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The interaction between product market and financing strategy the role of venture capital

The interaction between product market and financing strategy the role of venture capital
The interaction between product market and financing strategy the role of venture capital

The Interaction Between Product

Market and Financing Strategy:

The Role of Venture Capital

Thomas Hellmann

Manju Puri

Stanford University

Venture capital?nancing is widely believed to be in?uential for new innovative compa-nies.We provide empirical evidence that venture capital?nancing is related to product market strategies and outcomes of https://www.sodocs.net/doc/0018319283.html,ing a unique hand-collected database of Silicon Valley high-tech start-ups we?nd that innovator?rms are more likely to obtain venture capital than imitator?rms.Venture capital is also associated with a signi?cant reduction in the time to bring a product to market,especially for innovators.Our results suggest signi?cant interrelations between investor types and product market dimensions, and a role of venture capital for innovative companies.

Venture capital is widely believed to contribute to the competitive strength of the U.S.economy by promoting the development of innovative start-ups. Yet little is known about what kind of companies are most likely to receive venture capital and what its impact is on these companies.It is commonly argued that a distinguishing feature of venture capital is the close involve-ment of investor’s with the companies they?nance.If one thinks of?nancial institutions on a spectrum from“arm’s length”to“relational”investors,ven-ture capital is typically viewed as lying at the latter extreme.It is generally believed that venture capitalists are extensively involved in the businesses they?nance,not only closely monitoring their activities,but also providing valuable support and governance.The natural question to ask then is whether the involvement of a venture capitalist makes a difference in the development path of the entrepreneurial companies.

We thank Anat Admati,Bill Barnett,Chris Barry,Jim Brander,Bruno Cassiman,Darrell Duf?e,Paul Gompers, Steve Kaplan,Josh Lerner,Robert McDonald,Paul P?eiderer,William Sahlman,Andrea Shepard,Jim Van Horne,and seminar participants at the American F inance Association,New York,Arizona State University, Bocconi University in Milano,Humbolt University in Berlin,University of British Columbia,University of Chicago,University of Dresden,University of Maryland,University of Munich,Stanford University,Tilburg University,and the Western F inance Association,Monterey,for helpful comments.We received particularly helpful and insightful comments from Sheridan Titman(the editor)and an anonymous referee.We also thank Jim Baron,Diane Burton,and Michael Hannan for their generous permission to access their data,and Ali Hortacsu,Vlasta Pokladnik,Shu Wu,Mu Yang,and Muhamet Yildiz for excellent research assistance. We thank the Center for Entrepreneurial Studies at the Stanford Graduate School of Business for?nancial support.All errors are ours.Address correspondence to Manju Puri,Graduate School of Business,Stanford University,Stanford,CA94305-5015,or e-mail:mpuri@https://www.sodocs.net/doc/0018319283.html,.

The Review of Financial Studies Winter2000V ol.13,No.4,pp.959–984

?2000The Society for Financial Studies

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The issue of what kind of investor might help to promote innovative?rms is also important in the context of the emerging literature on the interaction between?nancing and product market behavior.This literature has focused mainly on the relationship between debt levels and product market behavior.1 However,the impact of equity investors,or more broadly,the importance of the investor type has received little attention.Hence the role of involved equity investors,such as venture capitalists,on product market dimensions is an interesting but unexplored avenue for research.

A large informal literature discusses the bene?ts and costs of venture cap-ital?nancing.Venture capitalists are said to bene?t their companies through a variety of activities such as mentoring,strategic advice,monitoring,cer-ti?cation to outside stakeholders,corporate governance,professionalization of the company,and recruitment of senior management.On the other hand, obtaining venture capital?nancing also has its costs.The close involvement of the venture capitalist can be time consuming for the entrepreneurs and the entrepreneurs can also experience a signi?cant loss of control.Moreover, venture capital is said to be an expensive source of capital(which in turn re?ects the bene?ts of having an involved investor).

In thinking about the product market dimension,we can draw on the large industrial organization literature.Among the competitive strategies of new companies,an important distinction is made in this literature between inno-vator and imitator strategies.Innovators are those?rms that are the?rst to introduce new products or services for which no close substitute is yet offered in the market.Imitators are also engaged in relatively new products and tech-nologies,but they are not the?rst movers in their markets,and therefore tend to compete on aspects other than innovation.2There is no general presuppo-sition that one strategy is systematically better than the other.3However,the choice of an innovator or imitator strategy has implications for the relative importance of strategic actions,such as the importance of being a?rst-mover or being quick to market.

1Brander and Lewis(1986),for example,examine the relationship between capital structure and pricing of an oligopolist.Other important theoretical contributions to this literature include Poitevin(1989)and Bolton and Scharfstein(1990),who examine endogenous?nancial constraints in predatory action games,Maksimovic and Titman(1991),who examine how?nancial policy affects?rms’incentive to maintain their product reputation; and Gertner,Gibbons,and Scharfstein(1988),who examine signaling across markets.Chevalier(1995a,b), Philips(1995),and Allen and Phillips(1999)provide some empirical evidence.See Ravid(1988)for a survey. Investors can be either equity or debt investors.F urther,they can be arm’s length or relational investors[see, e.g.,F ama(1985),Sharpe(1990),Diamond(1991),and Rajan(1992)].

2In a survey of the theoretical and empirical literature,Lieberman and Montgomery(1988)broadly classify the advantages of being a?rst-mover in terms of leadership in product and process technology,preemption of assets,and the development of buyer switching costs;the disadvantages of being a?rst-mover relate mainly to free-rider problems and to lock-in effects and sluggish responses of?rst-movers.See Fudenberg and Tirole (1996)and Tirole(1988)for some of the game-theoretic foundations of the?rst-mover advantages.

3Maggi(1996)shows that even with identical players there may be asymmetric equilibria where some?rms pursue innovator and other?rms pursue imitator strategies;expected pro?ts,however,are equalized across companies.A similar conclusion emerges from the literature on the endogenous timing of innovation,sum-marized in Reinganum(1989).

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Interaction Between Product Market and FinancingStrateg y With this distinction between innovator and imitator strategies,a number of questions arise relating to venture capital?nancing.F irst,does the choice of a product market strategy in?uence the type of?nancing obtained by a start-up company?There are a number of alternative hypotheses.For exam-ple,one alternative would be that the marginal value of obtaining venture capital is greater for innovator companies so that innovators obtain venture capital in equilibrium.This could be because innovators have a wider set of challenges on many fronts(e.g.,development of new business concepts,prod-uct and technological innovation,and development of new markets)and the business expertise provided by venture capitalists can be particularly help-ful in addressing some of these issues.An alternative hypothesis would be that innovators have greater dif?culties in attracting venture capital,because venture capitalists themselves are accountable to a set of investors who pre-fer more easily understood products.Second,does the choice of an investor affect outcomes in the product market?F or instance,venture capitalists may affect the time it takes a company to bring its product to market.Again,a number of alternative hypotheses are possible.One alternative would be that venture capitalists focus the entrepreneurs on the key strategic challenges and exert in?uence in order to speed up the time to market.Alternatively,ven-ture capitalists could be more patient investors that provide the entrepreneurs with additional breathing room,thus slowing down time to market.Because the theoretical considerations suggest that there might be various interac-tion effects pointing in different directions,it is appropriate to submit these questions to an empirical analysis.

