
come into play, further reducing this likelihood.
Close rivals in the product market space may pursue related innovation activities. R&D efforts of such rival firms may lead to the introduction of new
products or services, further intensifying product market competition (Bloom,
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Schankerman, and Van Reenen (2013)). Building on both Hoberg and Phillips’s
(2010) findings and our second hypothesis, our third hypothesis is thus: the positive effect of technological overlap on the likelihood of a merger pair formation
is reduced for firm pairs that also overlap in product markets.
D. Technological Overlap and Postmerger Innovation Output
So far, we have developed hypotheses about the ex ante selection effects of
innovation activities on transaction incidence and merger pairing, focusing on
the role of technological overlap. To establish that technological synergy is a
key driver behind M&As, we must ascertain the ex post treatment effect of a
merger on postmerger innovation output.
Prior work shows that relatedness of merger participants is critical for postmerger outcomes. Maksimovic, Phillips, and Prabhala (2011) find that productivity of acquired assets increases in industries in which the acquirer operates.
Hoberg and Phillips (2010) show that mergers between firms with product market similarities achieve bigger product range expansions, and higher operating
profitability and sales growth. Fan and Goyal (2006) find that vertical mergers are associated with positive wealth effects significantly larger than those
for diversifying mergers. Ahuja and Katila (2001) show that technological relatedness is associated with improved innovation output of acquiring firms in
the chemicals industry. Our fourth and final hypothesis is thus: the effect of a
merger on postmerger innovation output is positively related to the degree of
premerger technological overlap between merger participants.
In our empirical investigation, we test the above hypotheses, and also attempt to control for several alternative explanations for why and how mergers
take place. In the next section, we describe our empirical methodology, define
key innovation variables, and present a sample overview.
II. Methodology, Key Variables, and Sample Overview
A. Methodology
We test our first two hypotheses by estimating selection models of firms
becoming acquirers or target firms. We run a conditional logit regression7 using
cross-sectional data as of the fiscal year-end before the bid announcement:
Event Firmim,t = α + β1 Event Firm Innovation Characteristicsim,t−1
+ β2 Event Firm Characteristicsim,t−1 + DealF Em + eim,t .
(1)
7 See McFadden (1974) for an introduction to the conditional logit regression, and Kuhnen (2009)
and Dyck, Morse, and Zingales (2010) for recent applications in finance.
Corporate Innovations and Mergers and Acquisitions
1929
The dependent variable, Event Firmim,t , is equal to one if firm i is the acquirer
(target firm) in deal m, and zero otherwise. Independent variables Event Firm
Innovation Characteristicsim,t-1 and Event Firm Characteristicsim,t-1 are defined
in the Appendix. For each deal, there is one observation for the acquirer (target
firm), and multiple observations for the control acquirers (control target firms).
Finally, Deal FEm is the fixed effect for each acquirer (target firm) and its
control acquirers (control target firms).
We use three different control samples as pools of potential merger participants. First, to form the Random Control Sample, for each acquirer (target
firm) of a deal announced in year t, we randomly draw five firms from Compustat/CRSP in year t − 1 that were neither an acquirer nor a target firm in
the three-year period prior to the deal. Our pool of potential merger participants thus captures M&A clustering in time (Mitchell and Mulherin (1996),
Maksimovic, Phillips, and Yang (2013)).
Second, to form the Industry- and Size-Matched Control Sample, for each
acquirer (target firm) of a deal announced in year t, we find up to five matching
acquirers (matching target firms) by industry—where the industry definitions
are based on the narrowest SIC grouping that includes at least five firms8 —and
by size from Compustat/CRSP in year t − 1 that were neither an acquirer nor a
target firm in the three-year period prior to the deal. Such matching creates a
pool of potential merger participants that captures clustering not only in time,
but also by industry (Andrade, Mitchell, and Stafford (2001), Harford (2005)).
Third, to form the Industry-, Size-, and B/M-Matched Control Sample, for
each acquirer (target firm) of a deal announced in year t, we find up to five
matching acquirers (matching target firms)—first matched by industry, and
then matched on propensity scores estimated using size and book-to-market
(B/M) ratios—from Compustat/CRSP in year t − 1 that were neither an acquirer
nor a target firm in the three-year period prior to the deal.9 We add the B/M ratio
to our matching characteristics because the literature argues that it captures
growth opportunities (Andrade, Mitchell, and Stafford (2001)), overvaluation
(Shleifer and Vishny (2003), Rhodes-Kropf and Viswanathan (2004)), and asset
complementarity (Rhodes-Kropf and Robinson (2008))—all important drivers
of M&As.
To test our third hypothesis, we run a conditional logit regression using crosssectional data as of the fiscal year-end before the bid announcement, with one
8 Specifically, we start with four-digit SIC industry groups to search for matching acquirers
(target firms). If there are no more than five industry peers to the actual acquirer (target firm)
within the four-digit SIC industry group, we move up to the three-digit SIC industry group. If there
are no more than five industry peers to the actual acquirer (target firm) within the three-digit SIC
industry group, we move up to the two-digit SIC industry group. We find that 73% (19%) of the
acquirers are matched at the four-digit (three-digit) level, while 75% (18%) of the target firms are
matched at the four-digit (three-digit) level; the remaining matches are at the two-digit level. We
use historical SIC industry codes from Compustat.
9 We alternatively try with and without replacement, and use 1, 5, or 10 nearest neighbors (see,
for example, Maksimovic, Phillips, and Yang (2013)). Our results continue to hold.
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The Journal of FinanceR
observation for each deal and multiple observations for control deals:
Acquirer-T argeti jm,t = α + β1 T echnological Overlapi jm,t−1
+ β2 Acquirer InnovationCharacteristicsim,t−1
+ β3 T arget InnovationCharacteristics jm,t−1
+ β4 Acquirer Characteristicsim,t−1
+ β5 T arget Characteristics jm,t−1 + β6 Diversi f yingi jm
+ β7 Same Statei jm + DealF Em + ei jm,t .
(2)
The dependent variable, Acquirer-Targetijm,t , is equal to one if the firm
pair ij is the acquirer-target firm pair, and zero otherwise, and Technological Overlapijm,t-1 is one of the three pairs of technological overlap measures,
all of which are defined below in Section II.B. All other control variables are
defined in the Appendix.
Since technological overlap can only arise between innovative firms, to estimate equation (2) we employ samples of actual and control deals involving
acquirers and target firms that are innovative before the bid. We form the
Random Control Sample by pairing the target firm with five randomly drawn
control firms for the acquirer, and by pairing the acquirer with five randomly
drawn control firms for the target firm. We form the Industry- and Size-Matched
Control Sample (Industry-, Size-, and B/M-Matched Control Sample) by pairing the target firm with up to five of the closest matches to the acquirer, and
by pairing the acquirer with up to five of the closest matches to the target firm.
In addition to capturing M&A clustering in time and by industry, our pools
of matched potential merger pairs account for any possible influence that the
horizontal/vertical relatedness between industry pairs might have on the likelihood of a merger pair formation (Fan and Goyal (2006), Ahern and Harford
(2014)).
In summary, our conditional logit models, together with three different control samples, allow us to examine whether innovation characteristics are important drivers of transaction incidence and merger pairing after accounting
for both M&A clustering (in time and by industry) and size and B/M effects.
B. Measures of Technological Overlap
We employ three sets of variables to capture technological overlap. The first
set includes two symmetric measures: Technological Proximity, which follows
Jaffe (1986) and measures the closeness of any two firms’ innovation activities
in the technology space using patent counts in different technology classes, and
Knowledge Base Overlap, which measures the extent to which any two firms’
awarded patents cite the same set of past patents, that is, the extent to which
the two firms base their innovation activities on the same underlying body
of knowledge. The second set of variables, new to the literature, includes two
Corporate Innovations and Mergers and Acquisitions
1931
reciprocal measures: Acquirer’s (Target’s) Base Overlap Ratio, which captures
the importance of the common knowledge base relative to the acquirer’s (target
firm’s) knowledge base. The final set of variables also includes two reciprocal
measures: Acquirer’s (Target’s) Cross-Cites Ratio, which measures the extent
to which the target firm’s (acquirer’s) patent portfolio is directly cited by the
acquirer’s (target firm’s) patent portfolio and hence captures the immediate importance of a firm’s innovation activity to that of another firm. Notably, all our
technological overlap measures transcend traditional industry classifications
such as SICs or the Fama-French industries. Our overlap variables provide a
continuous measure of the pairwise relatedness of any two firms in the technology space, both within and across conventional industry affiliations—a critical
aspect of capturing technological synergy in our M&A setting.
To construct our targeted measures of technological overlap, we retrieve
patent and patent citation data from the worldwide Patent Statistical Database
(PATSTAT, April 2008). PATSTAT is the most comprehensive and detailed
database on patents designed for statistical analysis. It identifies all citations
received by a patent, as well as citations made by a patent, which are essential
for us to track patent citation activity between firm pairs.
