Entrepreneurial Exits and Innovation - INSEADfaculty.insead.edu/vikas-aggarwal/documents/[3]...

MANAGEMENT SCIENCEVol. 60, No. 4, April 2014, pp. 867–887ISSN 0025-1909 (print) � ISSN 1526-5501 (online) http://dx.doi.org/10.1287/mnsc.2013.1801

© 2014 INFORMS

Entrepreneurial Exits and Innovation

Vikas A. AggarwalINSEAD, 77305 Fontainebleau, France, [email protected]

David H. HsuThe Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104, [email protected]

We examine how initial public offerings (IPOs) and acquisitions affect entrepreneurial innovation as mea-sured by patent counts and forward patent citations. We construct a firm-year panel data set of all venture

capital-backed biotechnology firms founded between 1980 and 2000, tracked yearly through 2006. We addressthe possibility of unobserved self-selection into exit mode by using coarsened exact matching, and in two addi-tional ways: (1) comparing firms that filed for an IPO (or announced a merger) with those not completing thetransaction for reasons unrelated to innovation, and (2) using an instrumental variables approach. We find thatinnovation quality is highest under private ownership and lowest under public ownership, with acquisitionintermediate between the two. Together with a set of within-exit mode analyses, these results are consistentwith the proposition that information confidentiality mechanisms shape innovation outcomes. The results arenot explained by inventor-level turnover following exit events or by firms’ preexit window dressing behavior.

Keywords : entrepreneurial exits; innovation; information confidentialityHistory : Received February 16, 2012; accepted July 29, 2013, by Lee Fleming, entrepreneurship and innovation.

Published online in Articles in Advance December 19, 2013.

1. IntroductionEquity investments in entrepreneurial start-ups areilliquid until an exit (or liquidity) event such as aninitial public offering (IPO) or acquisition by anotherentity.1 As a result, a leading performance mea-sure that researchers in the entrepreneurship litera-ture investigate is the likelihood of an exit event.The main motivation for studying such outcomes isthat these events offer liquidity and financial returnsto the entrepreneurial founders, their investors, andother shareholders. We know little, however, aboutthe relationship between entrepreneurial exit modesand organizational innovation, particularly when tak-ing into account self-selection. Understanding the linkbetween exits modes and innovation outcomes isimportant to start-up entrepreneurs and managers atestablished companies alike. For entrepreneurs, alter-nate exit mode choices involve trade-offs in organi-zational structure, governance, incentives, resources,and degree of information disclosure—all of whichcan shape innovation outcomes. For industry incum-bents, a deeper understanding of the consequencesof organizational changes accompanying the goingpublic process and the entrepreneurial acquisition

1 We use the terms “exit event” and “liquidity event” interchange-ably. These refer to the ability of the entrepreneur or venture capi-talist (VC) to fully or partially sell their equity stake in a VC-backedstart-up firm.

process can be important in assessing the innova-tion profile of potential competitors.2 We thereforeexamine the research question of the relationshipbetween entrepreneurial exit mode and innovationwhile taking into account the role of (unobserved)entrepreneurial self-selection into exit mode.

To illustrate the phenomenon we study, considerthe example of Genentech. Tom Perkins, cofounderof the venture capital firm Kleiner Perkins and chair-man of Genentech’s board from 1976 through 1990,reflected on the company’s possible sale to Eli Lillyprior to Genentech’s 1980 public offering: “We didhave some preliminary discussions with Lilly. Theymade one of the biggest mistakes in business his-tory in that they didn’t try to push us very hard tosell the company. I think if Lilly, a year before thepublic issue, had made an attractive offer we prob-ably would have gone for it. Because there were no

2 Another motivation for investigating the relationship betweenentrepreneurial exit modes and innovation outcomes is to betterassess the public policy implications of the shifting balance ofentrepreneurial exit modes away from initial public offerings andtoward mergers and acquisitions (M&As). Panel A of Figure 1 plotsthe ratio of deals (and deal value) from VC-backed M&As to IPOsover the 1992–2007 time period. The same data series are plottedfor VC-backed biotechnology firms (the industry subject of thisstudy) in panel B of Figure 1. Acquisitions have clearly outstrippedIPOs as the modal form of entrepreneurial exit. Although assessingthe welfare implications of this shift is beyond the scope of thispaper, the innovation consequences are a key component to suchan analysis.

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Aggarwal and Hsu: Entrepreneurial Exits and Innovation868 Management Science 60(4), pp. 867–887, © 2014 INFORMS

precedents to follow; we would have had a goodreturn on the investment. That would be that. Butthey didn’t. So we gave up that idea and decided topursue the public issue” (Perkins 2002). On the likelyconsequences of such a buyout, Genentech’s CEO inthe early 1990s, Kirk Raab speculated: “Genentechwould not be the wonderful place it is today if somelarge pharmaceutical firm had bought us 0 0 0and likean amoeba absorbed us, which is what big companiesoften do” (Raab 2003).

This anecdote exemplifies a key difficulty in design-ing a study investigating the innovation consequencesof entrepreneurial liquidity mode: the possible issueof self-selection into mode based on unobserved fac-tors. Clearly, the gold standard of random assign-ment of ventures to exit mode is not available. Notonly is being in the position to consider a liquid-ity event (of any sort) not a random occurrence,the choice between exit modes may be importantlyinfluenced by unobserved factors. While we recog-nize that disentangling the comingling of exit modeselection and treatment effects is challenging, weemploy three approaches enabled by our panel dataset of the universe of VC-funded U.S. biotechnol-ogy start-ups founded between 1980 and 2000. First,we employ a coarsened exact matching (CEM) algo-rithm to our data to define more closely aligned treat-ment and control samples. Second, we conduct aquasi-experiment in which we compare the innova-tion profiles of firms experiencing a given exit eventto subsamples of firms that “nearly” experienced theevent, but for reasons unrelated to innovation, didnot complete the exit process. Finally, we employan instrumental variables strategy centered on therelative liquidity of alternative exit channels in thebiotechnology industry.

Across the range of our comparisons, we find adecline in innovation quality (as measured by patentcitations) as a causal effect of both the IPO and M&Atreatments, with the IPO effects larger in magnitude.Although the quantity of innovations (as measuredby patent counts) also declines following an IPO, wefind an increase in this measure following an M&A.These results are consistent with an information con-fidentiality mechanism, in which different levels ofinformation disclosure associated with alternative exitmodes influence innovation rates (going public entailsthe largest information disclosure, while remainingprivately held involves the least, with being acquiredin-between). We conduct within-exit mode analysesto sharpen our evidence for this mechanism. Forfirms going public, there is a significant negativeinteraction on innovation quality between stock mar-ket analyst attention and the level of preclinical trialproducts firms have in their pipeline. For biotechnol-ogy firms, the veil of secrecy may be most important

during the preclinical phase of drug development,and the interaction with analyst coverage is con-sistent with an information disclosure mechanism.Furthermore, among acquired firms, we find beingacquired by a private rather than a public acquirer(the latter associated with higher information dis-closure) results in higher innovation quality amongM&As. In addition, our results point to an importantrole for managerial incentives in M&As: greater tech-nology overlap between the acquiring and acquiredfirms boosts patent quantity but reduces quality, sug-gesting that in more competitive settings, once thefirm becomes part of another organization, acquiredfirm managers prefer short-run observable outcomes(patent quantity) at the expense of outcomes that maynot be observable until the longer run (patent quality).Finally, we investigate the extent to which inventorturnover following liquidity events might account forthese empirical patterns by constructing an inventor-year panel data set covering inventor histories bothin and out of sample with regard to our focal firms.We find that the inventor-level turnover effects cannotexplain the firm-level patterns, which are instead con-sistent with information confidentiality mechanisms.

2. LiteratureA key precondition to the entrepreneurial choiceamong exit modes is building a significant businessto warrant further expansion. Conditional on this,there have been just a few papers, to our knowl-edge, that deal with this choice; these papers suggestfour categories of explanatory factors. In the contextof significant VC involvement, a first set of expla-nations suggests that financing contractual designcan influence exit outcomes, because VCs negotiatecertain control rights based on their assessment ofentrepreneurial quality (e.g., Hellmann 2006, Cum-ming 2008). A second set of explanations centers onindustry or market characteristics, such as the indus-try degree of leverage and concentration, or publicequity hotness (e.g., Brau et al. 2003; Bayar and Chem-manur 2011, 2012). A third set of explanations relatesto the role of firm and product market characteristics,such as growth potential, capital constraints, degreeof information asymmetry, and complementarity withthe potential acquirer (e.g., Poulsen and Stegemoller2008; Bayar and Chemmanur 2011, 2012). Finally,founder characteristics, most notably entrepreneurialpreferences for control versus value creation, can playa role. Schwienbacher (2008) argues in a theoreti-cal model that because entrepreneurs value control,which is more likely under an IPO exit, they aredriven to be more innovative to reduce the likelihoodof being acquired.

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Aggarwal and Hsu: Entrepreneurial Exits and InnovationManagement Science 60(4), pp. 867–887, © 2014 INFORMS 869

Figure 1 Relative Intensity of M&As to IPOs, 1992–2007

Panel B: VC-backed biotechnology start-ups

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Ratio of M&A/IPO amount raised

Ratio of M&A/IPO number of deals

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Ratio of M&A/IPO amount raised

Ratio of M&A/IPO number of deals

Panel A: All VC-backed start-ups

Source. Dow Jones/VentureSource.Note. Data for M&A deal value for panel B is unavailable for 1992–1999.

Although there is a limited but growing literatureexamining the entrepreneurial choice among multi-ple exit modes, the paper by Schwienbacher (2008)is the only one, to our knowledge, that aims tolink this choice directly to entrepreneurial innova-tion. Although we do not believe that any empir-ical study has addressed this topic, there are twomechanisms through which this choice might impactinnovation: a first mechanism relates exit mode inno-vation outcomes to project selection incentives underdifferent ownership regimes; a second mechanismrelates to whom information is revealed under var-ied ownership structures to innovation outcomes.

Although both relate innovation to the degree ofinformation confidentiality the enterprise is able toretain without disclosing to various parties, we dis-cuss each mechanism and its associated empiricalimplications separately because each operates in a dif-ferent way.

2.1. Organizational Ownership, Project Selection,and Innovation

Under private ownership, the classic agency issuesassociated with the separation of ownership and cor-porate control are typically not as severe, becausethe (concentrated) insiders are also the managers.

