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ENTREPRENEURSHIP AND INFORMATION

ASYMMETRY: Theory and Evidence from the

University of California

Robert A. Lowe

Carnegie Mellon University

November 25, 2002

Abstract

Why do inventors found firms? I address this question by examining start-ups

founded by University of California faculty and graduate students. I first present a

theoretical framework to capture the contracting relationship between an inventor and

a potential licensee. I propose that information asymmetry between an inventor and

an outside firm raises the cost of licensing for the firm. This contracting problem

leads inventors to found firms to further develop their inventions and reduce problems

of information asymmetry. I empirically test two sources of information asymmetry,

technological uncertainty and tacit knowledge, and find that these characteristics influ-

ence the probability that a given university invention is licensed to an inventor-founded

firm. This study has implications for research on entrepreneurship, the economics of

information, and the impact of universities in national innovation systems.

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1. Introduction: Why do inventors found firms?

When an inventor discovers a new technology in her lab, such as a new semiconductor

material, she is confronted with several decisions on how to promote further development

and commercialization of her invention. First, the inventor faces the prospect of licensing

to an established firm and simply collecting rents on the estimated value of the invention

in its current, undeveloped form. The established firm must weigh the market opportunity

against several factors related to acquiring the technology, such as the cost of acquiring

the technology from the inventor, the cost of developing a similar invention in-house, and

uncertainty over whether an unproven laboratory model can eventually become a commercial

product.

For the inventor, licensing to an established firm has several advantages. An established

firm already has access to an existing customer base and complementary assets needed to

ultimately commercialize the product. Established firms also bring considerable market

experience and a portfolio of related products.

The inventor can also choose to found afi

rm to develop the invention, but foundinga firm can be a difficult route since many inventors initially lack resources and business

experience. Of course, these choices are not mutually exclusive; the inventor could start a

firm while also granting rights to use the technology to an established firm. However, this

latter outcome is rare since the inventor’s incentives are not aligned with the licensee’s to

transfer the invention to the licensee.

In this paper, I address the research question: why don’t inventors always license their

discoveries to established fi rms ? The decision to start a firm is complicated by a number

of factors, and to analyze this decision I develop a theoretical framework that captures the

inventor’s decision to found a firm in the context of other opportunities, namely licensing to

established firms. This framework recognizes an important counterfactual, that new firms

always could have licensed their underlying idea or invention to an established firm. Thereby,

the formation of a new firm represents a decision by either the entrepreneur or an established

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firm not to negotiate a license.

In this framework, I propose that inventors often found firms because certain features of

the invention, such as technological uncertainty and tacit knowledge, suffi

ciently raise thecost of licensing to an established firm. The critical problem is that the inventor maintains

valuable information that cannot easily be contracted upon to transfer to the licensee. When

the licensing cost becomes too high, inventors found firms to further develop the technology

and reduce problems of information asymmetry.

This paper off ers three contributions to the entrepreneurship, management, and eco-

nomics of innovation literatures. First, among previous research on entrepreneurship, there

does not exist a cohesive theoretical framework that explicates the inventor’s decision process.

Indeed, economists have long struggled to incorporate the entrepreneur into economic theory

(Barreto 1989). Moreover, William Baumol (1993) has suggested that a discussion of market

mechanisms that lead to new firms stands as an important, yet underdeveloped, contribu-

tion of economists to the entrepreneurship research agenda. This study off ers a step in this

direction by considering the information asymmetry and contracting mechanisms that drive

new firm formation.

Secondly, previous research and field work highlight the importance of tacit knowledge

in many inventions. The nature and management of knowledge remains an open and devel-

oping area of study, and the analysis below incorporates tacit knowledge associated with an

invention as an important consideration in a licensing transaction.

Finally, this paper contributes an important piece of evidence, university-based start-ups,

to the growing literature on university patenting and licensing. Recent studies have examined

the importance of vehicles such as co-publication (Zucker, Darby and Armstrong 1998) and

licensing (Mowery and Ziedonis 2001) for transferring inventions and knowledge out of the

university. Firms such as Genentech, Chiron, and Inktomi illustrate that start-ups are an

important vehicle for developing and commercializing university inventions. UC provides

a rich empirical setting to examine the role of start-ups firms in the invention-innovation

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process because, unlike most single-campus universities, the nine UC campuses include a

comprehensive range of technologies from medical schools; engineering colleges; agricultural

research centers; and biology, chemistry, and computer science departments.Another advantage of this data is that the unit of analysis is an invention. Much of the

theory and discussion in entrepreneurship and innovation focuses on inventions, but data

limitations dictate that empirical tests examine patents. To the extent that one invention

leads to many patents such analysis may speak more to a legal construction than to the

economic phenomena, invention and innovation, of study. In this paper, related patents are

mapped to a single invention based on a particular reporting process in the data.

The next section briefly reviews recent work on university start-ups relevant to the the-

oretical discussion and hypotheses presented in Section 3. Section 4 describes the data,

specification, and variables employed. Section 5 presents results and discusses limitations

to this research, and Section 6 concludes.

2. University Technology Transfer and Entrepreneurship

I use the term university technology transfer to describe the process of passing invention from

the university laboratory into the domain of a private or public organization for the purpose

of further developing and commercializing the invention. This definition includes formal-

ized patenting and licensing activities managed at a university office of technology transfer.

University technology transfer has recently attracted a great deal of attention in academic

research. This growing area of research is not reviewed in full here, and the interested reader

is directed to Eisenberg (1996); Henderson, Jaff e and Trajtenberg (1998); Mowery, Nelson,

Sampat and Ziedonis (2001); and Ziedonis (2001) among others for background.

Recent empirical studies of entrepreneurship in the university setting have been limited,

due to data availability, to a series of studies by Scott Shane of MIT’s licensing operations.

Shane (2002) examines the eff ects of appropriability regime on inventors licensing their in-

ventions from the university and the success of university licensing. Similar to the discussion

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presented in the next section, Shane argues that the skills involved in technology development

are "largely tacit" and cannot be easily sold or exchanged in an open market. In addition,

Shanefi

nds that inventions in that industries where patents are perceived as most eff

ectivewill be more likely to be licensed by someone other than the inventor. Since the level of

industrial class is fairly broad, this paper informs principally ”where” new firm formation is

likely to occur.

In a related paper, Shane (2001) studies several variables at the technology level, again

using proxies for various technological characteristics. Shane finds that inventions were more

likely to be licensed by start-ups when their patents were ”more important,” ”radical,” and

broader in scope (number of patent classes a given patent is assigned). Patent importance

is measured based on a count variable of forward citations (number of patents citing a given

patent) following Trajtenberg, Henderson and Jaff e (1997) and other papers by this trio who

employ Importance as a proxy for ”general” or ”basic” inventions. ”Radicalness” is based

on the breadth of patent classes covered by patents citing a given patent.