C. Sample Overview
To form our M&A samples, we begin with all announced and completed U.S.
M&As with announcement dates between January 1, 1984 and December 31,
2006 covered by the Mergers and Acquisitions database of Thomson Financial’s
SDC Database.10 We identify all deals where the form of deal was coded as a
merger, an acquisition of majority interest, or an acquisition of assets. We retain
an acquisition only if the acquirer owns less than 50% of the target firm prior to
the bid, is seeking to own more than 50% of the target firm, and owns more than
90% of the target firm after the deal completion. We require that: (1) both the
acquirer’s and the target firm’s total assets be valued at more than $1 million,
or that the transaction value be no less than $1 million (all in 1984 constant
dollars) to eliminate the many small and economically insignificant deals in
the sample; (2) neither the acquirer nor the target firm be from the financial
sector (SIC 6000–6999); and (3) for different analyses, either the acquirer or
both the acquirer and the target firm be covered by Compustat/CRSP. These
filters yield 2,621 deals where information on acquirers is available and 1,762
deals where information on both acquirers and target firms is available.
Table I presents the temporal distribution of acquirers/target firms/deals for
which we are able to form control samples using industry and size matching.
We also include subsamples in which the merger participants, as well as their
control firms, are active in patenting in the five-year period leading up to
10 Our sample period begins in 1984 because information on M&As in SDC is less reliable before
1984. Our sample period ends in 2006 because the GVKEY-patent number link available through
the NBER Patent Data Project (January 2011) ends in 2006.
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Table I
Corporate Acquisitions over Time, 1984–2006
This table reports the number of completed corporate acquisitions by the year of the bid announcement over the period January 1, 1984 to December 31, 2006, where the form of the deal was coded
as a merger, an acquisition of majority interest, or an acquisition of assets. We require that the
acquirer own less than 50% of the target firm prior to the bid, seek to own more than 50% of the target firm, and own more than 90% of the target firm after deal completion. We further require that
both the acquirer and the target firm be valued at more than $1 million (in 1984 constant dollars)
and be nonfinancials. A deal enters the Acquirer Sample (Target Sample) if its acquirer (target
firm) is covered by Compustat/CRSP and has at least one industry- and size-matched acquirer
(target firm) as of the fiscal year-end before the bid announcement—All Acquirers (All Targets).
A deal enters the Acquirer-Target Sample if both the acquirer and the target firm are covered by
Compustat/CRSP and have their respective industry- and size-matched firms (All Deals). For the
Acquirers with Patents (Targets with Patents) subsample, we require that the acquirer and the
matching acquirer (target firm and the matching target firm) be awarded at least one patent in
the period from year ayr-5 to year ayr-1, where ayr-1 is the calendar year with the largest overlap
with the fiscal year before the bid announcement, and ayr-5 is four years prior to ayr-1. For the
Acquirers or Targets with Patents (Acquirers and Targets with Patents) subsample, we require that
the acquirer or the target firm, or both firms and their respective matching firms (both the acquirer
and the target firm and their respective matching firms), be awarded at least one patent over the
same five-year period.
Acquirer-Target Sample
Acquirers
and
Targets
with
Patents
(7)
Year
All
Acquirers
(1)
Acquirers
with
Patents
(2)
All
Targets
(3)
Targets
with
Patents
(4)
All Deals
(5)
Acquirers
or
Targets
with
Patents
(6)
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
81
79
87
65
107
60
62
52
47
51
87
150
168
201
231
220
155
158
108
102
98
110
93
33
39
46
30
44
27
20
23
26
21
37
73
70
89
120
139
93
90
71
62
51
70
62
54
56
62
49
62
42
39
34
27
37
64
99
109
131
165
167
104
113
68
56
61
75
70
18
19
23
12
16
16
13
11
11
7
25
33
28
39
58
65
51
44
33
21
31
35
33
55
57
62
51
63
42
39
35
27
37
64
100
112
132
166
168
105
113
68
56
61
76
70
34
34
42
33
35
27
19
23
19
20
39
57
57
68
98
119
78
82
56
39
42
60
54
15
16
21
11
13
12
9
9
10
10
17
33
23
33
53
62
43
46
28
19
26
30
31
Total
2,572
1,336
1,744
642
1,759
1,135
570
Acquirer Sample
Target Sample
Corporate Innovations and Mergers and Acquisitions
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the bid.11 We show that deals with innovative acquirers/target firms exhibit
similar cyclicality as deals in general. In the Internet Appendix, we present
a breakdown of the same samples by industry, highlighting the prevalence of
innovation-driven acquisitions across industries.12
Table II presents descriptive statistics for the sample, as in Table I column (6)
and its industry- and size-matched control sample.13 We show that acquirers
exhibit significantly higher innovation output than their matching acquirers
as well as their target firms, as measured by both Patent Index and CitationWeighted Patents. The target firms’ R&D expenses are significantly higher
than those of their acquirers. Our univariate statistics suggest the importance
of distinguishing patenting output and R&D expenses when investigating the
role of innovation activities in M&As.
We further show that our sample firms are large (the mean total assets is in
the ninth and eight deciles of the Compustat universe over the same time period
for the acquirers and the target firms, respectively), and that the acquirers are
larger, and have higher sales growth, higher ROA, lower B/M ratios, and better
stock market performance than their industry- and size-matched control firms.
Moreover, we show that the acquirers are much larger, and have higher sales
growth, higher ROA, lower cash holdings, lower B/M ratios, and much better
stock market performance than their target firms. Overall, our samples are
similar to those used in other studies of mergers between public firms (see, for
example, Gaspar, Massa, and Matos (2005) and Harford, Jenter, and Li (2011)).
At the bottom of Table II, using six different measures, we show that there is
technological overlap between the acquirers and their target firms. The mean
Technological Proximity between the acquirer’s and the target firm’s patent
portfolios is 11.27%. The common knowledge base between the acquirers and
the target firms is more important to the latter than to the former (Target’s Base
Overlap Ratio is larger than Acquirer’s Base Overlap Ratio). Target firms cite
their acquirers’ patents more frequently than the reverse (Target’s Cross-Cites
Ratio is larger than Acquirer’s Cross-Cites Ratio).14 In contrast, the extent of
the technological overlap between merger participants in the control sample is
minimal.15
11 To be classified as active in patenting in the five-year period leading up to the bid, a firm
must have been awarded at least one patent in the period from ayr-5 to ayr-1, where ayr-1 is the
calendar year with the largest overlap with the fiscal year before the bid announcement, and ayr-5
is four years prior to ayr-1.
12 The Internet Appendix is available in the online version of the article on the Journal of
Finance website.
13 See the Internet Appendix for descriptive statistics for the other samples.
14 One or more measures of technological overlap are nonzero for the 329 deals (out of 1,135
deals).
15 We find a high correlation between the two measures of innovation output—Patent Index
and Citation-Weighted Patents. As a result, in our multivariate analyses of acquirer/target choices,
we include one innovation output measure at a time in a regression. We find moderate correlation
among the six technological overlap measures, and between the overlap measures and the measures
of innovation output. Therefore, in our multivariate analyses of acquirer-target firm pairing, we
include at most two technological overlap measures at a time in a regression.
Table II
Patent Index
Citation-Weighted Patents
Patent Index
Citation-Weighted Patents
R&D (%)
Self-Cites Ratio (%)
Age of Patent Portfolio
Total Assets (2006 USD billion)
࢞ Sales (%)
ROA (%)
Leverage (%)
Cash (%)
B/M
Stock Return (%)
6.44
33.98
140.86
814.69
5.65
6.19
5.62
12.96
16.71
14.42
19.45
16.85
0.42
12.45
Mean
31.52
182.11
Target Firms
444.00
2,961.47
6.79
8.52
4.44
32.68
27.73
11.68
15.62
18.83
0.30
53.68
Acquirers
SD
0.50
2.00
7.50
36.00
3.29
2.15
5.05
2.35
11.98
15.53
17.67
9.13
0.34
2.87
Median
SD
Median
317.21
2,096.00
6.86
8.55
4.26
24.04
26.83
12.00
15.90
19.01
0.35
56.09
5.83*
27.00*
3.87**
2.51
4.85
1.43***
10.34***
14.59***
17.38
10.14
0.39***
–0.87***
8.23*
44.40*
40.38
236.05
(Continued)
0.83***
3.00***
Industry- and Size-Matched Target Firms
90.17***
521.85***
6.09**
6.02
5.50
8.35***
14.52**
13.14***
19.12
17.73
0.48***
7.79***
Industry- and Size-Matched Acquirers
Mean
This table reports summary statistics of the acquirers and the target firms in the Acquirers or Targets with Patents sample, as in Table I column (6),
as well as their industry- and size-matched control firms. The control sample contains, for each deal, control deals formed by pairing the acquirer
with up to five target firm matches, and by pairing the target firm with up to five acquirer matches. If either the acquirer or the target firm is active
in patenting activities in the five-year period leading up to the bid, we require that their matches be active in patenting over the same time period as
well. Definitions of the variables are provided in the Appendix. *, ** and *** denote significance at the 10%, 5%, and 1% level, respcetively.
