Technological innovation and Acquisitions

Xinlei Zhao*

Department of FinanceKent State University

Kent, OH 44242330.672.1213

xzhao@kent.edu

September 2007

Abstract: .

I examine firms’ make-or-buy decision in the context of mergers and acquisitions via an

investigation of the relation between technological innovation and acquisition activities.

I find that firms engaging in acquisition activities are less innovative and have

experienced declines in technological innovation during the years prior to the bid. Among

the bidders, the relatively more innovative ones are less likely to complete a deal,

suggesting that these bidders may feel less pressure. Further, although experiencing less

decline in innovation output than bidders that cancel the deals, bidders that complete the

deals still trail non-bidding firms in technological innovation during the three years after

the acquisitions. This finding suggests that buying-innovation can only partially alleviate

the internal innovation deficiency problem.

Keywords: patents, citations, innovation, Coase Theorem, make-or-buy, R&D JEL Classification: G34

* I thank Kai Li for help with the M&A data and Bronwyn Hall for providing the NBER Patent Data. I thank Raj Aggarwal, Jarrad Harford, Kai Li, John Thornton, and seminar participants at the Kent State University for their helpful comments. All errors are mine.

1. Introduction

This paper examines firms’ make-or-buy decision in the context of mergers and acquisitions. This

question is one of the fundamental questions in modern economics and it goes back as far as Coase

(1937). Coase argues that the make-or-buy decision depends on the environments in which firms perform

relative to the market. Obtaining goods or services via the market entails a number of transaction costs,

including searching and information costs, bargaining costs, and contract enforcement costs, etc. Because

of the additional costs in the external market, the Coase Theorem argues that firms are formed to produce

goods or services more cheaply internally. This theorem also implies that firms resort to the external

market only when they are not able to internally produce the goods or service as efficiently in the market.

This argument has been reintroduced into economics by Williamson (1975); it has been more thoroughly

developed by Klein, Crawford, and Alchian (1978), Grossman and Hart (1986) and others. However,

empirically proving this theory is difficult. Some studies have tried to shed light on this theory by

examine how firm boundaries or asset ownership affect its behavior (for example, Baker and Hubbard

(1993) and Mullainathan and Scharfstein (2001)). Another stream of literature directly examines this

theory in the context of mergers and acquisitions (for example, Higgins and Rodriguez (2006)).

This paper belongs to the second stream of literature. If the Coase Theorem sufficiently describes

a firm’s make-or-buy decision, firms with successful internal innovation are more likely to rely on

internal growth and less likely to participate in the acquisition market. Consequently, firms would be

more likely to engage in acquisition activities following a lack of success in their internal innovation

efforts. Higgins and Rodriguez (2006) find some evidence in support of this buying-innovation prediction

in the pharmaceutical industry. However, their work is limited to one industry and it is not clear whether

the findings can be generalized to other industries and to different types of deals.

My study contributes to this literature in four ways. First, I examine firms’ make-or-buy decisions

across industries and over time. In contrast, the prior studies have been limited to specific industries,

such as the trucking industry (Baker and Hubbard (1993)), the vinyl chloride monomer (VCM) industry

(Mullainathan and Scharfstein (2001)), or the pharmaceutical industry (Higgins and Rodriguez (2006)).

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Second, I examine the role innovation plays in the likelihood of deal completion, which has not

been investigated before. The Coase Theorem implies that, among the firms involved in acquisition

activities, the less innovative firms should be more likely to complete a deal.

Third, an important question that has yet to be thoroughly investigated in the existing literature is

whether buying-innovation is detrimental to internal innovation and whether it can fully make up for the

lack of success in internal innovation. I try to answer this question by examining changes in innovation

output following a completed acquisition and a failed acquisition.

Finally, this paper uses citation data as a measure of innovation output quality. Prior studies

usually draw conclusions based on R&D expenditure (a measure of innovation input), or patent counts (a

measure of innovation output quantity). However, neither of these measures is able to sufficiently

describe the true technological innovation level of a firm.

This study uses a sample of 1,053 acquisitions during the period of 1984-1997. I combine this

sample with the NBER patent data set created by Hall, Jaffe, and Trajtenberg (2001). I find that,

controlling for other firm characteristics, the number of patents of the bidders is comparable to that of the

non-bidding firms. However, bidders have significantly fewer citation counts to their patents, and they

have also lagged behind in terms of citation growth in the past three years. This finding suggests that

bidders are as innovation-oriented, but the quality of their innovation is deficient, so they attempt to

acquire quality and innovation skills externally. The likelihood of deal completion is negatively

associated with the citation counts prior to the bid, suggesting that bidders with relatively higher-impact

innovations in the past are more confident with their own research productivity and feel less pressure to

complete the deal.

Further, acquisitions do not seem to stifle technological innovation since bidders that complete

the deals experience less decline in innovation output than those that cancel the deals. On the other hand,

bidders completing the deals still lag behind non-bidding firms during the three years after deal

completion. This finding suggests that buying-innovation can only partially alleviate the internal

innovation deficiency problem. In addition, I find that less innovative bidders experience more increases

in innovation after the acquisitions are completed.

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These results hold among both diversifying and non-diversifying deals, among bidders from more

innovative industries and during the sub-periods of 1980s and 1990s. Therefore, the findings indeed

confirm that the make-or-buy decision is determined by whether a firm performs better than the market or

not. The findings here also suggest that what motivates the make-or-buy decision is the innovation output

quality, not innovation input (R&D) or innovation output quantity (patent counts), further highlighting the

value of citation data. On the other hand, although acquisitions do not stifle technological innovation,

they can only partially make up for the internal deficiency. In other words, my findings suggest that firms

should rely primarily on internal innovation success rather than on acquisitions in the external market.

My study also contributes to the mergers and acquisitions literature. Theory has suggested many

possible answers to the question of why acquisitions occur: efficiency-related, market power, disciplining,

agency costs, and diversification. However, data have not strongly supported any one of these views as

consistently explaining a significant portion of acquisition activities over time. My paper sheds insight on

the motivations of acquisitions by examining the quantity and quality of technological innovation on the

bidder side, and my evidence suggests that buying-innovation is one reason why firms merge.

The plan of the paper is as follows. I review the literature on the role of technological innovation

in acquisition and develop my hypotheses in the next section. Section 3 describes the sample and

variables, and provides descriptive statistics. Section 4 examines how technological innovation affects a

firm’s takeover decisions, and Section 5 studies post-acquisition innovation. Section 6 discusses some

additional investigations, and Section 7 concludes.

2. Role of technological innovation in Acquisitions and Hypothesis Development

Acquisition activities and technological innovation each has a large impact on economic activity,

and technological innovation has often been mentioned as one of the justifications for mergers and

acquisitions (for example, Chesbrough (2003)). Yet, there is surprisingly little empirical study on the

interaction between the two. Typical studies of the efficacy of acquisitions focus on operating synergies

and performance post-acquisition, or announcement return and long-term returns.† My current paper tries † A large amount of literature argues that many acquisitions destroy value for the acquirer (see, for example, Loughran and Vijh (1997), Rau and Vermaelen (1998), and Moeller, Schlingemann, and Stulz (2005)). Bradley, Desai, and Kim (1988) shows that the increase in the gains to the target shareholders has come at the expense of the stockholders of acquiring firms. Nonetheless, there is a growing body of empirical literature documenting that

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to fill the void in the literature by investigating the interaction between technological innovation and

acquisitions.

Earlier studies investigating the role technological innovation plays in acquisition activities

usually relies on R&D expenditure. However, R&D expenditure is an input to innovation, not the output.