One of the dif?culties that typically plagues this line of research is the dearth of available datasets.In order to research these questions we use a unique hand-collected dataset of high-technology companies in Silicon Valley,where the high incidence of entrepreneurial activity provides a rich setting for studying our hypotheses.We use a variety of instruments,includ-ing surveys,interviews,and commercial databases,as well as any publicly available information.The sample design enables us to observe a timeline of events for each company,including whether it obtains venture capital,and if so,when,and how long it takes to bring its product to market.In our data we can hence compare venture capital-and non-venture capital-backed companies.Through interviews,we are also able to obtain product market information that is usually not available,such as a founder’s initial prod-uct market https://www.sodocs.net/doc/0018319283.html,panies are classi?ed into two groups according to whether their initial strategy is best described as an innovator strategy or an imitator strategy.The criterion for being an innovator is that the company is either creating a new market,is introducing a radical innovation in an existing market,or is developing a technology that will lead to products that satisfy either of the above criteria.4

4Imitators typically still have a certain amount of inventiveness,but they seek their competitive advantages not through innovation itself,but rather through differentiation,typically in terms of product features or marketing.

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The?rst part of the analysis examines the relationship between the product market strategy and the investor type.In a probit model,controlling for age and industry effects,we?nd that innovators are more likely to be?nanced by venture capital than are imitators.5In a Cox proportional hazard duration model we also?nd that innovators obtain venture capital earlier in the life cycle than do imitators.These results refute the sometimes voiced criticisms that venture capital does not support the most innovative start-ups,or that venture capitalists invest in innovative companies only when they are already older and less risky.

In the second part of the analysis,we examine the relationship between venture capital?nancing and the time it takes a company to bring its prod-uct to market.In a duration model with time-varying covariates that keeps track of when a company obtains venture capital,we?nd that the presence of venture capital is associated with faster time to market.This effect is par-ticularly strong for innovators but statistically insigni?cant for imitators.One interpretation of these results is that venture capitalists in?uence companies to bring their product to market faster,and this effect is more pronounced for innovators,for whom this might be particularly valuable.This also explains our?rst?nding,that innovators are more likely to obtain venture capital.

The third part of the analysis considers some alternative interpretations and additional robustness checks.F irst,we ask whether?rms themselves consid-ered obtaining venture capital important.In surveys,?rms were asked to list signi?cant milestones in the company’s history.We?nd that?rms are more likely to consider venture capital a milestone event than obtaining?nanc-ing from some other kind of?nancier.Second,we then examine whether our?nding of a faster time to market could be due to only companies with certain characteristics obtaining venture capital in equilibrium.It could be that venture capitalists select?rms with certain characteristics,or that?rms with certain characteristics select venture capital.Because we examine equi-librium outcomes,the same basic estimation procedure applies irrespective of who selects whom.However,for ease of exposition,in what follows we word the selection effect as selection by venture capitalists.We test for selec-tion by venture capitalists based on observable information,selection based on expert(predictive)industry knowledge by venture capitalists,and selec-tion based on product announcements,either publicly known or anticipated within the year.In all cases,we?nd that after controlling for these effects, venture capital is still associated with a faster time to market,particularly for innovators,suggesting that selection based on these aspects is not driving the results.F inally,we ask why imitators obtain venture capital,given that there

5Insofar as ex ante measures of innovation are likely to be correlated to ex post measures,our results are con-sistent with Kortum and Lerner(1998),who,in a somewhat different context,?nd that venture capital-backed ?rms are more likely to innovate(through ex post patenting activity)than are non-venture capital-backed ?rms.

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Interaction Between Product Market and FinancingStrateg y are also costs associated with venture capital,as discussed above.We?nd that venture capital is associated with signi?cantly greater amounts of exter-nal?nancing for imitators,but not for innovators.This result suggests that venture capital can play different roles in different companies.For imitators the provision of funds may be the more important aspect of venture capital, whereas for innovators,the product market dimension can be more important.

The remainder of the article is organized as follows.Section1provides some institutional background on venture capital.Section2lays out the hypotheses.Section3describes the data.Section4examines the effect of the founding strategy on the type of?nancing.Section5examines the rela-tionship between venture capital?nancing and product market outcomes. Section6discusses alternative interpretations and robustness checks. Section7concludes.

1.Institutional Background

It is frequently argued that venture capitalists are a distinct type of investor for entrepreneurial companies.In this section we give a brief description of venture capitalists,and the alternatives to venture capital.

Venture capitalists are full-time professional investors who invest for their partnership funds.Venture capitalists tend to closely follow the technol-ogy and market developments in their area of expertise in order to stay in the deal?ow and to be able to make an informed investment decision [F enn,Liang,and Prowse(1995)].Before making an investment,they care-fully scrutinize the founders and their business concepts[Fried and Hisrich (1994),Garmaise(1999)].When making the investment,they bring?nancial expertise to structuring the deal and setting appropriate incentive and com-pensation systems[Sahlman(1988,1990),Kaplan and Stromberg(1999)]. After the initial investment,venture capitalists tend to be very active in the process of raising additional funds for their portfolio companies[Gorman and Sahlman(1989)].They also continuously monitor their companies,both formally through participation at the board level and informally[Rosenstein (1988),Lerner(1995)].As monitors and through their access to private infor-mation,like banks,they can help provide certi?cation to outside stakeholders [James(1987),Puri(1996,1999),The Economist(1997)].They can provide valuable mentoring and strategic advice for the entrepreneurs and they fre-quently assist companies in providing business contacts and recruiting senior managers[Bygrave and Timmons(1992)].They tend to play an important role in corporate governance,frequently replacing the original founder as CEO[Hellmann(1998)].They help professionalize the company,both within the organization and at the CEO level[for empirical evidence,see Hell-mann and Puri(2000)].F inally,they often take an active role in guiding the exit decision,such as in?uencing a company’s initial public offering[Lerner (1994),Gompers(1995)].

