On the Matching of Companies and

Their Financial Intermediaries: Evidence from Venture Capital*

ALEXANDER W. BUTLER M. SINAN GOKTAN

September 6, 2008

* Butler is at University of Texas at Dallas; Goktan is at California State University – East Bay. Please send correspondence to the first author: Department of Finance and Managerial Economics, School of Management, SM 31, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX 75083. Email: butler@utdallas.edu. Acknowledgements: We thank Ekin Alakent, Andres Almazan, Lee Ann Butler, Chitru Fernando, Robert Kieschnick, Mark LaPlante, Volkan Muslu, Michael Rebello, David Robinson, and seminar participants at the University of Texas at Dallas for helpful comments. Any remaining errors are our own.

On the Matching of Companies and Their Financial Intermediaries: Evidence

from Venture Capital

ABSTRACT

We use the venture capital market to examine how companies and their financial

intermediaries match together, focusing on the tradeoff between the costs (due to

agency problems) and benefits (due to comparative advantage in information

production) of matches. Inexperienced VC firms—those with the largest

potential for agency problems—tend to match with young and small companies

within close proximity, presumably to enhance soft information production. This

finding suggests a tradeoff between the benefits of better information production

and costs of agency conflicts between company and financial intermediary. We

then quantify an outcome, IPO initial return, from matching that demonstrates

this tradeoff. We show empirically that, (only) after controlling for endogeneity

in the choice of the intermediary, venture-backed companies that are close to

their lead VC firm have substantially lower first day initial returns. Our findings

thus rationalize why companies choose to finance through inexperienced venture

capitalists that pose high agency costs (those with incentives to grandstand),

despite the large expected costs of doing so.

JEL Codes: G24, D80

Keywords: Venture capital, soft information, IPO, grandstanding

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How do the characteristics of financial intermediaries affect the matching

between companies and those intermediaries? The question of how the characteristics of

financial intermediaries affect how they function is central to the recent literature on how

banks’ locations (Petersen and Rajan [2002], Degryse and Ongena [2004]), incentive

structures (Brickley, et al. [2003]), and organizational form (Stein [2002] and Berger, et

al. [2005]) affect their lending activities. The main idea behind this literature is that the

production of information about a company, particularly soft information, by an

intermediary such as a bank is enhanced by the intermediary’s location and other

characteristics that favor strong incentives for investing in soft information gathering.

Soft information here refers to information that cannot be directly verified by anyone

other than the agent who produces it, whereas hard information refers to the type of

information that can be measured, recorded, and transferred to others (Stein [2002]).

If a financial intermediary’s characteristics, such as its incentives to produce soft

information, affect how it functions, do companies choose their intermediaries based on

observable characteristics? Put differently, how do companies and intermediaries choose

their mates? This is an important question in our framework because we argue that the

very characteristics that drive the matching also lead to possible agency conflicts. Our

paper addresses this issue using data on U.S. start-up companies and the venture firms

that fund them. The matching process should be particularly important for start-up

companies for two main reasons. First, start-ups have a clear need for production of soft

information and second, they may find themselves interacting with inexperienced venture

capitalists that have well-documented incentives to engage in “grandstanding” (Gompers

[1996]).

Grandstanding refers to the tendency of less experienced VC firms to take their

portfolio companies public “too early” in order to increase the VC firm’s reputation,

which in turn allows the VC firm to raise more capital in the future. Grandstanding is an

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agency problem that has the effect of making venture capital finance particularly

expensive for companies that use inexperienced VC firms, because companies that are

taken public early are penalized by greater underpricing at the IPO. Thus, grandstanding

represents a cost to the start-up company.

The “supply side” (why VCs might engage in this behavior) of grandstanding is

intuitive. But on the demand side, why would companies enter into financing agreements

with such VC firms if it is likely to be so expensive in terms of the going-public

strategies that grandstanding VC firms encourage? We show in this paper that the answer

to this “demand side” question is rooted in the more fundamental question of how firms

and intermediaries match with each other. In this particular matching between the VC

firm and the start-up company, there is a tradeoff between soft information production

and grandstanding. We provide evidence that the same characteristics—such as small

size and lack of reputation—of VC firms that create incentives for them to engage in

grandstanding also create incentives for such intermediaries to engage in soft information

production. Indeed, we show that it is the difficult-to-evaluate portfolio companies most

in need of soft information production that choose to match with inexperienced VC firms.

This matching provides a plausible explanation for why grandstanding can persist in

equilibrium.

To determine the benefits of this matching, we examine soft information

production by VC firms and the interaction between information production and

grandstanding incentives. There are many ways in which soft information production

might manifest itself. We focus on the effect of distance between the VC firm and the

portfolio company on the initial returns of portfolio companies that complete an initial

public offering. Others show that geographical distance correlates with soft information

production (Petersen and Rajan [2002], Berger et al. [2005], Malloy [2005], Butler

[2008]), and so we use distance as a proxy for monitoring costs and information

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production by VC firms. This research design allows us to gain direct insights on the

tradeoff between information production and grandstanding by venture capitalists.

We show that venture-backed companies that are close to their VC firms are, on

average, younger and smaller, with relatively low asset tangibility—the very companies

that most need soft information production. We stress that this is not just a Silicon

Valley effect because we control for both the state and the industry of the company in our

study. Further, VC firms that invest in close companies are less experienced VC firms—

the firms that are most likely to engage in grandstanding. This result is intuitive.

Monitoring a distant company may be more costly and require greater resources which

might be lacking in inexperienced VC firms (Gompers [1996] and Lee and Wahal

[2004]). Thus, young companies match with inexperienced VC firms, and they do so at

close physical proximity, presumably to enhance soft information production.

Our main result, described below, reveals how this matching on geographic

proximity affects an important economic outcome for start-up companies—the initial

return on their IPO. Can a nearby VC firm add value in the IPO process through reduced

underpricing? Simple ordinary least squares (OLS) techniques are inadequate to address

this issue because it is precisely those companies in close proximity that will both face

grandstanding and that will need the most “soft” information production. Companies with

such characteristics are likely to have the greatest underpricing at the IPO stage. This

selection bias potentially confounds any beneficial effect of proximity. Indeed, we find

this is the case: using an OLS framework as a benchmark model shows that there is no

net effect of venture capital distance on IPO underpricing. That is, our simple OLS tests

indicate that the near and distant VC firms have no different impact on IPO underpricing.

In our main statistical test we use a treatment effects model, a common technique

in labor and health economics research, to remove the “selection effect” due to the

endogenous matching of small companies choosing grandstanding VC firms and look at

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the “average treatment effect” of VC distance from the IPO company. Using a sample of

venture-backed IPOs from 1983-2004, we find that distance indeed matters. We show

that after controlling for the endogeneity of how firms and VCs match geographically,

venture-backed companies within close proximity to their VC firms leave less money on

the table at the IPO. Holding other factors constant and controlling for the endogenous

choice of distance, companies close to their VC firms have initial returns that are a

remarkable 12 percentage points (for the full sample; 7 percentage points when we

exclude the internet bubble) or, about 35% of the mean (for the full sample; 33% of the

non-bubble period mean), lower than companies distant from their VCs. This reduction

in initial return is an outcome of better soft information production arising from

proximity.

Through its focus on how intermediaries match with firms, our paper brings

together two literatures—the one on the effect of geography in financial intermediation

(Petersen and Rajan [2002], Berger et al. [2005], Butler [2008]) and the one on agency

problems in venture capital (Gompers [1996], Lee and Wahal [2004]). Closest to the

spirit of our paper is Berger, et al. [2005]. In their two-stage least squares approach, they

model both how borrowing companies choose the size of their bank and what economic

outcomes result as a function of (the exogenous portion of) bank size. Similarly, we

consider both how companies choose their intermediary (their VC firm) and the impact

those choices have on related economic outcomes. Fernando, Gatchev, and Spindt

[2005] are also interested in how firms and intermediaries (IPO lead managers) match,

but they model only the company’s choice of underwriter reputation, not the joint

decision by both company and the intermediary.