Shane’s research finds empirical support for many of the previous hypotheses in the

literature on innovation and industry life cycles as well as entrepreneurship. However, there

are at least two concerning issues in these literatures. First, while considerable attention is

paid to the role and actions of entrepreneurs in founding firms, the counterfactual question-

e.g. why didn’t the entrepreneur take his idea, invention, or innovation to an established firm-

is rarely addressed. Secondly, the economic mechanisms behind the series of transactions

leading up to new firm formation are often not explicated. In this paper, I address these

two points by examining the economic mechanisms behind the university technology transfer

process that lead inventors to found firms.

3. Theory and Hypotheses

I develop a simple theoretical framework based on contracting theory and the economics of

information to analyze the inventor’s decision to found a firm. This framework focuses on

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the transaction of transferring technology (including patent rights and related technological

knowledge) from an inventor to a hypothetical licensee to illustrate the costs of such a

licensing transaction.

3.1. Theoretical Framework: Formal Contracts and Personal Contacts

Consider the contract to license an invention to an established firm. In general, such contracts

have two payment components. First, licensees pay for intellectual property rights with a

fixed fee plus a stream of fees, such as royalty payments based on commercial sales. In

addition, fees are paid to the inventor for ongoing consulting. This second set of payments

has been explored empirically by Arora (1996) and Agrawal (2000) as a mechanism to access

the inventor’s experience and personal knowledge while the licensee continues to develop

the invention. Licensees can also access the inventor’s personal knowledge through other

arrangements such as appointing the inventor to a scientific advisory board.

The licensing transaction is a simple decision analysis for an outside firm. For a given

market opportunity, when the total cost of licensing is relatively high, the firm can invent

around or choose not to pursue the technology. Once the firm has decided to pursue the

technology, the critical comparison is between the transaction costs related to licensing and

the costs of in-house development.

Consistent with this decision analysis, Jensen and Thursby (2001) implicate the criti-

cal problem in university technology transfer as information asymmetry between inventor

and licensee. In their study, Jensen and Thursby model a university licensing transaction

as a case of information asymmetry, whereby the inventor privately maintains valuable in-

formation pertinent to the invention but not contained in the licensing contract or patent

documentation. Jensen and Thursby argue that a potential licensee must then be concerned

about moral hazard since the transfer of knowledge requires costly eff ort on the part of the

inventor, the inventor could choose to not transfer vital information related to the invention

after the license is executed.

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In this paper, I off er a more general framework to think about the impact of informa-

tion asymmetry. Information asymmetry raises the transaction costs of executing a license

because certain information is known to the inventor, but not the licensee, and it is costlyto fully specify a contract to transfer this information to the licensee. For the moment, I

remain vague about the explicit mechanisms by which certain information is particularly

costly to transfer or, stated diff erently, how certain categories of information lead to in-

creased transaction costs. The next subsection (3.2. Hypotheses) is devoted to articulating

these mechanisms.

For the present discussion, I merely stress that given high transaction costs, ceteris paribus

a firm will pass on licensing the invention and will instead choose to invent around or not

pursue the invention at all. However, the inventor can invest in her invention to overcome

the problems brought on by information asymmetry. In a specialized case of this action,

Leland and Pyle (1977) suggest that an entrepreneur who cannot ex ante convince investors

of the high quality of her abilities or services (as in a lemons problem) can signal her quality

by investing her own funds to start a firm. In a similar line of reasoning, I characterize the

founding of a firm by an inventor as a direct response to information asymmetry whereby

the inventor makes an investment to further develop the invention and reduce transaction

costs brought on by information asymmetry.

In summary, information asymmetry raises the transaction costs of transferring technol-

ogy from inventor to a licensee. As the cost of transferring technology increases, ceteris

paribus inventors will be more likely to license the technology in an eff ort to reduce prob-

lems of information asymmetry. This activity manifests as inventor-founded start-ups. To

be sure, the problems encountered with information asymmetry as described above are only

economically important for certain categories of information. The remainder of this paper,

and the empirical tests, focus on the sources of information asymmetry that raise transaction

costs. That is, when does information asymmetry pose significant contracting problems or

raise the cost of transferring technology? Two categories of information have already been

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implicated in the literature: technological uncertainty and tacit knowledge.

3.2. Hypotheses

3.2.1. Technological uncertainty

Jensen and Thursby’s (2001) work illustrates the early stage of development for many univer-

sity inventions. Inventions that are still in pre-prototype form present considerable challenges

to the licensing transaction because potential licensees will be concerned about the feasibil-

ity of developing a raw form invention into a commercial product. Inventors, who have

often worked on the invention for a number of years have better information regarding the

feasibility of the invention.

Suffice it to say that inventors need not actively pursue an active course of moral hazard.

Even with the best intentions, an inventor may not be able to convince the potential licensee

that the inventor will indeed pass along information to the best of her abilities. Jensen and

Thursby (2001) suggest that revenue or profit-based royalties paid to the inventor are one

way, albeit limited, to align the incentives of the inventor and licensee.

To operationalize technological uncertainty, I examine inventions that are closer to basic

research or “new science,” arguing that such inventions carry greater uncertainty since es-

tablished firms have by definition less experience with these technologies. Hence, we should

witness more start-ups founded on inventions in the category of basic research or ”new

science” than other inventions1.

Hypothesis 1: Ceteris paribus, inventor-founded start-up fi rms will be more 1 An alternative explanation not developed fully herein is the inventor’s lack of information regarding the

market opportunity. However, 44% of inventor-founded start-ups licensed inventions that were sponsored

(through research grants) or licensed previously by an established firm, and another 30% of the firms licensed

inventions for which an established firm had signed a Secrecy Agreement to review the technological merits of

an invention. These data can be interpreted as signals to an inventor from established firms that the market

for a technology is substantial enough to warrant devoting resources towards developing the invention.

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likely to license technologies developed on basic research or ”new science” than

other technologies.

Technological uncertainty is a reasonably straightforward implementation of information

asymmetry problems in university licensing. I now turn to a second hypothesis based on

how the type of knowledge required for further development of an invention aff ects the

contractual relationship between inventor and outside firm. A more detailed examination of

this transaction is included in Lowe (2002).

3.2.2. Tacit knowledge

In his seminal 1958 book Personal Knowledge , Polanyi discussed two complementary types

of knowledge that scientists maintain: that which can be easily communicated to someone

trained in the relevant scientific field(s) (”the articulate contents of science,” or codified

knowledge) and that which ”can be passed on only by example from master to apprentice”(or

the ”tacit” component) (1974: 53). For many inventions, there is little tacit knowledge

needed to further work with the technology, and once the patent is disclosed anyone in thescientific field can replicate the technology. Reverse engineering pharmaceutical drugs to

produce generic equivalents illustrates this case.