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The main alternatives to venture capital?nancing are so-called angels(i.e., private individuals),corporations,banks,government,and self-?nancing. Sahlman(1990)emphasizes the high-powered incentives of venture capi-talists and their high degree of specialization to the?nancing of young com-panies,often within only a very limited number of industry segments.Angel investors are independently wealthy individuals who diversify part of their wealth by investing in young companies.Typically they do not have any staff for supervising their investments and tend to rely on their preexisting net-works to?nd new deals.Although there is considerable heterogeneity within the angel community,many exercise some other position as their main pro-fessional activity[Benjamin and Sandles(1998),F enn,Liang and Prowse (1998)].Corporations also invest in entrepreneurial companies,either as part of an organized venture capital fund,or on an ad hoc basis.In addition to seeking?nancial gains,they frequently also pursue strategic objectives. Hellmann(1997a)shows that entrepreneurs may be quite reluctant to receive funding from corporations if there are potential con?icts of interest.More generally,while a corporate investor may,in principle,be in a good posi-tion to add value to an entrepreneurial company,incentive problems and bureaucracy are frequently believed to limit the usefulness of a corporate investor[Block and McMillan(1993),Gompers and Lerner(1998)].Com-mercial banks are an infrequent provider of funding to entrepreneurial com-panies.Apart from occasional loan commitments,banks sometimes engage in venture capital investments through wholly owned subsidiaries.Regula-tory constraints tend to make banks more conservative investors[Fiet and F raser(1994),Hellmann(1997b)].Some investment banks also make ven-ture capital investments,typically with an eye on future transactions,such as underwriting the initial public offering(IPO).Puri(1996,1999),Gande et al.(1997),and Gompers and Lerner(1999)examine the potential con-?ict of interest when investors are also https://www.sodocs.net/doc/0018319283.html,ernment?nancing is entirely passive by nature and consists mainly of grants[Lerner(1996)]. Self-?nancing comprises?nancing from the founders,their families,and their friends[F luck,Holtz-Eakin,and Rosen(1998)].Hence it seems reasonable to conjecture that venture capitalists are a somewhat distinct type of investor who specialize in the?nancing of entrepreneurial companies.

2.The Hypotheses

In this section we brie?y outline the hypotheses that underlie our analy-sis.Very little is known about the interaction between the product market attributes and the type of investor,particularly in the?nancing of start-up companies.Therefore an important question is whether any such inter-actions exist.In testing for interactions we will be careful to account for the timing structure of events.In particular,we distinguish between ex ante strategy prior to?nancing,the?nancing itself,and the ex post product mar-ket outcome.Thus we examine the interrelationship of the ex ante strategy

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Interaction Between Product Market and FinancingStrateg y (innovator or imitator)and the type of?nancing(venture capital or other) on the one hand,and the interrelationship of the type of?nancing and sub-sequent product market outcomes(in particular time to market)on the other.

Our?rst null hypothesis is that the type of?nancing is independent of product market strategy,that is,there is no relationship between the ex ante strategy and the type of?nancing of a start-up company.There are at least two alternative hypotheses.The?rst alternative is that venture capitalists prefer to invest in innovator companies,because they have a comparative advantage in identifying and then assisting innovator companies.In particular, their business experience may be particularly helpful to sort through the greater ambiguity that surrounds an innovator company.A second alternative is that venture capitalists,being accountable to their own investors,may prefer to invest in imitator companies,for which the business concepts are easier to comprehend and communicate.Under this view,venture capitalists may shy away from the uncertainty of an innovator company,but are eager to free ride on the learning of other companies by funding mainly imitator companies.6

Our second null hypothesis postulates that the type of?nancing is product market neutral,that is,there is no relation between the type of?nancing and product market outcomes of a start-up company.Arguably for start-ups one of the most important product market outcomes is the time it takes to bring a product to market.The null hypothesis would then argue that the type of ?nancing(or investor type)does not affect time to market.Again there are at least two alternate hypotheses.The?rst alternative is that the knowledge and the involvement of venture capitalists allow them to better identify promising companies and then assist them in their quest to quickly bring a product to market.Here we would then?nd venture capital being associated with faster time to market.This effect might be particularly strong for innovators whose strategy is predicated on being a?rst mover.Alternatively,it could be the case that venture capitalists are patient investors with a higher tolerance for long development cycles that would slow down entrepreneurs.In this case, venture capitalists would be associated with a longer time to market.

3.The Data

In order to test our hypotheses we need a sample of?rms chosen indepen-dently of?nancing(so that we have both venture and non-venture capital-backed?rms).F urther,we need data on ex ante strategies of?rms,the kind and timing of?nancing(venture capital or not)received,and the timing of bringing a product to market.Our task is complicated by the fact that existing commercial databases such as Venture Economics and Venture One contain data only on venture capital-backed?rms,hence we cannot use these

6Bylinsky(1995)and Deger(1996)suggest such behavior for the venture capital industry.

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databases to identify a sample of?rms to study.F urther,these databases have scant data on the timeline of events and ex ante strategies of?rms.

To conduct this study we therefore use a unique hand-collected dataset of start-ups in Silicon Valley culled from a combination of survey data as well as from publicly available data.This dataset is collated from com-bining two independent research efforts conducted over a period of several years,starting in1994.The initial sample selection of Silicon Valley?rms and data collection was organized by Baron,Burton,and Hannan(1996a,b) which we supplemented in1996and1997with an additional?nancing sur-vey and related data collection.7To generate the initial list of companies three main datasources were used.The?rst two databases that listed?rms in Silicon Valley were Rich’s Everyday Sales ProspectingGuide,published by Rich’s Guide,and Technology Resource Guide to Greater Silicon Valley, published by CorpTech.A strati?ed random sample was selected in which ?rms could have a legal age no older than10years and had to have more than10employees.Moreover,young and large?rms were oversampled and foreign?rms were excluded.The Silicon Valley business press was used as a third data source to identify very young?rms that were not even listed in the two databases mentioned above.The purpose of doing this was to alleviate concerns that relying exclusively on guidebooks such as Rich’s and CorpTech to construct the sample might underrepresent new start-ups since there is sometimes a considerable time lag before newly created?rms appear in these guidebooks.Hence the sample was supplemented by adding on22 very young?rms identi?ed through the Silicon Valley business press.

Our sample consists of173start-up companies that are located in California’s Silicon Valley.In order to collect the data,a number of sur-veys were sent to key people in each?rm,covering a wide range of questions about historic and current aspects of the companies.The overall response rate was80%.F urther,trained MBA and Ph.D.students conducted semistruc-tured interviews with key informants of the sample companies.An effort was made to interview the founders,the current CEO,and a human resource manager for each company.This data was then augmented with any infor-mation provided by the company.In addition,publicly available information about each of the?rms in the study was gathered from on-line data sources such as Lexis/Nexis,Dialog,Business Connection,or ABI Inform.F urther, for?rms that had gone public,annual reports and10-K or IPO prospec-tuses(where available)were also collected and used to augment the data. To obtain?nancing data,between autumn1996to October1997we sent out

7A more detailed description of the sampling procedures and their rationale can be found in Burton(1996) and in Baron,Burton,and Hannan,(1996a,b).These articles are based on a?rst round of interviews of some 100companies that were performed in the summer of1994.A second round of interviews was conducted in the summer of1995,and follow-up interviews were conducted in the summer of1996.This article obviously uses the updated information.Where possible,we also augmented the publicly available information up to the end of our observation period,which we de?ned to be October1997.