One straightforward aspect of our paper is that it is a continuation of Gompers

[1996]. His paper documents the strong incentives for inexperienced VC firms to

grandstand, but leaves as an open question why start-up companies would have a demand

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for inexperienced VC firms that are likely to grandstand. Gompers [1996] states, “While

this paper does not address the reasons entrepreneurs seek financing from young venture

capital firms who then rush them to the IPO market, the issue deserves greater

attention[…].” We fill this gap with our matching analysis, thereby supplying the

demand side explanation for grandstanding.

The remainder of this paper is organized as follows. Section 1 gives a brief

description of the importance of proximity and soft information in VC industry, Section 2

describes the data, Section 3 presents the results, and Section 4 provides concluding

remarks.

1. Importance of Proximity and Soft Information in Venture Capital Industry

How can better information production through close proximity add value to a

venture-backed company? This section discusses in detail how we anticipate soft

information production to be important in the venture capital industry. When information

about projects is soft and cannot be credibly transmitted, financing through intermediaries

that invest in research and create relationship-based lending with their clients is a better

alternative than arms-length finance. Under such circumstances, proximity is important

(Coval and Moskowitz [2001], Malloy [2005]). Butler [2008] shows that high-risk bonds

and non-rated bonds are more difficult to evaluate and that investment banks with a local

presence are better able to assess soft information. As a result, these local investment

banks charge lower fees and sell bonds at lower yields. These studies support the

positive correlation between proximity and better information production that we try to

examine in this paper.

In addition to financing, many VC firms provide intensive oversight for the

companies in their portfolios. VC firms heavily invest in research on the companies they

finance and are involved in soft information production which is needed to evaluate their

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investments. The fact that VC firms provide extensive monitoring and supervising

services along with financing makes distance an important factor in the VC industry.

Proximity enables VC firms to produce more accurate information and this information

will be conveyed to the company more effectively. We argue that distance is especially

important for young and small venture-backed companies that are in their early stages

since they require extensive supervising. Distance is also important for young and

inexperienced VC firms since they have relatively limited resources to monitor a distant

portfolio company and they have a comparative advantage at soft information production

due to their organizational form.

A close VC firm may be able to improve the valuation of a venture-backed

company through enhanced screening, monitoring, and certification. Proximity improves

screening because VC firms can receive more accurate and credible information about the

potential investment opportunities within their region through their network, and as a

result, make better investment choices.

VC firms may be able to provide close monitoring, through active involvement in

the daily business of the company, serving on the board of the company, and matching

the company with key customers and suppliers (Lerner [1995], Jeng and Wells [1997]).

In addition to those services, location specific advantages such as established networks

and industry expertise found in the close vicinity of the VC firm also benefit those

companies located in close distance. Consistent with the view that proximity enhances

monitoring ability, Tian [2007] documents that venture-backed companies located closer

to their VC firms receive fewer financing rounds, receive more investment amount per

round, have a higher chance of successful exit and have better operating performance in

the IPO year.

VC firms facilitate certification for underwriter firms and investors. As

Megginson and Weiss [1991] show, venture capitalists tend to use the same underwriters.

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Under these circumstances, a VC firm can credibly provide certification for the company

under its close supervision which will increase the perceived value of the company to

investors and underwriters.

We analyze the relationship between proximity and valuation by looking at the

effect of proximity on IPO underpricing. We hypothesize that better information

production/sharing as a result of proximity will manifest itself through IPO valuation and

proximity will create a decrease in the underpricing of the venture-backed companies at

the IPO stage.

2. Data

We gather information on an initial sample of 2,742 venture-backed IPOs from

the period 1983-2004 from the Securities Data Company (SDC) database. Following

previous researchers, we eliminate offerings (i) identified as unit offerings (ii) not

involving common stock, (iii) of financial firms with SIC codes between 6000-6999, (iv)

of very small issues with offer size below 20 million dollars, and (v) for which SDC did

not provide information required for our tests. Both the venture-backed IPO company

and the VC firm must have their main office or branch office within the U.S. We require

the company be in the Center for Research in Security Prices database (CRSP) and the

Compustat database. We obtain information on the founding year of the company, IPO

volume in a given year, underwriter rank, and a list of internet companies from Professor

Jay Ritter’s website. After applying all our filters and data requirements, the resulting

sample consists of 915 IPOs. To mitigate the effect of outliers or data errors, we

winsorize all variables in our models at 1% and 99% levels.

To determine the distance between the VC firm and the venture-backed IPO, we

first identify the lead investment firm which is the firm in the syndicate that typically

undertakes the main task of monitoring and consulting (Gompers [1996]). The co-

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investors in the syndicate are involved with the business of the financed firm to a

considerably lesser degree and so their proximity is, arguably, not as important (Wright

and Lockett [2003]). Measuring distance only between the lead VC firm and the

company (i.e., by excluding the distance effect of non-lead syndicate members) biases

against our finding a distance effect. We identify the lead investor following Lee and

Wahal [2004] and choose the VC firm with the largest investment in the syndicate as the

lead investor.

We obtain the zip codes for the lead VC firm headquarters and any branch offices

and the zip codes for the venture-backed IPO from SDC. We compute the zip code to zip

code distance between the IPO company and each of the VC firm’s offices and select the

minimum as the “shortest distance” in the analysis. We use the proprietary in-house

program of a company that is in the mail sorting business to calculate the distances

between the zip codes. To test the reliability of the distances, we randomly selected a

sample of observations and calculated the distances between zip codes through

www.gpsvisualizer.com and obtain very similar results.

We classify IPOs which are within 25, 50, and 100 miles to VC firms with

dummy variables. We prefer to use “close VC” dummy variables to capture the effect of

proximity instead of a continuous measure of distance (such as miles) because we do not

expect a linear relation between proximity and its effect on monitoring and/or

certification. Figure 1 shows that a disproportionate percentage of venture-backed

companies are within 25 miles of their lead VC firm. This finding is consistent with the

“local bias” result that Dai and Cumming [2006] document. Along these lines, some

observers argue that the effective geographical radius within which VC firms prefer to

make investments may be restricted to one to two hours’ travel time from their office

(Mason and Harrison [1992]) or less (Dai and Cumming [2006]).

<Insert Figure 1 here>

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Our main dependent variable is the initial return on the IPO, which we compute

as the percentage change from the SDC offer price to the first day closing price from

CRSP. Many factors besides VC firm distance affect initial returns, so we construct the

following control variables: price revision, measured as [offer price-midpoint of original

filing range] / midpoint of original filing range*100; the proportional IPO filing range,

computed as [original high filing price – original low filing price] / midpoint of original

filing price*100. All of the variables mentioned above are from SDC. We use the log of

total IPO proceeds (from SDC) and log of total assets (from COMPUSTAT, data item

#6) as measures of the size of the IPO and size of the company, respectively, and we

convert both of these measures to 2004 dollars. We calculate asset tangibility as net

plant, property, and equipment (COMPUSTAT, data item #7) / total assets

(COMPUSTAT, data item #6) *100.

As a proxy for uncertainty about the company, we use the standard deviation of

stock returns in the after-market. Because underwriters might provide price support at

first, we exclude the first several days, and calculate the standard deviation of returns

(from CRSP) for 10 through 180 calendar days post IPO. We control for market wide

movements from the filing date to the offer date using the percent change in the CRSP

equal weighted composite index during the filing period. We control for the IPO volume

by number of IPOs in a given year from Professor Jay Ritter’s website. The age of the

company at IPO is in years and we calculate this variable by subtracting the year the

company was founded from the IPO year. The top underwriter dummy takes a value of

one for lead managers with a tombstone ranking greater than 8 (Carter and Manaster

[1990], Carter, Dark, and Singh [1998], Loughran and Ritter [2002]). We also use an

internet company dummy that takes a value of one if the company is listed as an internet

IPO and a technology company dummy; both are defined in the website of Professor Jay

Ritter at University of Florida.

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We calculate the age of the VC firm at IPO by subtracting the founding year of

the VC firm (from SDC) from the IPO year. Following Gompers [1996], we create a

young firm dummy that takes a value of one for VC firms that are less than 6 years old

and a value of zero otherwise. We use the total investments ($ million) made by VC firm

prior to the first investment year in the IPO company as another proxy for VC firm

experience. (We note that we do not use the “total amount of investments that VC firm

participated” variable that SDC supplies. The SDC variable represents investments of

VC firms to the date the data are downloaded so it is not representative of the VC firm

experience when the company receives its funding.) We calculate the total prior VC firm

investments variable for each year for each VC firm by summing the total investments of

the VC firm in companies in all prior years within the SDC data. We then use the year

the VC firms makes its first investment in the portfolio company as the reference year to

assign the experience of the VC firm for each observation.