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By contrast, under public ownership, because of theexpanded number of possible shareholders, the reg-ulatory requirements associated with going publicinclude regular public disclosures on firm opera-tions. These disclosures may have innovation effects.If managers know that they will have to report projectstatus on a regular basis, they may be incentivizedto select projects that are more likely to yield steadyprogress. Developing important innovations, how-ever, is a process that not only involves a longertime horizon, but also offers returns with highervariance relative to more certain investment activi-ties. Moreover, innovation often requires experimen-tation, which may be curtailed if managers know theyhave to report results on a quarterly basis. As such,for situations in which managers want to incentivizeexploratory (rather than exploitative) behavior, pri-vate rather than public firm ownership might be opti-mal (Ferreira et al. 2012). The empirical results ofLerner et al. (2011) are consistent with these ideas. Inthat study, the authors use the private equity contextto evaluate whether firms’ innovation profiles changeas a result of being acquired via buyout, finding anoverall increase in the innovative output of privateequity-acquired firms over the long term (as a result,going private from a publicly held status improvesinnovation outcomes). Therefore, information disclo-sure to a broad audience under public ownershipcan negatively impact innovation quantity and qual-ity by reducing the tolerance for failure (Manso 2011,Ferreira et al. 2012).

With regard to acquisitions, whereas in con-cept there are synergies of personnel and organiza-tions that should benefit the acquisition target (theentrepreneurial firm), the act of merging, typicallyinto a larger organization, can impose costs that mightdampen innovation. Seru (2013) argues that as a divi-sion within a conglomerate, the acquired firm mayhave skewed managerial incentives to oversell thetrue prospects of a given technology in an effort toacquire more resources for the business unit (or to tar-get projects with near-term as opposed to longer-termpayoffs). The result is that managers in the conglom-erate are less willing to fund innovative projects inthe first place, because they are not able to assess thetrue quality of projects.

We would therefore expect the following order-ing of innovation outcomes associated with projectselection incentives resulting from varied ownershipstructures: privately held would be ahead of theother two exit modes of publicly held and acquisi-tion with regard to innovation quality. There is evi-dence in the literature consistent with this ordering,but little or no within-industry evidence taking intoaccount the full spectrum of entrepreneurial liquidityoptions, while also addressing issues of self-selection

into exit mode. There have, however, been a fewefforts to order innovation outcomes by ownershipstructure on a pairwise basis (publicly held versusprivately held and acquisition versus privately held)while taking into account possible selection effects.For example, research contemporaneous with ourstudy suggests that firms pursuing an IPO realize adecline in the quality of their innovations, largely dueto skilled inventor departures and post-IPO produc-tivity decreases (Bernstein 2012). However, the samestudy finds that more entrenched managers experi-ence a smaller decline in innovation productivity. TheBernstein (2012) study complements our own by eval-uating a multi-industry context, with a focus solelyon the IPO mode of exit (and so is unable to assesshow acquisitions fit in comparatively). In addition,in a study using the medical device industry as theempirical context, Wu (2012) finds similar post-IPOeffects as Bernstein (2012) does, with respect to inno-vation quality (a decrease in patent impact follow-ing an IPO), but at the same time finds an increasein the quantity of patents after an IPO (in contrastwith Bernstein (2012), who finds no effect of the IPOtreatment on this same metric). Likewise, for acquisi-tions, Seru (2013) finds lower patent grants and for-ward citations following acquisition as compared toexogenously uncompleted acquisitions, especially forfirms with active internal capital markets.3 Of course,studies comparing only one liquidity mode to pri-vate ownership cannot estimate the relative orderingof expected outcomes among a broader set of alter-natives in a causal way, which is our objective in thispaper.

Together, these studies point to the likely impor-tance of factors determining within-event hetero-geneity, as well as the need to examine multipledimensions of innovative outcomes—e.g., patentquantity and quality. Overall, according to thisfirst information confidentiality mechanism of projectselection, private ownership appears to dominateIPOs and acquisitions with regard to innovation out-put, though the latter two exit modes are not clearlyordered among themselves in this regard.

2.2. Organizational Ownership, InformationDisclosure, and Innovation

Under private ownership, details of a product or ser-vice innovation can more likely remain hidden from

3 According to this “dark side” explanation of internal capital mar-kets of conglomerates, however, it would seem that business unitmanagers (including those acquired) would have incentives tooverrepresent their innovation potential as measured by innovationquantity, even if doing so may be at the cost of developing higher-quality inventions. Seru (2013) does not find this effect, though wedo in our empirics.

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potential competitors. For acquired firms, such infor-mation is only disclosed to a small set of outsiderswho are evaluating the firm as a suitable acquisitiontarget.4 By contrast, publicly held organizations mustroutinely make public disclosures, which can provideimportant information to organizational outsiders.Consider the following quotes regarding the biotech-nology industry: “The biotech industry is fiercelycompetitive and disclosure costs are generally highbecause most companies develop only a few products,and the entrance of a competitor poses a serious sur-vival threat” (Guo et al. 2004, p. 320). Furthermore, asPerkins (2002) noted in regard to possible disclosureof contractual details with Eli Lilly around the timeof the company’s IPO, “We figured that our competi-tors would try to ferret out the details of those con-tracts. They were literally inked out in the SEC files.”Entrepreneurs therefore sacrifice the opportunity tooperate “under the radar” with respect to announc-ing their offerings, in exchange for liquidity and otherbenefits of a public offering. Nevertheless, the deci-sion to go public likely involves a trade-off betweenearly liquidity and the risks of information disclo-sure to product market competitors, as the theoreticalmodels of Bhattacharya and Ritter (1983), Maksimovicand Pichler (2001), and Spiegel and Tookes (2007)suggest.

In an empirical analysis of U.S. manufacturingfirms, Chemmanur et al. (2012) build on these models,finding that product market characteristics can drivefirms’ choice of exit mode in ways that are consis-tent with predictions based on the relative degree ofexpected information confidentiality under alternateownership structures (private, M&A, and public). TheChemmanur et al. (2012) study examines a cross-industry sample of manufacturing firms and findsevidence for a greater decrease in total factor produc-tivity following an IPO as compared to an acquisi-tion, consistent with the mechanism of informationconfidentiality. This study provides complementaryinsights to ours, with our study in the context ofentrepreneurial biotechnology firms differing in itsemphasis on innovative output, as compared to pro-duction and product market characteristics.

A small, related literature is the connectionbetween corporate governance and innovation out-comes. With private ownership, in addition to innateentrepreneurial preferences or benefits associatedwith control, less distributed control rights allow

4 If the acquirer is publicly held, however, the transaction couldreceive more scrutiny by antitrust authorities and/or shareholdersof the acquiring firm (in which case there would be more infor-mation disclosed to a broader audience). We exploit this within-acquisition event heterogeneity in our empirics to sharpen ourempirical evidence beyond across exit mode innovation orderingfor this information confidentiality mechanism.

entrepreneurs to retain relative autonomy in mak-ing decisions in the face of differences of opinionwith outsiders (Boot et al. 2006). The net impact ofconcentrated versus more distributed ownership (aswould be the case with public ownership) on innova-tion, however, is theoretically ambiguous because itdepends on the relative productivity differences asso-ciated with more versus less concentrated corporategovernance. Typically, the corporate board of direc-tors expands in the ramp-up to an IPO (Baker andGompers 2003). Unfortunately, there is little literatureon the direct impact of expanded boards or of tightercorporate governance more generally on innovation.Whereas earlier literature found a negative relation-ship between antitakeover provisions and innovationinvestments (e.g., Meulbroek et al. 1990), a recentstudy (O’Connor and Rafferty 2012) finds no rela-tion between broad measures of corporate governanceand innovation levels once simultaneity is taken intoaccount in their empirical models.

Taken together, this second information confiden-tiality mechanism, focusing on to whom informationis disclosed under different ownership structures, pre-dicts acquisitions as middling in innovation perfor-mance, with better outcomes than going public andworse outcomes relative to remaining private.

3. Methodology3.1. OverviewExamining the causal implications of alternate exitmode choices requires a methodology that takes intoaccount possible self-selection of firms into particu-lar modes based on unobserved factors. In additionto our aim of drawing causal inferences on the effectsof exit mode treatments, we also seek to frame ourresults in the context of the prior literature. As dis-cussed in the previous section, there are two streamsof work related to information confidentiality that arepotentially helpful in understanding the mechanismsat work: altered project selection and disclosures toexternal parties, both of which operate through man-agerial channels. Although these two mechanisms areconceptually distinct, they yield similar predictionswith regard to the relationship between ownershipstructure and innovation patterns. As a result, wewill not be able to untangle the mechanisms empiri-cally, especially because the first mechanism of projectselection from a choice set of alternatives is unob-servable to us. Nevertheless, the two mechanismsof information confidentiality imply an ordering ofinnovation outcomes across ownership modes andsome empirical patterns within exit mode. Our anal-yses are accordingly structured to test the empiricalsalience of the two information confidentiality mecha-nisms. To the degree that our effects might be alterna-tively explained solely through inventor-level changes

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(rather than managerial-level effects associated withproject selection incentives or direct information dis-closure), however, we supplement our firm-level anal-yses with inventor-level analyses, examining the roleof inventor movements and inventor productivityaround exit events.

3.2. SampleWe sample the universe of VC-funded biotechnol-ogy firms founded between 1980 and 2000, identi-fying these firms using the VentureXpert database.We focus on start-ups receiving venture capital fund-ing because the quality screen of VC involvement(Kortum and Lerner 2000) offers a desirable dimen-sion of homogeneity among firms in the sample, withliquidity needs arising from the venture capital cycle(Gompers and Lerner 2004, Inderst and Muller 2004)creating pressures to pursue exit opportunities. A sec-ond desirable dimension of homogeneity is the useof biotechnology as the industry context. The impor-tance of patenting to the appropriation and valuationof innovations is particularly important in biotechnol-ogy relative to other sectors (e.g., Levin et al. 1987).A single-industry context enables us to obtain rele-vant measures of the value and importance of inno-vations, an objective that would be significantly morechallenging in a multi-industry setting. We focus onfirms founded in the 21-year period between 1980 and2000 to ensure that our results are generalizable acrossa range of initial industry conditions, as well as toensure that we can observe firm outcomes for a suf-ficiently long period of time postfounding. The sam-ple consists of the 476 U.S.-based firms in the humanbiotechnology industry (Standard Industrial Classifi-cation codes 2833–2836) founded during these years.

