Technological innovation and Acquisitions

Technological innovation and Acquisitions Xinlei Zhao1 Department of Finance Kent State University Kent, OH 44242 330.672.1213 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 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)) 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 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 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 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 describes the sample and variables, and provides descriptive statistics Section examines how technological innovation affects a firm’s takeover decisions, and Section studies post-acquisition innovation Section discusses some additional investigations, and Section concludes 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 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 provide useful information about the generation of future technological impact of a given invention (Trajtenberg (1990), and Hall, Jaffe, and Trajtenberg (2005)).4 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 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 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 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 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.5 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-orbuy 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 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).6 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 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 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 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 main categories: categories: Chemical (excluding Drugs); Computers and Communications (C&C); Drugs and Medical (D&M); Electrical and Electronics (E&E); Mechanical; and Others 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 FamaFrench 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.10 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 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% 10 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 not contaminate the identity of the patent assignee 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 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 t to (2) the total number of patents applied in year t 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 t 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 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 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 innovation post acquisition This finding indicates that less innovative firms benefit the most from completed acquisitions, further rendering support to the buying-innovation hypothesis 15 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 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 withinindustry-year variation to yield any significant results, mainly because of the small sample size 15 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 In the sample, I 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 16 and I would leave these research questions to future investigations 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 so Further, I find that innovations 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 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 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 16 Matching the two datasets is an on-going project under the direction of Dr Bronwyn Hall 19 erasing the deficit against industry leaders in innovation output in the future To stay 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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: Year 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 Total Bidding Firms Completed Not Completed 147 65 76 39 58 10 68 12 63 55 69 74 82 76 58 58 988 65 23 Table (continued) Panel B: Acquisition Sample Distribution by Fama-French 48 Industries Industry 10 12 13 14 15 16 17 19 20 21 22 23 24 25 30 32 34 35 36 37 38 39 40 41 42 48 Total Food products Alcoholic beverages Tabacco Products Recreational Products Printing and publishing Consumer goods Apparel Medical equipment Pharmaceutical products Chemcials Rubber and plastic products Textiles Construction materials Steel works Fabricated products Machinery Electrical equipment Automobiles and trucks Aircraft Shipbuilding and railroad equipment Petroleum and natual gas Telecommunications Business services Computers Electronic equipment Measuring and control equipment Business supplies Shipping containers Transportation Wholesale Retail Miscellaneous Completed 38 20 34 20 54 58 42 21 17 58 45 78 33 37 14 12 12 59 95 45 43 15 57 50 988 Bidders Not Completed 1 5 0 11 0 3 65 24 Table Acquisition Sample Characteristics Mean Panel A: Firm characteristics MV Equity 1769.98 MV Assets 2944.26 Book Assets 2016.87 Sales 2294.82 M/B 1.70 ROA 10.98% Return 31.29% Market Leverage 0.16 Panel B: Deal Characteristics Relative Size 0.20 Diversify 65.24% All Cash 50.52% All Stock 15.76% Compete 3.42% Panel C: Innovation measures R&D expenditure Bidders 4.64% Non-bidders 5.82% Std Dev 25% Median 75% 5339.87 11644.33 7857.41 7433.32 1.12 21.41% 73.23% 0.14 60.19 101.67 71.07 86.47 1.10 7.45% -3.85% 