However, for other inventions, even disclosing patents and technical data does not ensure

that other scientists can replicate or further work with the technology. An example of this

class of inventions is discussed in Lowe (2001) where a UC start-up biotechnology company

producing genetically-engineered bacteria passed their technical information and ISO 9000

documented procedures on to their European distributor. The distributor’s scientists also

received training on the laboratory equipment used by the biotech company. The process

to manufacture the bacteria had been described in publicly-available patent documents and

journal articles. Moreover, the genetically-modified bacteria were based on an assay that has

been freely available to the scientific community and employed widely by pharmaceutical

firms for over 20 years. Despite complete documentation and training, the distributor’s

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scientists were unable to replicate the bacteria strains. As a result, the inventor visited the

distributor’s European labs and, using the distributor’s equipment, replicated a two-year

supply of the bacteria in a few days time. As the inventor indicated:

We have enhanced the sensitivity of the strains to the point where they’re

really fragile. It’s a good thing commercially, you can send them out, but then

(customers) cannot propagate them and have to come back and buy them from

you...You can write it down to the extreme detail, we have standard operating

procedures for the manufacturing... given all of that detail (the distributor) still

was not able to manufacture it.

This discussion raises a second possible explanation for why inventors found firms. In-

ventions that require significant inventor involvement to transfer tacit knowledge will be

less likely to be licensed by incumbent firms, due to high post-license transaction costs.

Rather, inventors can start a firm to further develop their invention, thus embedding their

tacit knowledge in a more developed form, and reduce the need for monitoring in a future

contract to transfer the invention to an established firm.

Hypothesis 2: Ceteris paribus, inventor-founded start-up fi rms will be more

likely to license inventions associated with high degrees of tacit knowledge than

inventions built on science with minimal tacit knowledge.

I now turn to the empirical specification and data used to test these two hypotheses.

4. Data and Specification

4.1. Data

The unit of analysis is an invention as disclosed by a faculty or graduate student to the

University. One invention can be the subject of several patents, and the majority of pre-

vious studies using patent data observe the patent as the unit of analysis even though the

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hypotheses being tested are often related to inventions or inventive activity. I correct for this

by collapsing data on multiple patents related to a single invention based on the maximum

score or quantity related to a given explanatory variable among all patents associated withthe same invention. Additional tests not reported in this paper were conducted on the mean

score or quantity and yielded similar results.

I began with the population of inventions disclosed to the university between 1986 and

1995. This group was truncated on the left to minimize the potential eff ects and learning

involved with building institutions in the wake of the Bayh-Dole Act passed in 1980. The

sample is truncated on the right to ensure sufficient patent data was available to construct

measures consistently across the sample, resulting in a cross-section dataset covering 10 years

of inventions at UC. Since the independent variables are constructed from patent measures,

I only study a sample of the inventions: those inventions that were patented by 1999. This

brings the final dataset to 488 inventions, of which 65 inventions were licensed or optioned

by an inventor-founded start-up.

Inventions were coded as ”licensed by a start-up” only if a license or option contract was

executed with the start-up firm during the first two years of existence for that firm. Therefore,

an invention licensed in 1993 for a UC start-up firm founded (on a licensing agreement) in

1990 would not be coded as “licensed to a start-up.” This methodology captures the decision

to license at the founding of the firm, while accounting for lags in license negotiations. Firms

were also coded as ”inventor-founded” if a member of the inventing team, including both

faculty and graduate students, founded the firm. Hence, the analyses below compare firms

founded by UC inventors (faculty or graduate students), those founded by outside parties

(”other start-ups”), and established firms.

4.2. Specification and Assumptions

To test for the eff ects of technological uncertainty and tacit knowledge on the likelihood

of an inventor licensing a technology, I use two specifications. First, I estimate a binomial

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logit model, whereby the dependent variable STARTUP=1 if the licensee is a start-up firm

founded by an inventor and 0 otherwise. The binomial logit specification provides a first-

order comparison between inventions licensed by inventor-founded start-ups and establishedfirms. This specification generates the main results of the paper. In addition, I use a binomial

logit specification to estimate the probability of start-ups not founded by inventors (after

removing inventions licensed by inventors from the dataset) licensing a given invention to

illuminate whether the eff ects we are interested in are descriptive of inventor-founded start-

ups only or all start-ups, more broadly. If the latter were true, the primary explanatory

variables would be correlated with the dependent variable in the same direction as in the

model of inventor-founded start-ups.

Second, I employ a multinomial logit model estimating the probability of a start-up licens-

ing and the probability of an established firm licensing compared to the pool of unlicensed,

although patented, inventions. I first estimate the probability of an inventor-founded start-

up licensing among all inventions, then estimate the probability of other (established firms

and start-ups founded by someone other than the inventor) firms licensing among all inven-

tions. Results are used to calculate relative risk ratios for the primary explanatory variables,

which compare the likelihood of a given outcome (inventor-founded firm or another firm

licensing) to an invention not licensed.

The multinomial logit specification is used primarily to test whether inventions licensed

by inventor-founded start-ups are similar to inventions that were not licensed. That is,

one alternative hypothesis mentioned above is that inventors are merely licensing "junk"

that established firms (correctly) do not assign any economic value. Comparing invention

(and by proxy patent) attributes to test whether unlicensed inventions are identical to inven-

tions licensed by inventor-founded start-ups provides importance evidence on this alternative

hypothesis.

To motivate the basic logit model, consider a decision function that models expected

payoff s to founding a firm, Di . This decision depends on hypothesized characteristics of the

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technology (tacit knowledge and technological uncertainty) X 1i and control variables X 2i

plus an error term.

Di = X 1iβ 1 + X 2iβ 2 + ei (1)

Define:

Y i = {1, if invention i is licensed by a start-up firm

0, otherwise

and express the probability that invention i is licensed by a start-up as:

P i(Y

i|X 1

iX 2

i) =

exp(X 1iβ 1 + X 2iβ 2)P

j∈J exp(X 1 jβ 1 + X 2 jβ 2)(2)

The latent variable of interest in this specification is information asymmetry, whereby an

index of information asymmetry is increasing in the probability that an inventor founds a

firm. In eff ect, I am interested in estimating to what extent increased information asymmetry,

entering through observed technological uncertainty and tacit knowledge (X 1i), will increase

the odds that an inventor will found a firm.