The primary data set is structured as an unbalancedfirm-year panel, with observations for each firm start-ing with the year of founding. Because the mostrecent founding year is 2000, and the data are col-lected through 2006, we observe each firm for a min-imum of seven years, except in cases where the firmis dissolved prior to 2006.5 Our data set thus includesobservations at the firm-year level for each year inwhich the firm is in operation, including those yearsfollowing an exit event (which can be either an IPO oran M&A). We do not, however, include observationsfor those years after which a firm ceases to exist asa consequence of a dissolution event. Left-censoringis not an issue because we observe firms beginningwith their date of founding. The final observation year

5 The average life span of a venture fund during this time frame is8 to 10 years and so VC-backed firms in this industry thus havestrong incentives to pursue an exit event within 5 to 7 years post-founding.

of 2006 is chosen in accordance with our use of for-ward citations as one of our two measures of inno-vative output (described in more detail in §3.4), forwhich we utilize a four-year postapplication observa-tion window. In addition to the firm-year panel, weassemble an inventor-year panel data set (describedin more detail in §3.9) to understand the role of indi-vidual inventors in influencing our results.

We utilize several archival sources to assemble ourdata sets. For exit events, this includes news arti-cle searches from Factiva, combined with data fromThomson One Banker, Zephyr, and U.S. Securities andExchange Commission (SEC) filings. For measures ofinnovation, we draw on the IQSS Patent Networkdatabase (see Lai et al. 2011 for a description), whichincorporates the U.S. Patent and Trademark Officedata on all patents applied for since 1975. This allowsus to construct patent-based measures of innovationoutput at the firm-year level, and in addition, to iden-tify unique inventors associated with these patents,thereby enabling the construction of inventor careerhistories. We also collect data on firms’ VC fundinghistories, strategic alliances, product pipelines, as wellas (for post-IPO firms) coverage from stock marketanalysts. These data draw, respectively, on the fol-lowing sources: VentureXpert, Deloitte Recap RDNA,Pharmaprojects and Inteleos, and I/B/E/S. Finally, toconstruct an instrument for the level of “heat” in theIPO market relative to the M&A market, we collectdata on IPO and M&A market volume from multi-ple sources, including Jay Ritter’s IPO data website6

and SDC.

3.3. Empirical StrategyOur main empirical strategy employs the CEM pro-cedure (Iacus et al. 2011, 2012) to construct treatmentand control samples that are balanced on pretreat-ment covariates (discussed in more detail in §3.8).We use the matched control group to run, for exam-ple, difference-in-differences estimates of the treat-ment effect of alternate exit modes.7 We employ twoadditional empirical strategies on the CEM-matcheddata to mitigate any additional concerns of bias due tounobserved pretreatment characteristics: (1) a quasi-experiment based on “near” exit events—those thatwere started but not completed for exogenous rea-sons; and (2) an instrumental variables strategy toaddress the possible endogenous selection of IPO ver-sus M&A liquidity events. To better understand the

6 This data, updated through 2012, uses the methodology inIbbotson et al. (1994), with the most recent version found athttp://bear.warrington.ufl.edu/ritter/ipoisr.htm.7 Recent examples of studies employing the CEM technique to con-struct matched control samples include Azoulay et al. (2010) andSingh and Agrawal (2011).

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mechanisms driving our results, we then conductwithin-exit mode analyses, along with an analysis atthe inventor-year level. We first describe the construc-tion of the various measures in our firm-year dataset, including innovation outcomes, exit events, firmcharacteristics, and an instrument for the IPO versusM&A choice. We then discuss the CEM process weemploy to generate the matched control samples. Weend the section by detailing how we construct ourinventor-year data set.

3.4. Innovation OutcomesWe begin with our measures of innovation, for whichwe utilize patent data. To identify all patents asso-ciated with the firms in our sample, we first extractfrom the IQSS Patent Network database (Lai et al.2011) all patents applied for between 1975 and 2010where the “assignee” name matches our focal firms’current or former name(s). To ensure that we are com-prehensive in our data collection process, we conductthe search using an algorithm that matches variouspermutations of the company name (e.g., we wouldcode patents from “Amgen” and “Amgen Inc.” asbeing associated with the same firm). The patentnumbers we collect for our focal firms enable us tocollect a range of other patent-based characteristicsincluding forward citations and patent classes. Oursample of 476 firms includes 15,439 patents and 45,789forward citations associated with these patents.

Identifying patents for firms undergoing an M&Aexit raises the issue that post-M&A patent applica-tions associated with inventions of the acquired firmmay be made with the acquirer listed as the assignee.As a consequence, it may be difficult to track the inno-vation outcomes of firms after an acquisition, unlessthe acquired firm operates as an independent entity,with future patents accruing to the subsidiary ratherthan to the parent. We use an inventor-matchingalgorithm to address this issue. We first assemble adatabase of inventors associated with preacquisitionpatents applied for by the focal (acquired) firm. Wethen search patent applications where the acquirer isthe assignee during the postacquisition period, andconsider patents from this set of inventors as havingoriginated from the acquired firm. Thus, the list ofpatents for a focal firm in our sample undergoing anM&A includes those patents associated directly withthe acquired firm before and after the acquisition, aswell the subset of the acquiring firm’s patents thatwere invented by the acquired entity (i.e., the focalfirm) after the acquisition.8

8 We verify that all of our results are robust to excluding all acquir-ers who assign any post-M&A patents to the corporate parent orother entity. We thank an anonymous reviewer for suggesting thisrobustness test.

We utilize two measures of patent-based innova-tion output: patent applications and forward citations.These two characteristics of firm-level output proxyfor the quantity and quality of innovation, respec-tively. Prior work (Trajtenberg 1990) suggests, more-over, that forward citations in particular have a strongcorrelation with economic value. We define the firm-year variables patent applications stock as the numberof patent applications applied for by the firm up toand including the firm-year, and forward patent cita-tions four years stock as the number of patent cita-tions within a four-year postissue window to patentsapplied for (and subsequently granted) by the focalfirm up to and including the firm-year.9 We measureboth through 2006 (the forward citations window con-straints our final observation year).10

3.5. Exit EventsWe observe variation in the modes by whichentrepreneurs and their stakeholders achieve exit.From the time of founding, each firm can undergomultiple exit or “near-exit” events (those for whichthe process was begun, but never consummated). ForM&A events we are concerned specifically with sit-uations in which the focal firm is the target in theacquisition (thereby creating a liquidity event for thefounders and investors). We conduct an exhaustivearchival search using news articles from Factiva, trian-gulated with Thomson One Banker, Zephyr, and SECfilings, to identify realized exit events for our focalfirms (from founding through 2006). We utilize in ourspecifications a set of indicator variables for subsam-ples of firms that underwent an IPO or M&A, as wellas indicator variables for the three-year period of timefollowing the IPO or M&A. These latter variables—focal, post-IPO (113) and focal, post-M&A (113)—allowus to obtain difference-in-differences estimates of theIPO and M&A effects on our CEM-matched sample,as we discuss in detail in §4.11

In addition to identifying realized exit events fromour archival data search, we also identify those exitevents that were “withdrawn” in the sense that theexit process started but was never taken to com-pletion. For IPOs, a withdrawn event represents

9 We also examine the robustness of our results to using our for-ward citation measure less self-citations (the two versions of thevariable are pairwise correlated at 92%). Removing self-citationsstrengthens the results, and so we report the more conservativefull-forward citations in our empirical tables.10 We use the stock versions of these variables because we believethese have a more natural interpretation given our difference-in-differences approach with firm fixed effects and firm age controls(as discussed later); our results are, however, robust to alternativelyusing the flow measures.11 We also researched the incidence of publicly held firms beingtaken private (as in the Lerner et al. 2011 study). Among our samplecompanies, we did not find a single such case.

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situations in which the firm filed for an IPO butsubsequently did not go public due to exogenousmarket conditions. Withdrawn M&A events representsimilar situations, in which a deal was announcedbut never consummated. These two sets of eventsenable us to conduct a quasi-experiment to identifythe treatment effect of exits (IPO or M&A) using sub-samples that pool realized-exit and near-exit events(IPO/near-IPO in one case and M&A/near-M&A inthe other). An assumption of this approach is thata firm’s withdrawal from a previously planned exitevent is uncorrelated with its innovation capacity andwith other firm-level characteristics. For withdrawnIPO events, we verify through news articles that thewithdrawal is a function of unstable or volatile mar-ket conditions, factors exogenous to our model spec-ifications. For withdrawn M&A events we similarlyverify that withdrawals are due to shareholder objec-tions or to regulatory oversight.12 Furthermore, weregress the likelihood of (IPO or M&A) withdrawalon our innovation variables and our full set of (time-lagged) firm characteristics (described later), and findall effects to be insignificant. This increases our con-fidence that exit withdrawals are not systematicallyrelated to either innovation or firm characteristics.

Our firm-year data set is structured to account forthe fact that a firm can undergo multiple “near”-and “realized”-exit events throughout its lifetime. Wecode the full history of such events, and can there-fore observe situations where, for example, the firmexperiences a withdrawn IPO or M&A event, andsubsequently exits via one of these modes. Similarly,we can observe situations in which one mode of exit(e.g., an IPO) is followed by another (an M&A). Oneadditional category of firm-level outcomes is the com-plete dissolution, or liquidation, of a firm in our sam-ple. Such situations differ importantly from our twomodes of exit (IPO and M&A) in that the firm ceasesto exist as a going concern and can thus no longercontinue its innovation output. For firms that are dis-solved, we use the year of dissolution as the finalobservation year for the firm in our firm-year paneldata set.

In addition to the indicator variables for differ-ent exit modes and the three-year postexit windows,we utilize two additional exit event-related measuresthat are specific to the subsample of acquired firms.First, we create an indicator variable for whether theacquiring entity is private (the private dummy). Sec-ond, following Jaffe (1986), we define technology over-lap as the angular separation between the primaryU.S. patent class vectors of the acquiring and acquired

12 Although we were able to confirm that the reasons for M&Awithdrawal were externally driven, we cannot determine whetherthe decision to withdraw came from the target or from the acquirer.

(focal) firms. Each vector has a dimension of 987 andis indexed by unique patent classes; a given valuewithin a vector represents the proportion of the firm’sstock of patents (applied for prior to and until thedate of acquisition) assigned to the patent class asso-ciated with the index for that value. The technologyoverlap measure is the angular dot product of thetwo vectors: a value of 1 represents vectors with per-fect overlap, whereas a value of 0 represents orthog-onal patent class vectors. We interact both the privatedummy and the technology overlap measure with thefocal, post-M&A (113) indicator variable to examinethe role of particular organizational mechanisms ininfluencing innovation output within the M&A modeof exit.