Summary Statistics
1934
The Journal of FinanceR
Technological Proximity (%)
Knowledge Base Overlap
Acquirer’s Base Overlap Ratio (%)
Target’s Base Overlap Ratio (%)
Acquirer’s Cross-Cites Ratio (%)
Target’s Cross-Cites Ratio (%)
R&D (%)
Self-Cites Ratio (%)
Age of Patent Portfolio
Total Assets (2006 USD billion)
࢞ Sales (%)
ROA (%)
Leverage (%)
Cash (%)
B/M
Stock Return (%)
11.27
0.94
0.39
2.44
0.71
3.35
7.89
3.19
3.75
1.11
13.44
8.02
18.08
21.34
0.61
−5.36
Mean
26.52
3.39
1.97
9.28
4.64
13.71
0.00
0.00
0.00
0.00
0.00
0.00
4.15
0.00
2.25
0.18
10.72
11.83
14.78
11.73
0.50
−16.56
Median
Acquirer-Target Firm Pairs
10.01
7.79
4.47
3.11
29.40
17.13
17.29
22.84
0.44
60.74
Acquirers
SD
Table II—Continued
SD
Median
9.50**
0.48***
0.14***
0.70***
0.22***
1.01***
8.15
3.61*
3.92
0.94*
12.84
6.95*
17.17*
23.03**
0.61
−3.30
24.06
2.76
1.26
4.21
2.61
7.18
0.00
0.00
0.00
0.00
0.00
0.00
4.73
0.00
2.78**
0.17
10.53
10.90***
12.33**
14.08**
0.49
−15.29
Industry- and Size-Matched
Acquirer-Target Firm Pairs
9.92
8.46
4.30
2.81
30.57
17.80
17.48
23.31
0.45
64.41
Industry- and Size-Matched Acquirers
Mean
Corporate Innovations and Mergers and Acquisitions
1935
1936
The Journal of FinanceR
III. Ex Ante Selection Effects
In this section, we implement various multivariate analyses to test our first
three hypotheses regarding the role of corporate innovations in M&As.
A. Which Firms Are Acquirers/Target Firms?
Table III, Panel A, presents coefficient estimates from the conditional logit
regression in equation (1) to predict acquirers. Columns (1) and (4) present
the median and standard deviation of the empirical distribution of coefficient
estimates from conditional logit models bootstrapping 1,000 randomly drawn
control groups of acquirers. (See Efron and Tibshirani (1993) for an introduction
to bootstrapping.)
Across different samples and using either Patent Index or Citation-Weighted
Patents as a measure of innovation output, we show that firms with higher innovation output are more likely to become acquirers. In all cases, the coefficients
on the measures of innovation output are significant at the 1% level. When we
use the random control acquirers, the coefficient on R&D expenses is positive
and significant (as it also is under the standard logit specification, as shown in
the Internet Appendix). The coefficient becomes negative and significant when
we use either sample of the matched control acquirers. This finding suggests
that the pool of potential acquirers obtained by matching on industry and size
(and book-to-market) to capture which firms typically participate in M&As is
different from the pool of potential acquirers drawn randomly. Importantly, we
show that acquirers are more R&D intensive compared to the average firm in
Compustat, but are less R&D intensive when compared to their industry- and
size-matched (and book-to-market-matched) peers at the time of the bid; hence,
we observe different results.
We also show that firms whose innovation activities rely more on their own
past innovation output (i.e., with high Self-Cites Ratio) are less likely to become
acquirers: the coefficients on Self-Cites Ratio are negative and significant at
the 1% level. In contrast, there is no systematic association between Age of
Patent Portfolio and a firm’s likelihood of becoming an acquirer. Other findings
not directly related to innovation are nonetheless consistent with prior work
on M&As (see, for example, Maksimovic and Phillips (2001), Moeller, Schlingemann, and Stulz (2004), and Gaspar, Massa, and Matos (2005)). In particular,
we show that larger firms, as well as firms with faster sales growth, better
operating performance, lower B/M ratios, and higher prior year stock returns,
are more likely to engage in M&As as acquirers.16 It is worth noting that
our findings that firms with large patent portfolios are more likely to become
acquirers remain after controlling for two measures of acquirer stock market performance (the B/M ratio and the prior year stock return) or employing
16 We obtain the same results: (i) using the linear probability model, (ii) using an expanded set
of control acquirers of up to 10 firms, and (iii) removing acquirers whose total assets are in the top
decile of Compustat firms. See the Internet Appendix.
Table III
Stock Return
B/M
Cash
Leverage
ROA
࢞ Sales
Total Assets
Age of Patent Portfolio
Self-Cites Ratio
R&D
Patent Index
0.273***
(0.017)
3.471***
(0.286)
−1.874***
(0.307)
0.015***
(0.004)
0.361***
(0.009)
0.776***
(0.050)
2.522***
(0.118)
0.527***
(0.086)
0.944***
(0.101)
−0.630***
(0.037)
0.225***
(0.028)
Random
(1)
Industry, Size, B/M
(3)
Random
(4)
0.676***
(0.095)
0.570**
(0.249)
−0.020
(0.162)
−0.356**
(0.181)
−0.681***
(0.078)
0.304***
(0.041)
0.541***
(0.026)
−2.853***
(0.613)
−1.869***
(0.530)
0.022***
(0.006)
0.175***
(0.042)
0.486***
(0.093)
1.830***
(0.210)
0.285
(0.187)
0.025
(0.181)
0.380***
(0.029)
−1.951***
(0.539)
−2.526***
(0.644)
0.013*
(0.008)
0.148***
(0.017)
1.606***
(0.294)
−1.119***
(0.252)
−0.026***
(0.005)
0.488***
(0.016)
0.575***
(0.066)
1.285***
(0.157)
−0.991***
(0.142)
0.514***
(0.121)
−0.603***
(0.060)
0.181***
(0.035)
Panel A: Innovation Characteristics Measured in Levels
Industry, Size
(2)
All Acquirers
0.749***
(0.146)
0.432
(0.360)
−0.582**
(0.253)
−0.581**
(0.243)
−0.834***
(0.129)
0.159***
(0.055)
0.579***
(0.030)
−4.265***
(0.746)
−1.995***
(0.601)
0.043***
(0.011)
Industry, Size
(5)
(Continued)
0.124**
(0.061)
0.457***
(0.167)
1.411***
(0.310)
−0.323
(0.310)
−0.378
(0.266)
0.365***
(0.029)
−3.384***
(0.687)
−2.469***
(0.631)
0.017
(0.012)
Industry, Size, B/M
(6)
Acquirers with Patents
This table reports coefficient estimates from conditional logit models in equation (1). The dependent variable is equal to one for the acquirer, and
zero for the randomly drawn or matched acquirers that form the control group. Columns (1) and (4) present the median and standard deviation of
the empirical distribution of coefficient estimates from conditional logit models using 1,000 randomly drawn control groups of acquirers. Measures
of innovation output (i.e., patents) and firm size are in natural logarithms. Panel A presents the baseline specification. Panel B presents coefficient
estimates for key variables of interest. Definitions of the variables are provided in the Appendix. All specifications include deal fixed effects. Robust
standard errors (clustered at the deal level) are reported in parentheses; *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively.
Which Firms Are the Acquirers?