Note that the dependent variable is specified as a decision to found a firm, not the type of

contract to sign. Keeping options and licenses together as one category (”investment to start

a firm”) avoids concerns regarding independence of irrelevant alternatives that a multinomial

logit specification invites since the decisions to sign options and licenses presumably share

many characteristics and are thus highly correlated. See Ziedonis (2001) for an analysis of

the decision to sign diff erent agreements.

One concern of a multinomial logit model is violation of the Independent of IrrelevantAlternatives (IIA) property. Following Hausman and McFadden (1984), I apply a standard

Hausman test to check whether the diff erences in coefficients between the multinomial logit

and a simple logistic model are systematic. Tests of the explanatory and control variables2

2 This test does not include the variable GEOGRAPHY , which does appear to make a diff erence between

the specifications. However, this variable is insignificant at a high level in the model. Further specifications

to account for these eff ects do not seem warranted for this paper.

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(χ2(5) = 4.20) and including dummies for year and patent class (χ2(36) ≈ 0.00) indicate

that we cannot reject that diff erences in the coefficients are random. Hence, a multinomial

logit provides an appropriate specifi

cation for my purposes.

4.3. Explanatory Variables

One of the major challenges in this research is to develop useful and credible measures of

technological uncertainty and tacit knowledge. The economics of innovation literature has

turned to patent data as proxies for technology characteristics, and the measures employed

below follow this tradition. Several papers by Rebecca Henderson, Adam Jaff e, and Manuel

Trajtenberg have demonstrated the usefulness of patent citation measures to study tech-

nology characteristics and knowledge flows (Henderson, Jaff e and Trajtenberg, 1998; Jaff e,

Trajtenberg and Henderson, 1993; and Trajtenberg, Henderson and Jaff e, 1997).

4.3.1. Technological Uncertainty

The first proxy of technological uncertainty to test Hypothesis 1 is SCIENCE . SCIENCE

is the ratio of references to scientific publications, e.g. journal articles, conference proceed-

ings, and books in Patent i to the total number of references to other patents and scientific

publications. This measure is used to indicate the contribution of scientific publications

relative to that of other patents as prior art to an invention under the assumption that refer-

encing a greater proportion of scientific journal articles indicates that the invention is closer

to basic science. As such, these patents will indicate inventions that carry greater techno-

logical uncertainty since established firms will presumably be less familiar with ”newer” or

untested technology. Formally, for Patent i:

SCIENCEi = POTHER_REF iPOTHER_REF i+PCITEDi

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POTHER_REF i is a count of books, journal articles, and conference proceedings3 cited

by patent i in the Other References field on a patent, and PC I TE Di is a count of other

patents cited by Patent i. The range for the SCIENCE measure is 0≤

SCIENCE ≤

1.When SCIENCE = 1, all citations in the patent are to journal articles and the like, implying

more basic science underlying the invention. Hypothesis 1 suggests that SCIENCE should

be positively correlated with the propensity for a start-up to license Patent i

A second measure of uncertainty is the average age of patents cited by Patent i. For

example, if Patent i is issued in 1990 and cites one patent from 1980 and one patent issued

in 1970, then AGE i equals 10+20

2= 15. AGE proxies for the age of the technology base upon

which a patent is built. To the extent that technological uncertainty coincides with ”newer”

technology, the AGE variable captures this aspect.

AGEi = 1

365.25∗k

K P

k=1

(ISSUE _DATE i − ISSUE _DATE k)

Patents k are cited by Patent i, and ISSUE _DATE k indicates the date of patent issue

for Patent k. Hypothesis 1 suggests that AGE should be negatively correlated with the

propensity for a start-up to license Patent i.

4.3.2. Tacit Knowledge

While often discussed in academic literature, measuring tacit knowledge has proven difficult

since by definition tacit knowledge refers to something that is unobservable. Arora (1996) and

Agrawal (2000) use contract characteristics and consulting hours respectively to proxy tacit

knowledge, whereby the authors assume that more consulting hours, for example, illustrate

tacit knowledge transfer. However, neither of these measures are available for inventor-

founded firms since any contracts or consulting would obviously be between the inventor and

3 Several scholars have noted that some citations in the Other References section of a patent do not

reference scientific literature. I removed technical abstracts and similar non-scientific ”Other References”

from the SCIENCE measure.

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him- or herself. Zander and Kogut (1995) and others employ direct survey data, however a

similar eff ort across 10 years of inventors would not be feasible for this study.

My strategy to identify tacit knowledge is then to infer the presence of tacit knowledgeby observing the characteristics of codified knowledge flows, as represented by patents. This

identification strategy is based on the argument that embedded tacit knowledge restricts

the spread of any related codified knowledge due to the need for experience and personal

interaction to transfer the tacit knowledge between parties. That is, when an invention is

associated with significant levels of tacit knowledge such that personal interaction with the

inventor and/or experience with the technology is required to work with the invention, then

the spread of any related codified knowledge (as embodied in a patent or journal article) will

be limited to those individuals with direct access to the inventor.

I employ a proxy for tacit knowledge based on measuring the geographic concentration

of knowledge flows related to a given invention. Subsequent or ”forward” citations to a

patent can be used to elicit information about the concentration of knowledge related to that

invention. If we observe inventors located in Miami citing a San Diego inventor’s patents,

then we can infer that the knowledge related to the San Diego invention has ”flowed” or been

transmitted to inventors in Miami who are able to successfully build upon this knowledge.

Two problems arise in constructing a citation-concentration measure. First, more recent

patents will have fewer years in which to be cited than older patents. A simple correction

for this problem is to construct the count measure of citations to Patent i relative to the

count of citations to other patents issued in the same year that Patent i was issued in (Hall,

Jaff e and Trajtenberg 2000).

Secondly, diff erent technology areas will demonstrate varying degrees of industry and

research concentration due to a number of other factors. This problem runs the risk that

geographic concentration measures merely proxy local industrial activity or an agglomeration

economy, where a number of local firms consistently cite each other’s patents. Therefore,

to reduce this problem, a measure of geographic concentration has to make comparisons

16



relative to the average citation-concentration level within a given technology area. This fix

is limited, and other considerations are discussed in the limitations section.

To control for these two issues, I construct the following geographic citation-concentrationmeasure, where PC I TE Rm

j refers to a count of patents j (”forward citations”) citing Patent

i with Patent j’s inventor located within m miles of Patent i0s inventor.

TACIT = P

j∈J

PCITER100

j

PCITER∞j− γ

In this measure, γ is the mean concentration among all patents issued in Patent i’s

International Patent Classification subclass and year of patent issue. This is eff ectively

a ”fixed eff ect” on patent issue year and technology subclass. The range for TACIT is

−1 ≤ TACIT ≤ 1. A negative tacit score indicates that Patent i received less forward

citations from inventors within a 100-mile radius of the inventor than the average patent

issued in the same year and in the same Subclass. And, T AC IT > 0 implies greater

concentration than the average comparable patent.