On the other hand, patents are innovation output and have long been recognized as a very rich and

potentially fruitful source of data for the study of innovation and technological change (see Griliches

(1990) for an excellent review of the literature on patents). In particular, patent data include citations to

previous patents and to the scientific literature, allowing researchers to create indicators of the

“importance” of individual patents and cope with the enormous heterogeneity in the “value” of patents.‡

Past research has shown that citations as a whole do provide useful information about the generation of

future technological impact of a given invention (Trajtenberg (1990), and Hall, Jaffe, and Trajtenberg

(2005)).§

acquisitions are efficient means for assets to be reallocated within the economy (Andrade and Stafford (2004)). Large sample evidence by Healy, Palepu, and Ruback (1992) on post-acquisition operating performance, as well as Jensen and Ruback (1983), Jarrell, Brickley, and Netter (1988), and Andrade, Mitchell, and Stafford (2001) for a review of the literature on announcement returns and long-term profitability, suggests that acquisitions on average increase value, and lead to improved profitability in subsequent years.‡ The main advantages are: each patent contains highly detailed information on the innovation itself; there are a very large number of patents; the patent system has been around for more than one hundred years; patents are supplied entirely on a voluntary basis; patent citations perform the legal function of delimiting the patent right by identifying previous patents whose technological scope is explicitly placed outside the bounds of the citing patent. The major limitations to the use of patent data are: not all inventions meet the patentability criteria set by the United States Patent and Trademark Office (USPTO) (the invention has to be novel, non-trivial, and has to have commercial application); the inventor has to make a strategic decision to patent, as opposed to rely on secrecy or other means of appropriability. Thus, not all inventions are patented.§ There is a small but growing finance literature that employs the patent data to examine various interesting questions. Lerner and Wulf (2006) examine the impact of the compensation incentives of the heads of corporate research and development on R&D output, and show more long-term incentives (e.g., stock options and restricted stock) are associated with more heavily cited patents. These incentives also appear to be associated with more patent filings and patents of greater originality. Seru (2006) uses patent-based metrics to examine the impact that the conglomerate form may have on the scale and novelty of corporate R&D activity. He concludes that conglomerates do stifle innovation. Atanassov, Nanda, and Seru (2006) hypothesize that established firms with innovative projects and technologies will make relatively greater use of arm’s length financing (such as public debt and equity); whereas less innovative firms will tend to use relationship based borrowing (such as bank borrowing). Using a large panel of US companies from 1974-2000, they find that consistent with their predictions, firms that rely more on arm’s length financing receive a larger number of patents and these patents are more significant in terms of influencing subsequent patents.

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Prior Literature

Hall (1990) uses data on R&D to examine the impact of acquisitions on industrial research and

development. She finds evidence of declines in R&D intensity as measured by the ratio of R&D to sales

after acquisition, but the declines are statistically insignificant. Hall (1999) extends her earlier work by

stratifying potential bidders based on their propensity to acquire. She finds that firms with a high

acquiring propensity that actually make an acquisition have a significantly higher increase in their R&D.

Hall concludes that the overall finding of no impact obscures some real heterogeneity across firms.

Hitt et al. (1991) examine the effect of acquisitions on R&D inputs (firm R&D/sales adjusted for

industry average) and outputs (patents/sales), based on a sample of 191 firms from 29 industries from the

period 1970 - 1986. They examine the seven-year window around the merger completion. They find that

acquisitions have a significant and negative effect on R&D intensity, and diversifying acquisitions

negatively affect patent intensity. However, this study needs to be updated, since technological

innovation really took off in the past two decades and more sophisticated statistical methods are now

available. Further, this study draws conclusions based on R&D and patent, measures that are not able to

fully capture innovation quality.

Hagedoorn and Duysters (2000) focus on a single, high-tech industry to examine the effect of

acquisitions on the technological performance of the combined firms, as measured by the number of

patents. They conclude that acquisitions can contribute to increases in innovative activities if there is

both the organizational and strategic fit of the companies involved in these mergers. They measure “fit”

using the SIC codes, the patent classification codes, R&D intensity, and firm size.

Higgins and Rodriguez (2006) examine the performance of 160 pharmaceutical acquisitions from

1994 to 2001. They find that firms experiencing declines in internal productivity are more likely to

engage in an outsourcing-type acquisition in an effort to replenish their research pipelines. They

document post-acquisition improvement in three performance measures: positive announcement period

returns, significantly positive changes in both the research pipeline and sales (the year of acquisition

versus the year after). Besides the fact that this study is limited to one specific industry, it is also unclear

whether the documented improvement can be attributed to better innovation success post acquisitions.

For example, the improved research pipeline can result from the additional drug candidates acquired from

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the target, which does not necessarily indicate an improvement in innovation output. Therefore, the

above study still leaves unaddressed the question whether acquisitions (or a lack of acquisition) affect

technological innovation.

My Hypotheses

This study examines the interaction between technological innovation and acquisition across

firms and over time. If the Coase Theorem provides a good description of a firm’s make-or-buy decision,

then bidders facing a decline in the success of internal innovation efforts would attempt to buy continued

innovation externally. I call this the buying-innovation hypothesis. This hypothesis has the following

predictions.

1) Less innovative firms are more likely to engage in acquisition activities.

2) Among the bidders, the relatively less innovative firms are more likely to complete a deal

because they feel more pressure to do so, while more innovative firms are less likely to complete a deal.

The buying-innovation hypothesis does not have clear predictions on the post-acquisition

innovation levels, which can be affected by many other factors. One factor is the strategic fit, which

plays an ambiguous role in the Coase Theorem. Post-acquisition innovation could increase if there are

economies of scale in R&D activities. On the other hand, acquisition activities can be affected by agency

costs such as the integration problem and post–acquisition innovation could also show a significant

decline as a result.**

Therefore, whether technological innovation increases or decreases following a successful

acquisition is an interesting empirical question. An answer to this question is important to the make-or-

buy decision because it can shed light on whether the ‘buy’ decision adds value or not, and whether the

‘buy’ decision can fully make up for internal innovation deficiency.

** A very good example of the latter case is IBM’s acquisition of Rolm, a leading maker of telephone switching equipment, in 1984. The purpose of the acquisition was to create a technology powerhouse. However, because of the enormous differences in business models, the acquisition was not successful and technical experts gradually left. IBM eventually sold the unit to Simens.

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3. Sample Formation, Variable Construction, and Sample Overview

My empirical design is to relate the quantity and quality of technological innovation to takeover

decisions and to explore the impact of takeover from a technological perspective.

Sample Formation

To form the sample of acquisitions, I begin with all announced (both completed and cancelled)

US acquisitions with announcement dates between January 1, 1984 and December 31, 1997 as identified

from the Mergers and Acquisitions database of the Securities Data Company (SDC).†† I identify all deals

where the bidder is a public firm and the form of deal was coded as a merger, an acquisition of majority

interest, or an acquisition of assets. I require that the transaction value be no less than 1 million.‡‡ Further,

I only retain an acquisition if the bidder owns less than 50 percent of the target prior to the bid and is

seeking to own greater than 50 percent of the target. For completed deals, I require that the bidder owns

more than 90 percent of the target after the deal completion. These filters yield 10,457 deals.

To clearly delineate the effect of each acquisition on innovation and reduce the risk of

contamination, I only include isolated acquisitions, i.e., those acquisitions that do not overlap when a

sample firm makes multiple acquisitions. More specifically, I only keep the first bid by the same bidder

within a three-year window. Note that this three-year time frame is chosen because I also compute

changes in patent/citation counts over the same three-year window both before and after the acquisition.