3.6. Firm CharacteristicsWe employ a set of firm-level controls to account forany residual time-varying unobserved heterogeneityin our models (we utilize firm fixed effects in mostspecifications). To account for firm-level quality andlife-cycle considerations we use firm age, which is theage of the firm since founding, along with VC inflowsstock, which measures the cumulative amount of VCfunding received by the firm through the currentfirm-year (collected using VentureXpert). In addition,we use the Deloitte Recap RDNA database to collectdata on the cumulative stock of strategic alliances afirm has entered up to the current firm-year, strategicalliance stock, a further measure of firm quality (e.g.,Stuart et al. 1999).13

In addition to age, VC funding, and strategicalliances, we use a firm’s product portfolio as afinal firm-level characteristic. In the empirical con-text of biotechnology, a relevant metric for prod-uct development is the stage of an individual drugcompound in the U.S. Food and Drug Administra-tion (FDA) approval process. To construct our twoproduct-related measures, we utilize the Inteleos andPharmaProjects databases to compile the number ofproducts each firm has at different stages of devel-opment in a given firm-year. We track the trajec-tory of an individual drug compound over timeby combining Inteleos, for which we have data foryears 1990–2001, and PharmaProjects (which we useto collect 2002–2006 data, matching these with drugcompounds identified in Inteleos).14 We measure the

13 Firms’ strategic alliance stock is correlated with VC inflows stockat the 66% level, and so in the empirical tables we only use thelatter variable, although the results are robust to using the formervariable instead.14 We compile product pipeline data only for firms founded post-1989 because of time-period coverage limitations associated withthese two data sources. However, since our unit of analysis is anindividual drug compound as it moves through the FDA approvalprocess, we are able to track product portfolios post-M&A as well.

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number of products in a given firm-year at four stagesof the FDA approval process: preclinical, stage 1,stage 2, and stage 3. Our measure of early-stage inno-vations, preclinical products, enables us to test the con-ditions under which information disclosure may bemost significant. In addition, as an aggregate measureof a firm’s product portfolio value in a given firm-year (which we use as a control variable), we con-struct a measure, weighted products, which weights thenumber of products based on their stage, putting arbi-trary values of 1, 2, 5, and 10, respectively, on the fourdevelopment stages, reflecting the relative degree ofeconomic value of the firm’s portfolio based on thelikelihood of eventual product commercialization (ourresults are similar with unweighted counts of firmproduct portfolios).

Finally, for the subsample of firms that undergo anIPO, we collect from I/B/E/S a measure of stock mar-ket analyst coverage, analyst reports, that measures thetotal number of analyst reports published about thefirm in the firm-year. Prior studies have discussedthe role that analysts play in influencing both infor-mation availability and incentive structures, whichcan influence innovation (Chemmanur et al. 2012,Ferreira et al. 2012, He and Tian 2013). We thus usethis variable, which measures the degree of scrutinyon the firm by outside parties, to examine the infor-mation confidentiality mechanism in our sample offirms that have gone public.

3.7. Instrumental VariableAs discussed previously, one component of our strat-egy for addressing the possibility of unobserved self-selection into exit mode involves instrumenting forthe endogenous selection between the IPO and M&Amodes of exit. We utilize as our instrument the rela-tive level of “heat” in one market as compared to theother within the biotechnology industry. Prior litera-ture has typically used volume-based measures of IPOmarket heat. Yung et al. (2008), for example, definemarket heat in two ways: first, by comparing thefour-quarter moving average to the historical quar-terly volume; and second, by examining IPO marketunderpricing relative to the historical average. Whileother studies of IPO market heat utilize variants ofthis approach, the commonality is using volume-basedmeasures (e.g., Helwege and Liang 2004). For M&As,“merger waves” are an analogous concept to “hot mar-kets” in IPOs (e.g., Harford 2005), and in this casetransaction volume is similarly used as the key met-ric. We thus focus on volume in the IPO and M&Amarkets because this offers an approach to measur-ing market heat that is common to both markets. Webuild on the methodology used in Yung et al. (2008) todevelop our metric for relative IPO market attractive-ness. Using IPO volume data from Jay Ritter’s website,and M&A volume data from SDC, we first identify

the number of quarters in each firm-year where thefour-quarter moving average of biotechnology IPO (orM&A) volume is 25% above the quarterly averagefrom the prior five years. We then construct a measureof IPO relative to M&A market heat (IPO versus M&Abiotechnology industry liquidity) by taking the ratio ofthe IPO measure to the M&A measure. When we dis-cuss our results using this instrumental variable (IV)in the next section, we will also discuss how the IV isboth correlated with the possibly endogenous variablebut satisfies the exclusion restriction by being unre-lated to firm-level innovation outcomes.

3.8. Coarsened Exact Matching ProcedureTable 1 summarizes the definitions and descrip-tive statistics of the measures used in our analyses.In Table 2, we show that the CEM procedure helpsbalance the preevent subsamples, which we use asthe basis for our main difference-in-differences spec-ifications. As Iacus et al. (2011) note, CEM is partof a general class of methods termed “monotonicimbalance bounding” (MIB), which has beneficial sta-tistical properties as compared to prior “equal per-cent bias reducing” (EPBR) models (Rubin 1976), ofwhich propensity score matching and Mahalanobisdistance are examples.15 MIB generalizes the EPBRclass, eliminating many of the assumptions requiredfor unbiased estimates of treatment effects, and out-performing EPBR in most situations, including thosespecifically designed to meet the EPBR assumptions(Iacus et al. 2011, 2012). A key difference in practicelies in the sequence of data preprocessing: whereasmethods such as propensity score matching (PSM)require determining ex ante the size of the matchedcontrol sample, then ensuring balance ex post, CEMperforms the balancing ex ante (Iacus et al. 2012).CEM entails “coarsening” a set of observed covari-ates, performing exact matching on the coarseneddata, “pruning” observations so that strata haveat least one treatment and one control unit, thenrunning estimations using the original (but pruned)uncoarsened data (Blackwell et al. 2009).

A key goal of any matching process is to ensure thatthe treated and control groups are “balanced” in thesense that their covariates have (approximately) equaldistributional characteristics. Table 2 shows the out-come of our application of the CEM process. We focuson four key pretreatment observables, age, VC inflowstock, strategic alliance stock, and weighted products,

15 In summarizing a series of analytical and numerical tests of theCEM method, Iacus et al. (2011, p. 359) note, “[CEM] 0 0 0generatesmatching solutions that are better balanced and estimates of thecausal quantity of interest that have lower root mean square errorthan methods under the older existing class, such as based onpropensity scores, Mahalanobis distance, nearest neighbors, andoptimal matching.”

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Table 1 Descriptive Statistics and Variable Definitions (Firm-Year Level of Analysis)

Variable Definition Mean Std. dev.

Dependent variablesPatent applications stock Stock of patent applications to firm i in year t 17017 57004Forward patent citations

four years stockForward patent citations to firm i ’s stock of patents within four years of patents granted in year t 55001 156045

Independent variablesEvent and time variables

Focal IPO sample Dummy = 1 only for all firm-years (pre- and postevent) associated with a firm undergoing an IPO 0048 0050Focal, post-IPO window Dummy = 1 for the time window one to three years (inclusive) after the IPO event 0008 0027IPO year indicator Dummy = 1 only for the year in which a firm undertook an IPO 0004 0020Focal M&A sample Dummy = 1 only for all firm-years (pre- and postevent) associated with a firm undergoing an M&A 0040 0049Focal, post-M&A window Dummy = 1 for the time window one to three years (inclusive) after the M&A event 0007 0026Focal, post-M&A window,

private acquirerInteraction term for the time window one to three years (inclusive) after the M&A event if acquired

by a privately held entity (indicator variable)0004 0020

Focal, post-M&A window,technology overlap

Interaction term for the time window one to three years (inclusive) after the M&A event with anormalized angular separation between vectors of primary patent classes of acquired andacquiring firms (see text; formula follows Jaffe 1986)

0010 0026

Biotechnology firm characteristicsAge Age in years of the focal firm as of year t 8042 6012VC inflows stock Cumulative VC inflows invested in the focal firm to year t (in $M) 16039 27087Strategic alliance stock Cumulative number of strategic alliances the focal firm had entered into as of year t as reported

by Deloitte Recap RDNA10039 17091

Weighted products a Aggregate measure of focal firm’s product portfolio in year t created by weighting the number ofproducts along the FDA approval process: preclinical (weighted 1), stage 1 (2), stage 2 (5), andstage 3 (10)

75054 143038

Preclinical products a Number of preclinical products in a firm-year 1005 3039Analyst reports For firms going public, number of analyst reports issued on focal firm in year t 61025 128094

Instrumental variableIPO vs. M&A biotechnology

industry liquidityRatio of number of quarters in a focal year in which the deal volume of IPOs in the biotechnology

industry exceeded by 25% the rolling average over the prior five-year window to the samecount for M&As

0056 0063

aData compiled only for firms founded post-1989.

creating separate treatment and control samples post-CEM for the IPO and M&A treatments. The fourvariables we use to balance the treatment and con-trol samples represent observable quality dimensionsthat we expect would be correlated with the IPOand M&A treatments. As the “pre-CEM” columnshows, the IPO and M&A treatment and control sub-samples are significantly different (at the 5% level)across the board for the full set of covariates. Thesedifferences are reduced, however, post-CEM, withnone of the treatment-control differences significant athigher than 5%, suggesting balance in the two sets ofsamples.

3.9. Inventor-Year Data SetFinally, although our primary aim is to empiricallyassess the role of information confidentiality in therelationship between entrepreneurial exits and inno-vation, an alternative to such mechanisms based inhuman resource turnover might instead be a primarydriver of innovation patterns. For example, Stuart andSorenson (2003) suggest that IPO and M&A liquid-ity events are organizationally disruptive for the focalenterprise, and link the geographic distribution ofnew firm foundings to the regional pattern of such

entrepreneurial liquidity events. They find supportfor an employee spinoff mechanism behind the empir-ical pattern. More generally, in acquisitions, there maybe personnel adjustment costs that can result fromchanges in corporate culture and/or from turnoverin personnel composition. Similarly, for employeesholding stock options, IPOs could loosen the bondsof employment for personnel not subject to lock-uprestrictions. We therefore wish to assess the degreeto which our firm-year results are wholly explainedby inventor-level turnover. If they are, the informa-tion confidentiality mechanisms, which operate at themanagerial policy level rather than at the inventorlevel, may be less important in explaining the firm-level empirical patterns. We therefore construct aninventor-year data set by identifying all inventorsassociated with patents of our focal firm sample andconstructing full inventor histories for each of theseindividuals.