Corporate Innovations and Mergers and Acquisitions
1937
࢞ R&D
R&D
࢞ Patent Index
Patent Index
Acquirer Controls
Deal FEs
No. of Observations
Pseudo R2
R&D
Deal FEs
No. of Observations
No. of Actual Acquirers
No. of Control Acquirers
Pseudo R2
Citation-Weighted Patents
Industry, Size, B/M
(3)
Random
(4)
Yes
14,500
2,572
11,928
0.11
0.345***
(0.018)
−2.647***
(0.607)
Yes
Yes
14,500
0.10
Yes
10,023
1,809
8,214
0.07
0.268***
(0.020)
−2.083***
(0.544)
Yes
Yes
10,023
0.07
Yes
8,268
1,474
6,794
0.30
0.114***
(0.013)
1.566***
(0.295)
Yes
Yes
8,268
0.30
Panel A: Innovation Characteristics Measured in Levels
Industry, Size
(2)
Yes
7,600
1,336
6,264
0.15
0.421***
(0.025)
−3.884***
(0.727)
Yes
Yes
7,600
0.13
Industry, Size
(5)
0.265***
(0.017)
0.075**
(0.041)
3.457***
(0.287)
−0.106
(0.087)
0.557***
(0.028)
−0.094
(0.062)
−2.986***
(0.619)
0.147**
(0.074)
0.370***
(0.030)
0.083
(0.076)
−2.083***
(0.546)
0.111
(0.075)
0.135***
(0.018)
0.130***
(0.035)
1.608***
(0.295)
0.221**
(0.100)
0.588***
(0.032)
−0.028
(0.065)
−4.457***
(0.753)
0.262***
(0.099)
(Continued)
0.359***
(0.030)
0.059
(0.073)
−3.591***
(0.689)
0.293***
(0.109)
Yes
4,808
853
3,955
0.10
0.274***
(0.023)
−3.380***
(0.683)
Yes
Yes
4,808
0.09
Industry, Size, B/M
(6)
Acquirers with Patents
Panel B: Innovation Characteristics Measured in Both Levels and Changes
Yes
14,447
2,621
11,826
0.29
0.200***
(0.011)
3.148***
(0.290)
Yes
Yes
14,447
0.29
Random
(1)
All Acquirers
Table III—Continued
1938
The Journal of FinanceR
Acquirer Controls
Deal FEs
No. of Observations
Pseudo R2
࢞ R&D
R&D
࢞ Citation-Weighted Patents
Citation-Weighted Patents
Acquirer Controls
Deal FEs
No. of Observations
Pseudo R2
Industry, Size
(2)
Industry, Size, B/M
(3)
Random
(4)
0.200***
(0.012)
0.000
(0.023)
3.150***
(0.290)
−0.114
(0.087)
Yes
Yes
14,447
0.29
Yes
Yes
14,447
0.29
0.357***
(0.020)
−0.057
(0.038)
−2.799***
(0.615)
0.123*
(0.072)
Yes
Yes
14,500
0.10
Yes
Yes
14,500
0.11
0.263***
(0.021)
0.027
(0.043)
−2.172***
(0.548)
0.093
(0.074)
Yes
Yes
10,023
0.07
Yes
Yes
10,023
0.07
0.104***
(0.014)
0.063***
(0.020)
1.599***
(0.295)
0.216**
(0.100)
Yes
Yes
8,268
0.30
Yes
Yes
8,268
0.30
0.426***
(0.025)
−0.007
(0.043)
−4.081***
(0.735)
0.241**
(0.096)
Yes
Yes
7,600
0.14
Yes
Yes
7,600
0.15
Industry, Size
(5)
0.267***
(0.023)
0.050
(0.046)
−3.569***
(0.684)
0.279***
(0.107)
Yes
Yes
4,808
0.10
Yes
Yes
4,808
0.10
Industry, Size, B/M
(6)
Acquirers with Patents
Panel B: Innovation Characteristics Measured in Both Levels and Changes
Random
(1)
All Acquirers
Table III—Continued
Corporate Innovations and Mergers and Acquisitions
1939
1940
The Journal of FinanceR
samples of control acquirers matched on industry, size, and B/M. We conclude
that our findings are unlikely to be due to market overvaluation.
In Panel B, we add the growth rates of innovation output and R&D expenses
to the baseline specifications presented in Panel A. The level variables remain
highly statistically significant, but the growth rate variables do not, suggesting that the level of innovation activity rather than its growth is positively
associated with the likelihood of a firm becoming an acquirer.
Table IV, Panel A, presents coefficient estimates from the conditional logit
regression in equation (1) to predict target firms. Columns (1) and (4) present
the median and standard deviation of the empirical distribution of coefficient
estimates from conditional logit models bootstrapping 1,000 randomly drawn
control groups of target firms.
In contrast to Table III, we show that there is no significant association
between innovation output and the likelihood of a firm becoming a target firm,
while there is a positive and significant association between R&D expenses
and the likelihood of a firm becoming a target firm (with one exception). Taken
together, our results suggest that target firms are active in innovation, but have
not yet converted their R&D expenses into patents at the time of a merger bid.
We also show that firms with better operating performance and firms with
lower prior year stock returns are more likely to become target firms.17
In Panel B, we add the growth rate of innovation output and R&D expenses
to the baseline specifications presented in Panel A. The level of R&D expenses
remains positive and highly statistically significant (with one exception). Interestingly, the coefficients on the growth rate of innovation output are always
negative and significant at the 10% level or better. These findings suggest that
firms with high R&D expenses and slow growth in innovation output are more
likely to become target firms.
Overall, our results provide strong support for our first hypothesis, that
innovative firms are more likely to be involved in merger transactions. We
show that innovation output and R&D expenses have different implications
regarding whether a firm will become an acquirer or a target firm. Our findings on R&D expenses are consistent with Phillips and Zhdanov (2013), who
show that small firms are R&D intensive when they can sell out to large
firms, while large firms optimally may decide to purchase small innovative
firms and conduct less R&D themselves. We further show that acquirers are
firms with large patent portfolios, while target firms have high R&D expenses
but experience slow growth in realized innovation output.
B. How Are Merger Pairs Formed?
Table V presents coefficient estimates from the conditional logit regression in
equation (2) to predict merger pairs. Columns (1) to (3) present the median and
17 Using the standard logit specification leads to a result analogous to those obtained using the
random control sample. We also obtain the same results: (i) using the linear probability model, (ii)
using an expanded set of control target firms of up to 10 firms, and (iii) removing acquirers and
target firms whose total assets are in the top decile of Compustat firms. See the Internet Appendix.
Table IV
Stock Return
B/M
Cash
Leverage
ROA
࢞ Growth
Total Assets
Age of Patent Portfolio
Self-Cites Ratio
R&D
Patent Index
−0.041**
(0.018)
5.584***
(0.280)
0.482*
(0.253)
0.011***
(0.003)
0.014*
(0.007)
0.140***
(0.046)
2.303***
(0.106)
0.630***
(0.077)
0.331***
(0.088)
−0.031
(0.026)
−0.189***
Random
(1)
Industry, Size, B/M
(3)
Random
(4)
0.034
(0.091)
0.803***
(0.229)
0.116
(0.180)
−0.504***
(0.188)
−0.028
(0.064)
−0.152***
0.029
(0.039)
1.898***
(0.476)
0.406
(0.403)
0.006
(0.007)
−0.248***
−0.035
(0.091)
1.771***
(0.200)
0.416**
(0.187)
−0.338*
(0.181)
0.109***
(0.035)
1.203***
(0.446)
0.332
(0.435)
0.000
(0.008)
−0.175***
(0.042)
3.928***
(0.682)
0.654
(0.504)
−0.021*
(0.011)
0.108***
(0.030)
0.048
(0.152)
1.649***
(0.301)
−0.128
(0.288)
0.074
(0.243)
0.082
(0.107)
−0.151*
Panel A: Innovation Characteristics Measured in Levels
Industry, Size
(2)
All Targets
0.007
(0.182)
0.601
(0.409)
0.493
(0.343)
−0.196
(0.310)
−0.055
(0.131)
−0.143*
−0.015
(0.050)
1.809**
(0.763)
0.045
(0.519)
0.015
(0.014)
Industry, Size
(5)
(Continued)
−0.233**
−0.000
(0.180)
1.320***
(0.332)
0.362
(0.366)
−0.522*
(0.306)
0.023
(0.045)
1.132
(0.701)
0.195
(0.540)
0.010
(0.015)
Industry, Size, B/M
(6)
Targets with Patents
This table reports coefficient estimates from conditional logit models in equation (1). The dependent variable is equal to one for the target firm, and
zero for the randomly drawn or matched target firms that form the control group. Columns (1) and (4) present the median and standard deviation of
the empirical distribution of coefficient estimates from conditional logit models using 1,000 randomly drawn control groups of target firms. Measures
of innovation output (i.e., patents) and firm size are in natural logarithms. Panel A presents the baseline specification. Panel B presents coefficient
estimates for key variables of interest. Definitions of the variables are provided in the Appendix. All specifications include deal fixed effects. Robust
standard errors (clustered at the deal level) are reported in parentheses; *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively.
Which Firms Are the Target Firms?