We create an industrial cluster dummy variable. An industrial cluster is a

geographic area that has a significant number of firms that belong to a particular industry.

This variable is motivated by Almazan, et al. [2006] who show the effect of geographical

industrial clusters on financial decisions of firms. In our context, the idea is that a

company located in an area where industry specific expertise is present is more likely to

work with a close VC firm that is specialized in that industry who can advise/monitor

more effectively and can make a greater use of close distance. For example, internet

companies have an industry cluster in “Silicon Valley,” near San Francisco, California

and telecommunications companies have an industry cluster in the “Telecom Corridor,”

near Dallas, Texas. We denote a company as being in an industrial cluster if there are at

least three IPOs with the same 2-digit SIC code within a 25 mile radius in our sample.

Finally, we control for the time and industry varying characteristics of the IPO by

using dummy variables representing the year of the IPO and industry dummy variables

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represented by two digit SIC codes, respectively. (We note that this does not create a

perfect collinearity with the internet and technology company dummies because these

classifications are defined with finer granularity, i.e., at the 3- and 4-digit SIC code

level.) We also control for the state the IPO company is in because of the well-known

concentrations of VC firms in certain parts of the country (we discuss this in more detail

below). Thus, all of our multivariate tests reflect within-state effects.

3. Results

This section introduces the descriptive statistics and regression results for our

sample. Sub-section A gives information on VC industry characteristics. Sub-section B

gives descriptive statistics of the variables used in our analysis. Sub-section C introduces

the characteristics of VC firms and venture-backed companies that are close to each

other. Sub-section D examines the effects of grandstanding. Sub-sections E, F and G

introduce the simple OLS regressions and average treatment effect regressions. Sub-

section H examines the effect of close proximity on initial returns in sub-samples and

Sub-section I discusses robustness checks.

A. Characteristics of the venture-backed IPOs: Geographic and industrial distribution

Figure 2 presents the geographic distribution of the venture-backed companies in

our sample on a U.S. map. Figure 2, Panel A presents companies that are not within 25

mile proximity to their VC firms whereas Figure 2, Panel B presents companies that are

within 25 mile proximity. The two figures show that companies that are within 25 mile

proximity to their VC firms do not present a specific region of the U.S., such as

California. They exist rather uniformly in regions where venture-backed IPO activity is

generally high.

<Insert Figure 2 here>

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Table 1, Panel A describes the composition of our sample of venture-backed IPO

companies by state. Consistent with conventional wisdom, California has by far the

largest number of venture-backed IPOs, with 324 of the 915 sample companies.

Massachusetts, Texas, and New York also have large numbers of venture-backed IPOs.

Venture-backed IPO companies are most likely to be within 25 miles of their VC firm in

New York (41% of companies), California (38% of companies) and New Jersey (37% of

companies).

<Insert Table 1 here>

Table I, Panel B reports the distribution of venture-backed IPO companies by

industry. The most represented industries are business services (SIC code 73) with 34%

of our sample of venture-backed IPOs and electronic equipment (SIC code 36) with 12%

of our sample. These two industries, along with industrial machinery (SIC code 35), are

also the ones that are most likely to be within 25 miles of their VC firm (33% of

electronic equipment companies, 32% of business services companies and 28% of

industrial machinery companies).

<Insert Figure 3 here>

Figure 3, reports the distribution of venture-backed IPOs by year. The “bubble”

period of 1999-2000 contains a disproportionate number of our sample companies (255

of 915, or 28%). Because this was an unusual period for initial return levels and IPO

numbers, we will discuss below the sensitivity of our results to the inclusion or exclusion

of this period.

B. Characteristics of venture-backed IPOs: Summary statistics

Table 2, Panel A presents descriptive statistics of some of the variables in our

sample. Note that the mean and median initial returns are quite high in the overall

sample, 34.9% and 14.7% respectively. This result is driven by both the fact that our

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sample consists of venture-backed IPOs (which are known to have relatively high initial

returns compared to the universe of IPOs) and by the huge initial returns during the

“bubble period” of 1999 and 2000. As we show in Table 2, Panel B, excluding the

bubble period, the average initial return for IPOs is around 18%. During the “bubble

period,” however, the initial return is 79% on average. These numbers are consistent with

the results in Lee and Wahal [2004] and reflect the non-stationary in the underpricing of

IPOs (Loughran and Ritter [2004]).

<Insert Table 2 here>

C. Characteristics of venture-backed IPOs: Close versus distant venture-backing

Simple univariate comparisons indicate that initial returns for venture-backed

IPO companies close to their VC firms are higher than those companies that are distant

from their VC firms. However, as we will explain later in detail, this result is due to

omitted variable bias and endogeneity in the choice of proximity.

<Insert Table 3 here>

The choice of whether to match with a close or a distant VC firm is endogenous.

In Table 3 we present some company and VC firm characteristics that may be related to

this choice. Table 3, Panel A has univariate tests for differences of characteristics for

close versus distant matches. Companies that match with close VC firms are smaller and

younger, have lower asset tangibility, and are more likely to be in industrial clusters than

companies that match with distant VC firms. The average age of a portfolio company

within 25 miles of its VC firm is 8 years at the IPO stage, whereas those that are more

distant have an average age of 13 years. This difference and, except as indicated, the

others we mention in the remainder of this sub-section are statistically significant at the

1% level. Similarly, companies within 25 mile proximity of their VC firm have average

total assets of $129 million whereas those that are not close have average total assets of

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$249 million. The asset tangibility ratio is 17% for companies within 25 mile proximity,

compared to 27% for those companies that are not. Companies that are located in an

industrial cluster location are more likely to be close to their VC firms: 82% of the IPO

companies in industrial clusters are close to their VC firms whereas 56% of off-cluster

IPO companies are close to their VC firms.

We also examine whether relatively inexperienced VC firms tend to invest in

portfolio companies within close proximity, which would be consistent with

inexperienced VC firms having limited resources and a comparative advantage at soft

information production. We use the dollar amount of all previous investments in which a

VC firm participated as a proxy for VC firm experience. The companies that are within

25 mile proximity have less experienced VC firms on average.

D. Is there grandstanding?

Grandstanding occurs when inexperienced VC firms, in an effort to establish a

reputation for bringing portfolio companies public, push their portfolio companies public

“too early.” The idea is that the VC firm will recoup any opportunity costs by being able

to raise larger subsequent investment funds sooner than they otherwise would. More

established VC firms have less need to try to build reputation in this way. In this section,

we document activities consistent with the grandstanding hypothesis of Gompers [1996].

Table 3, Panel B compares descriptive statistics for initial return and price

revision for the IPOs of young VC firms versus experienced VC firms. We follow

Gompers [1996] and define a young/inexperienced firm as one that is less than 6 years

old. Table 3, Panel B shows that IPO companies backed by experienced VC firms have

average initial returns of 33% whereas those backed by young firms have average initial

return of 58% and the difference in means is significant. This result is consistent with

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Gompers [1996] and shows that, on average, companies backed by young VC firms leave

more money on the table at the IPO.

Another implication of the grandstanding hypothesis is that young VC firms take

younger companies to IPO compared to experienced VC firms. In Table 4, we report

regressions where age of the company at the IPO (in years) is the dependent variable,

following Gompers [1996]. In the first regression we include both the 25 mile proximity

dummy together with the young VC dummy. The 25 mile proximity dummy is

significant at 5% level and the young VC dummy has a p-value of 10.7%. Interestingly,

the magnitudes of the coefficients of these two dummies are quite similar, the 25 mile

proximity dummy has a coefficient of -1.62 and the young VC firm dummy has a

coefficient of -1.49.

<Insert Table 4 here>

Overall, our data are consistent with grandstanding activities by relatively

inexperienced VC firms. Moreover, close VC firms are more apt to have the

characteristics of “grandstanders” than distant VC firms. Furthermore, it is the IPO

companies that are close to their VC firms that are youngest and smallest. Because these

characteristics may also affect IPO initial returns, we need to control for the endogenous

choice of proximity in our analysis.