These inventor histories include patenting activitiesboth within and outside our focal firms,16 with the

16 We track inventor histories starting from 1975 to ensure that wecapture a sufficient window of history for inventors prior to theirjoining the focal firm.

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Table 2 Firm Characteristics Before and After Coarsened ExactMatching Procedure

Pre-CEM Post-CEM

IPO Control Controlsample sample IPO sample

L Age 2004 1090∗∗ 2019 2020400835 400835 400605 400685

L VC inflow stock 2010 1055∗∗ 2016 2000410535 410405 410555 410495

L Strategic alliance stock 2011 1010∗∗ 2010 2025410185 410035 400795 400775

L Weighted products 1022 0091∗∗ 0059 0044420155 410685 410615 410405

M&A Control Controlsample sample M&A sample

L Age 2000 1094∗∗ 2047 2050400835 400835 400475 400435

L VC inflow stock 2005 1066∗∗ 2044 2037410485 410485 410345 410335

L Strategic alliance stock 1081 1058∗∗ 2023 2025410235 410215 400965 400775

L Weighted products 1012 1002∗∗ 1003 0099410865 410975 410765 410935

Notes. The mean and standard deviation (in parentheses) are reported. Thenatural logarithm of a variable, X , is denoted L X . The CEM procedureinvolves matching on the log values of age, VC inflow stock, alliance stock,and weighted products.

∗∗Indicates difference is significant at the 5% or higher level compared tothe “treated” sample.

resulting inventor-year data set consisting of 12,769inventors associated with 15,439 focal firm patents,each observed, on average, for 11.3 years (the totalnumber of patents within and outside the focal firmassociated with these inventors is 57,803). We definethe variables change in (mean: 0.46; s.d.: 0.50) andchange out (mean: 0.02; s.d.: 0.15) as indicators forwhether a given inventor either joined or departed afocal firm in a given year. For inventors joining a focalfirm in our sample, we set the variable change in toequal 1 in the first year in which the inventor appliesfor a patent in the focal firm. A departure, capturedby change out, is identified when an inventor who haspatented in one of our focal firms is observed to sub-sequently patent outside this same focal firm. Thisvariable is equal to 1 in the year the inventor patentsin the “new” firm. We additionally define the vari-able years since first invention at the inventor-year levelto reflect the length of the inventor’s career to date.Finally, we create patent outcome measures similar tothe firm-year measures discussed previously (patentapplications stock and forward patent citations four yearsstock), except that these are specific to the inventorand defined at the inventor-year level.

4. Empirical Results4.1. Post- vs. Preevent ComparisonsWe begin our analysis in Table 3 with a simple regres-sion analysis of the innovation patterns for firms thatexperienced an IPO or an acquisition, comparing thepost- and preevent innovation profiles. This analysisdoes not confine the sample to observations matchedvia CEM, because we initially want to describe theinnovation patterns comparing post- versus preeventsfor the sample of firms undergoing each event. In sub-sequent analyses, we will adopt methods to addresspossible selection issues associated with firms of dif-ferent characteristics choosing liquidity modes. Weexamine two innovation outcomes throughout ourempirics, patent applications stock and forward patentcitations four years stock, with the former measure cor-responding to innovation quantity and the latter aproxy for innovation quality. We take the log valueof these outcome variables and run firm fixed effectsOLS regressions on our firm-year sample. Negativebinomial count models (of unlogged outcomes) yieldsimilar estimates for the specifications that convergein estimation. For the sake of consistency throughoutthe tables, we report OLS results.

We first compare the innovation profiles of the202 firms in our sample undergoing an IPO in the firstfour columns of Table 3. The first two columns reportthe effect of being in the post-IPO period, with thefirst column including no controls beyond the firmfixed effects and the second adding to the model avariety of (logged) time-varying firm controls: age, VCinflows stock, and weighted products. VC inflows stockproxies for differential firm resource inputs, whereasage and weighted products aim to control for possibleinnovation rate differences across the firm and prod-uct life cycle. Chemmanur et al. (2010), for example,find that IPOs occur at the peak of firms’ productivitycycle. The key independent variable, focal, postevent(113), is negative and significant in both specifica-tions, with the estimate in (3-2) suggesting a 36%decline in patent applications in the three years post-IPO. The analogous specifications for the forwardpatent citation outcome are contained in the next twocolumns of Table 3. The only difference is that we nor-malize these forward patent citations regressions byincluding the log of patent applications stock as a regres-sor (a structure we adopt throughout our empiricalspecifications when we analyze this outcome vari-able). Dropping this normalization does not alter thestatistical significance of the estimates, although theindependent variable of interest is typically estimatedwith a larger coefficient. The key independent vari-able, focal, postevent (113), is positive, but only sig-nificantly so in specification (3-4), with the estimatesuggesting a 5% increase in forward patent citations

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Table 3 Post- vs. Preevent Innovation Comparisons (Firm-Year Level of Analysis) OLS Regression Coefficients Reported

Post- vs. pre-IPO innovation comparisons Post- vs. pre-M&A innovation comparisons

L Forward patent citations L Forward patent citationsDependent variable: L Patent applications stock four years stock L Patent applications stock four years stock

(3-1) (3-2) (3-3) (3-4) (3-5) (3-6) (3-7) (3-8)

Focal, postevent 41135 −10069∗∗∗ −00361∗∗∗ 00022 00049∗∗ 00508∗∗∗ 00223∗∗∗ −00066∗∗∗ −00073∗∗∗

4000545 4000325 4000225 4000235 4000515 4000325 4000205 4000215Firm-level controls No Yes No Yes No Yes No YesEvent year FE No Yes No Yes No Yes No YesFirm FE Yes Yes Yes Yes Yes Yes Yes YesConstant 20339∗∗∗ −00308∗∗∗ 00276∗∗∗ 00219∗∗∗ 10759∗∗∗ −00320∗∗∗ 00278∗∗∗ 00264∗∗∗

4000215 4000345 4000185 4000245 4000215 4000335 4000155 4000225No. of observations (firms) 3,498 (202) 3,498 (202) 3,498 (202) 3,498 (202) 2,934 (180) 2,934 (180) 2,934 (180) 2,934 (180)

Notes. Firm-level controls include L Age, L VC inflows stock, and L Weighted products. L Patent applications stock is also a control for (3-3), (3-4), (3-7), and(3-8). The samples are not CEM matched because the comparisons simply reflect innovation rates post versus preevent for the sample of firms undergoingeach event.

∗∗ and ∗∗∗ indicates statistical significance at 5% and 1%, respectively.

stock within four years of patent application in thethree years post-IPO.

The final four columns of the table report analo-gous specifications for the 180 firms undergoing anM&A, comparing post- with pre-M&A innovationrates. With the full slate of controls, we find that thepost-M&A (113) window is associated with a 22%increase in patent applications and a 7% decrease inforward patent citations (both estimates are statisti-cally significant at the 1% level). These estimates havenot taken into consideration the possible self-selectioninto exit mode based on unobservables, however. Wetherefore employ several strategies including CEMmatching, an instrumental variables analysis, and acomparison of actual versus “near” liquidity eventsto better understand the relationship between exitmodes and innovation patterns.

4.2. Coarsened Exact Matching EstimatesIn Table 4, we use the CEM technique, balanced onthe log values of age, VC inflows stock, alliance stock,and weighted products to define an IPO treatment andcontrol sample (we omit the alliance stock variableas a regressor in our models because it significantlyreduces our sample size and because it is significantlycorrelated with our VC inflows variable). We also testthe robustness of our results to using CEM matchingon preevent stocks (as of the year prior to the event)of the dependent variables. We find that our keyresults hold, although we report our results withoutmatching on the stocks of preevent outcomes, becausethey are more conservative. The first three columns ofTable 4 examine the outcome variable log patent appli-cations stock. Each OLS specification contains our fullset of firm controls, event year fixed effects, and firmfixed effects. The specifications differ on the sample

analyzed. We start with the entire CEM-balanced sam-ple employing 328 firms. The difference-in-differencesestimate, focal, post-IPO (113), after controlling for thefocal IPO sample, is negative and significant, with animplied 40% drop in patent applications post-IPO(the comparison group is therefore firms which wereeither private or experienced an M&A). The nexttwo columns restrict the sample successively by firstremoving firms that remained privately held over theduration of the study window (reducing the sam-ple size to 200 firms and 1,872 firm-years, with thecomparison group as firms undergoing an M&A) andthen examining just the subsample of firms experi-encing both an IPO and an M&A (yielding 79 firmsand 817 firm-year observations). In both cases, focal,post-IPO (113) is negative and significant at the 1%level, though the estimated effect drops to 35% and28%, respectively. These estimates are in line withthe estimates produced from the simple post- ver-sus pre-IPO analysis of Table 3. We also note thatthe CEM-balancing procedure seems successful, as thecoefficient on the focal event sample in these andsubsequent specifications is not different than zero,suggesting no preevent differences in trends in thecomparison groups. A final note is that in (4-3), sincethe sample contains firms undergoing both liquidityevents (almost always in the order of IPO followedby M&A), we can also estimate a focal, post-M&A(113) variable. That estimated coefficient is not differ-ent than zero.

The final three columns of Table 4 examine the for-ward patent citations outcome, following a parallelmodel structure and subsample comparison as thefirst group of analyses in this table. Here, we finda reversal of the empirical patterns produced by asimple post- versus pre-IPO comparison. Recall thatin that analysis, we found a positive and significant

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Table 4 IPO Treatment vs. Control Sample (Post-CEM) Innovation Comparisons (Firm-Year Level of Analysis) OLS Regression Coefficients Reported

Dependent variable: L Patent applications stock L Forward patent citations four years stock

Removing firms Firms experiencing both Removing firms Firms experiencing bothSample: All remaining private an IPO and M&A All remaining private an IPO and M&A

(4-1) (4-2) (4-3) (4-4) (4-5) (4-6)

Focal, post-IPO 41135 −00399∗∗∗ −00352∗∗∗ −00279∗∗∗ −00190∗∗∗ −00155∗∗∗ −00243∗∗∗

4000385 4000395 4000505 4000275 4000295 4000385Focal IPO sample 10076 −10391 10710 00112

4202575 4108725 4105885 4103565Focal, post-M&A 41135 00035 −00119∗∗∗

4000555 4000415Firm controls Yes Yes Yes Yes Yes YesEvent year FE Yes Yes Yes Yes Yes YesFirm FE Yes Yes Yes Yes Yes YesConstant −00778 −10190 −10762∗∗∗ 00063 00066 −00437

4202075 4005405 4005855 4105555 4103085 4004435No. of observations (firms) 2,702 (328) 1,872 (200) 817 (79) 2,702 (328) 1,872 (200) 817 (79)

Note. Firm-level controls include L Age, L VC inflows stock, and L Weighted products; L Patent applications stock is also a control for 4-4, 4-5, and 4-6 only.∗∗∗Indicates statistical significance at 1%.

effect of citations post-IPO. Using the CEM-balancingprocedure, we instead find a negative and signifi-cant effect at the 1% level across the various samples.Using the entire sample, we find a 19% drop. Underthe logic that firms remaining private for the entirestudy period may be qualitatively different (in unob-servables) compared to firms achieving liquidity, andso should be left aside in the analysis, we estimate a15% drop in forward citations. Finally, restricting thesample to firms undergoing both events produces a24% estimated decline in forward citations post-IPOas compared to a 12% (and statistically significant)decline post-M&A (the two coefficients are statisti-cally different from each other). Therefore, using aCEM-balanced sample of IPO treatment versus con-trol, we find that IPOs are associated with both worseinnovation quantity and quality.