Corporate Innovations and Mergers and Acquisitions
1941
࢞ R&D
R&D
࢞ Patent Index
Patent Index
Industry, Size, B/M
(3)
Random
(4)
0.01
0.029
(0.025)
1.853***
(0.477)
Yes
Yes
9,637
(0.049)
Yes
9,637
1,744
7,893
0.01
0.03
0.094***
(0.023)
1.116**
(0.447)
Yes
Yes
8,341
(0.051)
Yes
8,341
1,517
6,824
0.03
−0.012
(0.018)
−0.252***
(0.040)
5.563***
(0.280)
−0.040
(0.080)
0.079*
(0.041)
−0.326***
(0.080)
1.841***
(0.477)
0.055
(0.071)
0.144***
(0.037)
−0.255***
(0.085)
1.087**
(0.451)
0.122*
(0.067)
−0.147***
(0.044)
−0.188***
(0.079)
3.889***
(0.683)
−0.028
(0.146)
0.049
(0.052)
−0.339***
(0.094)
1.734**
(0.759)
0.063
(0.122)
0.01
0.017
(0.035)
1.714**
(0.764)
Yes
Yes
2,605
−0.076**
(0.030)
3.788***
(0.683)
Yes
Yes
1,503
0.04
(0.087)
Yes
2,605
642
1,963
0.01
Industry, Size
(5)
(0.076)
Yes
1,503
554
949
0.05
Panel A: Innovation Characteristics Measured in Levels
Industry, Size
(2)
(Continued)
0.049
(0.047)
−0.161*
(0.094)
1.126
(0.706)
−0.013
(0.127)
0.03
0.050
(0.033)
1.043
(0.702)
Yes
Yes
2,072
(0.093)
Yes
2,072
552
1,520
0.02
Industry, Size, B/M
(6)
Targets with Patents
Panel B: Innovation Characteristics Measured in Both Levels and Changes
0.05
Pseudo R2
Target Controls
Deal FEs
No. of Observations
R&D
0.005
(0.012)
5.435***
(0.282)
Yes
Yes
9,777
(0.023)
Yes
9,777
1,762
8,015
0.05
Citation-Weighted Patents
Deal FEs
No. of Observations
No. of Actual Targets
No. of Control Targets
Pseudo R2
Random
(1)
All Targets
Table IV—Continued
1942
The Journal of FinanceR
Target Controls
Deal FEs
No. of Observations
Pseudo R2
࢞ R&D
R&D
࢞ Citation-Weighted Patents
Citation-Weighted Patents
Target Controls
Deal FEs
No. of Observations
Pseudo R2
Industry, Size
(2)
Industry, Size, B/M
(3)
Random
(4)
Industry, Size
(5)
0.032***
(0.012)
−0.189***
(0.023)
5.362***
(0.281)
−0.037
(0.079)
Yes
Yes
9,777
0.05
Yes
Yes
9,777
0.05
0.066**
(0.027)
−0.193***
(0.043)
1.747***
(0.481)
0.053
(0.071)
Yes
Yes
9,637
0.01
Yes
Yes
9,637
0.01
0.124***
(0.024)
−0.176***
(0.048)
0.980**
(0.455)
0.124*
(0.068)
Yes
Yes
8,341
0.03
Yes
Yes
8,341
0.03
Yes
Yes
2,605
0.02
0.066*
(0.038)
−0.184***
(0.051)
1.539**
(0.773)
0.057
(0.124)
Yes
Yes
2,605
0.02
Yes
Yes
1,503
0.05
−0.041*
(0.032)
−0.158***
(0.045)
3.696***
(0.684)
0.009
(0.146)
Yes
Yes
1,503
0.05
0.078**
(0.035)
−0.121**
(0.057)
1.032
(0.714)
−0.015
(0.128)
Yes
Yes
2,072
0.03
Yes
Yes
2,072
0.03
Industry, Size, B/M
(6)
Targets with Patents
Panel B: Innovation Characteristics Measured in Both Levels and Changes
Random
(1)
All Targets
Table IV—Continued
Corporate Innovations and Mergers and Acquisitions
1943
Table V
Technological
Proximity
Knowledge Base
Overlap
Acquirer’s Base
Overlap Ratio
Target’s Base
Overlap Ratio
Acquirer’s
Cross-Cites Ratio
Target’s
Cross-Cites Ratio
Acquirer, Target Innovation
Acquirer, Target Controls
Diversifying, Same State
Deal FEs
No. of Observations
No. of Actual Deals
No. of Control Deals
Pseudo R2
Yes
Yes
Yes
Yes
2,040
536
1,504
0.61
2.941***
(0.541)
1.780***
(0.302)
(1)
Yes
Yes
Yes
Yes
2,040
536
1,504
0.57
43.879
(68.899)
20.159**
(11.986)
(2)
Random
23.534
(33.042)
7.209**
(3.586)
Yes
Yes
Yes
Yes
2,040
536
1,504
0.56
(3)
Yes
Yes
Yes
Yes
4,089
570
3,519
0.14
0.791***
(0.207)
0.970***
(0.107)
(4)
Yes
Yes
Yes
Yes
4,089
570
3,519
0.12
9.612***
(2.401)
5.537***
(1.080)
(5)
Industry, Size
4.776***
(1.585)
2.480***
(0.423)
Yes
Yes
Yes
Yes
4,089
570
3,519
0.11
(6)
Yes
Yes
Yes
Yes
2,805
507
2,298
0.12
1.099***
(0.235)
0.966***
(0.123)
(7)
Yes
Yes
Yes
Yes
2,805
507
2,298
0.10
9.036***
(3.434)
6.075***
(1.286)
(8)
(9)
4.001**
(1.816)
2.422***
(0.508)
Yes
Yes
Yes
Yes
2,805
507
2,298
0.08
Industry, Size, B/M
This table reports key coefficient estimates from conditional logit models in equation (2) using the Acquirers and Targets with Patents of the AcquirerTarget Sample as in Table I, column (7). The dependent variable is equal to one for the acquirer-target firm pair, and zero for the control firm pairs
that form the control group. Columns (1) to (3) present the median and standard deviation of the empirical distribution of coefficient estimates from
conditional logit models using 1,000 randomly drawn control groups of acquirer-target firm pairs. Knowledge Base Overlap and firm size are in natural
logarithms. Definitions of the variables are provided in the Appendix. All specifications include deal fixed effects. Robust standard errors (clustered
at the deal level) are reported in parentheses; *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively.
Acquirer-Target Firm Pairing
1944
The Journal of FinanceR
Corporate Innovations and Mergers and Acquisitions
1945
standard deviation of the empirical distribution of coefficient estimates from
conditional logit models bootstrapping 1,000 randomly drawn control groups
of deals. Due to space constraints, we report only coefficient estimates on measures of technological overlap. We show a positive and significant association
between any of the six measures of merger participants’ technological overlap and the likelihood of a merger pair formation (with two exceptions using
the random control sample). This finding is both important and new in the
literature, suggesting that the reciprocal relatedness of two firms’ respective
innovation activities leads to merger pairing.18
Our evidence in Table V provides strong support for our second hypothesis,
that mergers are more likely to take place between parties with overlapping
innovation activities. Our findings provide direct evidence on how asset complementarity triggers merger pairing, as argued by Rhodes-Kropf and Robinson (2008). By focusing on a specific form of complementarity—technological
synergy—we complement the results on product market synergy established
by Hoberg and Phillips (2010).
C. Different Sources of Merger Synergy
Table VI, Panel A, presents coefficient estimates from the conditional logit
regression in equation (2), where we add Hoberg and Phillips’s (2010) measure
of Product Market Relatedness19 and its interactions with our measures of technological overlap. We show that Product Market Relatedness is an important
driver of merger pairing decisions, as are our measures of technological overlap. Interestingly, we find that the coefficients on the interaction terms between
Product Market Relatedness and the technological overlap measures are negative (with one exception), and in five cases are significantly different from zero
at the 5% level or better. This finding suggests that, when firms overlap in two
different core activities—product markets and technological innovations—the
positive effect of technological (product market) overlap on the likelihood of a
merger pair formation is reduced for firm pairs that overlap in both.
To further explore the interaction effect, in Table VI, Panel B, we report the
average probability of a merger pair formation based on Panel A specifications
(columns (1), (4), and (7)) for specific values of Product Market Relatedness and
Technological Proximity. In column (2), we show that, when we set the value of
Product Market Relatedness to zero (one), the effect of increasing Technological
18 We obtain the same results: (i) using a specification with the growth rates of technological
overlap measures rather than their levels; (ii) using the linear probability model; (iii) using an
expanded set of control deals obtained by including the control acquirers paired up with the
control target firms; (iv) using the sample of All Deals (as shown in Table I, column (5)); (v)
using the sample of Acquirers or Targets with Patents (as shown in Table I, column (6)); and (vi)
removing acquirers and target firms whose total assets are in the top decile of Compustat firms.
See the Internet Appendix.
19 We thank Jerry Hoberg for making the data available at the following website:
http://www.rhsmith.umd.edu/industrydata/.
Table VI
Product Market
Relatedness (PMR)
Technological
Proximity
Knowledge Base
Overlap
Technological
Proximity × PMR
Knowledge Base
Overlap × PMR
Acquirer’s Base
Overlap Ratio
Target’s Base
Overlap Ratio
4.751***
(0.537)
3.516***
(0.498)
1.732***
(0.297)
−2.512**
(1.492)
−0.700
(4.831)
(1)
43.441**
(25.788)
20.828*
(13.007)
4.660***
(0.628)
(2)
Random
4.655***
(0.474)
(3)
2.054***
(0.194)
0.912***
(0.264)
0.931***
(0.144)
−0.540
(0.386)
−0.233
(0.192)
(4)
10.223***
(3.865)
5.638***
(1.276)
2.037***
(0.181)
(5)
Industry, Size
2.080***
(0.178)
(6)
Panel A: Interaction of Product Market and Technological Overlaps
2.339***
(0.221)
1.501***
(0.311)
0.889***
(0.166)
−1.277***
(0.458)
−0.177
(0.208)
(7)
9.369
(5.933)
6.887***
(1.547)
2.294***
(0.205)
(8)
2.247***
(0.206)
(9)
(Continued)
Industry, Size, B/M
This table reports coefficient estimates from conditional logit models using the Acquirers and Targets with Patents of the Acquirer-Target Sample as
in Table I column (7). The dependent variable is equal to one for the acquirer-target firm pair, and zero for one of the control firm pairs that form
the control group. Panel A, columns (1) to (3), and Panel B, column (1), present the median and standard deviation of the empirical distribution of
coefficient estimates from conditional logit models using 1,000 randomly drawn control groups of acquirer-target firm pairs. Knowledge Base Overlap
and firm size are in natural logarithms. Panel A presents the baseline specification. Panel B presents the average predicted probabilities, computed
using estimates of the linear probability model. Definitions of the variables are provided in the Appendix. All specifications include deal fixed effects.