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Interaction Between Product Market and FinancingStrateg y a survey addressed to the most senior member of the company in charge of ?nance.The survey asked for a complete?nancing history of the company since the time of founding.The information was augmented with data avail-able from two commercial databases,Venture Economics and Venture One, largely for the purpose of ascertaining which?rms in our sample received venture capital.8We performed additional cross checks on the data by using the interview transcripts,researching public sources,and placing calls to the companies to resolve remaining ambiguities.We also continued to augment the data coming in from the companies,again using public information as well as the interview and survey material.Considerable emphasis was put on measuring the timing of events such as the date of founding,the date of the ?rst product sale,and the timing of all?nancing rounds.

This experiment design has three distinct advantages.F irst,the sampling is independent of the form of?nancing.One-third of the sample is non-venture-backed?rms,allowing us to compare and contrast the behavior of venture-backed?rms with non-venture-backed?rms.Second,we obtain a clear timeline of events.The design of the study allows us to observe compa-nies over time,including retrospective data all the way back to the founding events.Third,the use of surveys and interviews,though imperfect for the usual reasons,allows us to obtain data,such as the founding strategy,that is normally not available to researchers.In addition,Silicon Valley is an inter-esting environment to study since it has the highest start-up activity in the United States.The narrow focus of our sample,speci?cally technology com-panies in Silicon Valley,has the advantage that we can control for common economic conditions,such as geography,labor markets,or regulation.The disadvantage is that the results may,obviously,have limited applicability for companies under different economic base conditions.

3.1The variables

In what follows,we describe the main variables and the way they are de?ned and collated.Table1shows the descriptive statistics.

AGE is the age of the company in October1997measured from the birth date of the company.The date of legal incorporation is often taken as the birth date for companies and would appear to be a natural choice.However, for entrepreneurial?rms this is far from obvious.In particular,in our sample, over half of the companies had some other signi?cant event that preceded the date of incorporation,such as the beginning of normal business operations or the hiring of a?rst employee.Moreover,there does not appear to be any clear sequence of events that these companies follow in this initial period of creation.In this article we therefore take a conservative approach and use the

8See Lerner(1994,1995)for a discussion of the Venture Economics database and Gompers and Lerner(1997) for a discussion of the Venture One database.We found107of the sample companies in Venture One and95 in Venture Economics.Some66companies(38%)replied to our?nancing survey.

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Table1

Descriptive statistics

Number of Mean Mean Mean Variable observations full sample innovator sample imitator sample INNOV ATOR1490.503355710

VC1490.69127520.78666670.5945946 VC(P)1490.42281880.49333330.3513514 TIME-TO-VC1032.6264622.461966 2.847038 TIME-TO-MARKET1322.5887982.832472 2.337626 AMOUNT918.10736911.05957 4.663135 COMPUTER1490.47651010.46666670.4864865 TELECOM1490.21476510.14666670.2837838 MEDICAL1490.1476510.240.0540541 OTHER1490.16107380.14666670.1756757 SAMPLE-AGE1496.745066.3665287.128707 AGE14910.125939.70319510.55438 INNOV ATOR is a dummy variable which takes the value1if the founding strategy of the?rm was an innovator strategy, 0otherwise.VC is a dummy variable that takes the value1if a?rm has received venture capital,0otherwise.VC(P)is a dummy variable that takes the value1if the company obtained venture capital before the date of?rst product sale.TIME-TO-VC measures the time in years from the birth of the company to the date of obtaining venture capital for the?rst time. TIME-TO-MARKET measures the time in years from the birth of the company to the date of?rst product sale.AMOUNT measures(in$millions)the amount of?nancing that a company obtains prior to bringing its product to https://www.sodocs.net/doc/0018319283.html,PUTER, TELECOM,and MEDICAL are dummy variables that take the value1if the?rm is in the computer,telecommunications,or medical-related industry respectively,0otherwise.OTHER is a dummy variable for other industries.SAMPLE-AGE is the age of the company at the time of sampling.AGE is the age of the company in October1997.

earliest date recorded in any of our data sources corresponding to the earliest evidence of?rm activity as the date of birth.

SAMPLE-AGE is the age of the company at the time of sampling,mea-sured from the date of birth of the company.

VC is a dummy variable that takes the value1if a?rm has received ven-ture capital,and0otherwise.F rom the interviews,surveys,and commercial databases we identify which?rms are?nanced by venture capitalists and the timing of such?nancing.Venture capitalists are professional investors who specialize in the?nancing of young private companies.We also create other venture capital-related variables based on the timing of the venture capital. Thus,VC(P)is a dummy variable that takes the value1if the company obtained venture capital before the date of?rst product sale.

TIME-TO-VC measures the time from the birth of the company to the date of obtaining venture capital for the?rst time.

TIME-TO-MARKET measures the time from the birth of the company to the date of?rst product sale.This is an important strategic variable.We obtain this variable from informants’interviews at the company.These inter-views included a targeted question of when the company brought its prod-uct to market.We augment this information with publicly available data on the company’s product,using in particular the earliest product mentioned in Rich’s guide or other public sources.

COMPUTER,TELECOM,and MEDICAL are dummy variables that take the value1if the?rm is in the computer,telecommunications,or medical-related industry,respectively,0otherwise.OTHER is a dummy vari-

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Interaction Between Product Market and FinancingStrateg y able that takes the value1if the?rm is in another industry(mostly semicon-ductors),0otherwise.