In Table 5, we report a similar table, but for M&Atreatment and control samples using CEM balancing.We follow an analogous structure as in Table 4 withregard to model specification and sample compar-isons. For patent applications, our results are similarto what we find in the post- versus pre-M&A sam-ple: a positive and significant effect.17 However, acrossthe range of samples used in this table, our estimated

17 At the suggestion of an anonymous reviewer, we investigatethe importance of an alternative “window dressing” mechanismin the preevent period in which the focal firm files many patentapplications to attract the relevant audience. This alternative holdsonly for patent applications rather than forward patent citations,because only the former is contemporaneously observed by theaudience (we also checked for any preevent spikes in forwardpatent citations—we did not find any). Recall that our findings onpatent applications are declines in the time window following IPO,but are an increase in the time window following the average M&A.Therefore, this alternative explanation only applies to our post-IPO

effects here are 25% to 50% of the economic size ofthe prior analysis, which did not account for selection.On the other hand, our analysis of forward patentcitations yields both similar statistical and economicsignificance as the simple post- versus pre-M&A anal-ysis: a negative and significant decline in forwardpatent citations. In addition, the negative and signifi-cant effect of the post-IPO window for (5-3) and (5-6)associated with patent applications and forward cita-tions, respectively, is consistent with the results fromTable 4 (the former coefficient is statistically differentand of opposite sign than the focal, post-M&A (113)coefficient in the same specification; the latter coeffi-cient is statistically lower than its corresponding focal,post-M&A (113) coefficient). Finally, note that the focalM&A sample dummy is also not statistically differ-ent than zero in all the specifications in Table 5, againimplying a successful CEM-balancing procedure.

4.3. Endogenous Choice of IPO vs. M&AOne concern with the CEM-balanced estimates pre-sented in the prior two tables is that the match-ing procedure is only as good as the observablesupon which we could possibly balance the treatedand control samples. As a result, there could still beunobserved selection issues associated with those esti-mates. We therefore employ two additional empiri-cal strategies to estimate our effects, both of which

patent application results. To evaluate it, we include dummies forvarious time window dummies prior to the event year (in additionto the post-IPO time dummy—and so the interpretation of the timewindow variables is relative to the event year). We find either nopreevent spike for a majority of the preevent windows, or for afew of the windows, a small (relative to the post-IPO window) andnegative coefficient. We therefore conclude that preevent windowdressing is unlikely to explain the empirical patterns.

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Table 5 M&A Treatment vs. Control Sample (Post-CEM) Innovation Comparisons (Firm-Year Level of Analysis) OLS Regression Coefficients Reported


Removing firms Firms experiencing both Removing firms Firms experiencing bothSample: All remaining private an M&A and IPO All remaining private an M&A and IPO

(5-1) (5-2) (5-3) (5-4) (5-5) (5-6)

Focal, post-M&A 41135 00171∗∗∗ 00177∗∗∗ 00106∗∗∗ −00037∗∗∗ −00036∗∗∗ −00078∗∗∗

4000225 4000225 4000275 4000135 4000145 4000185Focal M&A sample 00693 00522 00011 10431 10430 −10432

4301985 4302225 4001895 4109325 4109565 4200825Focal, post-IPO 41135 −00504∗∗∗ −00083∗∗∗

4000475 4000335Firm controls Yes Yes Yes Yes Yes YesEvent year FE Yes Yes Yes Yes Yes YesFirm FE Yes Yes Yes Yes Yes YesConstant −00290 00372 −00188 00009 00361 00153

4202625 4202805 4202355 4103665 4103835 4104725No. of observations (firms) 4,711 (396) 3,681 (260) 1,369 (96) 4,711 (396) 3,681 (260) 1,369 (96)

Note. Firm-level controls include L Age, L VC inflows stock, and L Weighted products; L Patent applications stock is also a control for 5-4, 5-5, and 5-6 only.∗∗∗Indicates statistical significance at 1%.

use CEM matching as the first step to sample con-struction. In our first strategy, we conduct extensiveresearch into the firms within our original sample thatnearly completed a liquidity event, but for reasonsunrelated to innovation did not complete the event.

Table 6 Endogenous Choice of IPO vs. M&A Innovation Comparisons (Firm-Year Level of Analysis) “Near” vs. Actual Events and Instrumental VariableAnalyses (Post-CEM) OLS Regression Coefficients Reported

2SLS IV analysis on firms undergoingOLS analysis of “near” vs. OLS analysis of “near” vs. either an IPO or M&A,

Estimation method and sample: actual IPOs, post-CEM actual M&As, post-CEM post-CEM balancing (IPO treatment)

L Patent L Forward L Patent L Forward L Patent L Forwardapplications patent citations applications patent citations applications patent citations

Dependent variable: stock four years stock stock four years stock stock (2SLS) four years stock (2SLS)

(6-1) (6-2) (6-3) (6-4) (6-5) (6-6)

Focal, post-IPO 41135 −00320∗∗∗ −00151∗∗∗ −00409∗∗∗ −00150∗∗∗

4000415 4000305 4000585 4000435Focal, post-M&A 41135 00180∗∗∗ −00050∗∗

4000215 4000145Focal event sample −00429 00045 00457 −00985 −00145 00152

4009105 4004575 4201825 4104495 4100335 4007515IPO year indicator (instrumented) 10157∗∗ −00069

4005275 4003845Firm-level controls Yes Yes Yes Yes Yes YesEvent year FE Yes Yes Yes Yes Yes YesFirm FE Yes Yes Yes Yes Yes YesConstant −20373∗∗∗ 00807∗ −00457∗∗∗ 00985∗∗∗ −10000 00809

4007495 4004355 4001245 4001695 4007005 4005105No. of observations (firms) 1,612 (168) 1,612 (168) 2,154 (175) 2,154 (175) 1,049 (179) 1,049 (179)

Notes. Firm-level controls include L Age, L VC inflows stock, and L Weighted products; L Patent applications stock is also a control for 6-2, 6-4, and 6-7 only.For (6-5) and (6-6), the first-stage logit regression of the endogenous variable, IPO year indicator, on the instrumental variable, IPO vs. M&A biotechnologyindustry liquidity, yields a positive and significant coefficient of 0.036 with a standard error of 0.013 (p < 0001). The F statistic of the first stage is 22.7,suggesting that the instrument is not weak.

∗, ∗∗, and ∗∗∗ indicate statistical significance at 10%, 5%, and 1%, respectively.

We compare actual versus “near” IPO events, post-CEM matching, in the first two columns of Table 6 forboth of our outcome variables (in unreported analy-ses, we find that the balance between the treatmentand control samples for the actual versus near-IPOs

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and M&As reported in Table 2 is maintained). Weinclude in each specification our full set of time-varying firm controls and report firm fixed-effectsOLS models. Our results are consistent with the CEManalyses in Table 4, in which we find negative and sig-nificant difference-in-differences post-IPO time win-dow effects for patent quantity and quality. In thethird and fourth columns of Table 6, we conductan analogous examination using actual versus nearacquisitions. Again, our results echo our findingsfrom Table 5, with a positive and significant post-M&A window effect on patent applications, but anegative and significant coefficient for the same win-dow on forward patent citations. For both pairs ofactual versus near event analyses, we regressed thelikelihood of withdrawal on our innovation variablesand our full set of firm characteristics (with time lags)and found all regressors insignificant (available uponrequest from the authors). This lends support to ourquasi-experimental strategy in that withdrawn eventsare not systematically related to innovation or firmcharacteristics in a regression framework.

Our second empirical strategy to address selectionof liquidity mode based on unobservables adds aninstrumental variables strategy to an IPO-treatmentCEM-balanced sample. For this analysis, we confinethe sample to firms experiencing either an IPO orM&A liquidity event and instrument for the poten-tially endogenous variable, IPO year indicator. We doso by constructing a variable, IPO vs. M&A biotechnol-ogy industry liquidity. As noted above, this variable isdefined at the biotechnology industry level and is ameasure of the comparative deal volume of each liq-uidity mode over a rolling time window. The higherthe value of IPO vs. M&A biotechnology industry liquid-ity, the “hotter” is the IPO market relative to the M&Amarket for biotechnology transactions. As a result, allelse equal, the higher the instrumental variable (IV),the more likely a given firm will choose an IPO as aresult of the comparative “money-chasing deals” IPOenvironment. This logic is borne out when we regressIPO year indicator on IPO vs. M&A biotechnology indus-try liquidity and our slate of firm controls. The result-ing coefficient is positive and statistically significant atthe 1% level. This is the first-stage regression in bothspecifications (6-5) and (6-6) in which we run two-stage least squares (2SLS) regressions. The F -statisticfor our first-stage regression is 22.7, strongly suggest-ing that our IV is not weak. Durbin and Wu-Hausmantests (with values of 19 and 15) reject the null hypoth-esis that IPO year indicator is exogenous.