0.05 267.23 430.25 297.57 415.75 1.37 11.49% 18.73% 0.13 1216.94 1769.80 1248.08 1681.20 1.90 16.74% 45.49% 0.23 0.34 0.03 0.08 0.22 17.61% 22.10% 0.00% 0.87% 1.24% 2.62% 4.61% 6.24% Number of Patents Bidders Non-bidders 9.78 12.45 42.83 35.89 0.00 1.03 0.00 3.00 3.23 8.61 Number of Citations to Patents Bidders Non-bidders 0.49 0.91 0.96 0.94 0.00 0.27 0.00 0.76 0.72 1.25 25 Table Innovation Measures by Fama-French 48 Industries Industry 10 12 13 14 15 16 17 19 20 21 22 23 24 25 30 32 34 35 36 37 38 39 40 41 42 48 Food products Alcoholic beverages Tabacco Products Recreational Products Printing and publishing Consumer goods Apparel Medical equipment Pharmaceutical products Chemcials Rubber and plastic products Textiles Construction materials Steel works Fabricated products Machinery Electrical equipment Automobiles and trucks Aircraft Shipbuilding and railroad equipment Petroleum and natual gas Telecommunications Business services Computers Electronic equipment Measuring and control equipment Business supplies Shipping containers Transportation Wholesale Retail Miscellaneous Mean Number of Patents per Firm-year 0.914 3.716 0.187 1.869 0.110 7.570 0.369 3.618 5.641 11.208 0.517 0.585 1.483 2.958 0.423 4.217 1.851 9.429 12.780 Mean Number of Citations per Patent 0.200 0.234 0.216 0.168 0.077 0.321 0.183 0.510 0.387 0.452 0.213 0.156 0.245 0.242 0.154 0.391 0.266 0.435 0.395 R&D Expenditure 0.003 0.004 0.003 0.019 0.004 0.030 0.002 0.228 2.761 0.043 0.011 0.008 0.010 0.008 0.009 0.045 0.053 0.018 0.024 2.246 5.567 7.525 0.927 11.150 9.268 0.203 0.126 0.067 0.133 0.427 0.334 0.010 0.005 0.013 0.004 0.170 0.078 2.090 2.950 2.612 0.049 0.116 0.073 0.291 0.336 0.366 0.328 0.024 0.044 0.034 0.143 0.101 0.007 0.015 0.000 0.013 0.002 0.021 26 Table The Propensity to Engage in a Acquisition This table presents results from the following probit model: Pr(Bidit )   findustry  ft  1Patentsit    2Change in Patentsit   3Citationsit    4Change in Citationsit  (1)  5 Salesit-1  6 M / Bit   7 R & Dit   8 ROAit   9 Re turnit   10 Leverageit   eit , where the LHS variables is equal to for a bidder and 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 Patentt-3 Patentt-1-Patentt-3 (1) [0.713] [0.915] Citationt-3 Citationt-1-Citationt-3 Log Sales M/B R&D Expenditure ROA Return Market Leverage Intercept Observations -0.062*** [0.002] -0.024 [0.372] -0.612** [0.025] -0.271 [0.204] 0.164*** [0.000] -0.266 [0.197] -0.680*** [0.002] 8851 Models (2) -0.451*** [0.000] -0.212*** [0.000] -0.001 [0.943] -0.006 [0.832] -0.121 [0.635] 0.012 [0.959] 0.170*** [0.000] -0.277 [0.174] -0.913*** [0.000] 8851 (3) 0.001 [0.177] 0.001 [0.620] -0.455*** [0.000] -0.213*** [0.000] -0.009 [0.647] -0.006 [0.819] -0.143 [0.580] 0.003 [0.991] 0.170*** [0.000] -0.273 [0.178] -0.853*** [0.000] 8851 27 Table The Propensity to Engage in an Acquisition - Sub-Samples This table presents results from the same probit models as in Table 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 (1) Panel A: Non-Diversifying Deals Patentt-3 [0.905] Patentt-1-Patentt-3 0.001 [0.557] Citationt-3 Citationt-1-Citationt-3 Panel B: Diversifying Deals Patentt-3 Patentt-1-Patentt-3 Citationt-1-Citationt-3 Panel C: Top 15 innovative Industries Patentt-3 [0.689] Patentt-1-Patentt-3 [0.960] Citationt-3 (3) -0.556*** [0.000] -0.368*** [0.000] 0.001 [0.190] 0.001 [0.737] -0.564*** [0.000] -0.373*** [0.000] -0.423*** [0.000] -0.160*** [0.001] 0.001 [0.414] [0.803] -0.426*** [0.000] -0.161*** [0.001] -0.385*** [0.000] -0.196*** [0.000] 0.001 [0.273] [0.747] -0.388*** [0.000] -0.197*** [0.000] [0.845] [0.910] Citationt-3 Citationt-1-Citationt-3 Models (2) 28 Table Deal Completion This table report results from the following model: Pr(Complete)it   findustry  f t  1Patentsit    2Change in Patentsit   3Citationsit    4Change in Citationsit   (2) 5 Salesit-1  6 M / Bit    R & Dit   8 ROAit   9 Re turnit   10 Leverageit   11 Diversifyit  12 Re lative Sizeit  13 All Cashit  14 All Stockit  15Competingit  eit where the LHS variable is equal to if the deal is completed and 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 Patentt-3 Patentt-1-Patentt-3 (1) [0.920] 0.001 [0.588] Citationt-3 Citationt-1-Citationt-3 Log Sales M/B R&D ROA Return Market Leverage Diversify Relative Size All Cash All stock Compete Intercept Observations 0.143*** [0.003] 0.085 [0.362] -0.185 [0.794] 0.175 [0.755] 0.298* [0.063] -0.566 [0.282] -0.271* [0.060] -0.247* [0.096] -0.718*** [0.003] -0.747*** [0.006] -1.144*** [0.000] 0.622 [0.185] 883 Models (2) -0.305*** [0.001] -0.02 [0.772] 0.195*** [0.000] 0.128 [0.219] 0.207 [0.775] 0.471 [0.409] 0.309* [0.062] -0.557 [0.302] -0.277* [0.067] -0.309** [0.037] -0.812*** [0.001] -0.876*** [0.001] -1.099*** [0.000] 0.33 [0.498] 883 (3) 0.001 [0.766] 0.001 [0.528] -0.306*** [0.001] -0.019 [0.778] 0.191*** [0.000] 0.128 [0.218] 0.193 [0.789] 0.464 [0.417] 0.309* [0.061] -0.552 [0.305] -0.277* [0.067] -0.308** [0.038] -0.810*** [0.001] -0.875*** [0.001] -1.091*** [0.000] 0.354 [0.490] 883 29 Table 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 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 Panel A: All Completed Deals Patent Counts 0.804 S.E 1.030 P-value 0.435 CYR+2 CYR+3 0.529 1.059 0.618 0.391 1.100 0.722 Citation Counts S.E P-value -0.152 0.027