In the tests for the influence of tacit knowledge, a subset of the data is used. To allow for

enough forward citations to existing patents, I restrict the sample to inventions with patents

granted before 1998. Forward citations through the end of 2000 are included in the TACIT

measure. If no citations were received by the end of 2000, the invention’s TACIT score was

coded equal to zero.

This measure raises a third concern that the concentration of forward citations is merely

a reflection of industrial activity local to the university— that is, TACIT might be merely a

proxy for for an agglomeration economy. To address this issue, I include a control variable

GEOGRAPHY that is the proportion of patents in a Patent i’s IPC class whose inventors

are within 100 miles of the UC inventor. GEOGRAPHY proxies for the concentration of

industrial patenting activity near the university.

An added complication with this measure is that an issued patent may be endogenous to

the concentration of local industrial activity. That is, one might be concerned that once a

17



patent issues, firms will move near the university. To construct this measure, I calculate the

proportion of patents local to Patent i during the five years preceding Patent i’s application

date (and including Patent i’s year of application). To summarize, GEOGRAPHY is theproportion of patents in Patent i’s patent class whose inventors are located within 100 miles

of Patent i’s inventor.

4.4. Control Variables

Several variables are used to control for both technological and inventor characteristics.

BACKCITES is a count of patents cited by Patent i to control for the total number of

backward patent citations in testing the SCIENCE and AGE variables.

NON-UC is a dummy equal to 1 if a non-UC inventor is listed on the invention. This is

a rough measure for the involvement of established firms and other potential licensees

in the research. Non-UC inventors can also be from other universities and research

institutions. Hence, unfortunately the current form of the data does not allow me to

separate these two populations, and direct corporate research involvement on a given

invention cannot be fully identified.

GEOGRAPHY controls for geographic concentration of patents in a given patent class

as described above. Geography is the proportion of patents in a given IPC class that

is within 100 miles of a UC inventor.

PAT_CLASS is a control for technology class, I use dummy variables for the primary

International Patent Classification (IPC) Class for a given patent. IPC Class codes

used are at the 3-character level, such as A01 (Agriculture, Forestry, and Animal

Husbandry) or C08 (Organic Macromolecular Compounds).

YEAR represents the year in which an invention was disclosed to the University.

18



5. Results and Discussion

5.1. Results

Results are displayed in the Appendix. Tables 1a and 1b list summary statistics and corre-

lations among independent variables. Two sets of regression models are displayed in Table

2, one set for inventor-founded firms (Models 1-5) and one set for other start-ups (Models

6-10). The latter category primarily includes firms founded by professional entrepreneurs

(people who have founded previous firms in the same field), many of whom have advanced

degrees in biology, chemistry, or related fields. However, in these cases the inventor was not

actively involved with the founding of the firm.

In Models 1-5, the dependent variable is STARTUP=1 if an inventor founded a firm

to develop the invention and 0 otherwise. In Models 6-10, UC inventor-founded start-ups

are dropped from the analysis and the dependent variable is coded STARTUP=1 if a non-

inventor-founded start-up licensed the invention. The arguments and hypotheses advanced

in this paper focus on the inventor’s information. Based on the theoretical discussion above,

the predictions in this paper should hold for inventor-founded start-ups, but not other start-

ups. Indeed, the results indicate these two sets of firms are quite diff erent. This result,

that inventor-founded and other start-ups diff er significantly, is an important test of the

hypotheses and provides evidence for diff erent categories of entrepreneurs.

As noted above, the number of observations among each subset of models in the binomial

logit regressions (Models 1-2 vs. 3-5, for example) diff er because a number of observations are

dropped to control for various aspects of the data and estimation. For example, inventions

patented in 1998 or later were dropped from the regressions on TACIT to allow for a minimal

number of years for forward citations. In addition, other observations (7-12%) were dropped

due to a lack of information to identify the model, specifically several technology classes did

not include any inventor-founded start-ups. This lack of variation in outcomes leads to a

19



model identification problem since the explanatory variable perfectly predicts the outcomes4.

Models 1 and 6 simply include control variables. Model 2 tests scientific uncertainty.

The positive odds ratio and signifi

cance of the primary explanatory variable, SCIENCE ,supports the hypothesis that inventors develop inventions closer to basic science within their

own start-up firms rather than through licensing. By interpretation, inventions (associated

with patents) that cite a higher proportion of scientific literature, such as journal articles,

are closer to basic research or ”newer science” and are more likely to be licensed by inventor-

founded start-ups that are other inventions.

Surprisingly, AGE is positively correlated, though only significant at 18%, with the like-

lihood that an inventor licensed a given technology. This result indicates that technologies

based on older prior art are more likely to be licensed by a start-up than other technologies.

Case studies conducted on several start-ups corroborate these results (Lowe 2001). For ex-

ample, the genetically-engineered bacteria described above was based on research that had

been underway for decades. The inventor merely applied a new technology, genetic engineer-

ing, to a standardized assay. The average AGE for this invention was 15 years, compared

to 7 years for the entire sample.

The contrast between SCIENCE and AGE is consistent with Jensen and Thursby’s

(2001) discussion alluded to earlier. Jensen and Thursby’s survey results highlight the un-

derdeveloped nature of particular inventions. This notion is captured by the SCIENCE

measure in that inventions closer to basic science are by definition underdeveloped and

untested. However, AGE is also capturing the eff ects of an industry or research field. To the

extent that a given research field, broadly defined, has existed for a considerable amount of

time, one could still imagine recent inventions in that fi eld as characterized by technological

uncertainty, consistent with the SCIENCE measure.

4 An unreported analysis of the dropped observations indicates that dropping these observations did not

bias results in favor of the hypotheses, and including this data would actually support the hypothesis with

respect to the AGE variable. These results are available from the author upon request.

20



Models 3-5 test for the influence of tacit knowledge on the likelihood that an inventor-

founded firm licenses a given invention. Three points are important in these results. First,

TACIT is indeed signifi

cant and positively signed, with an odds ratio ranging from 3.195to 3.323 depending on which other variables are included in the regression. These results

suggest a strong relationship between concentrated citations (hence concentrated knowledge

flows) and the probability that an inventor founds a firm.

Second, Model 5 indicates that TACIT is indeed robust to the inclusion of the GEOGRAPHY

control. Even when controlling for the concentration level of patenting near a given invention,

inventions associated with highly concentrated citations, as a proxy for codified knowledge

flows, are more likely to be licensed by inventor-founded firms. Stated diff erently, inventors

are more likely to found firms on inventions associated with concentrated knowledge. I in-

terpret such concentrated knowledge to indicate tacit knowledge, or that knowledge which is

difficult to appropriate by an outside party based on published documents alone. Alternative

explanations are discussed in the next section.