In addition, the requirement that the IV is uncor-related with firm innovation outcomes is likely satis-fied in our case. The IV is a measure of industry-levelrelative liquidity, whereas our ultimate outcome vari-ables are at the firm level. Furthermore, the IV is a

measure of relative liquidity of exit mode rather thana measure of differences in factor inputs that mightbe correlated with firm-level innovation outcomes.Finally, it is not only notoriously difficult to predictthe degree to which a financing channel will be “hot”(e.g., Lowry 2003), but also the relative degree towhich one market will be more active than another.This suggests that it will be very difficult or not pos-sible for entrepreneurs with (possibly unobserved)innovation expectations to correctly anticipate rela-tively “hot” financing modes. Although our instru-mental variable allows us to meet the order conditionfor identification, there is no direct statistical test ofthe exclusion restriction. Using this empirical frame-work, our results on innovation quantity and qualityare consistent with the estimates we obtained fromusing CEM matching alone (Table 4) and CEM match-ing coupled with actual versus near IPOs (first twocolumns of Table 6). Furthermore, the 2SLS results arerobust to omitting the CEM-balancing scheme (whichhas the effect of nearly tripling the number of usablefirm-year observations).

The results thus far are consistent with the informa-tion confidentiality mechanism in that innovation out-comes are worse post-IPO relative to post-M&A, andseem to be best under private ownership. This patternholds for patent applications (comparing (6-1) to (6-3)and (6-5)) and for forward patent citations (comparing(6-2) to (6-4) and (6-6)), even after addressing the roleof possible self-selection into liquidity mode. Infor-mation confidentiality is best preserved under pri-vate ownership and is partially compromised underan acquisition (information is spread to the acquireror candidate acquirers). IPOs represent the structurewith the most information revelation to the greatestnumber of outsiders among the ownership structures,consistent with the predictions of the information con-fidentiality mechanisms. We now examine situationswithin liquidity mode in which the information confi-dentiality effects are likely to be more or less severe toprovide another dimension of empirical evidence forthis mechanism because it might connect to firm-levelinnovation outcomes.

4.4. Within-Event HeterogeneityWe begin by examining heterogeneous within-IPOeffects. Whereas all IPOs in the United States necessi-tate regulatory compliance with the SEC with regardto information disclosure, we believe that the negativeeffect of information confidentiality on innovationoutcomes may be most salient under two concurrentconditions: namely, when the focal biotechnology firmhas many early-stage projects (as proxied by the num-ber of preclinical products), and at the same timethe firm itself receives considerable scrutiny (leadingto increased information flows to outsiders) by stock

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Table 7 Within-Event Heterogeneity (Firm-Year Level of Analysis, Post-CEM Matching) OLS Regression Coefficients Reported

Within IPO sample Within M&A sample

L Patent L Forward L Patent L Forward L Patent L Forwardapplications patent citations applications patent citations applications patent citations

stock four years stock stock four years stock stock four years stock

(7-1) (7-2) (7-3) (7-4) (7-5) (7-6)

L Analyst reports 00060∗∗∗ 00033∗∗∗

4000135 4000095L Preclinical products −00134 −00022

4002385 4001625L Analyst reports ∗ 00046 −00084∗∗

L Preclinical products 4000605 4000415Focal, post-M&A 41135 00191∗∗∗ −00062∗∗∗ 00043 00137∗∗∗

4000245 4000165 4000545 4000395Focal, post-M&A 41135, −00051 00075∗∗

private acquirer 4000525 4000345Focal, post-M&A 41135, 00143∗∗ −00298∗∗∗

tech overlap 4000755 4000545Firm controls Yes Yes Yes Yes Yes YesEvent year FE Yes Yes Yes Yes Yes YesFirm FE Yes Yes Yes Yes Yes YesConstant −00545 10956∗∗∗ 00123 10410 00821 00607

4005515 4003755 4201855 4104375 4200605 4104965No. of observations (firms) 891 (129) 891 (129) 2,089 (170) 2,089 (170) 1,593 (119) 1,593 (119)

Note. Firm-level controls include L Age and L VC inflows stocks; L Weighted products is included in specifications 7-3 through 7-6, and L Patent applicationsstock is also a control for 7-2, 7-4, and 7-6 only.

∗∗ and ∗∗∗ indicate statistical significance at 5% and 1%, respectively.

analysts. Stock analysts therefore work in the oppo-site direction as information confidentiality, exposinginformation and firm analysis to the outside. In thefirst two columns of Table 7, we analyze the interac-tion effect of log analyst reports and log preclinical prod-ucts on our two innovation outcomes. Although wedo not find a significant patent applications effect, wedo find a negative and statistically significant effect ofthis interaction on our measure of innovation quality.Although the direct effect of analyst coverage is posi-tive on innovation, the interaction effect suggests thatfor a given level of preclinical products, the marginalimpact of increasing analyst attention as measuredby analyst reports by one standard deviation resultsin a decrease of 2.2% in forward patent citations.This effect is consistent with other research highlight-ing the risks of information disclosure in the earlystages of the biotechnology product development pro-cess: “At an early stage in the product developmentcycle, the firm’s lead time over potential competitorsis short, and managers may accordingly view the riskof adverse competitor action as high and thereforebe reluctant to disclose extensive proprietary informa-tion” (Guo et al. 2004, p. 326).

To probe the within-IPO sample for possible evi-dence of organizational governance effects, we col-lected information on whether the executive officers(including the chief executive officer) of the firm

at the time of IPO were also founders of the firm.Although there could be varied reasons for observ-ing such instances, we examine whether there areconsequences for innovation depending on such exec-utive officer status. On one hand, we might conjec-ture incentive alignment because founders typicallypossess a large share of equity, even at the time ofIPO. On the other hand, the literature has reportedfounder control tendencies (e.g., Boot et al. 2006,Schwienbacher 2008), and so the net effect is theoret-ically ambiguous. We define two variables to capturethe phenomenon: (1) an indicator variable for whetherthe CEO at the time of IPO is also a founder, and(2) the percentage of executive officers at the time ofIPO who were founders. In both cases (when inter-acted with the post-IPO time window), we find nosignificant effect on forward patent citations, althoughwe do find a significant positive effect using the CEOvariable on patent applications (results available uponrequest).

Similarly, we examine heterogeneity within theM&A sample with an eye to testing the informa-tion confidentiality mechanism. First, we conjecturethat there might be differential information disclo-sure effects associated with acquisition by a publicversus private acquirer. Because such an acquisi-tion happens only once, to estimate the effect, weinteract an indicator for private acquirer with the

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key difference-in-differences variable focal, post-M&A(113) in our OLS panel firm fixed-effects framework.Although we do not find an effect of this inter-action on patent applications, the effect is positiveand significant for forward patent citations. This sug-gests that relative to the post-M&A window of publicacquirers, biotechnology targets acquired by privateentities receive a nearly 8% boost in innovation qual-ity. Naturally, private acquirers retain more informa-tion confidentiality relative to public acquirers.

Taken together, these two empirical patterns ofwithin-event heterogeneity provide additional evi-dence consistent with the information confidentialitymechanism. With regard to M&As, the Seru (2013)and related theories suggest an additional within-M&A pattern. Recall that this theory relates businessunit manager incentives for innovation in the contextof a competitive internal capital and labor market of aconglomerate (which the acquired innovator joins inthe case of an acquisition). Due to such competitionat least in the short run, individual managers mayhave the incentive to overrepresent their unit’s inno-vation prospects. We empirically examine acquisitionsthat differ in the degree to which such incentives mayplay out by measuring the degree of technologicaloverlap between acquirer and target. We do so byconstructing the tech overlap measure at the time ofacquisition, which follows the Jaffe (1986) method of

Table 8 Exit Event Window Robustness Regressions (Firm-Year Level of Analysis)


2SLS IV on IPO or 2SLS IV on IPO orCEM, IPO CEM, M&A M&A, post-CEM CEM, IPO CEM, M&A M&A, post-CEM

Comparison: treatment treatment (IPO treatment) treatment treatment (IPO treatment)

Nonwindow RHS same as: (4-1) (5-1) (6-6) (4-4) (5-4) (6-7)

(8-1) (8-2) (8-3) (8-4) (8-5) (8-6)

Focal, post-IPO 41135 −00399∗∗∗ −00406∗∗∗ −00190∗∗∗ −00179∗∗∗

4000385 4000685 4000275 4000505Focal, post-IPO 41145 −00479∗∗∗ −00427∗∗∗ −00202∗∗∗ −00234∗∗∗

4000375 4000665 4000275 4000495Focal, post-IPO 41155 −00499∗∗∗ −00405∗∗∗ −00207∗∗∗ −00263∗∗∗

4000375 4000685 4000285 4000505Focal, post-IPO 411105 −00645∗∗∗ −00474∗∗∗ −00245∗∗∗ −00310∗∗∗

4000445 4000805 4000335 4000585Focal, post-M&A 41135 00171∗∗∗ −00037∗∗∗

4000225 4000135Focal, post-M&A 41145 00187∗∗∗ −00037∗∗∗

4000215 4000135Focal, post-M&A 41155 00200∗∗∗ −00051∗∗∗

4000215 4000135Focal, post-M&A 411105 00259∗∗∗ −00018

4000245 4000155

Notes. Values are regression coefficients (standard errors). Each cell represents a different (full) regression equation with only the focal time window changedrelative to the specification listed in the third row in the table.

∗∗∗Indicates statistical significance at 1%.

comparing patent classifications of the entire portfo-lio of patents between the acquirer and the acquiredfirms. High values of tech overlap suggest more simi-lar technical alignment between the parties, and thisis a situation in which the incentives for business unitmanagers are more likely to be competitive. We inter-act tech overlap with the focal, post-M&A (113) variablein the final two columns of Table 7. Consistent withthe Seru (2013) project selection mechanism on inter-nal incentives, we find that the interaction effect issignificantly positively correlated with patent appli-cations but significantly negatively correlated withforward patent citations. This suggests that in suchcompetitive settings, managers are incentivized to dis-play outcomes that are observable in the near term(a 3.7% increase in patent applications for a one stan-dard deviation increase in tech overlap) while sacrific-ing quality, which is only apparent over the longerrun (a 7.7% decrease in forward patent citations forthe same tech overlap increase).

4.5. Event Window Result RobustnessThroughout the analyses thus far, we have mainlyemployed a one- to three-year postevent window inassessing our results. In Table 8, we report resultsthat vary this event window. Each cell in the tablerepresents a different regression using all the same

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nonwindow right-hand-side (RHS) regressors as thespecification stated in the third row of the table, withonly the estimated coefficient associated with the rel-evant time window variable reported. For ease ofcomparison, we repeat the estimates using the (113)window under the different estimation strategies. Wethen show the results of the same models, but replacethe (113) window with (114), (115), and (1110) timewindows. The results are quite robust to these alter-native time windows, and the longer time windowssuggest that the effects we report are not necessar-ily transitory—but rather are more consistent with aregime shift (as would hold under the informationconfidentiality mechanisms).