E. Modeling the choice of proximity

We further analyze the matching between the VC firm and the venture-backed

company in a multivariate setting. We run a probit regression using the 25 miles

proximity dummy as our dependent variable to analyze the characteristics of both VC

firms and companies that choose to be close to one another. (We note that other

proximity cutoffs give similar results.) Table 5 gives the results for this regression.

Consistent with the univariate tests, the results here suggest that venture-backed

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companies within close proximity to their VC firms are smaller, younger, more opaque,

and are more likely to be in an industrial cluster. In addition, close companies match

with VC firms that are less experienced, as evidenced by the dollar amount of previous

investments, which is negative and statistically significant.

<Insert Table 5 here>

This result is interesting because the characteristics of the VC firms and the

venture-backed companies that are in close proximity to each other coincide with those

most likely to be involved in grandstanding. Previous evidence on grandstanding

suggests that young and inexperienced VC firms take companies earlier to IPO in order to

establish reputation and successfully raise capital for new funds (Gompers [1996], Lee

and Wahal [2004]). Moreover, Lee and Wahal [2004] show that the companies backed

by relatively young/inexperienced VC firms are younger, smaller, and more underpriced

at their IPO than those of established VC firms.

F. Matching based on soft information production

The evidence in the previous section suggests smaller and younger venture-

backed companies and inexperienced VC firms tend to match within close proximity.

This evidence is consistent with matching based on the start-up company’s soft

information production needs.

However, the matching between the VC firm and the start-up company might

simply be a case of lower quality companies not having access to high quality

intermediaries. If so, such an outcome would be consistent with the findings of Fernando

et al. [2005]. They document that IPO issuers and IPO underwriters associate by mutual

choice: lower quality companies tend to match with underwriters with lower reputation.

It is difficult to distinguish between these two (not mutually exclusive)

explanations—that less established companies pair with less established VC firms (a)

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because they benefit from doing so due to those VC firms’ comparative advantage at soft

information production, or (b) because they have no better opportunity available to them.

To distinguish between the two, we examine the determinants of company size at the

IPO, a proxy for company quality and transparency. The idea here is that under case (b)

above (the “no better opportunity available” story), the reasons for matching between

company and VC firm comes primarily from similarities in “quality” and the ability of a

VC firm to produce soft information is secondary at most. In contrast, under case (a)

above (the “comparative advantage” story), soft information production is of primary

importance.

To test this argument, we model the size of the venture-backed company with

independent variables that we use in Table 5, and include an interaction term between

geographic proximity and VC firm experience. The intuition for our approach is as

follows. Imagine a small venture-backed company located in, say, California. Under the

comparative advantage story, this company is unlikely to match with an inexperienced

VC firm that is distant, such as one that has its nearest office in, say, New York. Under

the alternative story, geographic proximity is a secondary concern, and matches between

companies and VC firms arise on the basis of their having comparable positions on their

respective quality spectra.

We regress start-up company size on the interaction of proximity and VC firm

experience, variables for the direct effects of proximity and VC firm experience, and

control variables. If the comparative advantage story holds in the data, we expect the

interaction term to be statistically significant. That is, the effect of geographic proximity

(our proxy for soft information production) on start-up company size at the IPO should be

stronger for inexperienced VC firms because these are the ones with an advantage at soft

information production.

18

We present the results in Table 6. The dependent variable is the natural

logarithm of total assets of the venture-backed company. The first column in Table 6

shows that distance between the VC firm and the venture-backed company increases with

company size. Also, VC firm experience, measured by the total dollar amount of

investments previously completed, is positively related to company size. Both of these

results are consistent with our previous findings in Table 5 and suggest that, on average,

smaller venture-backed companies are closer to their VC firms and are more likely to

work with inexperienced VC firms.

As discussed above, our primary interest is in the interaction between distance

and VC firm experience. If smaller companies choose to match with inexperienced VC

firms within close distance to enhance soft information production, then the interaction

variable should be significantly and negatively related with company size. We define a

VC firm with less than $100 million in previous investments as inexperienced. (We note

that our results are not sensitive to this cutoff: using $200 million or $300 million as our

cutoff gives similar results.) Consistent with the comparative advantage story, the size of

the venture-backed company is significantly and negatively related with this interaction

variable. When we include some additional control variables (second column of Table

6), we reach similar conclusion.

<Insert Table 6 here>

This result confirms our argument that smaller companies and inexperienced VC

firms choose to match with one another within close proximity and this finding is not

driven by only a “good matches with good, bad matches with bad” equilibrium. If soft

information production played no part, then the interaction term in this analysis would be

insignificant. The fact that the interaction is significant is consistent with our hypothesis

that firm and intermediary choose to match with one another based on soft information

production abilities and needs.

19

The economic magnitude of the effect of the matching within close distance is

difficult to interpret with the analyses in Table 6 because the interaction term is a dummy

variable whereas the components of the interaction term are continuous variables in the

model. To ease interpretation, we substitute the continuous distance measure with a

dummy variable that represents being close within a certain radius (25 miles, 50 miles or

100 miles). The magnitude of the interaction is economically large: in unreported

regressions, the results suggest that the effect of being close and working with an

inexperienced VC firm has a 52% to 127% (depending on the cutoff for proximity)

greater effect on the size of the venture-backed company compared to the effect of just

being close to the VC firm.

G. Endogeneity: Average treatment effect model

Our results in the previous section show that the choice of proximity between

venture-backed company and VC firm is endogenous. Indeed, it is precisely those

companies, which are likely to have the greatest underpricing at the IPO stage that will

need the most soft information production. This soft information production can be done

most easily by a close and small VC firm. However, because those small VC firms are

also likely to be young and/or inexperienced, they are more likely to have incentives to

grandstand and this creates a confounding effect. Under these circumstances, OLS

estimates are inconsistent because the proximity dummy is correlated with the error term

(Greene [2003]).

We deal with the endogeneity of proximity with a treatment effects model

(Heckman [1976], Heckman [1978], Manning [2004]). Treatment effects models are

common in labor and health economics research and are gaining popularity in finance

applications (Ciamarra [2006], Li and Prabhala [2005]). In our model, the treatment is

having a proximate VC firm. If we have Y1 and Y0 as the outcome (IPO initial return)

20

with and without treatment, respectively, then we are interested in the effect of proximity

on Y. Thus, the effect of treatment is ATE = E(Y1 - Y0). If we know a set of variables X

that affect the decision to receive treatment, then our average treatment effect will be

ATE = E(Y1 - Y0 | X). This specification gives us an unbiased estimate only if the decision

to receive treatment is randomized across firms that receive treatment and those that do

not. However, in our model, the decision to receive the treatment (i.e., choice of

proximity) is not random, which creates a selection bias. For example, as we have seen

from the previous analysis, companies that choose to receive the treatment are younger

and smaller.

To overcome the bias that we mention in the above discussion, we use the

“endogenous dummy variable” approach developed by Heckman [1976, 1978]. We

model the probability of receiving the treatment, equation (2) below, with the structural

outcome, equation (1) below:

Yi = α + β1Xi + β2Di + εi (1)

Di* = δZi + ui (2)

Di = 1 if Di* > 0

Di = 0 if Di* < 0

Equation (2) reflects the decision to receive treatment, where Zi is a set of characteristics

that affect the choice of receiving treatment. Di in the first equation is an endogenous

dummy variable, indicating whether the treatment is received and it is determined with

Di* from the second equation which is an unobserved latent variable. The variables in X

can overlap with variables in Z, but it is assumed that at least one component of Z is

unique. The individual error terms εi and ui are assumed to have bivariate normal

distribution. We use full information maximum likelihood to solve the model (Heckman

[1976], Heckman [1978]).

21

H. IPO Initial returns: The effect of proximity

To apply the average treatment effects model, we first estimate a model to predict

the choice to receive the treatment: being close to the VC firm. This step is directly

analogous to the probit model estimated in Table 5, though here the treatment equation is

estimated jointly with the structural equation of initial returns. The dependent variable is

a proximity dummy, with explanatory variables identical to those in Table 5 (log of total

assets, total previous investments of the VC firm, company age at the IPO date, industrial

cluster dummy, and asset tangibility ratio).