This filter yields 4,269 deals.

I then match the bidder sample with Compustat and CRSP data, and the NBER patent data. The

NBER patent data set was created by Hall, Jaffe, and Trajtenberg (2001).§§ This dataset provides among

other items, annual information on patent assignee names, on the number of patents, on the number of

†† The sample period is chosen because the information in SDC is less reliable before 1984. The patent data end in 2002. For completed deals, I impose the additional filter based on the acquisition effective date to be no later than the end of 1997 due to the fact that the average lag between application and grant is about two years, and I would like to have up to three years of citation data post acquisition for the sample firms. This helps mitigate the post-1989 bias to some extent. ‡‡ Given that the success of a knowledge-based firm is not its size, and a small firm may hold many patents or have the potential for many patents.  So I do not impose any relative size filter that may eliminate many interesting and innovation-enhancing acquisitions.§§ There are 400 3-digit main patent classes by USPTO, and Hall et al. further refine them into 36 2-digit technological sub-categories, and 6 main categories: categories: Chemical (excluding Drugs); Computers and Communications (C&C); Drugs and Medical (D&M); Electrical and Electronics (E&E); Mechanical; and Others.

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citations received by each patent, on the technology class of the patent, and on the years that the patent

application was filed and was granted. Hall, Jaffe, and Trajtenberg (2001) match the assignees of the

patents in the NBER dataset, by name, to manufacturing firms from Compustat, as of 1989 – the

NBER/Compustat population is the base for my analysis. The fact that the matching occurs for firms that

existed on or before 1989 might introduce a new listing bias, since firms that went public after 1989 (for

both bidding and non-bidding firms) are not included in the study and older firms dominate the latter half

of the sample. (I address this problem in a sub- sample analysis in Section 6.) Using these cusip numbers,

I merge the financial data in Compustat and SDC M&A data with the NBER patent dataset. I only keep

non-utility manufacturing firms (SIC codes 2000- 4899 and 5000-5999) and I require a firm to have data

three years before the announcement date and three years after the completion date or the announcement

date. I also require that failed acquisitions are not due to regulation reasons. These requirements reduce

the sample size to 1,349 deals.

The number of bidding firms in the population is quite small relative to the total number of firms.

To overcome the possible existence of non-linearity and ensure that I am comparing bidders to their most

comparable non-bidding competitors, I rely on the matching method in this paper. I require that the

control group of non-bidding firms 1) have no bid within a seven-year period, 2) are from the same Fama-

French 48 industry-year as the bidding firm, and 3) have total sales within 25% deviation of the bidding

firm. This requirement reduces the final sample to 1,053 deals, including 988 completed deals and 65

failed deals. There are 7,798 non-bidding firm-year observations in the study.*** Note both bidders and

non-bidders are restricted to firms established before 1989 because of the NBER patent data.

Innovation Variable Construction

I focus on the following two measures of technological innovation success: patent counts and

citation counts.††† Both measures are based on the application year, as it is closer to the time of the actual

innovation than the grant year (Griliches, Pakes, and Hall (1987)). The first measure is a simple patent

count for each firm year and measures a firm’s innovation intensity. Because only patents that have been

*** I allow for more than one matching firm for each bidder as long as they are from the same industry-year and have total sales within 25% deviation of the bidding firm. Results do not change if I pick only one matching firm for each bidder and if I change the size cutoff point from 25% to 30% or 15%.††† I have checked with the U.S. patent office and confirmed that if a firm applied for a patent and then was acquired before the patent was granted, the patent was still granted to the old firm instead of the combined firm. Therefore, mergers and acquisitions do not contaminate the identity of the patent assignee.

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granted are reported in the dataset, the patent data are truncated; i.e., there is a declining number of

patents towards the end of the sample period. I correct for this truncation bias following Hall et al.

(2001). Further, there is an increasing trend in the number of patents granted over the past a few decades,

and the number of patents from different years will not be directly comparable. To adjust for the time

trend, I deflate the patent counts by the average number of patents of that year, using 1990 as the base

year. This measure captures the quantity of a firm’s innovation output.

The second metric, the number of citations to patents, measures the quality of a firm’s innovation

output. This measure is motivated by the recognition that patent counts do not distinguish breakthrough

innovations from less significant ones. Past research has shown that the distribution of the impact of

patents is extremely skewed, with most of the value concentrated in a small number of patents (Griliches,

Pakes, and Hall (1987)). Trajtenberg (1990), and Hall et al. (2005) among others have shown that patent

citations captures the true value of innovations. This measure is calculated as the ratio of (1) the total

number of citations (as recorded in the dataset) received by a firm’s patents applied in year to (2) the

total number of patents applied in year . For example, a firm may applied for five patents in year 1988,

and future patents citing these five patents could come from any year after 1988. I count all future

citations as citations of year 1988.

However, the raw citation data suffer from a serious truncation bias, since citations to patents

tend to arrive over time, and the citation lag can take decades. Therefore, the number of citations to more

recent patents is not comparable to the number of citations to older patents. Further, different industries

might have different practices in citing patents. I correct for these biases using the fixed-effect method

recommended by Hall et al. (2001). This method divides the citations to year patents by the average

citations to patents applied in the same technology group and the same year. Therefore, the mean citation

counts are normalized to be 1 across all firm-years in the NBER citation database. The NBER patent data

end in 2002, so the data after 2000 are more subject to the data truncation problem. To further alleviate

this problem, I stop the acquisition sample in 1997, so that patent data after 2000 are not used in the

study.

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Sample Overview

Table 1, Panel A presents the temporal distribution of my sample of announced acquisition deals

over the 1984-1997 period. It is clear that acquisitions tend to be highly cyclical, as the total number of

acquisitions closely follows the business cycle expansion over most of the sample period. The large

number of deals in the first year of the sample period, 1984, is due to my requirement that only the first

deal by the same bidder over a three-year window is kept in the sample. Panel B presents the industry

breakdown based on the Fama and French (1997) classification. I find a high concentration of bidders in

electronic equipment, machinery, computers, construction materials industries, and wholsale, suggesting

that a large proportion of the bidding firms are not from the high innovation industries.

Panels A and B of Table 2 present bidding firm and deal characteristics. All firm characteristics

are as of the year prior to the announcement year, and all dollar values are in 1990 dollars. The median

market value of assets is $430 million. My measure of firm size, sales, has a median of $416 millions. I

use as the measure of R&D intensity as the ratio of R&D expenses (data 46) to total sales (data 12).

Median R&D expenditure is 1.24 percent. The bidders have relatively low market leverage and are

profitable, with a median market leverage ratio of 13 percent and median ROA at 11 percent.‡‡‡ The

bidders experience median annual stock return of 18.7 percent. In the sample, 94 percent of the

announced deals are eventually completed. Almost half of the deals use only cash as the method of

payment, and less than 20 percent of the deals are pure stock swaps. Approximately 3 percent of the

targets receive competing bids within one year. There are more diversifying deals than within-industry

deals. The mean relative deal size, defined as the ratio of the transaction value to the market value of the

bidder, is 20 percent, while the median relative size ratio is 8 percent. These statistics are quite

comparable to those in the existing literature.