4.6. Inventor-Level AnalysisTo examine the extent to which these firm-year pat-terns are driven by inventor-level effects, we rebuildour entire database at the inventor-year level (ratherthan the firm-year level) and construct inventor careerhistories for the focal inventors who have invented inour focal set of firms. As described in §3, we constructthe inventor histories backward (to 1975, when theelectronic patent records are first available) and for-ward (to 2006, when the inventor database has beendisambiguated) in time.

We explore two sets of outcomes at the inventorlevel: patent applications and forward patent cita-tions. Note that although these mirror the outcomeswe examine at the firm level, the results in this tableshould be interpreted at the inventor-year level ofanalysis. We wish to evaluate these outcomes forinventors as they transition into an IPO or M&A own-ership regime. In particular, we want to assess threedimensions of inventor-level impact: average inventorproductivity within firms in the time window follow-ing the liquidity event, average productivity of inven-tors being hired into firms after the liquidity event,and average productivity of inventors departing thefirm after a given liquidity event.

To estimate the first dimension, we examine thefocal, postevent (1, 3) variable, as in our firm-year anal-ysis. This captures the change in inventor innova-tion productivity for the focal sample in the timewindow after the event. To estimate the second andthird dimensions, we interact focal, postevent (1, 3) witheither inventor change out or inventor change in, respec-tively. After preparing the inventor-year data in themanner described in §3, we use a CEM algorithmto define a treatment and control sample. We matchbased on log years since first invention (a proxy forinventor age), log firm age, and log VC inflows stock.In each specification in Table 9, we include firm- andevent-year fixed effects, as well as controls for firm age,VC inflow stock, and years since first invention. In addi-tion, when we analyze the outcome variable log for-ward patent citations four years stock, we include an

additional regressor as before, log patent applicationsstock. All models are estimated via OLS.

The first four columns of Table 9 examine post-IPOinventor effects by analyzing each of the two innova-tion outcome variables using two different samples—first, the CEM sample defining an IPO treatment ver-sus control sample, and second, an actual- versusnear-IPO sample following the CEM process (the for-mer subsample is “treated”). Across the two esti-mating techniques, we find a fairly consistent setof results. First, patent applications decline whileforward patent citations increase, on average, forinventors in the firm post-IPO. Second, following anIPO, the inventors departing the firm are the onesunderperforming with regard to patent applications(although departing inventors did not differ withregard to forward patent citations). Finally, follow-ing an IPO, inventors joining the newly public firmunderperform with respect to both patent applica-tions and patent citations. Taken together, these anal-yses suggest that the firm-level drop in innovationquantity and quality, when evaluated from the dimen-sion of inventor-level productivity, results both fromfactors related to changes in innovative productivityof the technical staff within firms undergoing an IPO,and also from the quality of inventors attracted toand departing from the firm in the postevent window.In the case of forward citations post-IPO, the nega-tive effect of the quality of inventors entering the firmoverwhelms the positive productivity effect experi-enced by the scientist-inventors at the firm. Althoughthe net result of the inventor analysis is consistentwith Bernstein (2012), that study finds that inventorsentering the firm produce higher-quality innovations,whereas the quality of those staying declines. Thedifference may be partially due to the cross-industrysetting used in that analysis. The results of inven-tor fixed-effects models are similar to what we havereported, although because such fixed-effects modelscannot also accommodate the CEM weighting, we donot formally report those models.

The final four columns of Table 9 report simi-lar model specifications, again employing the sametwo sampling strategies, except analyzing post-M&Ainnovation effects. Here, we find much less withregard to changes in inventor productivity in thetime window post-M&A. There is no effect in thepost-M&A window of inventor productivity changes(in contrast to Seru’s (2013) finding of a drop in post-M&A inventor productivity). Similarly, there is no dif-ference in the quality of inventors departing the firmpost-M&A. There is mixed evidence suggesting thatinventors hired into the newly merged company areslightly worse as measured by forward patent cita-tions (but only using the actual- versus near-M&Acomparison). Taken as a whole, these results suggest

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Table 9 Inventor-Level Analyses (Inventor-Year Level of Analysis)

Post-IPO effects Post-M&A effects

L Patent applications L Forward patent citations L Patent applications L Forward patent citationsDependent variable: stock four years stock stock four years stock

Actual vs. Actual vs. Actual vs. Actual vs.CEM, IPO near IPO, CEM, IPO near IPO, CEM, M&A near M&A, CEM, M&A near M&A,

Sample: treatment post-CEM treatment post-CEM treatment post-CEM treatment post-CEM

(9-1) (9-2) (9-3) (9-4) (9-5) (9-6) (9-7) (9-8)

Focal, postevent 41135 −00050∗∗∗ −00051∗∗∗ 00036∗∗ 00030∗∗ −00002 00009 −00003 000044000205 4000195 4000165 4000155 4000225 4000245 4000185 4000145

Focal, postevent 41135 ∗ −00157∗∗∗ −00189∗∗∗ 00010 00002 00073 00072 −00044 −00059Inventor change out 4000635 4000625 4000515 4000495 4000815 4000915 4000645 4000545

Focal, postevent 41135 ∗ −00044∗∗ −00059∗∗∗ −00068∗∗∗ −00070∗∗∗ 00041∗ 00034 −00018 −00036∗∗

Inventor change in 4000215 4000205 4000175 4000165 4000235 4000265 4000185 4000155Focal event sample −00998 −10655∗∗∗ 00770 00257 −20225∗∗∗ −00498 −40188∗∗∗ −10876∗∗∗

4100105 4004435 4008055 4003515 4007585 4003125 4006005 4001845Inventor change out −00035∗∗ −00007 00058∗∗∗ 00064∗∗∗ −00034∗∗∗ −00050∗ 00019∗ 00042∗∗

4000165 4000195 4000135 4000155 4000145 4000295 4000115 4000175Inventor change in −00081∗∗∗ −00056∗∗∗ −00003 −00001 −00041∗∗∗ −00057∗∗ −00025∗∗∗ 00005

4000085 4000085 4000055 4000065 4000075 4000135 4000055 4000355Firm and inventor controls Yes Yes Yes Yes Yes Yes Yes YesFirm FE Yes Yes Yes Yes Yes Yes Yes YesEvent year FE Yes Yes Yes Yes Yes Yes Yes YesConstant −10675∗ −00888∗∗∗ −00583 00032 00721 −00474∗∗∗ 30120∗∗∗ 00659∗∗∗

4009585 4003195 4007635 4002525 4007075 4000905 4005595 4000535No. of observations 18,583 10,500 18,583 10,500 10,532 3,692 10,532 3,692

Notes. Values are regression coefficients (standard errors). Firm-level controls include L Firm age and L VC inflows stock; inventor-level control is L Yearssince first invention. L Patent applications stock is also a control for 9-3, 9-4, 9-7, and 9-8 only. The CEM procedure involves matching on the L Years sincefirst invention, L Firm age, and L VC inflows stock.

∗, ∗∗, and ∗∗∗ indicate statistical significance at 10%, 5%, and 1%, respectively.

that inventor-level effects are not driving the over-all firm-level patterns (the same conclusion applies toinventor fixed-effects models, which we do not for-mally report for the same reason as before). As aconsequence, there is little support for the alternativeexplanation that inventor-level turnover as a result ofthe entrepreneurial exit events (both IPO and M&A)explains the firm-level empirical patterns.

5. ConclusionWe examine the impact of entrepreneurial exit modeon innovation outcomes, as measured by patent quan-tity and quality. We construct a firm-year panel dataset of all venture capital funded biotechnology firmsfounded between 1980 and 2000, tracking these firmsthrough the end of 2006, to evaluate the innovationimplications of entrepreneurial firms’ choice among amenu of alternative exit mode options. Our empiri-cal methods address the challenge of inference basedon self-selection effects by controlling for firm-levelqualities using CEM alone, and variously coupledwith a quasi-experiment utilizing both exit event and“near-exit” event observations, as well as an instru-mental variable approach that instruments for the exit

event using the relative “hotness” of different exitmode channels within the biotechnology industry. Wefind that innovation quality is highest under privateownership and lowest under public ownership, withacquisition intermediate between the two. Within theIPO sample, innovation quality suffers when firmssimultaneously have more analyst attention and morepreclinical stage products. Within the M&A sample,innovation quality is bolstered by private (as com-pared to public) acquirers, and when there is lesstechnology overlap between the acquirer and target.The collection of across- and within-exit mode resultsis consistent with information confidentiality mecha-nisms in which project selection and information dis-closure stratified by ownership structure impact firminnovation outcomes. Moreover, these patterns arenot driven entirely by inventor-level turnover behav-ior, nor are they explained by preevent window dress-ing behavior.

Three caveats to the analysis are important to note.First, we rely on patent data for our measures ofinnovation. If private companies are less likely topursue the patenting channel for invention appropri-ability relative to public firms, our estimates may be

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biased. However, the stylized fact is that patents area primary means of appropriation in the biotechnol-ogy industry. Furthermore, Hsu and Ziedonis (2013)report an important patent signaling value to financialinput providers for private firms in the semiconductorindustry, which is particularly salient in the earlieststages of venture development. Second, we examineonly a single industry, biotechnology, and so resultgeneralizability will be left for future research. Finally,we rely on public sources to code M&A withdrawalannouncements for our quasi-experimental analysis,and these sources could be incomplete. Neverthe-less, we conclude from the collection of evidence thatentrepreneurial firms’ exit modes affect subsequentinnovation outcomes.

AcknowledgmentsThe authors thank Iain Cockburn, Florian Ederer, JoanFarre-Mensa, Josh Lerner, the editorial review team, andaudience members at the Conference on Entrepreneur-ship, Firm Growth and Ownership Change held at theSwedish Research Institute of Industrial Economics; theNational Bureau of Economic Research productivity lunch;Queen’s University Economics of Entrepreneurship andInnovation Conference; Stanford University; the StrategicManagement Society annual meeting; Temple Fox School;Tilburg Entrepreneurial Finance Conference; and the Uni-versity of California, Los Angeles Entrepreneurship andInnovation seminar for helpful comments. The authors alsothank Sean Nicholson and Simon Wakeman for provid-ing biotechnology product and alliance data; and PhoebeChen and especially Andy Wu for excellent research assis-tance. The authors acknowledge funding from the WhartonEntrepreneurship and Family Business Research Centre ofExcellence for Applied Research and Training, the MackInstitute for Innovation Management, the Centre of Excel-lence for Applied Research and Training Term Fund, andthe Wharton–INSEAD Center for Global Research andEducation.

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