Robust standard errors (clustered at the deal level) are reported in parentheses; *, **, and *** denote significance at the 10%, 5%, and 1% level,
respectively.
Different Sources of Merger Synergy
1946
The Journal of FinanceR
Target’s
Cross-Cites Ratio
Acquirer’s
Cross-Cites Ratio × PMR
Target’s
Cross-Cites Ratio × PMR
Acq./Targ./Deal Controls
Deal FEs
No. of Observations
No. of Actual Deals
No. of Control Deals
Pseudo R2
Target’s Base Overlap Ratio × PMR
Overlap Ratio × PMR
Acquirer’s Cross-Cites Ratio
Acquirer’s Base
Yes
Yes
2,040
536
1,504
0.60
(1)
Yes
Yes
2,040
536
1,504
0.56
−25.261
(45.412)
−12.757
(22.974)
(2)
Random
32.676*
(23.391)
7.266**
(4.087)
−19.290
(40.824)
–4.725
(12.826)
Yes
Yes
2,040
536
1,504
0.55
(3)
Yes
Yes
4,089
570
3,519
0.20
(4)
Yes
Yes
4,089
570
3,519
0.19
−3.306
(4.403)
−1.990
(1.829)
(5)
(6)
6.588***
(1.989)
2.582***
(0.498)
−4.827**
(2.401)
−0.304
(0.889)
Yes
Yes
4,089
570
3,519
0.18
Industry, Size
Panel A: Interaction of Product Market and Technological Overlaps
Table VI—Continued
Yes
Yes
2,805
507
2,298
0.20
(7)
Yes
Yes
2,805
507
2,298
0.19
−3.061
(6.893)
−4.517**
(1.892)
(8)
(9)
(Continued)
9.347***
(3.223)
2.040***
(0.630)
−8.239**
(3.428)
0.271
(1.214)
Yes
Yes
2,805
507
2,298
0.18
Industry, Size, B/M
Corporate Innovations and Mergers and Acquisitions
1947
Product Market Relatedness = 1 & Technological Proximity = 1
Product Market Relatedness = 0 & Technological Proximity = 1
Product Market Relatedness = 1 & Technological Proximity = 0
Product Market Relatedness = 0 & Technological Proximity = 0
Technological Proximity = 1
Technological Proximity = 0
Product Market Relatedness = 1
Product Market Relatedness = 0
0.198***
(0.004)
0.930***
(0.025)
0.230***
(0.005)
0.782***
(0.048)
0.146***
(0.005)
0.925***
(0.026)
0.764***
(0.054)
0.993***
(0.055)
Random
(1)
0.091***
(0.005)
0.299***
(0.016)
0.126***
(0.006)
0.209***
(0.025)
0.073***
(0.008)
0.294***
(0.019)
0.173***
(0.029)
0.322***
(0.041)
Industry, Size
(2)
Panel B: Probabilities of Acquirer-Target Firm Pairing
Table VI—Continued
0.113***
(0.007)
0.390***
(0.019)
0.160***
(0.009)
0.312***
(0.047)
0.080***
(0.012)
0.389***
(0.023)
0.283***
(0.057)
0.398***
(0.058)
Industry, Size, B/M
(3)
1948
The Journal of FinanceR
Corporate Innovations and Mergers and Acquisitions
1949
Proximity from its minimum (zero) to maximum (one) increases the average
probability of a merger pair formation by 10% (0.173 – 0.073) (3%; 0.322 –
0.294). This increase suggests that the effect of technological overlap on the
probability of merger pairing is reduced for firm pairs that overlap in product
markets. The same countervailing effect occurs for Product Market Relatedness
when we condition on different values of Technological Proximity.20
This effect might arise when two firms are similar in their product offerings and pursue related innovation activities; their innovation efforts will then
likely further intensify product market rivalry by introducing similar new products or services. As shown by Hoberg and Phillips (2010), close rivalry in product markets has a negative impact on the likelihood of firms merging. As a
result, the positive effect of technological overlap on the likelihood of a merger
pair formation is reduced for firm pairs that also overlap in product markets.
These findings highlight the importance of capturing how firms simultaneously
interact along multiple dimensions when we study transaction incidence.
IV. Ex Post Treatment Effect
So far, we have examined and established the ex ante selection effects of innovation characteristics on merger participants, focusing on the role of technological overlap. We now investigate whether and how mergers with premerger
technological overlap affect the innovation output of the combined firm following deal completion—the ex post treatment effect. We do not examine R&D
expenses as, a priori, it is unclear how they change after the merger. Similarly,
combining merging firms’ innovation activities may not deliver improved operating performance in the short run, and we cannot attribute the long-run
performance solely to the merger event.
A. The Quasi-Experiment
The identification challenge of our treatment effect analysis is that the association between premerger technological overlap and postmerger innovation
output could be due to the endogenous selection of firm pairs into a treatment
group, rather than to the impact of technological synergy on postmerger innovation output. As we show above, acquisitions are more likely to occur between
firms with technological overlap. As a result, comparing the average innovation output of merged firms with technological overlap to that of merged firms
without overlap could lead to biased estimates.
To address such selection concerns, we exploit a quasi-experiment. Specifically, following Seru (2014), we employ a control sample of withdrawn bids
20 We obtain the same results: (i) using the linear probability model; (ii) using an expanded
set of control deals obtained by including the control acquirers paired up with the control target
firms; (iii) using the sample of All Deals (as shown in Table I, column (5)); (iv) using the sample of
Acquirers or Targets with Patents (as shown in Table I, column (6)); and (v) removing acquirers and
target firms whose total assets are in the top decile of Compustat firms. See the Internet Appendix.
1950
The Journal of FinanceR
that failed for reasons exogenous to the innovation activities of either merger
partner. In this case, the assignment of firm pairs to the treatment sample
(completed deals) versus the control sample can be treated as random with
respect to the innovation output variable that we examine.21
To examine the role of technological overlap in the effect of a merger on
postmerger innovation output, we employ samples of treatment and control
deals involving acquirers and target firms that are innovative before the bid.
To form the control sample, we begin with 116 withdrawn bids with necessary
firm-level information in Compustat/CRSP announced over the period 1984 to
2003.22 We then read news articles for each withdrawn bid, excluding those
bids that could fail due to the innovation activities of either merger partner,
including disagreement over growth strategy, restructuring, or valuation; news
of negative developments; bids for which the reason for failure cannot be determined; and bids that were expected to fail. Table VII, Panel A, provides a
detailed description of how we arrive at our sample of 60 bids withdrawn due
to reasons exogenous to innovation, including competing bids (70%), objections
by regulatory bodies (20%), and macroeconomic shocks and adverse market
conditions (10%).
Next, we form a treatment sample of friendly completed deals over the period 1984 to 2003 that: (i) involve innovative acquirers and target firms (444
deals); (ii) occur in acquirer-target industry pairs that match industry pairs of
the bids in the control sample, and are announced within the three-year window centered at the announcement year of the bids in the control sample (166
deals). We find no industry pair- and event window-matched completed deal for
22 bids in the control sample. To form the treatment sample for the remaining
38 control bids, we select the closest completed deal in terms of the relative
size ratio (i.e., target firm’s total assets divided by acquirer’s total assets) from
the industry pair- and event window-matched completed deals. Using this approach, we ensure that the treatment and control samples are similar along
key dimensions relevant for M&As—industry composition and time clustering
(see Roberts and Whited (2011)).23
B. Postmerger Innovation Output
We estimate a difference-in-differences regression using a panel data set
that contains information on deals in the treatment and control samples
21
Seru (2014) exploits a sample of withdrawn bids to examine whether and how conglomerate
mergers stifle innovation. Bernstein (2014) employs a sample of withdrawn IPOs to investigate
whether and how going public affects innovation.
22 To mitigate the potential truncation bias in our postmerger innovation output measure, we
only include bids in our control sample with an announcement date (and deals in our treatment
sample with a transaction completion date) on or before December 31, 2003, which is three years
before our patent data end in 2006.
23 We verify that none of the technological overlap, innovation output, and R&D expenses variables, measured prior to bid announcement, predict deal completion in the combined sample of
treatment and control deals.