Panel C of Table 2 reports the three measures of technological innovation - (1) R&D expenditure,

(2) the number of patents and (3) the number of future citations generated by these patents - during the

‡‡‡ Market leverage is defined as the ratio of total book value of debt (data34+data9) to the market value of assets, which is defined as the market value of equity plus the book value of asset minus the book value of equity, and the book value of equity is defined as stockholders’ equity (data 216) or common equity (data 60) + preferred stock par value (data 130) or total asset (data 6) – total liabilities (data 181), plus balance sheet deferred taxes and investment tax credit (if available, data 35) and post-retirement benefit liabilities (if available, data 330), minus the book value of preferred stocks (estimated in the order of the redemption (data 56), liquidation (data 10), or par value (data 130), depending on availability). ROA is defined as the ratio of data13 (operating income before depreciation) to data12.

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year before the announcement date for both the bidders and non-bidders. The distributions of all three

measures are quite skewed, both among the bidding and non-bidding firms. Second, bidders seem to be

less innovation-oriented, as R&D expenditure and both the numbers of patents and citations are

significantly lower among the bidders than among non-bidders. This panel presents the first piece of

evidence suggesting that bidding firms might intend to acquire innovation from the market.

Table 3 presents averages of my measures of technological innovation, patent counts and citation

counts per firm year, as well as R&D expenditure, broken down by industry. The top five industries for

patents are aircraft, chemicals, computers, automobiles, and electronic equipment. The list is slightly

different for citations: medical equipment, chemicals, automobiles, computers, and aircraft. For

comparison, I also present the average R&D expenditure normalized by sales across different industries.

The top five industries for R&D expenditure are pharmaceuticals, medical equipment, computers,

measuring and control equipment, and electronic equipment. It is clear that the ranking of industries by

innovation input, R&D, is quite different from those based on measures of innovation output, patents and

citations to patents. This evidence suggests that there is a difference between measuring R&D input and

output. In this paper, I focus on innovation output in my examination of the interaction between

innovation and acquisitions.

4. Innovation and Acquisition Decisions

I investigate two questions in this section. First, I examine the role innovation plays in a firm’s

decision to make a bid. Second, I study whether innovation is a factor in a firm’s likelihood of completing

a deal. In all models, I include industry and year fixed effects and use heteroskedasticity-adjusted

standard errors that are robust to clustering at the firm level. In all regression models, I include both the

level of past innovation measures and the change in the innovation measures to capture innovation

dynamics. Further, I use patent counts (citations counts) from year instead of patent counts

(citations counts) from year to allow for the uncertainty about the innovation quality, as it may take

time for the impact of the innovation to be realized, particularly since the application-grant lag is around 2

years (Hall, Jaffe, and Trajtenberg (2001).

4.1 Propensity to Engage in an Acquisition

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I base the empirical model on previous work such as Higgins and Rodriguez (2006), and add my

measures of innovation as explanatory variables,

where the change in patents and the change in citation counts are computed from year to year ,

where is the bidding year.

I present the probit results in Table 4. It is clear from this table that bidders have a significantly

lower number of citations in the past and have experienced a lackluster growth in citations during the

three years prior to the bid. The relation between citation counts and the propensity to become a bidder is

also economically significant. The marginal effect of model (3) suggests that an increase of one citation

count decreases the propensity to bid by 7.5%. This magnitude is substantial since bidding firms

constitute about 13% of the observations in the regression. Further, boosting the citation growth from

year t-3 to t-1 by one citation count reduces the propensity to bid by 3.5%. Therefore, Table 4 indicates

that bidders have significantly lower quality innovations than the non-bidding competitors, consistent

with the first prediction of the buying-innovation hypothesis.

On the other hand, although there is a significant difference in R&D expenditure and numbers of

past patents between bidding and non-bidding firms (Table 2), after controlling for other firm

characteristics, I do not find a significant difference in R&D expenditure, the number of patents and past

changes in patent counts between bidding and non-bidding firms. These results imply a distinctive role

played by innovation output quality versus innovation input or innovation output quantity.

However, the results in Table 4 may hide possible heterogeneity among bidding firms. In

particular, technological innovation may not play a role for bidders engaging in diversifying deals.

Further, technological innovation should be a concern primarily only for highly innovative industries. If

the results in Table 4 are driven by firms from less innovative industries, they may not provide much

insight to the role that technological innovation plays in acquisition activities.

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To address these concerns, I conduct the same analysis across various sub-samples, and present

the probit results in Table 5. To save space, I only report coefficient estimates of the patent and citation

measures. Panels A and B report results for diversifying deals and non-diversifying deals, respectively.

Panel C presents results among the 15 most innovative industries, defined by number of patents as per

Table 3.§§§ The evidence that bidders have lower innovation quality and have experienced a decline in

innovation quality is quite robust across the different sub-samples.

It is interesting that, in both Tables 4 and 5, neither lagged patent counts nor past changes in

patent counts play any role in a firm’s decision to bid. Also, although not reported in Table 5, there is no

significant difference in R&D expenditure between bidding and non-bidding firms. These findings

further imply that it is the quality of technological innovation, rather than the input or innovation output

quantity, that plays the major role in firms’ make-or-buy decision.

4.2 Deal Completion

I now investigate the effect of bidder innovation on the likelihood of deal completion. I estimate

the following model:

Note that I include a few variables on the right-hand-side to describe deal features. The results from the

probit model are reported in Table 6.

This table shows that bidders with more citations in year are less likely to complete the deal.

The marginal effect of model (3) suggests that an increase of one citation count in year t-3 increases the

likelihood of deal incompletion by 1.9%. This magnitude is economically significant given the

unconditional deal incompletion rate at about 6%. This finding is consistent with the second prediction of

the buying-innovation hypothesis. However, bidders completing and cancelling the deals do not differ

statistically in terms of citation changes from year t-3 to year t-1. Since bidding firms cancelling the deals

§§§ Results do not change when I use top 10 or top 20 innovative industries.

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have more citation counts in year t-3, with a similar magnitude of citation count deterioration, these firms

still have relatively more internal innovative power than bidders completing the deals.

The observation that patent counts and the change in patent counts turn out to be statistically non-

significant is consistent with the finding from Tables 4 and 5, again suggesting that the quantity of

innovation is not of primary importance. Results from sub-samples, i.e., diversifying and non-diversifying

deals, and bidders from more innovative industries, are very similar to those presented here and are not

reported because of space limitations.

Table 5 also shows that large firms with high prior year stock returns are more likely to complete

the deal, and R&D expenditure is not related to the likelihood of deal completion. On the other hand,

diversifying deals, deals with smaller relative size differences, and deals involved either pure cash or pure

stock swaps are less likely to complete. Not surprisingly, deals with competing bids are less likely to

complete.

Results from this section add further support to the buying-innovation hypothesis. They imply

that less innovative firms are more likely to complete a deal possibly because they feel more pressure; or

more innovative firms are more careful and would not complete if the deal is not right. The findings here

provide additional evidence that it is the innovation output quality, rather than innovation input or output

quantity, that is of primary concern in firms’ make-or-buy decision.

However, it shall be interesting to examine 1) whether acquisitions stifle technological

innovations among bidders that complete the deals, and 2) whether the ‘buy’ decision can fully make up

for internal innovation deficiency. We answer these questions in sections 5.1 and 5.2, respectively, via an

examination of the post-event innovation levels.

5. Post-Event Innovation

5.1 Do acquisitions stifle technological innovations?

I illustrate in Figures 1.1 and 1.2 the number of patents and citations three years before and three

years after the acquisition (acquisition announcement) for bidders that complete (cancel) the deals. Figure

1.1 shows that the average patent counts of bidders completing the deals remains stable during the seven

14

years. By contrast, bidders canceling the deals experience an almost monotonic drop in the average

number of patents during the same period. The gap between these two groups of bidders is 2.4 three years

before the bid, which grows to 6.3 in the year of the bid. Following the bid, the gap in the number of

patents between the two groups widens to 8.5 by the third year.