INNOV ATOR is a dummy variable that takes the value1if the founding strategy of the?rm was an innovator strategy,0otherwise.This is an impor-tant strategic variable as we are interested in measuring ex ante strategies. There is no easy way of obtaining this data,hence this variable deserves some discussion.9The founders’interviews contained a speci?c section that asked about their perceived“distinctive competence and competitive advantage.”Whenever possible,the same questions were asked of other people involved in the founding of the company to cross-check the information.The classi?-cation was then done based on content analyses of their answers.F urther,this classi?cation was supplemented by other objective measures.These include secondary sources(e.g.,newspaper articles and industry analyst reports)and other material provided by the?rm(such as a business plan).Two trained coders were then asked to separately read through all the interview transcripts as well as all the other collected material to assign a company to a strategy.10 Both of the coders coded all of the sample companies independently.In cases in which they disagreed,a third person was asked to resolve the differences. Conceptually the coding of the innovator variable captures the notion of?rms that introduce a new product that is considered not to be a close substitute to any product or service already offered on the market;?rms that introduce a new product or service that is considered to perform an order of magnitude better than any substitute products already offered in the market;or?rms that are developing new technologies that could lead to products satisfying either of the two criteria above.11We put the remaining companies into a single category of imitators.12Although these?rms may still have some degree of

9This coding was performed under the supervision of Baron,Burton,and Hannan.They actually used a more ?ne-grained strategy coding that further subdivided the group of what we call imitators.Details can be found in Burton(1996)and Burton,Lam,and Sellars(1996).

10The instructions included“buzzwords”that the coders were asked to look out for.Buzzwords for innovators include new technology,forefront,new paradigms,brand new discovery,patented technology,pioneer,unique, no competition,?rst in the?eld,?rst-mover advantage,technological breakthrough,new idea,and technolog-ical innovation.Buzzwords for imitators include feature-rich,integration,me-too,better design,outperform existing competitors,improvements in quality and features,second source,superior sales/marketing,clone, and cheap,low-cost assembly.

11Some typical quotes from the interviews that were associated with innovators are

?“[A]new and unique introduction without a de?ned market.”

?“It was a brand new discovery that could be patented.”

?“[A]completely new idea,so there was no competition in the area.”

?“There is intense competition in the drugdelivery business;they are clearly in a technolog ical race with many companies.”

?“They appear to be at the forefront of developing technology for the information superhighway...

Initial core competence was technology developed at Stanford.”

12Representative quotes for imitators include

?“The initial competence of the company was a customer focus.”

?“U nderstandingtechnolog y,customers’needs and how to apply the former to the latter.”

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novelty in their product offering,this is typically not the main source of their perceived competitive advantage.We were able to obtain founding strategies for149companies and hence this is our effective sample size.

3.2Robustness checks on the data

We next discuss some potential limitations of the data and a number of robustness checks we performed to deal with these concerns.A common issue with the use of survey data is the use of retrospective data and the bias such data can impose.Although the innovator variable measures the initial strategic intent,it still relies on retrospective information and could be subject to retrospective biases.Some argue[Bhide(1998)]that entrepreneurs may have a tendency to distort or dramatize their company’s history.To avoid such problems,a number of measures were taken,both during the data-gathering stage and afterwards.

F irst,the interview subjects were not asked to classify themselves as inno-vators or imitators.Instead,this information was extrapolated from a variety of information sources that included not only the founders’technical descriptions, but also the descriptions of other parties involved in the founding of the company and other available information about the company.Second,if there is retrospec-tive bias it is likely to be stronger for older companies,so that older?rms are more likely to portray themselves as innovators.Hence we examine the age composition of the innovator variable.We divide the sample into its younger and older halves and perform a test to determine whether older?rms are more likely to be classi?ed as innovators.Table2,panel A,performs a chi-sqaured test and reveals no signi?cant differences,suggesting that retrospective biases are unlikely to be driving this result.Third,we examine whether companies formalized the strategies that they intended to pursue at the time of receiving venture capital,in terms of a written detailed business plan.

Table2,panel B,shows some descriptive statistics of how many entrepreneurs had a business plan at the time of obtaining venture capital. Although the number of observations is somewhat low,two messages emerge from this table.Most entrepreneurs have a business plan at the time of receiving venture capital.And innovators are more likely to have a busi-ness plan than imitators(the chi-squared test is statistically signi?cant at 1%).Venture capitalists typically use the business plan to carefully scruti-nize a company’s plans before investing in it.This is particularly important for an innovator company,because it is likely to be inherently more dif?cult to evaluate.The writing of a business plan also forces entrepreneurs to de?ne

?“Competitive advantage is providing both hardware and software expertise...[their]product is the most feature-rich in the industry.”

?“[T]hey knew that there was a market for optical character recognition and they wanted to outperform their existingcompetitors on accuracy and speed.It was a me-too approach.”

?“[It was a]service industry emphasizingquality and low cost.Hard working,longhours,low margins.”

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Table2

Some differences in innovators and imitators

Panel A

AGE INNOV ATOR IMITATOR Total Above median384583 Below median372966

Total7574149

p-value.213

Panel B

BUSINESS-PLAN INNOV ATOR IMITATOR Total

YES391958

NO4711

Total432669

p-value.053

Panel C

PATENT INNOV ATOR IMITATOR Total

YES483785

NO273764

Total7574149

p-value.084

Panel D

NUMBER OF PATENTS Number of observations Mean Standard deviation INNOV ATOR75 1.2838 1.311502 IMITATOR740.8838137 1.112151

p-value.0948

Panel A provides descriptive statistics and a chi-squared test for the breakdown of AGE into innovators and imitators.Panel B provides descriptive statistics and a chi-squared test for the breakdown of BUSINESS-PLAN into innovators and imitators.The variable BUSINESS-PLAN is a dummy variable that takes the value1if a company had a business plan at the time that it?rst raised venture capital,0otherwise.Panel C provides descriptive statistics and a chi-squared test for the breakdown of PATENT into innovators and imitators.The variable PATENT is a dummy variable that takes the value1if a company had received a patent,0otherwise.Panel D provides descriptive statistics and a t-test for the difference in means for the number of patents. The variable,NUMBER OF PATENTS,is the natural logarithm of(1+the number of patents).

their strategy more explicitly.The fact that these companies formalize their intention in writing would also limit the extent to which their recollections become distorted.

Last,but not least,while a measurement of the ex ante intent is most rel-evant to our hypothesis,the question arises whether this intent is re?ected in the realized behavior of these companies.It is reasonable to expect that ex ante strategies are correlated with ex post outcomes.We examine this by analyzing the?rm’s patenting behavior,because patenting data reveals infor-mation about the ex post behavior of the companies.[See Griliches(1990) and Kortum and Lerner(1998)for a detailed discussion of the advantages and disadvantages of using patenting data as a measure of innovation.]We gathered additional data to examine patenting behavior of the?rms in our sample.We identify patent activity by using the Lexis/Nexis on-line database.

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F or each individual?rm,we identify all patents for which that particular?rm is de?ned as the assignee-at-issue over the entire sample period,that is,from the birth of the company until October1997.We classify companies by whether or not they receive patents.We create a dummy variable PATENT that is1if the company received a patent,0otherwise.Table2,panel C, shows the relationship between an innovator strategy and the propensity to patent.The chi-squared test?nds that innovators are indeed more likely to obtain patents,showing that more often than not,ex ante intent is translated into a realized measure of innovation.F urthermore,we examine the natural logarithm of the number of patents,which is a widely used measure of inno-vation[see,e.g.,Kortum and Lerner(1988)].Table2,panel D,shows a t-test of the equality of means between the innovator and the imitator sample.We ?nd that innovators do have a signi?cantly higher number of patents than do imitators.Thus both of our ex post measures(of innovation based on patents) correlate well with our ex ante measure of innovation.