In our treatment analysis, we use as an instrument a dummy variable for whether

the start-up company is geographically located in an industrial cluster. Companies and

venture firms tend to coexist within industry clusters, such as Silicon Valley, so

companies are more likely to be close to their VC firms when they are located within

industry clusters. On the other hand, we do not see a direct connection between IPO

underpricing and being in an industry cluster, other than its indirect effect through

proximity. The treatment equation results are very similar to those reported in Table 5,

so we do not report them in a separate table. (The treatment equation results are available

from the authors upon request.)

Table 7 gives the results for the initial return regressions for both OLS and the

average treatment effects model. In these regressions the dependent variable is the IPO

initial return in percentage terms. We examine how proximity to the VC firm effects

underpricing of the venture-backed IPO by regressing initial return, defined as percentage

change from the offer price to the first day closing price, on a proximity dummy and

several control variables. We control for the filing range, after-market variability of the

stock, market return during the filing period (CRSP-Equal weighted index), a dummy for

internet companies, IPO volume, price revision, offer size, age of company, young VC

firm dummy and a dummy for top underwriter. We also include dummy variables for

22

each state, two digit SIC code, and year of the IPO company to capture industry, state,

and time-specific effects. It is important to control for state effects in the regression

because this control ensures us that the results are not driven by venture-heavy states such

as California.

<Insert Table 7 here>

The first regression model is a baseline OLS regression that does not control for

the potential endogeneity problem. In this regression, the coefficient of the 25 mile

proximity dummy is not significantly different from zero. Consistent with other studies,

the coefficients on the control variables show that price revision, the internet company

dummy, standard deviation of return and the young VC firm dummy, are all positively

and significantly related to initial return whereas offer size, IPO filing range, top

underwriter dummy and market return are negatively and significantly related. All of the

coefficients of the variables have the expected sign and are consistent with prior studies

on IPO underpricing. The R-squareds for these regressions are 60%.

The second regression in Table 7 gives the results for the average treatment

effects model that explicitly corrects for the endogenous choice of proximity. This

regression is jointly estimated with the treatment equation described above. We reject the

hypothesis of independent equations (exogeneity) at the 5% level. Thus, the treatment

effects model is a more correct specification than OLS.

All control variables have the predicted signs and do not differ much from the

OLS regression results. Our main variable of interest is the coefficient on the proximity

dummy in the average treatment effects model and the young VC firm dummy. The

regression results show that company proximity has a negative and significant effect on

the initial return of a venture-backed company at the IPO stage. Overall, we see that once

we account for the characteristics of the companies that choose to be close to their VC

firms and correct for the selection bias in the model, we are able to show that IPOs that

23

are close to their VC firms are significantly less underpriced. (We note that other

specifications produce similar results.) The average treatment effect on the treated is

-12.3% for the 25 mile proximity specification. This means that, absent the offsetting

endogeneity issues, IPOs backed by close VC firms have underpricing of 12.3 percentage

points less, on average, than those backed by distant VC firms. Compared to the whole

sample average initial returns of 44.7% for those companies within 25 mile proximity,

this treatment effect is about one fourth of the overall initial return.

The coefficient on the young VC firm dummy in the treatment regression is

about 9.8% and is significant, similar in magnitude to the coefficient in Gompers [1996].

Overall, our results show that being close to the VC firm can decrease the underpricing at

the IPO stage in a magnitude that can offset the negative effects of an inexperienced VC

firm that is likely to grandstand. This result is important because it suggests that

proximity can partially explain why companies choose to work with young VC firms

despite the risk of grandstanding.

We then estimate the robustness of our results to alternative definitions of

“close.” We change the definition from “within 25 miles” to “within 50 miles” and then

“within 100 miles.” Models (3) and (4) are average treatment effects models just like

model (2), except using different distance cutoffs for what is denoted as “close.” The

results are similar, with each producing a statistically significant distance effect of about

the same order of magnitude: 15 percentage points for a 50 mile cutoff, 16 percentage

points for a broader 100 mile cutoff. We do not report the OLS regressions for these

robustness tests because no matter what distance cutoff we use, proximity is always

insignificant due to the offsetting endogeneity problem.

To assess whether this distance effect is unique to the internet “bubble period,”

we re-run the tests omitting the IPOs that occurred during 1999-2000. The coefficient for

the 25 mile proximity dummy remains significant at the 10% level. The magnitude of the

24

effect is much smaller, down to 7 percentage points from 12. Of course, the non-bubble

period initial returns are also much smaller (21% mean outside the bubble period for

IPOs with close VC firms), and 7 percentage points corresponds to approximately one

third less underpricing compared to the overall sample mean.

I. Sub-sample tests: Who benefits the most from proximity?

One of our main arguments in this paper is that young venture-backed companies

are those that will benefit most from soft information production and thus, are likely to

see the largest benefit from being close to their VC firms. To test this hypothesis, we

repeat our regression tests from Table 7 with two sub-samples of the data: one sub-

sample comprises only those companies that are 6 years or younger at the IPO, and the

other sub-sample comprises the complement. (We note that, due to our treatment effects

model approach, simply including an interaction term would produce inconsistent

estimates; see Wooldridge [2002, p. 236].) The results are reported in Table 8. We do not

report the treatment regression results since these results are similar to those in Table 5.

<Insert Table 8 here>

The first column in Table 8 represents the regression for the sub-sample of young

companies. The coefficient on the 25 mile proximity is -28.4% and statistically

significant (p = 6.2%). Compared to the coefficient of -12.2% reported in the whole

sample, we see that the gain from being close to the VC firm is more than doubled for

companies that are younger. Looking at column 2 in Table 8, we see that the regression

for the sub-sample comprising older companies has a coefficient of -6.2% (not

statistically significant) for the 25 mile proximity dummy. A visual comparison across

these two sub-sample regressions indicates that the effect of VC firm proximity on

underpricing is substantially greater for the young portfolio companies, which are the

companies that most need soft information production.

25

Overall, these results are consistent with the arguments in this paper. Younger

companies benefit from being close to their VC firms. For that purpose, they tend to

match within close proximity with VC firms that are relatively less experienced who have

greater incentives to produce information. Overall we see that, especially for younger

companies, benefits from close proximity outweigh the cost of grandstanding.

J. Robustness

Our results are robust to different specifications of close proximity. Instead of

using a “close” dummy variable, we also try actual distance in miles (logged and non-

logged) as the independent variable to measure the effect of proximity on underpricing.

Under a linear specification, the difference between 1 and 101 miles distance is treated

the same as a difference between 2400 and 2500 miles distance. Not surprisingly, the

distance variable is not statistically significant when we replace our proximity dummy

with distance in miles. This result is not surprising because even a casual visual

inspection of the distribution of distance in Figure 1 suggests that the effect of distance is

likely to be non-linear.

Companies that are located where VC firms develop industry-specific expertise

are also more likely to be closer to their VC firms. We control for this factor with the

cluster location dummy in our treatment equation. We also control for 1-digit SIC

industry code in our treatment equation and find consistent results. (We note that the

maximum likelihood estimation procedure does not converge if we use 2-digit SIC

codes.) Finally, including IPO year dummies in the treatment equation does not change

our results.

4. Conclusion

26

Why do companies choose to work with inexperienced financial intermediaries

that have high potential for agency conflicts? We argue that the answer partially lies in

the need for information production. Financial intermediaries produce information about

the companies that they work with and information is valuable, especially for smaller

companies that are more opaque and rely on soft information. Due to their organizational

form [Stein (2002)], inexperienced financial intermediaries have greater advantage at

producing soft information. As a result, smaller companies and less experienced

financial intermediaries match together to achieve better information production. In

equilibrium, there is a tradeoff between the benefits of better information production and

costs due to agency conflicts between the company and the financial intermediary.