Figure 1.2 tells a similar story. Both groups of bidders experience a decline in citation counts;

however, the drop is more severe for bidders canceling the deals. These bidders actually have slightly

more citations per patents than the deal-completing bidders three years before the bid, consistent with the

findings in Table 6. Nevertheless the difference reverses to 0.10 fewer citations for the bidders not

completing the deals in the year of the bid, a gap which remains for at least three years following (the

completion of) the bid.

Bidders not completing the deals experience a decline of 5.6 in patent counts and a decline of

0.35 in citation counts during this seven-year period, both of which are statistically significant. By

contrast, bidders completing the deals do not experience a drop in patent counts and a reduction of a much

smaller magnitude (0.09) in terms of citation counts. The difference in patent count changes between the

two groups of bidders is not statistically significant, but the difference in citation count changes is

statistically significant at the 5% level.

I also conduct the same analysis among diversifying deals, non-diversifying deals, and bidders

from more innovative industries. All findings, not reported here because of space limitations, suggest that

bidders that do not complete the deals experience a reduction in patent counts, although the decline is

only marginally significant. Further, these same bidders face an economically and statistically significant

drop in citation counts. Although the bidders completing the deals also have a decline in citation counts,

the magnitude is significantly smaller than that among bidders canceling the deals. These results suggest

that acquisitions do not stifle technological innovations, since bidders completing the deals experience

less decline in technological innovations than bidders cancelling the deals.

5.2 Can the ‘buy’ decision fully make up for internal innovation deficiency?

The decline in citations experienced by bidders completing the deals in Section 5.1 is difficult to

interpret, since there is also an overall decline in citations among the non-bidding firms in the sample.

Because I normalize the average citation counts to be equal to 1 for each technology category of each year

15

(see appendix), the decline only suggests that the technological innovation by firms in the sample (firms

in existence before 1989) becomes less important over time compared to new firms - firms established

after 1989 and thus are not included in the sample. Further, the bidding and non-bidding firms differ in

terms of patent counts and citation counts even before the deal year. Therefore, directly comparing the

post-acquisition innovation level is subject to the selection bias problem, and a more sophisticated tool is

called for. For this purpose, I use a matching estimator recently proposed by Abadie and Imbens (2004)

(referred to as the AI estimator in this paper). **** This method belongs to the family of matching

estimators. It is more recently developed and more sophisticated than the propensity score matching

method that is more widely used in the literature.

For each bidder that completes the deal, I choose four matching non-bidding firms based on the

same firm characteristics used in the previous regressions (sales, market-to-book, R&D, profitability,

return, market leverage, with 25% sales deviation of the bidding firm and from the same industry-year)

plus patent counts and citation counts during the deal completion year. I also tried matching based on

characteristics in the years prior to the bid, but the results do not change. I report the results using the deal

completion year in Table 7 to ensure that I compare innovation levels of the combined firms before and

after. I present in the first row the average difference in patent counts, citation counts, and R&D

expenditure during the three years after the deal between the bidding firm and matching firms.

Heterogeneity-corrected standard errors are reported in the second row, and p-values are reported in the

third row.

Panel A reports that, although bidding firms completing the deals experience some decline in the

R&D expenditure, this decline does not affect the innovation output quantity (patent counts). However,

these firms encounter a significant deterioration in innovation output quality (citation counts). Compared

to non-bidding firms, these firms have 0.15 fewer citations during the three years after deal completion,

compared to an average of 0.47 citation counts before the deal completion (Figure 1.2). I have also

conducted the same analysis among diversifying and non-diversifying bidders and bidders from the 15

most innovative industries, and obtain similar results. These results are not reported here to save space.

**** I have also used a difference-in-difference method, and the results are very similar.

16

To help put the above results in context, I report in Panel B of Table 7 the parallel results on

bidders that do not complete the deals; †††† except in this case, I match these bidders with non-bidding

firms based on characteristics in year AYR-3 to capture the change during the entire seven-year period

around the announcement. Compared to similar non-bidding firms, bidders canceling the deals have

significantly fewer patent counts and fewer citation counts during the three years after the acquisition

announcement. By the third year after the event, the bidders canceling the deals have an average of 4.1

fewer patents and 0.54 fewer citations than their matches. These numbers are economically significant

since these bidders have an average of 7.38 patents and 0.64 citation counts three years before the bid.

These magnitudes are also much larger than those for completed deals. However, bidders cancelling the

deals do not experience a drop in R&D expenditure, which suggests that the deterioration in innovation

output is not due to a decrease in innovation input. Results using AYR-2 and AYR-1 are close , but the

drops in innovation output are of a smaller magnitude than those reported here, which is not surprising

because of the nearly monotonically decreasing trend in innovation measures from AYR-3 to AYR+3 as

depicted in Figures 1.1 and 1.2. Therefore, bidders that cancel the deals significantly underperform

similar non-bidding firms both in terms of innovation output quantity and quality.

Therefore, the findings in Table 7 suggest that lack of innovation power can be partially address

by acquisitions, as the bidders completing the deals seem to outperform those canceling the deals.

However, technological innovation cannot be effectively outsourced, as bidder that managed to buy

innovation from the market still lag behind firms that achieve innovation from within.

Finally, bidders with different levels of prior innovation output may experience different impacts.

To address this question, I employ a multivariate regression framework to examine how past innovation

affects the change in patents and citations post acquisition, similar in specification as equation (2). The

regression is based on all completed acquisitions, and the dependent variable is either change in patent

counts or citation counts over the three year period from the year of completion to three years after the

deal completion. If the buying-innovation hypothesis is robust, then the formerly less innovative firms

should benefit the most from the acquisitions in terms of both future patent count changes and future

citation count changes.

†††† I choose not to compare bidders completing the deal versus bidders cancelling the deals, because matching estimators (including the AI estimator) are very data-intensive, requiring a large pool of non-event firms. I am able to find less than a dozen matches under the latter approach, which mostly yields non-significant results.

17

Table 8 presents these results. Results from sub-samples are very similar and are not reported

because of space limitations. I find that bidders with lower numbers of patents prior to the bid experience

more increase in future patent counts. Further, Columns 3 and 4 of the table shows that bidders with

lower patent counts, lower citations and a decreasing trend in citations tend to produce more high quality

innovation post acquisition. This finding indicates that less innovative firms benefit the most from

completed acquisitions, further rendering support to the buying-innovation hypothesis.‡‡‡‡

6. Additional Investigation and Discussions

This section discusses the additional investigations that I have conducted.

Sub-periods

I conduct the previous analyses during the two sub-periods: 1980s and 1990s. There are two

reasons for the sub-period analysis. First, acquisitions in 1980s and 1990s may be driven by different

reasons (Holmstrom and Kaplan (2001)) and innovation may play a different role in the 1980s and 1990s.

Second and more important, the results in 1980s may be more reliable for two reasons. First, firms in the

sample are established in or before 1989, so they may not be representative of the universe in the later

period. Second, even though I adjust for the data truncation problem, the data quality, and the citation

data in particular, may be less reliable in the more recent years. Consequently, my conclusion would be

more reliable if my results are not mainly driven by the post-1990 period.

I find that the results generally hold among the two sub-periods, and the results are stronger in the

1980s than 1990s, indicating that my results are robust and are not driven by the potential sample problem

in the recent years.