Another concern with the data is the potential survivorship bias,because companies are not sampled at birth.A number of arguments,however,sug-gest that this survivorship bias is relatively minor.F irst,a particular effort was made to include many young companies,precisely to reduce any sur-vivor bias.As a consequence,our sample captures?rms at a much earlier stage than do most other databases.13Second,unlike many other studies in ?nance relating to venture capital,we are able to sample companies indepen-dent of their?nancial choices.In fact,our sampling criterion is essentially based on the existence of the company,and not on any endogenous?nancial measure.In particular,our sampling criteria are not affected in any way by the?rm getting venture capital.F inally,a number of companies fail within our sample and we estimate a probit to see whether the probability of failing is systematically related to any known characteristic.We?nd that the strategy (innovator or imitator),the presence of venture capital,the age of the?rm, or any industry effects are not statistically signi?cant in predicting exit from the sample.The within-sample behavior thus suggests that selection issues are unlikely to have a major effect on our results.

F inally,although we had a high overall response rate,we were still unable to code every company in the sample.We identi?ed a strategy for149compa-nies in the sample.This raises the question whether there are response biases. We test for response biases by estimating a probit in which the dependent variable is whether we obtain an ex ante strategy for the?rm.We?nd that the presence of venture capital,the age of the?rm,and any industry effects are not statistically signi?cant in predicting responses received.This suggests that there is little systematic response bias.

13An interesting issue that emerged in this research is that even at a conceptual level,it is not clear how one could eliminate all survivorship bias.As we discussed,there is no objective date at which a company is born.Given this inherent ambiguity about if and when entrepreneurial activities should be considered a new company,it seems conceptually impossible to eliminate all survivor bias.

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4.Does Strategy Affect the Type of Financing?

In this section we examine the?rst null hypothesis that concerns the rela-tionship between initial product market strategy and the type of?nancing.

A univariate chi-squared test was conducted to test for independence between innovators and venture capital.This was rejected at the2%level,with more innovators taking venture capital.Although the univariate test is suggestive, we clearly need to control for other characteristics of the?rm.Hence we esti-mate a probit regression in which the dependent variable is a dummy variable, VC,that measures whether a?rm has obtained venture capital?nancing.The main independent variable is whether a company is an innovator or imitator, and we control for factors such as the age of the?rm and the industry the ?rm is in.Table3reports the probit regression results.We?nd that inno-vators are more likely to receive venture capital?nancing,and this result is statistically signi?cant at the5%level.

The probit model estimates the overall likelihood of a company receiving venture capital?nancing,but it does not use information on the timing of the venture capital investment.We are interested in the length of time it takes to obtain venture capital and the in?uence of the strategy on that duration.

A standard procedure for dealing with duration data is to employ a hazard model[Kalb?eisch and Prentice(1980),Kiefer(1988)].To proceed we must specify the exact nature of our hazard model.We can choose from a num-ber of parametric models(such as Weibull)or we can use a semiparametric model.The parametric models are attractive because of their simplicity,but by imposing as much structure as they do,the models can distort the esti-mated hazard rate.Because fewer restrictions can result in a more accurate Table3

Determinants of venture capital?nancing

Dependent variable:venture capital

Marginal increase

Independent variables in probability Coef?cients T-ratio CONSTANT N/A?0.3696116?0.405 INNOV ATOR0.1894893??0.5573478??2.304 LNAGE0.0171640.05008310.134 COMPUTER0.12769390.37594891.244 TELECOM0.2706599???0.9673541???2.561 MEDICAL0.2531136??0.9464507??2.214 Number of?rms=149Pseudo R2=0.0880

χ2(5)=15.34Model p-value=.0090

The table presents the results of a probit.The dependent variable is VC,which is a dummy variable taking the value of1if a company has ever received venture capital,0otherwise.The independent variables are INNOV ATOR,which is a dummy variable taking the value of1if the founding strategy of the company was an innovator strategy,0otherwise;LNAGE,which is the natural logarithm of the company’s age;and COMPUTER,TELECOM,and MEDICAL,which are dummy variables that take the value 1if the?rm is in the computer,telecommunication,or medical industry,respectively,0otherwise.Pseudo R2=1?log L/log L o, where log L is the maximized value of the log-likelihood function,and log L o is the log-likelihood computed only with a constant term.Model p-value reports the joint signi?cance of the coef?cients of the independent variables.T-ratios are computed using White’s heteroskedasticity-adjusted standard errors.?,??,or???mean the coef?cient is signi?cant at the10%,5%,or1%level, respectively.

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representation,we use the proportional hazard model—a common choice among researchers for modeling duration.The formal model is

h(t)=h0(t)exp{β X}.

The Cox proportional hazard model does not impose any structure on the baseline hazard h0(t).Cox’s partial likelihood estimator provides a way of estimatingβwithout requiring estimates of h0(t).Suppose the complete dura-tions are ordered t1

exp{β X i}

j∈R i exp{β X j}

.

This conditional probability is independent of the baseline hazard function.14 A distinguishing feature of our duration data is that some of the observations are right censored;that is,at the end of the sample period the?rm has not obtained venture capital,yet there is a positive probability of such an event occurring.Our hazard model explicitly takes such censoring into account. We report both the coef?cients and the hazard ratios(i.e.,the relative risks).A positive coef?cient on x implies a higher x is linked to a higher hazard rate and thus a lower expected duration.For ease of interpretation, we also give the hazard ratios.The hazard ratio tells us how much the hazard (i.e.,the instantaneous risk)of the event increases for a unit change in the independent variables.In the case of a dummy variable,this is equal to the ratio of the(instantaneous)probabilities of the two possible states.

Table4shows the results from the Cox regression.The dependent variable is TIME-TO-VC,that is,the time to obtaining venture capital.15We control for industry effects.The regression shows that the INNOV ATOR variable is signi?cant.Innovators are faster to obtain venture capital,with an estimated hazard ratio of1.69.This says that,relative to imitators,innovators are1.69 times more likely to obtain venture capital in any given period of time.This result is statistically signi?cant at1%.

These two results reject the?rst null hypothesis that there is no relation-ship between an entrepreneurial company’s strategy and its propensity to obtain venture capital?nancing.Firms pursuing an innovator strategy are

14The partial log likelihood is ln L= n

i=1[β X i?

j∈R i

exp{β X j}].

