The Effect of Market Structure on Specialization

Decisions in Venture Capital

Yael Hochberg∗, Michael J. Mazzeo†, and Ryan C. McDevitt‡

Draft of June 10, 2008

INITIAL NOTES–PRELIMINARY AND INCOMPLETE

Abstract

In making its investment decisions, a venture capital firm must consider whether to be ageneralist or a sector specialist. While spreading investments across sectors has benefits – suchas diversifying the firm’s holdings and opening up a larger pool of investment opportunities– it reduces the benefits of specialization, namely that a specialist has greater access to itssector’s most promising ventures, either because it can more accurately assess a project’s valueor because, in this two-sided market, the venture is more likely to accept an investment from aVC with specialized knowledge of its sector. In addition, competition from other VC firms willaffect a firm’s specialization choice in the sense that its relative standing among competitors willinfluence a startup’s decision to accept its investment. We find empirical evidence suggestingthat firms alter their specialization decisions in response to competitors and market structureusing a panel dataset of VC investments from 1975 - 2003.

∗Kellogg School of Management, Northwestern University, y-hochberg@northwestern.edu†Kellogg School of Management, Northwestern University, mazzeo@northwestern.edu‡Department of Economics, Northwestern University, r-mcdevitt@northwestern.edu

I. Introduction

Over the past decade, the empirical industrial organization literature has extensively examined

the tradeoffs firms face when making endogenous product choice decisions in competitive mar-

kets. As outlined in Mazzeo (2002), this literature has addressed a classic challenge facing all

firms: the need to differentiate themselves from competitors to profitably capture market share,

while operating in a space that holds profitable opportunities – and hence, will attract more

competitors, thus eroding margins. In equilibrium, we observe that differentiation is a profitable

strategy for firms, but that popular product space locations are also very attractive.

In this paper, we examine the product type decision in the context of the venture capital

(VC) industry. In venture capital, the “product type” decision of an investment firm can be

characterized as the accumulated experience of its investors. As such, venture capitalists en-

dogenously select their “types” over time based on their stock of previous investments and their

new investment decisions. In particular, a VC firm faces an interesting tradeoff when deciding

whether to specialize in a particular sector or remain a generalist – that is, making investments

across several sectors. A firm might specialize – defined as making the majority of its invest-

ments in one sector, such as software – for a number of reasons: For example, its principals

might hold sector-specific expertise that affords them advantages when selecting or managing

ventures, or the sector simply has many profitable ventures in the firm’s location, to name just

a few.

In the latter case, however, an abundance of profitable opportunities in a particular sector

will no doubt attract several competing venture funds to the area, resulting in higher bids or

valuations for ventures, which will necessarily reduce each fund’s ability to capture profits. In

this situation, a firm might find investing in less-crowded sectors preferable, as less competition

will make its ventures relatively more attractive despite its relative lack of sector expertise, all

else equal. Or, in contrast, a firm might continue to specialize in the crowded sector, making itself

more attractive to ventures that seek funding from firms with the most extensive experience in

their sectors (though at the expense of forgoing the opportunity to invest in promising ventures in

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other sectors).1 As investment competition increases, this tradeoff for investors can potentially

intensify.

To guide intuition, consider a firm that monopolizes a VC market. This firm will want to

be a generalist since it can then select the most attractive opportunities irrespective of sector.

In response to competition, however, it might want to specialize if ventures are more likely to

accept funds from firms that specialize in their sector. Thus, a firm will give up some flexibility

in terms of sector choice to reap the gains from specialization, with the gains being that a firm

will have access to the most attractive ventures in its sector of specialization.

Recent work has shown that a firm’s degree of specialization has a significant economic and

statistical effect on its profitability. Gompers, Kovner, Lerner & Scharfstein (2005) find that, on

average, more specialized venture capital firms achieve greater returns on their investments. In

their analysis, they treat a firm’s degree of specialization as an exogenous characteristic, while

noting, however, that the choice of specialization in actuality is likely endogenous. Here we

consider a complementary topic: what influences a venture capital firm’s decision to specialize?

Recent work has shown that market structure can significantly impact endogenous decisions

in venture capital. For instance, Hochberg, Ljungqvist & Lu (2008) have shown that highly

networked incumbents in a market lead to lower rates of entry in that market. Here, we focus on

the effect of market structure (in the form of competition) on the endogenous choice to specialize

investment activity. We present reduced-form evidence that firms respond to competition by

altering their specialization decisions.

Based on a sample of all US VC investment data from 1975-2003, we investigate sector

specialization at the geographic market level as well as for individual VCs, focusing on how

specialization is related to various measures of market competition. Increased competition tends

to correlate with more specialization in markets overall. However, the specialization choice varies

widely with individual firm-level characteristics, in particular, incumbency and experience.

The need to establish credibility and access to information in a new market puts entrants

1In addition, this decision will have dynamic effects for the firm, since a firm’s degree of specialization in thecurrent period will influence its access to promising investments in future periods, both because ventures prefer fundsthat have expertise in their sector and because a venture capitalist’s network affects her access to deals, and thusa more specialized venture capitalist will potentially have a more specialized network (Hochberg, Ljungqvist & Lu(2007)). The dynamic aspects of this decision remain to be explored in future work.