We look into the venture capital industry where benefits from soft information

production are likely to be high and where high agency costs due to “grandstanding”

exist. Our results provide a “demand side” argument for grandstanding (Gompers

[1996]) by relatively inexperienced VC firms. Grandstanding is costly for the portfolio

companies because their IPOs are underpriced substantially more than their counterparts

that are brought to market by more seasoned VC firms. Why would portfolio companies

be drawn to venture capitalists that are likely to grandstand? We argue that companies

most in need of soft information production choose to pair with VC firms that have

characteristics most conducive to producing such soft information—relatively small and

inexperienced VC firms that are physically close to the company. But inexperienced VC

firms also have the strongest incentives to grandstand. Thus, there is a tradeoff—the

companies most in need of soft information production will naturally pair with VC firms

most likely to engage in costly grandstanding.

We directly examine the nature of the matching between VC firms and portfolio

companies and our conclusions strongly support the argument above. We find that when

soft information production is most in demand (i.e., companies that are young, small, and

27

have low asset tangibility) VC firms and portfolio companies match in close proximity.

These geographically close matches, though, are with relatively inexperienced VC firms.

This is not surprising, as these firms may be best suited for soft information production

(e.g., they may have organizational forms that are less hierarchical than well-established

VC firms (Stein [2002]), but it also bears out our tradeoff argument.

We also note that the matching between the VC firm and the start-up company is

not simply a case of lower quality companies not having access to high quality

intermediaries, like what Fernando et al. [2005] find. We document evidence that

matching between the inexperienced VC firm and the small company is taking place

within close distance, supporting our argument that the matching is rather driven by soft

information needs.

We document an effect of this tradeoff between information benefits and agency

costs by examining the effect of proximity between venture-backed IPO companies and

their lead VC firm. In OLS results, we find no difference in the initial returns of the

venture-backed companies which are close to their VC firms compared to those that are

distant. However, we show that this (non-)result is driven by the fact that portfolio

companies and VC firms choose to be close to one another based on soft information

production needs. After controlling for the endogeneity in the proximity choice, we show

that venture-backed IPOs that are within a short commute distance to their VC firms have

substantially lower underpricing. One interpretation of the result is that the lower

underpricing of the IPO is a result of the enhanced monitoring and/or certification role of

the close VC firm. Our results also suggest that the benefits from being close are higher

for smaller companies, which strengthen our argument that matching within close

distance is driven by soft information needs.

These findings combine to provide a demand side rationale for grandstanding.

Gompers [1996] documents strong incentives for inexperienced VC firms to grandstand,

28

but leaves as an open question why start-up companies would have a demand for

inexperienced VC firms that are likely to grandstand. Gompers [1996] states, “While this

paper does not address the reasons entrepreneurs seek financing from young venture

capital firms who then rush them to the IPO market, the issue deserves greater

attention[…].” We fill this gap with our matching analysis, thereby supplying the

demand side explanation for grandstanding. The organizational characteristics that

encourage grandstanding by VC firms are the same characteristics that encourage soft

information production.

29

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32

Figure 1 Distribution of venture capital firm and the venture-backed company

Panel A shows the distribution of the distance between the venture capital firm and the venture-backed company in our whole sample; each bin represents 25 miles. Frequency is the number of companies within each bin. Panel B shows the distribution of the distance between the VC firm and the venture-backed company in our sample for observations where distance between the venture capital firm and venture-backed company is less than 500 miles. Each bin in panel B represents 5 miles.

Panel A

050

100

150

200

250

Freq

uenc

y

0 1000 2000 3000Distance between VC firm and company (miles)

Panel B

020

4060

80Fr

eque

ncy

0 100 200 300 400 500Distance between VC firm and company (miles)

33

Figure 2 Geographic distribution of venture capital-backed companies

This figure shows the geographic distribution of venture capital-backed companies on the U.S. map (Hawaii and Alaska are omitted). The midpoint of each circle represents the zip code that the venture capital-backed company is located in and the circle around it has a radius of 25 miles. Panel A gives the distribution of the venture capital-backed companies whose lead venture capital firm is distant (i.e., not within the 25 mile radius). Panel B gives the distribution of companies that have their lead venture capital firm close (i.e., within the 25 mile radius).

Panel A

Panel B

34

Figure 3 Distribution of venture-backed IPOs by year

This figure shows the time series distribution of 915 venture-backed initial public offerings in our sample from 1983-2004. The figure shows the distribution of IPOs for the whole sample and for those companies that are within 25 mile proximity to their VC firms for each year.

020406080

100120140160180

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Year

Num

ber o

f IPO

s

Number of IPOs Number of IPOs within 25 mile prox.

35

Table 1 Distribution and characteristics of venture-backed IPOs

Panel A shows the geographic distribution of 915 venture-backed initial public offerings in our sample between 1983-2004 by state. The number of venture-backed IPOs within each state and the percentage of venture-backed IPOs within 25 mile proximity in each state are given. States in which there are less than 1% of the entire sample are not shown. Panel B shows the distribution of venture-backed IPOs across 2 digit SIC codes. The percentage of venture-backed IPOs within 25 mile proximity in each 2 digit SIC code are also given. 2 digit SIC codes in which there are less than 1% of the entire sample are not shown.

Panel A: Geographic distribution of venture-backed IPOs

Company state No. of venture-

backed IPOs

No. of venture-backed IPOs

within 25 mile prox. to VC firm

% of venture-backed IPOs

within 25 mile prox. to VC firm

NY 44 18 40.91 CA 324 124 38.27 NJ 24 9 37.50 MA 82 28 34.15 MN 17 5 29.41 OR 12 3 25.00 WA 30 7 23.33 TN 16 3 18.75 IL 22 4 18.18 MD 17 3 17.65 VA 20 3 15.00 NC 15 2 13.33 GA 23 3 13.04 CO 25 3 12.00 PA 28 3 10.71 TX 59 6 10.17 OH 16 1 6.25 CT 22 1 4.55 All other states 119 3 2.52

36

Panel B: Industry distribution of venture-backed IPOs

2-digit SIC code

Number of venture-

backed IPOs % of venture-backed IPOs

Number of venture-

backed IPOs within 25 mile

proximity

% of venture-backed IPOs

within 25 mile proximity

36 (electronic equipment) 106 11.58 35 33.02 73 (business services) 308 33.66 99 32.14 35 (industrial machinery) 67 7.32 19 28.36 38 (instruments) 73 7.98 17 23.29 28 (chemicals) 61 6.67 14 22.95 48 (communications) 57 6.23 13 22.81 87 (engineering services) 27 2.95 6 22.22 20 (food) 10 1.09 2 20.00 80 (health services) 29 3.17 3 10.34 27 (publishing) 10 1.09 1 10.00 59 (retail) 31 3.39 3 9.68 All other industries 136 14.86 17 12.5

37

Table 2 Summary statistics for control variables

Panel A presents the summary statistics of our control variables for the whole sample. Panel B presents descriptive statistics for the initial return and price revision for sub periods. Data consist of 915 venture-backed initial public offerings from 1983 to 2004. Excluded from the full sample are IPOs identified as unit offerings, offerings not involving common stock, offerings that are in the fields of finance, insurance, and real estate, and offerings having gross proceeds below $20 million. Descriptive statistics are reported for distance, initial return, price revision, IPO filing range, standard deviation of returns for the days between 10 and 180 after IPO, CRSP equal weighted index return during the file date and offer date of IPO, young VC firm dummy representing venture capital firms less than 6 years old, top underwriter dummy, internet company dummy, and offer size as the dollar value of proceeds amount (SDC) adjusted for inflation. Panel A: Descriptive statistics for the whole sample

Variables 25 mile

proximity Mean Median Std. dev. 5th 95th N

Distance (miles) Distant 1050.51 766.56 889.67 36.59 2574.14 686 Close 10.60 10.17 6.21 0.54 22.77 229 Total 789.11 391.30 892.22 4.83 2556.70 915

Initial return % Distant 31.60 12.50 53.97 -4.55 153.75 686 Close 44.65 20.00 66.66 -5.00 201.79 229 Total 34.87 14.71 57.65 -4.55 179.17 915

Price revision % Distant 5.77 4.62 23.46 -27.78 45.45 686 Close 13.65 8.33 28.42 -26.92 77.78 229 Total 7.74 6.25 25.01 -27.27 54.55 915

Offer size Distant 35.08 21.86 44.76 11.04 105.78 686 Close 28.91 19.47 43.49 10.97 56.43 229 Total 33.53 21.07 44.51 11.01 86.36 915