Technically this is for the simplest case where exactly one?rm exits at each distinct time and there are no censored observations.The partial log-likelihood can handle censoring easily.Censored observations enter the risk set at each observation(in the denominator)but do not enter in the numerator of the partial likelihood. 15There may be some intervening events that imply that a company will no longer obtain any venture capital, namely,if it goes public,out of business,or gets acquired.We model this as a standard competing risk,so that either of these events constitutes a terminal noncensored event.

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Table4

Cox estimations for time-to-venture capital

Dependent variable:Time-to-VC

Independent variable Hazard ratio Coef?cient T-ratio

INNOV ATOR1.695095??0.5277389??2.532

COMPUTER1.3341810.28831780.889

TELECOM2.375416??0.8651728??2.468

MEDICAL2.094569??0.7393477??1.970 Number of?rms=149χ2(4)=16.65Model p-value=.0023 The table presents the results of a Cox regression.The dependent variable is TIME-TO-VC,which measures the time from the birth of a company to the date of obtaining venture capital for the?rst time.The independent variables are INNOV ATOR,which is a dummy variable taking the value of1if the founding strategy of the company was an innovator strategy,0otherwise;and COMPUTER,TELECOM,and MEDICAL,which are dummy variables that take the value1if the?rm is in the computer, telecommunication,or medical industry,respectively,0otherwise.Model p-value reports the joint signi?cance of the coef?cients of the independent variables.?,??,or???mean the coef?cient is signi?cant at the10%,5%,or1%level,respectively.

more likely to obtain venture capital and to obtain it more quickly.This suggests that venture capitalists are not shying away from the uncertainty of innovative business concepts,but rather seem to embrace them.Nor does it seem to be true that if they invest in innovator companies they invest at a later stage when much of the uncertainty may already have been resolved. Instead we?nd that innovators are faster to obtain venture capital.The natu-ral question to ask is,what is it that attracts innovators and venture capitalist to each other?Is there any particular role that venture capitalists can play in the development of innovator companies?To examine this we will now turn to the relationship between venture capital and product market outcomes.

5.Type of Financing and Product Market Outcomes

In this section we examine the second null hypothesis,that the presence of a venture capitalist does affect product market outcomes.We consider the effect of venture capital on the time it takes a company to bring its product to market.We estimate a Cox duration regression.The dependent variable is TIME-TO-MARKET,which measures the time from founding up to the date of the?rst product sale.

When we examine duration data with respect to the length of time it takes to get a product to market,we have an additional complication.We are inter-ested in the in?uence of venture capital on the time to product market,and venture capital is obtained by?rms at different points of time.We therefore modify the original Cox proportional hazard model to allow for time-varying covariates.The model being estimated now takes the form of

h(t,X(t))=h(t,0)exp[(β X(t)],

where h(t,X(t))is the hazard rate at time t for a?rm with covariates X(t). The Cox regression estimates the coef?cient vectorβ.Again,our model also takes into account the right censoring of our data.

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Hence,apart from the usual industry controls,the main independent vari-able is a dummy variable VC(t),that indicates whether the?rm has received any venture capital?nancing by time https://www.sodocs.net/doc/0018319283.html,panies do not necessarily start with venture capital,but obtain venture capital after variable lengths of time.

A company may thus change its status from not being venture capital backed to being venture capital backed.Note that depending on strategy and industry, a company may have a shorter or longer time to market,which is a function of the underlying technological parameters.Indeed,from Table1we see that on average innovators take more time to bring a product to market than imi-tators.The question we are interested in here is,for a company with a given technology,whether venture capital reduces the time to market.

Table5reports the coef?cients and the hazard ratios of the estimated models.Looking?rst at the entire sample,we see that the likelihood of the?rst product sale increases by a factor of1.88with the advent of a venture capitalist.This is statistically signi?cant at the1%level.Table5also shows the results of the Cox regression for the subsample of innovators and imitators.The presence of a venture capitalist increases the likelihood of a ?rst product sale by a factor of3.37in the innovator sample(this is again statistically signi?cant at the1%level),while it has no statistically signi?cant effect in the imitator sample.

As a robustness check we reran all of our duration models as Weibull regressions,which is a standard fully parametric model(and which takes time dependency into account in the same way as does the Cox model).All of our results are very similar,and the signi?cance levels were frequently even stronger.16

These results reject the second null hypothesis that venture capital is prod-uct market neutral.We?nd that venture capital is associated with faster time to market.This association is particularly strong for innovator companies. These results tie in with the?ndings in the previous section.We found that innovators are more likely to take venture capital.We then asked the ques-tion,what is it that attracts innovators to venture capital?An interpretation consistent with the results of this section is that the presence of a venture capitalist helps companies in their quest to be faster to market,especially for innovators,whose strategy is predicated on?rst-mover advantages.This interpretation helps explain why innovators are more likely to take venture capital in the?rst place.

6.Extensions

One of the main advantages of the use of this dataset is the existence of a clear timeline of events that allows us to establish whether preceding events

16As another robustness check,we reran all of our regressions dropping those companies that received no external?nancing at all.We also classi?ed companies in the computer industry into hardware and software ?rms,gave them a separate industry dummy,and reran our regressions.Again,for all of these our qualitative results are unaffected.

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Table5

Cox estimations for time to market

Independent variables Hazard ratio Coef?cients T-ratio

Panel A:Full sample

Dependent variable:TIME-TO-MARKET

VC(t)1.884990.6339223???3.185 COMPUTER1.2783820.24559510.972 TELECOM0.8627586?0.1476204?0.502 MEDICAL0.4970323?0.6991003???2.140 Number of?rms=149χ2(4)=18.09Model p-value=0.0012 Panel B:Innovator sample

Dependent variable:TIME-TO-MARKET

VC(t)3.371708???1.215419???4.282 COMPUTER2.38884??0.8708079??2.257 TELECOM2.533499??0.9296012??1.969 MEDICAL0.4268758??0.8512621??1.884 Number of?rms=75χ2(4)=32.78Model p-value=0.0000 Panel C:Imitator sample

Dependent variable:TIME-TO-MARKET

VC(t)1.4792750.39155231.269 COMPUTER0.7754147?0.2543572?0.746 TELECOM0.4915214??0.7102497??1.837 MEDICAL0.7624329?0.2712407?0.467 Number of?rms=74χ2(4)=4.64Model p-value=.3261 The table presents results from a Cox regression with time-varying covariates.The dependent variable is TIME-TO-MARKET, which measures the time from the birth of a company to the date of?rst product sale.The independent variables are VC(t), which is a time-dependent dummy variable that takes the value0as long as a?rm has not received venture capital and1 thereafter;and COMPUTER,TELECOM,and MEDICAL,which are dummy variables that take the value1if the?rm is in the computer,telecommunication,or medical industry,respectively,0otherwise.Panel A reports the results when the regression is run for the full sample,panel B reports the results for the innovator subsample,and panel C reports results for the imitator subsample,respectively.Model p-value reports the joint signi?cance of the coef?cients of the independent variables.?,??,or ???mean that the coef?cient is signi?cant at the10%,5%,or1%level,respectively.

are correlated with subsequent events.This is advantageous over standard cross-sectional analysis,in which two variables are observed at the same point in time,and disentangling the impact of one on the other can be dif-?cult.Our results indicate that an engagement of venture capital leads to a subsequent increase in the likelihood that a company brings its product to market.Therefore a natural interpretation is that the presence of venture capital helps these companies to bring their product to market.Nonetheless, other explanations are possible.In this section we discuss the robustness of our results and some alternative interpretations.