Third, in the regressions for other start-ups, Models 8-10, TACIT is not significant.

That TACIT and SCIENCE are not significant in the regressions of other start-ups is

an important result because these findings reinforce the arguments advanced in this paper

that inventor-founded firms are a direct consequence of information asymmetry between the

inventor and a potential licensee firm. If the results had been the same regardless of who

founded the firm, TACIT and SCIENCE would merely be picking up some unobserved

technological or environmental characteristics not necessarily related to the inventor’s infor-

mation that encouraged start-ups, in general, to be formed around a given invention.

One valid concern in using patent citation data is that self-citations may skew the local-

ization of citations. Thus, an alternative hypothesis in this paper might be that inventors

who start firms tend to cite themselves more often than other UC inventors, leading to a

number of citations concentrated near the inventor. This outcome could be generated be-

cause (a) inventors might tend to found firms near their home university and/or (b) the type

21



of inventor interested in founding a firm may have an unobserved personal characteristic

such that they site themselves more often. In either case, increased self-citation would lead

to geographically-concentrated citations for reasons other than tacit knowledge.The hypotheses discussed in the previous paragraph, if true, imply that self-citation

would be higher for inventions and patents licensed by inventor-founded firms. A simple test

for the impact of self-citations is to check whether the proportion of self-citations is diff erent

between patents licensed by inventor-founded firms and patents licensed by all firms. A

citation was categorized as a self-citation if one inventor, regardless of whether he or she was

a lead inventor, is listed on both the UC patent and the citing patent. To be consistent with

the above analysis, inventions are compared by collapsing patents into inventions by taking

the maximum score of the proportion of self-cites among all patents assigned to a given

invention; however, patents were also tested as the unit of analysis. T-tests comparing the

percentage of citations that are self-cites for both relevant units of analysis, inventions and

patents, indicate no significant diff erence in means between inventions licensed by inventor-

founded firms5. These results suggest that self-citation is not driving the results captured

by the TACIT variable.

Finally, I specify a multinomial logit to include unlicensed but patented inventions to

test whether unlicensed inventions are more akin to inventions licensed by inventor-founded

start-ups. That is, I recognize that for many inventions the expected profits from the owning

or licensing the patent do not justify licensing the invention. This recognition raises the

concern that unlicensed inventions may be very similar to inventions licensed by inventor-

founded firms, and that results of the simple logit model merely reflect inventors licensing

"junk" technologies that are similar to unlicensed inventions. In short, while I cannot

observe the "expected commercial value" of an invention or its related patents, a reasonable

5 The self-citation rate was among inventions licensed by inventors was 0.156 for inventions as the unit of

analysis and 0.131 for patents. Self-citation for inventions licensed by other firms was 0.164 for inventions

and 0.121 for patents. The t-test statistic was -0.191 and 0.327 for inventions and patents, respectively.

22



test is a comparison of the three categories (licensed by inventor-founded start-up, licensed

by established firm only, or unlicensed) on the primary explanatory variables.

The results of the multinomial logit are striking. These three categories diff

er remarkablyacross the explanatory variables SCIENCE and TACIT as reported in Table 3 which lists

results from the multinomial logit model6. Table 3 lists coefficients, with relative risk ratios

calculated for key variables. The relative risk of licensing is calculated against unlicensed

inventions.

Inventions licensed by start-ups and established firms are far more "scientific" and "tacit"

than unlicensed inventions, demonstrated by the relative risk ratios greater than 1 for both

inventions licensed by established firms and start-ups. Interestingly, the diff erence across

many of the control variables is random; for example, relative risk is approximately 1 for

BCITE and AGE .

Unlicensed inventions were characterized by their high proportion of coinventors who were

not affiliated with the University of California, represented in Models 11-12 by significant

relative risk ratios less than 1 on the N ON − U C variable. However, the coefficient is not

significantly diff erent among inventions licensed by start-ups and established firms in the

binomial logit models. One plausible interpretation for these results that demands greater

investigation is that coinventors from companies are more able to appropriate the value

of the invention without owning the intellectual property right simply by working on the

invention itself. Technology transfer, under this possible scenario, takes place during the

initial research, and licensing does not appear as necessary to facilitate technology transfer

as compared to inventions that were discovered entirely by UC inventors.

To summarize, based on these results, we cannot reject the hypotheses that inventors

found firms to reduce information asymmetry problems related to technological uncertainty

6 Additional regressions including dummies for Y EAR and PAT _CLASS were also run for the multino-

mial logit specification, but did not substantively change the results. These results are available upon request

from the author.

23



and tacit knowledge. The diff erences between regressions on inventor-founded start-ups and

other start-ups highlight a stark contrast in the types of inventions licensed by these two

groups. These results do not, of course, rule out the impact of other variables motivatingboth inventors and others to found firms, particularly as discussed in Roberts (1991) in the

university context. These results do support the notion that many inventors found firms

based on information asymmetry problems for which they alone are uniquely able to solve.

5.2. Limitations

In this subsection, I discuss two categories of limitations to the above analysis, and oppor-

tunities for refinements. The next section concludes the paper.

Proxy Measures: Alternative Explanations The measures employed in this paper are

constructions of arguably artificial indicators. As with any study, one must be concerned that

any results, positive, negative, or insignificant, reflect more on the merits of the measures used

than the actual phenomenon under study. Hence, results and conclusions of this paper should

still be viewed with some criticism, while recognizing the absence of any direct measures of

technological uncertainty and tacit knowledge.

The proxy for tacit knowledge, patent citation-concentration, raises more specific con-

cerns. I control for concentration among patent subclass, but not for who the inventor of

a citing patent is (e.g. university faculty or corporate lab scientist, or self-citation). This

raises the concern that concentrated citations reflect characteristics of who the inventor is

rather than knowledge flows. For example, other university faculty may tend to cite their

department colleague’s patents for reasons other than to recognize prior art.

The problems are quite believable in the context of journal citations, where some scholars

or departments use a faculty’s citation count as a measure of research performance. His-

torically, patent citations did not carry as much currency in the academic climate, though

this may have changed in the years since Bayh-Dole. These patent citation practices raise

concerns to the extent that such behavior is correlated with patents licensed by inventor-

24



founded firms. I have no a priori reason to believe that this would be the case, but the

source of citations is an important issue and deserves further consideration.

Selection Bias I analyze the relative eff

ect of various technological characteristics on thepropensity for an inventor to found a firm, conditional on that technology being patented.

This potentially represents a censored-sampling process that may result in selection bias on

those inventions that did not receive a patent (all licensed inventions are filed for a patent).