Std. dev. of returns Distant 5.04 4.44 2.30 2.23 9.68 686 Close 5.76 5.13 2.29 2.69 10.33 229 Total 5.22 4.65 2.32 2.35 9.84 915

Market return % Distant 3.74 3.37 6.38 -5.10 14.48 686 Close 3.74 3.02 6.61 -6.10 14.52 229 Total 3.74 3.16 6.43 -5.25 14.48 915

IPO filing range % Distant 15.43 15.38 4.20 9.52 22.22 686 Close 16.23 16.67 4.35 9.52 22.22 229 Total 15.63 15.38 4.25 9.52 22.22 915

Top underwriter dummy Distant 0.77 1 0.42 0 1 686 Close 0.76 1 0.43 0 1 229 Total 0.77 1 0.42 0 1 915

Young VC firm dummy Distant 0.23 0 0.42 0 1 686 Close 0.22 0 0.41 0 1 229 Total 0.23 0 0.42 0 1 915

Internet company dummy Distant 0.27 0 0.45 0 1 686 Close 0.34 0 0.48 0 1 229 Total 0.29 0 0.45 0 1 915

38

Panel B: Descriptive statistics for sub periods

Variables Sample 25 mile

proximity Mean Median Std. dev. 5th 95th N

Initial return % 1999-2000 Distant 72.97 39.58 83.63 -8.33 243.33 181 Close 94.06 68.24 93.67 -10.58 282.81 74 Total 79.09 47.81 87.01 -8.65 282.81 255 Initial return % Excluding

1999-2000 Distant 16.78 9.56 25.04 -2.88 58.33 505

Close 21.06 13.28 26.20 -4.17 75.00 155 Total 17.78 10.36 25.36 -3.53 65.59 660 Price revision % 1999-2000 Distant 16.82 11.11 32.77 -33.33 81.82 181 Close 25.25 20.20 36.40 -33.33 100.00 74 Total 19.27 14.29 34.01 -33.33 83.33 255 Price revision % Excluding

1999-2000 Distant 1.81 0.00 17.46 -26.67 30.00 505

Close 8.11 7.69 21.73 -25.00 46.67 155 Total 3.29 3.39 18.73 -26.67 33.33 660

39

Table 3 Comparative summary statistics

Data consist of 915 venture-backed initial public offerings from 1983 to 2004. Excluded from the full sample are IPOs identified as unit offerings, offerings not involving common stock, offerings that are in the fields of finance, insurance, and real estate, and offerings having gross proceeds below $20 million. All variables have been winsorized at the 1% and 99% levels. Panel A presents the summary statistics of the variables used in the treatment model. Descriptive statistics are reported for the dollar value of total assets (Compustat item # 6) adjusted for inflation, tangibility computed as net PPE (Compustat item #7) / total assets (Compustat item #6)*100, total investments ($ million) made by venture capital firm prior to the first year of investment in company, age of the company at IPO calculated by subtracting the IPO year from the founding year of the company and industry cluster location dummy which takes value of one if there are at least 3 other IPOs with the same 2 digit SIC industry code within 25 mile radius of the company in our sample. Panel B presents descriptive statistics for initial return and price revision for companies backed by young versus experienced VC firms in the sample. Young VC firm represents venture capital firms less than or equal to 6 years old. Experienced VC firm represents venture capital firms greater than 6 years old. Panel A: Summary statistics by proximity

Variables 25 mile

proximity Mean Median Std. dev. 5th 95th N

p-value for differences in means

Total assets Distant 249.27 81.29 572.08 26.34 933.46 686 0.00 Close 129.40 64.84 224.99 20.15 468.52 229 Total 219.27 77.24 510.49 23.72 829.79 915

Total prior VC firm investments

Distant 3695.57 885.11 5660.36 2.74 18086.88 686 0.09 Close 2996.10 704.67 4954.46 5.50 18031.16 229 Total 3520.51 834.68 5497.94 3.50 18086.88 915

Age of company at IPO

Distant 12.88 7 16.87 2 55 686 0.00 Close 7.98 6 8.11 2 19 229 Total 11.65 6 15.31 2 49 915

Cluster location dummy

Distant 0.56 1 0.50 0 1 686 0.00 Close 0.82 1 0.38 0 1 229 Total 0.62 1 0.48 0 1 915

Tangibility

Distant 26.94 16.92 24.89 3.36 83.63 686 0.00 Close 17.17 11.27 15.87 3.16 48.56 229 Total 24.49 15.57 23.35 3.22 76.93 915

Panel B: Summary statistics by venture capital firm experience

Variables Firm experience Mean Median

Std. dev. 5th 95th N

p-value for differences in means

Initial return % Experienced 33.21 13.73 56.76 -4.17 164.92 461 0.00 Young 58.01 25.00 76.58 -4.17 225.89 88 Total 37.18 14.71 60.97 -4.17 192.86 549

Price revision % Experienced 7.97 6.67 24.00 -25.93 50.00 461 0.25 Young 11.28 7.50 28.38 -30.30 75.00 88 Total 8.50 6.67 24.75 -27.27 53.85 549

40

Table 4 Determinants of the age of venture-backed companies at their IPO

Data consist of 915 venture-backed initial public offerings from 1983 to 2004. Excluded from the full sample are IPOs identified as unit offerings, offerings not involving common stock, offerings that are in the fields of finance, insurance and real estate and offerings having gross proceeds below $20 million. All variables have been winsorized at the 1% and 99% levels. The dependent variable in regressions is age of the company at IPO calculated by subtracting the IPO year from the founding year of the company. The independent variables are: an intercept, industry cluster location dummy which takes value of one if there are at least 3 previous IPOs with the same 2 digit SIC code within 25 mile radius of the company, offer size as the natural logarithm of global proceeds amount adjusted for inflation, top underwriter dummy that represents underwriters with ratings greater than 8 in the Carter and Manaster ranking. Technology and internet company dummies are from Jay Ritter’s website. 25 mile proximity is a dummy variable indicating that the venture-backed company is within 25 mile proximity of the venture capital firm. Young VC firm dummy represents venture capital firms less than 6 years old. All estimations include industry effects (2 digit SIC dummies), year effects, and state effects. We report p-values in parentheses below the parameter estimates; p-values are calculated using White’s (1980) heteroskedasticity consistent standard errors.

Age of company Age of company Age of company 25 mile proximity -1.623 -1.568 (0.046)** (0.054)* Young VC firm dummy -1.494 -1.43 (0.107) (0.122) Ln (Offer size) 3.228 3.287 3.25 (0.007)*** (0.006)*** (0.007)*** Number of rounds comp. received -0.47 -0.45 -0.456 (0.002)*** (0.002)*** (0.002)*** Top underwriter dummy -0.999 -0.954 -1.01 (0.365) (0.388) (0.361) Internet company dummy 0.087 0.05 -0.043 (0.947) (0.969) (0.973) Tech company dummy -5.451 -5.508 -5.365 (0.001)*** (0.001)*** (0.001)*** Cluster location dummy 0.583 0.432 0.572 (0.684) (0.765) (0.689) Year, state, 2 digit SIC dummies Yes Yes Yes Observations 915 915 915 R-squared 0.456 0.454 0.455

41

Table 5 Probit regression for close proximity (dependent variable=1 if the company is within 25 mile

proximity of the VC firm) Data consist of 915 venture-backed initial public offerings from 1983 to 2004. Excluded from the full sample are IPOs identified as unit offerings, offerings not involving common stock, offerings that are in the fields of finance, insurance, and real estate, and offerings having gross proceeds below $20 million. All variables have been winsorized at the 1% and 99% levels. The dependent variable is 25 mile proximity dummy indicating that the venture-backed company is within the 25 mile proximity of the VC firm. The independent variables are: an intercept, the natural logarithm of the total assets (Compustat item#6) adjusted for inflation, total investments ($ billion) made by VC firm prior to the first year of investment in company (SDC), age of the company at IPO calculated by subtracting the IPO year from the founding year of the company, tangibility defined as net PPE (Compustat item #7) / total assets (Compustat item #6)*100 and industry cluster location dummy which takes value of one if there are at least 3 other IPOs with the same 2 digit SIC code within 25 mile radius of the company. We report p-values in parentheses below the parameter estimates; p-values are calculated using White’s (1980) heteroskedasticity consistent standard errors.