We?rst ask whether?rms themselves believe that venture capital is impor-tant to https://www.sodocs.net/doc/0018319283.html,panies were asked in surveys to list the events that they considered milestones in the development of their?rm.We consider all the ?rms that replied to this part of the survey and that received some kind of external?nancing.For the venture capital-backed companies we?nd that 59%list obtaining venture capital as a milestone.For the other companies we ?nd that27%list obtaining?nancing(from some source other than venture capital)as a milestone.This difference is signi?cant at the5%level.This is an interesting and signi?cant result.It shows that entrepreneurs consider

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obtaining venture capital a more signi?cant event than obtaining?nance from other sources.17

Although our results are consistent with venture capitalists in?uencing ?rms to bring their product to market earlier,an alternative interpretation of our results could be that venture capitalists select companies with faster time to market.Given that we have already taken into account the timeline of events(i.e.,we have already established Granger causality),it would have to be the anticipation of a faster time to market that would lead to such an equilibrium.The informal literature typically suggests that venture capitalists are involved both in selecting good companies and in adding value to them, and theory suggests that these may well be complementary activities.18As such,it is unlikely that one would be operating to the exclusion of the other. Nonetheless,we conduct some preliminary tests to rule out some potential explanations.Note that since we examine equilibrium outcomes,the same basic estimation procedure applies irrespective of whether venture capitalists select?rms with certain characteristics,or?rms with certain characteris-tics select venture capital.However,for expositional ease,in the rest of the article,when we talk of selection,we describe it as selection by venture cap-italists,though the estimation procedure is the same if?rms select venture capitalists.

F irst,venture capitalists may be selecting?rms based on particular observ-able characteristics,suggesting that a two-stage regression approach might be appropriate[see Puri(1996)for an application].F or that,our probit analysis provides the?rst step.Table3shows that only?rm strategy and industry affect selection.The cleanest way to control for these in the second step is simply to rerun the duration model within the respective subsamples of innovators and imitators,as in Table5.We also rerun our results within the computer industry(which accounts for about50%of our sample).We obtain qualitatively similar results,with venture capital being associated with faster time to market for innovators.These?ndings effectively rule out selection of venture capital based on observable information as an explanation for the reduced time to market.

Second,we test for selection of?rms based on the notion that venture capitalists have expert knowledge of industry conditions,which helps them to pick the right industries at the right time.We augment the duration model by adding as an independent variable the P/E ratio of the industry at the birth of the company,where the P/E ratio is taken to be a price-based measure of the

17F urther,in the venture capital-backed companies we?nd that66%(21of32)of the innovators and50% (11of22)of the imitators considered obtaining venture capital to be a milestone.Although the sample is too small to obtain statistical signi?cance,the numbers nonetheless suggest that innovators are also more likely to consider obtaining venture capital to be a signi?cant milestone.

18In particular,the marginal return to assisting a company should increase in the expected quality of the company,which is an increasing function of the amount of screening.

978

四年级一件难忘的事400字作文【五篇】

四年级一件难忘的事400字作文【五篇】 四年级一件难忘的事400字精选作文篇一 在童年里有许许多多的事情就像在海滩上捡贝壳,有绚烂的笑,是开心的往事;有黯淡的,像是勾起一段伤心的往事,都使我非常难忘。 早晨,风和日丽,我去帮妈妈和爸爸买早餐,等我去到早餐店,我发现早餐店旁有一大群人,我带着好奇心走去,让我大吃一惊,我发现那有一位老奶奶绊倒在地上。 我心想:为什么一位老奶奶搬绊倒在地上,但每一个人都无动于衷,还围观老奶奶,真是不尊重老人!我气得暴跳如雷,我就问我旁边的大人:“你们为什么不扶老奶奶”那人就说:“因为最近在新闻里,有很多老人成心绊倒在地上让路人扶起她。”谁知老人就说:扶起他的人是推他的人。老人还说:赔我钱。我听完就有一点不敢拉老奶奶了。 这时,一位青年人走过来,马上扶起老奶奶,旁边的人说,那个青年人真笨,青年人就说:“难道金钱比生活还重要吗?”青年人问奶奶要紧吗?奶奶说,不要紧,谢谢你,小伙子。青年人就说不用谢,说完就走了,我看到青年人的身影,我觉得很惭愧。 这一件事使我非常难忘,假如社会的人都献出一点爱心,那么社会就是充满爱心的。

四年级一件难忘的事400字精选作文篇二 每一个人都有自己最难忘的事,我最难忘的事就是三年级那次集体打连响儿。 记得那一次我们拿着漂亮的连响儿,排着整齐的队伍,伴随着美好的音乐,踏着坚决的步伐,熟练地打了起来。 看到这样整齐划一的队伍,我不由心生骄傲:看来我的努力还是没有白费呀!但放眼又一望,我们四周可以说是人山人海,有无数双眼睛正盯着我们,我心里不知从哪里来了一种莫名的紧张,我头上冒着冷汗,腿不由的发着抖。就在这一霎时,我做错了一个动作,登时,我感觉所有人的目光都投向了我一个人,我这颗本来就不平静的心,现在显得更加紧张了、更加不安了,我一下子都不知该干什么了,我真想有一个洞,让我钻到洞里去,我以前打的是那么灵敏,可现在却有气无力,仿佛有一个小精灵把我这活泼的精神给夺走了,经过漫长的煎熬,我好不容易才等到了结束的那一刻。 我回家把这件事告诉我妈妈,妈妈说:想做好一件事就要加倍的努力,不要认为自己会做了就可以了,妈妈的这次教导,将使我一生受益。 四年级一件难忘的事400字精选作文篇三 我家有各种各样的陶瓷器皿,比如:碗,花瓶,勺子,水杯等。它们形态各异,五彩缤纷。它们站在一起,仿佛都在展示自己曼妙的

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人 教 版 四年级(上册) 数 学 基 础 知 识 专 项 训 练

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