Targets

This paper only investigates the innovation output of bidding firms. A natural question relates to

the innovation output of targets. Because the patent dataset compiled by Hall, Jaffe, and Trajtenberg

(2001) only includes firms in existence before 1989, I can find patent information for only 264 targets.

‡‡‡‡ The second column of the table also shows that bidders with more citations and an increasing trend in citations prior to the bid have more patent increases over the three-year period post acquisition. This relation holds among both the bidding and non-bidding firms. This result suggests that firms with more high-quality innovations, as measured by citation counts, tend to have more follow-up patents.

18

These targets are scattered across the 48 Fama-French industries, with each industry often having only 2-

3 targets. Once I add industry and year dummies in the regression models, there is not enough within-

industry-year variation to yield any significant results, mainly because of the small sample size.

In the sample, I do not require a target to be a public firm. Once I impose this restriction, the

sample size is reduced to below 400 observations. Of these deals, even fewer (less than 100) have patent

information on targets. This difficulty is mainly caused by the restriction of pre-1989 firms in the dataset.

Matching post-1989 firms with the NBER patent data is a very challenging task because a patent often is

assigned to a division or a subsidiary of a firm, and therefore it is very difficult to match patent assignees

to Compustat firms. Although I would love to incorporate target innovation activities to conduct a more

complete study, it is not feasible at this stage §§§§ and I would leave these research questions to future

investigations.

7. Summary and Conclusions

This paper investigates the role of technological innovation in acquisition activities. I find that

bidding firms are less innovative and experience a significant decline in technological innovation

compared to similar non-bidding firms, suggesting that firms resort to the external market for

technological innovation when their internal effort is not fruitful. Among the bidders, I find that more

innovative firms are less likely to complete a deal, possibly because they feel less pressure to do so.

Further, I find that innovations do not stifle technological innovation since bidders completing the deals

experience less decline in innovation output than bidders cancelling the deals; and among bidders

completing the deals, less innovative firms do benefit more in terms of future innovation output.

However, buying-innovation can only partially help to alleviate the internal innovation deficiency

problem: compared to firms that build innovation internally, bidders completing the deals still lag behind

in innovation quality during the three-year post-acquisition period. These results are robust among

diversifying deals, non-diversifying deals, among bidders from more innovation-oriented industries, and

across the sub-periods of 1980s and 1990s.

§§§§ Matching the two datasets is an on-going project under the direction of Dr. Bronwyn Hall.

19

My findings suggest that buying innovation is a major consideration in a firm’s acquisition

decisions. Further, the make-or-buy decision seems to be based on innovation output quality, rather than

innovation input or output quantity. My findings also suggest that outsourcing innovation seems to be

only partially effective, in the sense that it merely alleviates significant declines, instead of completely

erasing the deficit against industry leaders in innovation output in the future. To stay technologically

competitive, firms should focus on ‘make’, rather than ‘buy’.

20

References:

Abadie, Alberto, David Drukker, Jane Leber Herr, and Guido W. Imbens, 2001, Implementing matching estimators for average treatment effects in Stata, The Stata Journal 1, 1-18.

Abadie, Alberto, and Guido W. Imbens, 2004, Large sample properties of matching estimators for average treatment effects. NBER and UC Berkeley working paper.

Andrade, Gregor, Mark Mitchell, and Erik Stafford, 2001, New evidence and perspectives on mergers, Journal of Economic Perspectives 15, 103-120.

Andrade, Gregor, and Erik Stafford, 2004, Investigating the economic role of mergers, Journal of Corporate Finance 10, 1-36.

Atanassov, Julian, Vikram Nanda, and Amit Seru, 2006, Finance and innovation: The case of publicly traded firms, University of Oregon working paper.

Baker, George and Thomas Hubbard, 1993, Make versus buy in trucking: Asset ownership, job design, and information, American Economic Review 93, 551-572.

Bradley, Michael, Anand Desai, and E. Han Kim, 1988, Synergistic gains from corporate acquisitions and their division between the stockholders of target and acquiring firms, Journal of Financial Economics 21, 3-40.

Chesbrough, H.W., 2003, Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Boston, MA.

Coase, Ronald, 1937, The nature of the firm, Economica 4, 386-405.

Fama, Eugene, and Kenneth French, 1997, Industry costs of capital, Journal of Financial Economics 43, 153-193.

Griliches, Zvi, 1990, Patent Statistics as Economic Indicators: A survey, Journal of Economic Literature 28, 1661-1707.

Griliches, Zvi, Ariel Pakes, and Bronwyn H. Hall, 1987, The value of patents as indicators of inventive activity, in: Dasgupta P., and Stoneman P. (eds.), Economic Policy and Technological Performance, Cambridge: Cambridge University Press.

Grossman, Sanford and Oliver D. Hart, 1986, The costs and benefits of ownership: A theory of vertical and lateral integration, Journal of Political Economy 94, 691-719.

Hagedoorn, John, and Geert Duysters, 2000, The effect of mergers and acquisitions on the technological performance of companies in a high-tech environment, University of Maastricht working paper.

Hall, Bronwyn H., 1990, The impact of corporate restructuring on industrial research and development, Brookings Papers on Economic Activity 1, 85-136.

Hall, Bronwyn H., 1999, Mergers and R&D revisited, UC Berkeley working paper.

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Hall, Bronwyn H., Adam Jaffe, and Manuel Trajtenberg, 2001, The NBER patent citation data files: Lessons, insights and methodological tools, NBER working paper 8498.

Hall, Bronwyn H., Adam Jaffe, and Manuel Trajtenberg, 2005, Market value and patent citations, Rand Journal of Economics 36, 16-38.

Healy, Paul M., Krishna G. Palepu, and Richard S. Ruback, 1992, Does corporate performance improves after mergers? Journal of Financial Economics 31, 135-175.

Higgins, Matthew J., and Daniel Rodriguez, 2006, The outsourcing of R&D through acquisitions in the pharmaceutical industry, Journal of Financial Economics 80, 351-383.

Hitt, Michael A., Robert E. Hoskisson, R. Duane Ireland, and Jeffrey S. Harrison, 1991, Effects of acquisitions on R&D inputs and outputs, Academy of Management Journal 34, 693-706.

Holmstrom, Bengt, and Steven Kaplan, 2001, Corporate governance and merger activities in the United States: Making sense of the 1980s and 1990s, Journal of Economic Perspective 15, 121-144.

Jarrell, Gregg A., James A. Bradley, and Jeffry M. Netter, 1988, The market for corporate control: The empirical evidence since 1980, Journal of Economic Perspective 2, 49-68.

Jensen, Michael C., and Richard S. Ruback, 1983, The market for corporate control: The scientific evidence, Journal of Financial Economics 11, 5-50.

Klein, Benjamin, Robert G. Crawford, and Armen A. Alchian, 1978, Vertical integration, appropriable rents, and the competitive contracting process, Journal of Law & Economics 21, 297-326.

Lerner, Josh and Julie Wulf, 2006, Innovation and incentives: Evidence from corporate R&D, Review of Economics and Statistics forthcoming.

Loughran, Tim, and Anand M. Vijh, 1997, Do long-term shareholders benefit from corporate acquisitions? Journal of Finance 52, 1765-1790.

Moeller, Sara B., Frederik P. Schlingemann, and René M. Stulz, 2005, Wealth destruction on a massive scale: A study of acquiring firm returns in the merger wave of the late 1990s, Journal of Finance 60, 757-782.

Mullainathan, Sendhil and David Scharfstein, 2001, Do firm boundaries matter? American Economic Review 91, 195-199.

Rau, Raghavendra P., and Theo Vermaelen, 1998, Glamour, value and the post-acquisition performance of acquiring firms, Journal of Financial Economics 49, 223-253.