Corporate Innovations and Mergers and Acquisitions
1951
Table VII
Postacquisition Innovation Output
This table reports our investigation of the ex post treatment effect of a merger on postmerger
innovation output. Panel A provides the steps taken to form the sample of control deals involving
bids withdrawn for reasons exogenous to innovation. Panel B presents coefficient estimates from
OLS regressions obtained using a panel data set that, for each deal in the treatment sample
(i.e., completed deals) and the control sample (i.e., bids withdrawn due to reasons exogenous to
innovation), has observations running from three years prior to bid announcement (ayr-3) to three
years after deal completion/withdrawal (cyr+3). The dependent variable is, in each year, the sum
of the acquirer’s and the target firm’s innovation output as measured by Patent Index. Columns (1)
and (2) present estimates from the difference-in-differences regression in equation (3) using the
subsample of treatment and control deals with premerger technological overlap, and the subsample
of treatment and control deals without premerger technological overlap, respectively. Column (3)
presents estimates from the regression in equation (4) using both treatment and control deals with
and without premerger technological overlap. Treat is an indicator variable equal to one for the
firm pairs in the treatment sample, and zero otherwise. Any Tech Overlap is an indicator variable
equal to one if any of the six measures of technological overlap is nonzero, and zero otherwise.
Panel C presents the results from falsification tests where we falsely assume that the onset of
treatment (i.e., bid announcement) occurs three or four years before it actually does. Definitions
of the variables are provided in the Appendix. All specifications include deal fixed effects and year
fixed effects. Robust standard errors are reported in parentheses; *, **, and *** denote significance
at the 10%, 5%, and 1% level, respectively.
Panel A: Control Sample Construction for Quasi-Experiment
116
All bids withdrawn
−5
−6
−13
−21
−11
Difference in opinion on growth strategy
Difference in opinion over restructuring including top personnel matters
Disagreement over valuation
News of negative development or unexpected legal actions
Not enough information or expected to fail
60
Bids withdrawn due to reasons exogenous to innovation
Panel B: Postacquisition Innovation Output
After
After × Treat
Deals with Any
Tech Overlap
(1)
Deals without Any
Tech Overlap
(2)
−0.247***
(0.074)
0.365***
(0.086)
−0.032
(0.165)
−0.221
(0.165)
Yes
301
18
25
0.95
Yes
231
20
13
0.84
Any Tech Overlap × After
Any Tech Overlap × After × Treat
Deal and Year FEs
No. of Observations
No. of Treatment Deals
No. of Control Deals
R2
All Deals
(3)
−0.022
(0.135)
−0.216
(0.156)
−0.239*
(0.141)
0.552***
(0.178)
Yes
532
38
38
0.94
(Continued)
The Journal of FinanceR
1952
Table VII—Continued
Panel C: Falsification Tests
Onset of Treatment in ayr-3
Onset of Treatment in ayr-4
Deals
Deals
Deals with
Deals
with Any without Any
Any Tech without Any
Tech Overlap Tech Overlap All Deals Overlap Tech Overlap All Deals
(1)
(2)
(3)
(4)
(5)
(6)
After
After × Treat
Any Tech
Overlap × After
Any Tech
Overlap × After × Treat
Deal and Year FEs
No. of Observations
No. of Treatment Deals
No. of Control Deals
R2
−0.084
(0.113)
0.165
(0.174)
−0.132
(0.169)
0.098
(0.230)
Yes
160
17
15
0.97
Yes
220
21
23
0.93
−0.050
(0.102)
0.061
(0.125)
−0.059
(0.106)
0.054
(0.166)
Yes
380
38
38
0.96
−0.156*
(0.075)
0.186
(0.114)
−0.214
(0.147)
0.143
(0.187)
Yes
135
13
14
0.99
Yes
245
25
24
0.93
−0.118
(0.100)
0.152
(0.117)
−0.210**
(0.099)
0.014
(0.146)
Yes
380
38
38
0.96
from three years prior to bid announcement (ayr-3) to three years after deal
completion/withdrawal (cyr+3):
Patent Indexi jt = α + β1 Af teri jt + β2 Af teri jt × T reati j
+DealF Ei j + Y ear F Et + ei jt .
(3)
The dependent variable, Patent Indexijt , is the sum of acquirer i’s and target firm j’s innovation output in each year t.24 The indicator variable Afterijt
equals one for the postmerger time period (from cyr+1 to cyr+3), and zero
otherwise. The indicator variable Treatij equals one for treatment deals, and
zero otherwise (i.e., for control bids). We include deal fixed effects to difference
away any time-invariant differences among deals.25 As a result, our approach
estimates the differences over time in Patent Index for the same cross-section
24 There are several reasons to focus on Patent Index when examining innovation output postmerger. First, the point in time at which we count patents (and hence Patent Index) is when the
patent application is filed with the patent office. This point in time is closest to when the innovation
was actually created. Second, Citation-Weighted Patents is constructed by counting citations over
a three-year window, starting with the year a patent is awarded. In the United States, patents are
awarded, on average, two to three years after applications are filed. The patent is then revealed to
the public and starts to be cited only at the time the patent is awarded. As such, Citation-Weighted
Patents cannot capture rapid changes in innovation output of the combined firm immediately
following deal consummation, which is the objective of our analysis.
25 We cannot estimate the coefficient on Treat as it is subsumed by deal fixed effects Deal FE .
ij
ij
Corporate Innovations and Mergers and Acquisitions
1953
Figure 1. Innovation output around M&As. This figure plots the average of the combined
innovation output of the acquirer and the target firm as measured by Patent Index, computed
separately for deals in the treatment sample (i.e., completed deals) and the control sample (i.e.,
bids withdrawn due to reasons exogenous to innovation). We use panel data running from three
years prior to bid announcement (ayr-3) to three years after deal completion/withdrawal (cyr+3).
Panel A uses only deals with premerger technological overlap. Panel B uses only deals without
premerger technological overlap. Year 0 is the year of deal announcement.
units (Wooldridge (2002), p. 284). We also include year fixed effects to difference away a common trend affecting deals in both the treatment and control
samples.
Before estimating equation (3), we follow Roberts and Whited (2011) by
checking whether our outcome variable exhibits “parallel trends” in the premerger period, which is a necessary condition for identifying the treatment
effect. We introduce an indicator variable Any Tech Overlap, which is equal to
one if at least one of our six technological overlap measures is greater than zero,
and zero otherwise. We then plot the average level of Patent Index separately
for the treatment and control subsamples with and without Any Tech Overlap
in Figure 1, Panels A and B, respectively.
In both panels, we show that, prior to bid announcement, the treatment and
control samples exhibit very similar trends in innovation output. Furthermore,
while the average level of innovation output is higher for the treatment sample
compared to the control sample without Any Tech Overlap, it is strikingly
similar between the treatment and control samples with Any Tech Overlap.
1954
The Journal of FinanceR
For deals with Any Tech Overlap after bid announcement, the average level of
innovation output stays flat for the treatment sample, while it drops and has a
strong decreasing trend for the control sample. We conclude that our treatment
and control samples satisfy the necessary conditions to estimate equation (3).
We acknowledge that there might still be omitted time-varying variables that
affect deals in the treatment and control samples differently; in such a case,
the coefficient estimates in equation (3) would be inconsistent.
Table VII, Panel B, columns (1) and (2) present coefficient estimates from
the OLS regression in equation (3) using subsamples of deals with and without
Any Tech Overlap, respectively. For the subsample with Any Tech Overlap, the
coefficient on Afterijt is negative and significant at the 1% level, suggesting
that the combined innovation output is, on average, lower in the postmerger
period. Importantly, we show that the coefficient on the interaction term Afterijt
× Treatij is positive and significant at the 1% level. We conclude that not completing a deal between firms with technological overlap leads to a considerable
drop in innovation output. None of these effects are present in the subsample without Any Tech Overlap. These findings suggest that the presence of
premerger technological overlap is a crucial determinant of the effect of a
merger on postmerger innovation output.
Next, we directly investigate the heterogeneity in the treatment effect of a
merger on postmerger innovation output. We estimate the following regression
using a panel data set that contains deals with and without Any Tech Overlap:
Patent Indexi jt = α + β1 Af teri jt + β2 Af teri jt × T reati j
+ β3 Any Tech Overlapi j × Af teri jt + β4 Any Tech Overlapi j
× Af teri jt × T reati j + DealF Ei j + Y ear F Et + ei jt .
(4)
Table VII, Panel B, column (3) presents coefficient estimates from the OLS
regression in equation (4). We show that the coefficient on the interaction
term Any Tech Overlapij × Afterijt is negative and significant at the 10% level.
Importantly, this decline is reversed for firm pairs with Any Tech Overlap in the
treatment sample: the coefficient on the interaction term Any Tech Overlapij
× Afterijt × Treatij is positive, significant at the 1% level, and twice as large
in magnitude as that on Any Tech Overlapij × Afterijt .26 Our findings, which
show improvement in innovation output postmerger for deals with premerger
technological overlap compared to the average outcome, support our fourth
hypothesis.