25 mile

proximity Ln (Total assets) -0.117 (0.020)** Total prior VC firm investments -0.017 (0.061)* Age of company at IPO -0.009 (0.029)** Cluster location dummy 0.604 (0.000)*** Tangible ratio -0.008 (0.002)*** Constant Yes Observations 915 Pseudo R2 0.0869

42

Table 6 Regression results for the age of the venture-backed company

Data consist of 911 venture-backed initial public offerings from 1983 to 2004. Excluded from the full sample are IPOs identified as unit offerings, offerings not involving common stock, offerings that are in the fields of finance, insurance, and real estate, and offerings having gross proceeds below $20 million. All variables have been winsorized at the 1% and 99% levels. The dependent variable is the natural logarithm of the total assets (Compustat item#6) adjusted for inflation. The independent variables are: an intercept, total investments ($ billion) made by VC firm prior to the first year of investment in company (SDC), inexperienced VC firm dummy indicating a VC firm that has less than $100 million in total investments prior to the first year of investment in company (SDC), age of the company at IPO calculated by subtracting the IPO year from the founding year of the company, 25 mile proximity dummy indicating that the venture-backed company is within the 25 mile proximity of the VC firm, tangibility defined as net PPE (Compustat item #7) / total assets (Compustat item #6)*100 and industry cluster location dummy which takes value of one if there are at least 3 other IPOs with the same 2 digit SIC code within 25 mile radius of the company. We report p-values in parentheses below the parameter estimates; p-values are calculated using White’s (1980) heteroskedasticity consistent standard errors.

Ln (Total assets) Ln (Total assets) Ln (Distance) 0.040 0.013

(0.021)** (0.430) Total prior VC firm investments 0.029 0.033

(0.000)*** (0.000)*** 25 mile proximity*Inexperienced VC firm -0.284 -0.226

(0.028)** (0.090)* Age of company at IPO 0.017

(0.000)*** Cluster location dummy -0.170

(0.024)** Tangible ratio 0.006

(0.000)*** Constant Yes Yes Observations 911 911 R-squared 0.034 0.147

43

Table 7 Regression results for the effect of proximity on initial returns

Data consist of 915 venture-backed initial public offerings from 1983 to 2004. Excluded from the full sample are IPOs identified as unit offerings, offerings not involving common stock, offerings that are in the fields of finance, insurance and real estate and offerings having gross proceeds below $20 million. All variables have been winsorized at the 1% and 99% levels. The dependent variable in all regressions is initial return. The independent variables are: an intercept, price revision, IPO filing range, standard deviation of returns for the days between 10 and 180 after IPO, CRSP equal weighted index return during the file date and offer date of IPO, IPO volume as the number of IPOs in a given year, offer size as the natural logarithm of global proceeds amount adjusted for inflation, age of the company at IPO variable, young VC firm dummy representing VC firms less than 6 years old, top underwriter dummy that represents underwriters with ratings greater than 8 in the Carter and Manaster ranking, and internet company dummy from Jay Ritter’s website. 25 mile proximity is a dummy variable indicating that the venture-backed company is within 25 mile proximity of the VC firm. All estimations include industry effects (2 digit SIC dummies), year effects, and state effects. We report p-values in parentheses below the parameter estimates; p-values are calculated using White’s (1980) heteroskedasticity consistent standard errors. For the average treatment effect (ATE) regressions we report rho, sigma and lambda. Rho is the correlation between the error terms of the two equations, sigma is the standard error of the outcome regression and lambda equals rho multiplied by sigma. Column (1) presents the result from an OLS regression and columns (2), (3), and (4) present the results for the structural equation which is jointly estimated together with the treatment equation with full information maximum likelihood method.

44

Number of obs. within proximity

Initial Return %

(1)

Initial Return %

(2)

Initial Return %

(3)

Initial Return %

(4) OLS ATE ATE ATE Proximity dummies 25 mile proximity 229 -3.609 -12.217 (0.280) (0.053)* 50 mile proximity 279 -15.469 (0.053)* 100 mile proximity 312 -16.728 (0.054)* Price revision % 1.297 1.299 1.296 1.295 (0.000)*** (0.000)*** (0.000)*** (0.000)*** Ln (Offer size) -2.656 -2.956 -2.949 -2.978 (0.343) (0.264) (0.265) (0.263) Age of company 0.039 0.019 0.011 0.013 (0.733) (0.799) (0.89) (0.179) Young VC firm dummy 9.626 9.825 9.959 10.003 (0.006)*** (0.008)*** (0.007)*** (0.006)*** Internet company dummy 11.805 11.685 11.75 11.686 (0.014)** (0.017)** (0.017)** (0.017)** IPO volume -0.039 -0.04 -0.04 -0.04 (0.473) (0.125) (0.128) (0.117) IPO filing range % -0.954 -0.942 -0.945 -0.942 (0.007)*** (0.006)*** (0.006)*** (0.006)*** Sd. of ret. (+10 to +180) 2.122 2.117 2.11 2.102 (0.039)** (0.046)** (0.047)** (0.048)** Market return % -0.617 -0.622 -0.616 -0.616 (0.010)** (0.006)*** (0.007)*** (0.007)*** Top underwriter dummy -6.099 -6.093 -6.042 -6.026 (0.073)* (0.062)* (0.064)* (0.065)* Year, state, 2 digit SIC dummies Yes Yes Yes Yes Observations 915 915 915 915 R-squared 0.603 Rho 0.14 0.22 0.25 Sigma 36.49 36.82 37.01 Lambda 5.26 8.21 9.41 Test of independent equations p>chi2 (0.050)** (0.039)** (0.046)**

45

Table 8 Regression results for the effect of proximity on initial returns for sub-samples

Data consist of 915 venture-backed initial public offerings from 1983 to 2004. Excluded from the full sample are IPOs identified as unit offerings, offerings not involving common stock, offerings that are in the fields of finance, insurance and real estate and offerings having gross proceeds below $20 million. All variables have been winsorized at the 1% and 99% levels. The dependent variable in all regressions is initial return. The independent variables are: an intercept, price revision, IPO filing range, standard deviation of returns for the days between 10 and 180 after IPO, CRSP equal weighted index return during the file date and offer date of IPO, IPO volume as the number of IPOs in a given year, offer size as the natural logarithm of global proceeds amount adjusted for inflation, age of the company at IPO variable, young VC firm dummy representing VC firms less than 6 years old, top underwriter dummy that represents underwriters with ratings greater than 8 in the Carter and Manaster ranking, and internet company dummy from Jay Ritter’s website. 25 mile proximity is a dummy variable indicating that the venture-backed company is within 25 mile proximity of the VC firm. All estimations include industry effects (2 digit SIC dummies), year effects, and state effects. We report p-values in parentheses below the parameter estimates; p-values are calculated using White’s (1980) heteroskedasticity consistent standard errors. Column (1) presents the result from an ATE regression in the sub-sample where age of the venture-backed companies at the time of IPO is less than or equal to 6 years and column (2) presents the result from an ATE regression in the sub-sample where age of the venture-backed companies at the time of IPO is greater than 6 years.

Initial return % Age of company ≤ 6

(1)

Initial return % Age of company > 6

(2) ATE ATE 25 mile proximity -28.399 -6.282 (0.062)* (0.274) Price revision % 1.374 1.185 (0.000)*** (0.000)*** Ln (Offer size) -5.186 -0.029 (0.307) (0.992) Age of company -1.12 -0.035 (0.476) (0.728) Young VC firm dummy 8.701 11.082 (0.13) (0.046)** Internet company dummy 5.674 18.806 (0.406) (0.020)** IPO volume -0.117 -0.047 (0.122) (0.111) IPO filing range % -1.838 -0.199 (0.002)*** (0.615) Sd. of ret. (+10 to +180) 3.001 1.391 (0.089)* (0.283) Market return % -0.72 -0.456 (0.060)* (0.185) Top underwriter dummy -9.452 -3.337 (0.068)* (0.421) Year, state, 2 digit SIC dummies Yes Yes Observations 467 448