Pertinent to this discussion is the following observation: non—patented, licensed inventions

were almost all licensed to established firms rather than inventor-founded start-ups.

Inventions do not receive patents for two reasons. First, the invention may have been

denied patent status by the U.S. Patent and Trademark Office. Hence, this selection process

is on the quality or patentability of the underlying invention. Secondly, since OTT maintains

policies such that licensees often must pay for patenting costs, if a licensee decides they are

no longer interested in a given technology during the patent application process, the patent

application can be withdrawn.

The potential identification problem is that ”low-quality” or unpatentable inventions may

have demonstrated similar patent citation patterns as those patented inventions licensed or

optioned by start-ups. For inventions that were not deemed worthy of patent by the USPTO,

an identification problem exists if we believe that such inventions mirror the technological

characteristics and citation patterns of patents based on basic research or new science. This

seems an unlikely scenario since, if it were true, this scenario implies that inventions charac-

terized by basic research and new science are more likely to be deemed unworthy of a patent

by the USPTO, even though novelty is one of the primary criteria for granting a patent.

For inventions that were abandoned by the licensee during the patenting process, one must

be concerned that the established firms who license patents related to more basic research

or associated with significant tacit knowledge also tend to cancel their interest in these

technologies. One could argue that this scenario actually supports the theoretical argument

advanced above: established firms do not pursue inventions with these characteristics.

25



6. Concluding Remarks

This paper empirically examined the aff ect of information asymmetry on who develops an

invention, an inventor or an outside firm. Characteristics of an invention that raise the

cost of licensing lead inventors to found a firm to further develop their invention. In the

presence of considerable information asymmetry, inventors are more likely to develop an

invention themselves in a start-up firm. The development process reduces such problems of

information asymmetry in the university context by proving the (technological) feasibility

of a commercial product and by embedding the inventor’s own tacit knowledge into the

invention. Regression results cannot refute the null hypotheses that inventions characterized

by technological uncertainty and tacit knowledge are more likely to be licensed by inventor-

founded start-ups.

The arguments in this paper implicate a subtle point about the development of start-ups.

A start-up founded by an inventor to address an information asymmetry problem appears

to be a transitory governance structure. The inventor signals the quality of the invention

(Hypothesis 1) and/or embeds her tacit knowledge (Hypothesis 2) for the purpose of reducingtransaction costs during the development stage. After initial development, the inventor can

then sell or license the firm or technology to an established firm. Indeed, inventions developed

by UC start-ups, with few exceptions, have not historically generated commercial sales until

after the start-up developed the technology and was sold to an established firm (Lowe 2002).

This description characterizes the firms both founded at UC and that we observe in

general: start-ups sold off to established firms after their initial technology or idea is further

developed. There are a few start-ups that eventually grow to become large companies with

substantial complementary assets. However, these firms appear to be more the exception

than the rule.

This paper was motivated by the university licensing setting, and data and observations

draw from this context. However, the mechanisms leading to new firms that are discussed are

not necessarily specific to university licenses. To the extent that many would-be entrepre-

26



neurs gain ownership of valuable assets, they are faced with the opportunity to license those

assets to an established firm. For researchers working for universities and government labs,

potential licensees include establishedfi

rms or venture capitalists and other entrepreneursseeking to start a new firm. For scientists in the private sector, a potential licensee is the

scientists’ own employer or perhaps even a competitor. Alternatively, many such individu-

als do create new companies. I suggest that the founding of a new firm is often related to

difficulties in contracting on the valuable asset in question, including but not limited to the

high transaction costs of transferring the asset.

Why do inventors found fi rms? The inventor’s personal preferences and characteristics

assuredly influence this decision. This paper demonstrates that traditional market and

contracting mechanisms are at work, as well. To be sure, the arguments advanced in this

paper are not new. Rather, I draw on traditional economic doctrines to analyze relatively

new research topics: personal knowledge and entrepreneurship. I propose that applying

a rigorous framework to analyze how information and institutional incentives aff ect the

inventor’s (or innovator’s in other contexts) decision to found a firm stands to make an

important contribution to the entrepreneurship research agenda. I look forward to more

research of this kind.

Acknowledgements This research was funded by the Andrew Mellon Foundation, the

Center for Studies in Higher Education, and the Industry University Cooperative Research

Program. I am grateful to David Mowery, Severin Borenstein, Rui deFigueirdo, Bronwyn

Hall, Bill McEvily, Scott Shane, David Teece, Catherine Wolfram, and Brian Wright for their

helpful comments and advice. Special thanks to Suzanne Quick and others at the University

of California Office of Technology Transfer for their assistance in compiling and interpreting

the licensing data.

27



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29



OBSERVATIONS MEAN

STANDARD

DEVIATION MIN MAX

SCIENCE 488 0.582 0.362 0.000 1.000

AGE 488 7.346 6.016 0.000 43.800

TACIT 414 -0.008 0.281 -0.412 0.858

BCITE 488 7.471 9.619 0.000 104.000

NON UC 488 0.213 0.410 0.000 1.000

GEOGRAPHY 414 0.050 0.023 0.004 0.154

SCIENCE AGE TACIT BCITE NON UC

SCIENCE 1

AGE -0.1418 1

TACIT -0.0466 0.0102 1

BCITE -0.3463 0.3666 0.0613 1

NON UC 0.0953 0.0213 0.0812 -0.0795 1

GEOGRAPHY 0.1166 -0.1242 0.0727 -0.0617 0.0343

Table 1a Descriptive Statistics for Independent Variables

Table 1b Correlations for Independent Variables

30



( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

( 1 0 )

S C I E N C E

3 . 3 2 2

3 . 9 1 7

1 . 6 0 1

1 .

1 4 3

( 2 . 1 6 ) * *

( 2 . 1 0 ) * *

( 0 . 8 9 0 )

( 0 .

2 4 0 )

A G E

1 . 0 3 9

1 . 0 6 3

1 . 0 2 4

1 .

0 3 4

( 1 . 3 6 0 )

( 1 . 8 9 ) *

( 0 . 7 8 0 )

( 1 .

0 3 0 )

T A C I T

3 . 1 9 5

3 . 1 9 9

3 . 3 2 3

2 . 0 3 2

2 . 0 3 7

1 .

9 6 9

( 2 . 2 3 ) * *

( 2 . 2 3 ) * *

( 2 . 2 1 ) * *

( 1 . 2 4 0 )

( 1 . 2 4 0 )

( 1 .

1 7 0 )

G E O G R A P H Y

2 . 0 9

0 . 0 6 8

0 . 2 9 9

0 .

1 0 2

( 0 . 0 8 0 )

( 0 . 2 7 0 )

( 0 . 1 1 0 )

( 0 .