Trajtenberg, Manuel, 1990, A penny for your quotes: Patent citations and the value of innovations, Rand Journal of Economics 21, 172-187.

Williams, Oliver E., 1975, Understanding the employment relation: The analysis of idiosyncratic exchange, The Bell Journal of Economics 6, 250-278.

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Table 1 Acquisition Sample Distribution

The sample consists of 1,053 acquisitions announced during the period January 1, 1984, to December 31, 1997. The bidders are listed in the SDC’s Mergers and Acquisitions database and are manufacturing firms appearing in the Compustat database before 1989. I keep an acquisition if (1) the transaction value is no less than 1 million, and (2) the bidder owns less than 50 percent of the target prior to the bid and is seeking to own greater than 50 percent of the target. For completed deals, I require that the bidder owns more than 90 percent of the target after the deal completion. I only keep the first bid by the same bidder within a three-year window. I further require that, for each bidding firm, at least one non-bidding matching firm can be found from the same Fama-French 48 industry-year, with total sales within 25% deviation of the bidding firm, and have no bid within a 7-year period.

Panel A: Acquisition Sample Distribution by Year:

Bidding FirmsYear Completed Not Completed1984 147 51985 65 51986 76 31987 39 61988 58 101989 68 121990 63 11991 55 41992 69 31993 74 71994 82 11995 76 31996 58 41997 58 1Total 988 65

23

Table 1 (continued)

Panel B: Acquisition Sample Distribution by Fama-French 48 Industries

BiddersIndustry   Completed Not Completed

2 Food products 38 64 Alcoholic beverages 2 15 Tabacco Products 1 06 Recreational Products 9 18 Printing and publishing 20 09 Consumer goods 34 410 Apparel 20 012 Medical equipment 54 213 Pharmaceutical products 58 314 Chemcials 42 615 Rubber and plastic products 21 116 Textiles 17 017 Construction materials 58 519 Steel works 45 120 Fabricated products 5 021 Machinery 78 522 Electrical equipment 33 423 Automobiles and trucks 37 024 Aircraft 14 225 Shipbuilding and railroad equipment 4 030 Petroleum and natual gas 12 132 Telecommunications 12 034 Business services 3 035 Computers 59 236 Electronic equipment 95 1137 Measuring and control equipment 45 238 Business supplies 43 039 Shipping containers 7 040 Transportation 15 141 Wholesale 57 342 Retail 50 348 Miscellaneous 0 1

Total 988 65

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Table 2 Acquisition Sample Characteristics

Mean Std Dev. 25% Median 75%Panel A: Firm characteristics        MV Equity 1769.98 5339.87 60.19 267.23 1216.94MV Assets 2944.26 11644.33 101.67 430.25 1769.80Book Assets 2016.87 7857.41 71.07 297.57 1248.08Sales 2294.82 7433.32 86.47 415.75 1681.20M/B 1.70 1.12 1.10 1.37 1.90ROA 10.98% 21.41% 7.45% 11.49% 16.74%Return 31.29% 73.23% -3.85% 18.73% 45.49%Market Leverage 0.16 0.14 0.05 0.13 0.23

Panel B: Deal Characteristics        Relative Size 0.20 0.34 0.03 0.08 0.22Diversify 65.24%All Cash 50.52%All Stock 15.76%Compete 3.42%

Panel C: Innovation measures        R&D expenditure Bidders 4.64% 17.61% 0.00% 1.24% 4.61% Non-bidders 5.82% 22.10% 0.87% 2.62% 6.24%

Number of Patents Bidders 9.78 42.83 0.00 0.00 3.23 Non-bidders 12.45 35.89 1.03 3.00 8.61

Number of Citations to Patents Bidders 0.49 0.96 0.00 0.00 0.72 Non-bidders 0.91 0.94 0.27 0.76 1.25

25

Table 3 Innovation Measures by Fama-French 48 Industries

Industry  

Mean Number of Patents per

Firm-year

Mean Number of Citations per

PatentR&D

Expenditure2 Food products 0.914 0.200 0.0034 Alcoholic beverages 3.716 0.234 0.0045 Tabacco Products 0.187 0.216 0.0036 Recreational Products 1.869 0.168 0.0198 Printing and publishing 0.110 0.077 0.0049 Consumer goods 7.570 0.321 0.03010 Apparel 0.369 0.183 0.00212 Medical equipment 3.618 0.510 0.22813 Pharmaceutical products 5.641 0.387 2.76114 Chemcials 11.208 0.452 0.04315 Rubber and plastic products 0.517 0.213 0.01116 Textiles 0.585 0.156 0.00817 Construction materials 1.483 0.245 0.01019 Steel works 2.958 0.242 0.00820 Fabricated products 0.423 0.154 0.00921 Machinery 4.217 0.391 0.04522 Electrical equipment 1.851 0.266 0.05323 Automobiles and trucks 9.429 0.435 0.01824 Aircraft 12.780 0.395 0.024

25Shipbuilding and railroad equipment 2.246 0.203 0.010

30 Petroleum and natual gas 5.567 0.126 0.00532 Telecommunications 7.525 0.067 0.01334 Business services 0.927 0.133 0.00435 Computers 11.150 0.427 0.17036 Electronic equipment 9.268 0.334 0.078

37Measuring and control equipment 2.090 0.336 0.101

38 Business supplies 2.950 0.366 0.00739 Shipping containers 2.612 0.328 0.01540 Transportation 0.049 0.024 0.00041 Wholesale 0.116 0.044 0.01342 Retail 0.073 0.034 0.00248 Miscellaneous 0.291 0.143 0.021

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Table 4 The Propensity to Engage in a Acquisition

This table presents results from the following probit model:

where the LHS variables is equal to 1 for a bidder and 0 for a non-bidder. I include industry and year fixed effects in the regression models and use heteroskedasticity-adjusted standard errors that are robust to clustering at the firm level. Coefficient estimates are reported in the first row, and p-values are reported in brackets in the second row. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Models  (1) (2) (3)Patentt-3 0 0.001

[0.713] [0.177]Patentt-1-Patentt-3 0 0.001

[0.915] [0.620]Citationt-3 -0.451*** -0.455***

[0.000] [0.000]Citationt-1-Citationt-3 -0.212*** -0.213***

[0.000] [0.000]Log Sales -0.062*** -0.001 -0.009

[0.002] [0.943] [0.647]M/B -0.024 -0.006 -0.006

[0.372] [0.832] [0.819]R&D Expenditure -0.612** -0.121 -0.143

[0.025] [0.635] [0.580]ROA -0.271 0.012 0.003

[0.204] [0.959] [0.991]Return 0.164*** 0.170*** 0.170***

[0.000] [0.000] [0.000]Market Leverage -0.266 -0.277 -0.273

[0.197] [0.174] [0.178]Intercept -0.680*** -0.913*** -0.853***

[0.002] [0.000] [0.000]Observations 8851 8851 8851

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Table 5 The Propensity to Engage in an Acquisition - Sub-Samples

This table presents results from the same probit models as in Table 4 but on different sub-samples. To save space, I only report coefficient estimates of the innovation output measures. I include industry and year fixed effects in the regression models and use heteroskedasticity-adjusted standard errors that are robust to clustering at the firm level. Coefficient estimates are reported in the first row, and p-values are reported in brackets in the second row. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Models  (1) (2) (3)Panel A: Non-Diversifying DealsPatentt-3 0 0.001