Finally, to check the internal validity of our difference-in-differences estimator, we conduct falsification tests following the suggestion in Roberts and
26 We cannot estimate the coefficients on Treat or Any Tech Overlap × Treat as both terms
ij
ij
ij
are subsumed by deal fixed effects Deal FEij . We always obtain a positive coefficient estimate
(significant in three cases) on Technological Overlapij × Afterijt × Treatij using any of the six
measures of technological overlap when we use them (one at a time) in equation (4), instead of
using the summary measure Any Tech Overlap. Also, we obtain the same results as those in
Table VII, Panel B, when we remove acquirers and target firms whose total assets are in the top
decile of Compustat firms. See the Internet Appendix.
Corporate Innovations and Mergers and Acquisitions
1955
Whited (2011). Specifically, we falsely assume that the onset of treatment (i.e.,
bid announcement) occurs three or four years before it actually does. In each
case, we re-estimate equation (4) using a five-year panel data set that ends
before the actual bid announcement. Table VII, Panel C, shows that the coefficient on the interaction term Any Tech Overlapij × Afterijt × Treatij is statistically indistinguishable from zero, suggesting that the observed improvement
in innovation output (shown in Table VII, Panel B) is more likely due to the
treatment, as opposed to some alternative force.
In summary, we identify an important type of synergy—technological
overlap—that leads to both merger-pairing decisions and subsequent improvement in innovation output.
V. Conclusions and Implications for Future Research
Using a large and unique patent-merger data set over the period 1984 to
2006, we uncover a specific source of synergy—corporate innovation activity—
that drives acquisitions and has a positive impact on merger outcomes. We
first show that firms with large patent portfolios and low R&D expenses are
more likely to be acquirers, while R&D-intensive firms with slow growth in
patent output are more likely to be acquired. Furthermore, technological overlap between firms’ innovation activities has a positive and significant effect
on the likelihood of a merger pair formation. We then show that the positive
effect of technological overlap on the likelihood of a merger pair formation is
reduced for firm pairs that also overlap in product markets. Finally, using a
quasi-experiment involving withdrawn bids that failed for reasons exogenous to
innovation, we show a positive treatment effect of a merger on postmerger innovation output when there is premerger technological overlap between merging
firms. We conclude that synergies obtained from combining innovation capabilities are an important impetus for corporate acquisitions.
The findings of this paper suggest the following new directions for future research. First, our paper highlights that many merger transactions are driven
by efficiency motives. Future research can focus on studying whether and
how synergistic gains in areas other than product markets and technological innovations are created through M&As. Second, we show that technological
synergy-driven acquisitions improve postmerger innovation outcomes, consistent with value creation. Exploring whether and how governance mechanisms
in acquirer and target firms facilitate the realization of synergistic gains will be
important. Finally, future theoretical work can help ascertain whether and how
multiple sources of synergy affect the likelihood of transaction incidence, perhaps capturing the tension between competition due to product market rivalry
and complementarities due to overlapping technologies.
Initial submission: January 18, 2012; Final version received: April 16, 2013
Editor: Campbell Harvey
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The Journal of FinanceR
Appendix: Variable Definitions
All firm characteristics are measured as of the fiscal year-end before the bid
announcement, and are winsorized at the 1% and 99% levels.
Innovation Measures
Patent Index
Patent Index
Citation-Weighted Patents
Citation-Weighted
Patents
R&D
R&D
Self-Cites Ratio
This measure is constructed in three steps. First, for each
technology class k and patent application year t, we
compute the median value of the number of awarded
patents in technology class k with application year t
across all firms that were awarded at least one patent in
technology class k with application year t. Second, we
scale the number of awarded patents to the
acquirer/target firm in technology class k with
application year t by the corresponding (technology
class- and application year-specific) median value from
the first step. Third, for the acquirer/target firm, we sum
the scaled number of awarded patents from the second
step across all technology classes and across application
years from ayr-3 to ayr-1. When assessing the
postmerger quantity of innovation, the measurement
window for patent application years in the third step is
cyr+1 to cyr+3. Year ayr-1 is the calendar year that has
the largest overlap with the last fiscal year before the
bid announcement, and year ayr-3 is two years prior to
ayr-1. Year cyr+1 is the calendar year that has the
largest overlap with the first fiscal year after the deal
completion, and year cyr+3 is two years after cyr+1.
The growth rate of Patent Index between the three-year
period ending three years prior to the bid announcement
(i.e., ayr-5 to ayr-3) and the three-year period ending one
year prior to the bid announcement (i.e., ayr-3 to ayr-1).
The sum of the citation-weighted number of awarded
patents to the acquirer/target firm with application
years from ayr-3 to ayr-1. For each patent, the weight is
the number of citations received within a three-year
period starting from the patent award year.
The growth rate of Citation-Weighted Patents between the
three-year period ending three years prior to the bid
announcement (i.e., ayr-5 to ayr-3) and the three-year
period ending one year prior to the bid announcement
(i.e., ayr-3 to ayr-1).
Research and development expenses scaled by total assets.
The growth rate of research and development expenses.
First, we compute the number of awarded patents to the
acquirer/target firm with award years from ayr-3 to
ayr-1 that cite any of the acquirer’s/target firm’s other
awarded patents. Second, we scale the number from the
first step by the total number of awarded patents to the
acquirer/target firm with award years from ayr-3 to
ayr-1.
Corporate Innovations and Mergers and Acquisitions
1957
Innovation Measures
Age of Patent Portfolio
Technological Proximity
First, for each patent awarded to the
acquirer/target firm prior to or in year ayr-1, we
compute the number of years since the patent
was awarded as of year ayr-1. Second, we take
the average of each patent’s age computed in the
first step across all patents awarded to the
acquirer/target firm prior to or in year ayr-1.
Following Jaffe (1986), the correlation coefficient is
computed as
√
Knowledge Base Overlap
Acquirer’s (Target’s) Base Overlap Ratio
Acquirer’s (Target’s) Cross-Cites Ratio
Sacq Starg
Sacq Sacq
√
Starg Starg
,
where the vector Sacq = (Sacq,1 , . . . , Sacq,K ) captures
the scope of innovation activity for the acquirer,
the vector Starg = (Starg,1 , . . . , Starg,K ) captures
the scope of innovation activity for the target
firm, and k ࢠ (1,K) is the technology class index.
Sacq,k (Starg,k ) is the ratio of the number of
awarded patents to the acquirer (the target firm)
in technology class k with application years from
ayr-3 to ayr-1 to the total number of awarded
patents to the acquirer (the target firm) in all
technology classes applied over the same
three-year period.
First, we determine the set of patents that received
at least one citation from any of the acquirer’s
patents with award years from ayr-3 to ayr-1
(“the acquirer’s knowledge base”), the set of
patents that received at least one citation from
any of the target firm’s patents awarded over the
same three-year period (“the target firm’s
knowledge base”), and the intersection of these
two sets as the set of patents cited by both the
acquirer and the target firm (“the common
knowledge base”). Second, we compute the
number of patents in “the common knowledge
base.”
First, we compute the number of citations from any
of the acquirer’s (target firm’s) patents with
award years from ayr-3 to ayr-1 made to the
patents in “the common knowledge base.” Second,
we scale the number from the first step by the
number of citations from any of the acquirer’s
(the target firm’s) patents with award years from
ayr-3 to ayr-1 made to the patents in “the
acquirer’s knowledge base” (“the target firm’s
knowledge base”).
First, we compute the number of the acquirer’s
(target firm’s) awarded patents with award years
from ayr-3 to ayr-1 that cite any of the target
firm’s (acquirer’s) awarded patents. Second, we
scale the number from the first step by the
number of the acquirer’s (target firm’s) awarded
patents with award years from ayr-3 to ayr-1.
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The Journal of FinanceR
Firm Characteristics
Total Assets
Sales
ROA
Leverage
Cash
B/M
Stock Return
The natural logarithm of total assets in millions of 2006
constant dollars.
The growth rate of sales.
Earnings before interest, taxes, depreciation, and
amortization scaled by total assets.
Total debt scaled by total assets.
Cash and short-term investment scaled by total assets.
The book value of common equity scaled by the market
value of common equity.
The difference between the buy-and-hold stock return
from month −14 to month −3 relative to the month of
the bid announcement and the analogously defined
buy-and-hold stock return on the value-weighted CRSP
index.
Deal Characteristics
Product Market Relatedness (PMR)
Diversifying
Same State
Equal to one if a given firm pair is from the same industry,
constructed using the text-based analysis of the firms’
product descriptions in the 10-K filings by Hoberg and
Phillips (2010), and zero otherwise. (We downloaded the
data from Hoberg-Phillips’s Data Library website:
http://www.rhsmith.umd.edu/industrydata/.)
Equal to one if the acquirer and the target firm operate in
different two-digit SIC industries, and zero otherwise.
Equal to one if the acquirer and the target firm are
incorporated in the same state, and zero otherwise.
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Supporting Information
Additional Supporting Information may be found in the online version of this
article at the publisher’s website:
Appendix S1: Internet Appendix.