2 1 0 )

B C I T E

1 . 0 2 3

1 . 0 2 6

1 . 0 3 2

1 . 0 2

1 . 0 2

0 .

9 9 5

( 1 . 6 2 0 )

( 1 . 6 9 0 )

( 1 . 4 9 0 )

( 1 . 3 4 0 )

( 1 . 2 5 0 )

( 0 .

2 1 0 )

N U C

0 . 7 8 2

0 . 7 2 1

0 . 4 3 5

0 . 9 0 9

0 . 8 5 2

0 .

9 3 3

( 0 . 6 5 0 )

( 0 . 8 5 0 )

( 1 . 6 8 ) *

( 0 . 2 5 0 )

( 0 . 4 2 0 )

( 0 .

1 6 0 )

Y E A R

Y E S

Y E S

Y E S

Y E S

Y E S

Y E S

Y E S

Y E S

Y E S

Y

E S

P A T_

C L A S S

Y E S

Y E S

Y E S

Y E S

Y E S

Y E S

Y E S

Y E S

Y E S

Y

E S

C O N S T A N T

O b s e r v a t i o n s

4 5 1

4 5 1

3 6 8

3 6 8

3 6 8

3 6 7

3 6 7

3 1 4

3 1 4

3

1 4

L R f o r χ

2 T e s t

3 7 . 6 4 * *

4 3 . 7 3 * * *

3 8 . 7 0 * * *

3 8 . 7 1 * * *

5 1 . 0 9 * * *

3 7 . 2 6 * *

3 8 . 6 3 * *

3 5 . 5 8 * *

3 5 . 5 9 * *

3 5

. 6 8 *

P s e u d o R - S q u a

r e d

0 . 1 0 3 2

0 . 1 1 9 9

0 . 1 3 5 8

0 . 1 3 5 8

0 . 1 7 9 3

0 . 1 1 7 6

0 . 1 2 1 8

0 . 1 3 2 5

0 . 1 3 2 5

0 . 1

3 6 6

* s i g n i f i c a n t a t 1

0 % ; * * s i g n i f i c a n t a t 5 % ; * * * s i g n i f i c a n t a t 1 %

" N o n - i n v e n t o r - f o

u n d e d s t a r t - u p s " a r e a s u b s e t o f t h e d a t a w h e r e a l l l i c e n s e s a n d o p t i o n s t o " i n v e n t o r - f o u n d e d s t a r t - u p s " h a v e b e e n r e m o v e d

" L R f o r χ

2 T e s t " r e p o r t s t h e t e s t s t a t i s t i c f o r a χ

2 t e s t t h a t a l l c o e f . =

0

I n v e n t o r - f o u n d e d S t a t -

u p s

N o n - i n v e n t o r - f o u n d e d S t a t - u p s

T a b l e

2

L o g i s t i c

r e g r e s s i o n r

e s u l t s

( L

o g - o d d s

r a t i o s

r e p o r t e d , a b s o l u t e

v a l u e

o f z - s t a t i s t i c

i n p

a r e n t h e s e s )

31



O t h e r

I n v - F o u n d e d

S t a r t - u p

O t h e r


S t a r t - u p

O t h e r


S t a r t - u p

O t h e r

I n v - F o u n

d e d

S t a r t - u p

S C I E N C E

0 . 4 5 3

1 . 8 0 9

1 . 5 7 2

6 . 1 0 4

0 . 6 8 9

1 . 9 8 7

1 . 9 9 2

7 . 2 9 2

( 2 . 2 5 ) *

( 3 . 8 8 ) * *

( 2 . 9 0 ) * *

( 3 . 7 6 ) * *

A G E

0 . 0 1 3

0 . 0 4 1

1 . 0 7 2

1 . 0 4 1

0 . 0 0 4

0 . 0 3 9

1 . 0 0 4

1 . 0 4 0

( 1 . 1 1 0 )

( 2 . 0 6 ) *

( 0 . 3 3 0 )

( 1 . 7 9 0 )

T A C I T

1 . 9 1

2 . 9 7 8

6 . 7 5 5

1 9 . 6 5

1

( 5 . 7 8 ) * *

( 5 . 7 3 ) * *

G E O G R A P H Y

- 2 5 . 3 4 7

- 2 4 . 0 7 5

0 . 0 0 0

0 . 0 0 0

( 9 . 8 6 ) * *

( 4 . 7 3 ) * *

B C I T E

- 0 . 0 0 9

0 . 0 0 6

0 . 9 9 1

1 . 0 0 1

- 0 . 0 0 8

0 . 0 1 2

0 . 9 9 2

1 . 0 1 2

( 1 . 6 3 0 )

( 0 . 7 4 0 )

( 1 . 1 5 0 )

( 1 . 1 7 0 )

N U C

- 0 . 3 7 9

- 0 . 6 7 1

0 . 6 8 4

0 . 5 1 1

- 0 . 5 9 2

- 1 . 1 1 4

0 . 5 5 3

0 . 3 2 8

( 2 . 4 2 ) *

( 1 . 9 8 ) *

( 3 . 0 7 ) * *

( 2 . 6 2 ) * *

C o n s t a n t

1 . 4 0 5

- 1 . 7 9 8

1 . 9 2 7

- 2 9 . 7 7

( 5 . 4 6 ) * * *

( 3 . 2 7 ) * * *

( 3 . 0 9 ) * * *

( 8 . 2 5 ) * * *

O b s e r v a t i o n s

9 8 9

9 8 9

8 3 0

8 3 0

L R f o r χ

2 T e s t

3 1 . 9 7 * * *

3 1 . 9 7 * * *

2 0 6 . 2 2 * * *

2 0 6 . 2 2 * * *

P s e u d o R - S q u a r e d

0 . 0 1 8 2

0 . 0 1 8 2

0 . 1 4 1 7

0 . 1 4 1 7

U n l i c e n s e d i n v e n t i o n s a r e t h e c o m p a r i s o n g r o u p

* s i g n i f i c a n t a t 1 0 % ; * * s i g n i f i c a n t a t 5 % ; * * * s i g n i f i c a n t a t

1 %

" L R f o r χ

2 T e s

t " r e p o r t s t h e t e s t s t a t i s t i c f o r a χ

2 t e s t t h a t

a l l c o e f . = 0

T a b l e 3

M u l t i - n o m i a l l o g i s t i c r e g r e s s i o n r e s u l t s ( A b s o l u t e v a l u e o f z

- s t a t i s t i c i n p a r e n t h e s e s )

C o e f f i c i e n t s

( 1 2 )

( 1 1 )

R e l a t i v e R i s k R a t i o s

C o e

f f i c i e n t s

R e l a t i v e R i s k R a t i o s

entrepreneurship and information asymmetry - theory and evidence from the university of california...

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