[0.905] [0.190]Patentt-1-Patentt-3 0.001 0.001

[0.557] [0.737]Citationt-3 -0.556*** -0.564***

[0.000] [0.000]Citationt-1-Citationt-3 -0.368*** -0.373***

[0.000] [0.000]

Panel B: Diversifying Deals    Patentt-3 0 0.001

[0.845] [0.414]Patentt-1-Patentt-3 0 0

[0.910] [0.803]Citationt-3 -0.423*** -0.426***

[0.000] [0.000]Citationt-1-Citationt-3 -0.160*** -0.161***

[0.001] [0.001]

Panel C: Top 15 innovative Industries    Patentt-3 0 0.001

[0.689] [0.273]Patentt-1-Patentt-3 0 0

[0.960] [0.747]Citationt-3 -0.385*** -0.388***

[0.000] [0.000]Citationt-1-Citationt-3 -0.196*** -0.197***    [0.000] [0.000]

28

Table 6 Deal Completion

This table report results from the following model:

where the LHS variable is equal to 1 if the deal is completed and 0 if a deal is cancelled. I include industry and year fixed effects in the regression models and use heteroskedasticity-adjusted standard errors that are robust to clustering at the firm level. Coefficient estimates are reported in the first row, and p-values are reported in brackets in the second row. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Models  (1) (2) (3)Patentt-3 0 0.001

[0.920] [0.766]Patentt-1-Patentt-3 0.001 0.001

[0.588] [0.528]Citationt-3 -0.305*** -0.306***

[0.001] [0.001]Citationt-1-Citationt-3 -0.02 -0.019

[0.772] [0.778]Log Sales 0.143*** 0.195*** 0.191***

[0.003] [0.000] [0.000]M/B 0.085 0.128 0.128

[0.362] [0.219] [0.218]R&D -0.185 0.207 0.193

[0.794] [0.775] [0.789]ROA 0.175 0.471 0.464

[0.755] [0.409] [0.417]Return 0.298* 0.309* 0.309*

[0.063] [0.062] [0.061]Market Leverage -0.566 -0.557 -0.552

[0.282] [0.302] [0.305]Diversify -0.271* -0.277* -0.277*

[0.060] [0.067] [0.067]Relative Size -0.247* -0.309** -0.308**

[0.096] [0.037] [0.038]All Cash -0.718*** -0.812*** -0.810***

[0.003] [0.001] [0.001]All stock -0.747*** -0.876*** -0.875***

[0.006] [0.001] [0.001]Compete -1.144*** -1.099*** -1.091***

[0.000] [0.000] [0.000]Intercept 0.622 0.33 0.354

[0.185] [0.498] [0.490]Observations 883 883 883

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Table 7 Acquisition Treatment Effect using the Abadie and Imbens (AI) Estimator (2004)

This table reports results on the treatment effect after completed acquisitions using the AI estimator. For each bidding firm, I choose four non-bidding matching firms from the same industry-year based on the same firm characteristics used in the previous regression models plus patent and citation counts during the deal completion year (CYR). The table reports the mean difference in future patent and citation counts between the bidding firms and the matching firms in the first row, the heterogeneity-adjusted standard errors in the second row, and p-values in the third row. Panel A reports results based on the entire sample of completed deals. Panel B reports the parallel results based on bidders that do not complete the deals and matched by characteristics three years prior to the announcement date. Please refer to Abadie and Imbens (2004) and Abadie, Drukker, Herr, and Imbens (2001) for details of the AI estimator.

  CYR+1 CYR+2 CYR+3Panel A: All Completed Deals    Patent Counts 0.804 0.529 0.391S.E 1.030 1.059 1.100P-value 0.435 0.618 0.722

Citation Counts -0.152 -0.118 -0.151S.E 0.027 0.034 0.034P-value <0.001 0.001 <0.001

R&D -0.165 0.075 -0.039S.E 0.065 0.120 0.018P-value 0.011 0.531 0.033

Panel B: Unsuccessful Bidders  AYR+1 AYR+2 AYR+3Patent Counts -3.114 -2.336 -4.058S.E 1.282 1.448 1.195P-value 0.015 0.107 0.001

Citation Counts -0.446 -0.451 -0.535S.E 0.103 0.099 0.089P-value <0.001 <0.001 <0.001

R&D -0.909 -0.037 0.010S.E 0.475 0.024 0.044P-value 0.056 0.133 0.814

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Table 8 Changes in Innovation Measures from the Completion Year (CYR) to Three Years After the Completion Year (CYR+3)

This table regresses changes in future innovation measures on past innovation measures and firm and deal characteristics. Patent counts and citation counts are from the NBER patent data set created by Jaffe, and Trajtenberg (2001). I include industry and year fixed effects in the regression models and use heteroskedasticity-adjusted standard errors that are robust to clustering at the firm level. Coefficient estimates are reported in the first row, and p-values are reported in brackets in the second row. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Dependent Variables

 Change in Patent Counts

from CYR to CYR+3  Change in Citation Counts

from CYR to CYR+3Patentt-3 -0.308*** -0.313*** -0.001**

[0.007] [0.007] [0.026]Patentt-1-Patentt-3 -0.124 -0.125 0

[0.173] [0.175] [0.889]Citationt-3 2.671** -0.301*** -0.295***

[0.014] [0.000] [0.000]Citationt-1-Citationt-3 2.172* -0.232*** -0.229***

[0.060] [0.001] [0.001]Log Sales 2.703*** 2.378*** 0.017 0.027

[0.001] [0.003] [0.316] [0.119]M/B -0.834 -0.796 -0.039* -0.040*

[0.267] [0.285] [0.067] [0.063]R&D 24.832* 21.971* 0.11 0.156

[0.065] [0.094] [0.649] [0.514]ROA 12.359 11.588 0.045 0.067

[0.200] [0.228] [0.795] [0.697]Return 1.296** 1.213* -0.046* -0.044*

[0.043] [0.052] [0.063] [0.075]Market Leverage -0.744 0.506 -0.118 -0.119

[0.844] [0.896] [0.528] [0.526]Diversify 0.532 0.821 -0.001 -0.003

[0.719] [0.589] [0.983] [0.957]Relative Size -0.824 -1.068 -0.017 -0.016

[0.592] [0.500] [0.723] [0.744]All Cash 0.088 0.33 0.063 0.064

[0.958] [0.845] [0.319] [0.314]All stock 1.062 1.274 0.164** 0.162**

[0.718] [0.665] [0.015] [0.016]Compete 2.771 1.445 0.003 -0.007

[0.605] [0.791] [0.984] [0.958]Intercept -15.779*** -15.093*** 0.2 0.142

[0.001] [0.002] [0.131] [0.289]Observations 988 988 988 988R-squared 0.217 0.223   0.166 0.17

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Figures 1.1 and 1.2 show patent and citation counts of bidders completing the deals and bidders canceling the deal around the announcement year (AYR). After the bid, I use completion year (CYR) for bidders that complete the deal and announcement year (AYR) for bidders that do not complete the deal.

Figure 1.1 Bidder Patent Count Around the Announcement Year

0.00

2.00

4.00

6.00

8.00

10.00

12.00

AYR-3 AYR-2 AYR-1 AYR CYR+1 (AYR+!)

CYR+2 (AYR+2)

CYR+3 (AYR+3)

Bidders completing the deal

Bidders canceling the deal

Figure 1.2 Bidder Citation Count Around the Announcement Year

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

AYR-3 AYR-2 AYR-1 AYR CYR+1 (AYR+!)

CYR+2 (AYR+2)

CYR+3 (AYR+3)

Bidders completing the deal

Bidders canceling the deal

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