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at an obvious disadvantage to incumbents, who are already established and networked within

a local market. One way of gaining credibility with local entrepreneurs may be to offer a more

specialized investment product (which presumably allows the VC firm to offer better value-

added services), as a way to distinguish themselves from already established players. Consistent

with this notion, we find that new entrants into the VC industry or into a given geography tend

to make investments that increase their specialization when entering a competitive market. In

contrast, incumbent VC firms appear to respond to competition by diversifying their sector

portfolio, thus decreasing their overall level of specialization. This finding is consistent with the

notion that once established in the market, local VC firms enjoy the advantage of visibility and

credibility, and thus can respond to increased specialized competition by new entrants by seeking

good local investment opportunities in other sectors. Similarly, we find that less experienced

VCs tend to be more specialized in the face of increased competition, while more experienced

VCs respond to increased competition by diversifying into new sectors and becoming more

generalist.

Our findings provide a number of contributions to the academic literature. First, our study

contributes to the literature exploring VC investment decisions. While there is a large literature

exploring the relationship between VCs and their portfolio companies (see e.g. Gorman &

Sahlman (1989), Sahlman (1990), Gompers & Lerner (1999), Hellmann & Puri (2002), Kaplan

& Stromberg (2004) and Hochberg (2006)), much less is known about VC decisions to invest in

particular companies, or even in particular industries. A notable exception is recent work by

Sorensen (2008), who explores the role of VC learning in resolving uncertainties about investment

opportunities. Our work adds nuance to the findings in Sorensen (2008), and expands the body of

knowledge regarding VC selection of investment opportunities. Second, our work contributes to

the emerging literature studying the industrial organization of the VC market (see e.g. Sorenson

& Stuart (2001), Hochberg et al. (2007) and Hochberg et al. (2008)). Our study complements the

evidence in Gompers et al. (2005), who show evidence of a strong positive relationship between

specialization and success. In contrast to Gompers et al. (2005), who take the VC firm’s level

of specialization as an exogenous decision, our work explores how the competitive structure of

the market influences VC specialization decisions. Finally, our study contributes to the large

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literature on product differentiation in the industrial organization literature.

The remainder of this paper is structured as follows. Section II. describes our dataset.

Section III. presents our analysis of market-level specialization. Section IV. presents our analysis

of firm-level specialization decisions. Section V. concludes.

II. Data

The data for our analysis come from Thomson Financials Venture Economics database. Ven-

ture Economics began compiling data on venture capital investments in 1977, and has since

backfilled the data to the early 1960s. Gompers & Lerner (1999) investigate the completeness

of the Venture Economics database and conclude that it covers more than 90% of all venture

investments.

Most VC funds are structured as closed-end, often ten-year, limited partnerships. They are

not usually traded, nor do they disclose fund valuations. The typical fund spends its first three

or so years selecting companies to invest in, and then nurtures them over the next few years

(Ljungqvist & Richardson (2003)). In the second half of the fund’s life, successful portfolio

companies are exited via IPOs or sales to other companies generating capital inflows that are

distributed to the funds investors. At the end of the fund’s life, any remaining portfolio holdings

are sold or liquidated and the proceeds distributed to investors.

We concentrate solely on investment actvity by U.S. based VC funds, and exclude invest-

ments by angels and buyout funds. We distinguish between funds and firms. While VC funds

have a limited (usually ten-year) life, the VC management firms that manage the funds have

no predetermined lifespan. Success in a first-time fund often enables the VC firm to raise a

follow-on fund (Kaplan & Schoar (2005)), resulting in a sequence of funds raised a few years

apart. Note that the experience, contacts and human capital acquired in the running of one

fund carry over to the firm’s next fund. In addition, startups seeking capital generally seek this

capital from a VC firm, rather than a particular fund within that firm. We therefore measure

specialization at the parent firm rather than the fund level.

Our final estimation dataset contains 3,469 VC funds managed by 1,974 VC firms which

participate in 47,705 investment rounds involving 16,315 portfolio companies.

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A. Data Construction and Variables

A.1. Markets

We define markets at the MSA level. VE provides data on the zip code of the startup in which

the investment is made. We then match the zip code of the investment to the MSA. Investments

made in non-MSA areas are not included in the analysis.

A.2. Firm-Level Investments

We consider both the total number of investments a firm makes in a five-year period in a market,

as well as the dollar amount invested over this period.

A.3. VC Firm Experience

We calculate a venture firm’s experience as the number of years since its first investment in a

market. Under this construction, the same venture firm can potentially have several different

levels of experience across the markets in which it invests.

A.4. Competition

We provide two measures of competition in our analysis: the number of venture capital firms

investing in a market over a five-year rolling period and the aggregate dollar amount those firms

invested in a market over a rolling five-year period. Thus, if a firm invests in a venture in year

t, that firm is considered to be a competitor in the market until year t + 5, even if it does not

make another investment in that market in the subsequent four years.

A.5. Sectors

We define a venture investment’s sector as one of six categories, using the Venture Economics

(VE) industry categorization for the investment. The six VE industry categories are: “Biotech-

nology,” “Communications/media,” “Computer related,” “Medical, health, life sciences,” “Non-

high-technology,” and “Semiconductors, other electronics.”

A.6. Specialization Index

Following the literature (i.e., Gompers et al. (2005)), we compute a firm’s specialization index

as a Herfindahl-Hirschman Index such that SIjmt = 10, 000 ∗∑6

s=1 I2jsmt

(∑6

s=1 Ijsmt)2in market m in year

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t. Here, Ijsmt represents the dollar amount of investments made by firm j in sector s in market

m over the five-year period prior to and including year t. Thus, a firm that makes investments

exclusively in one sector will have SIjmt = 10, 000, while a firm that spreads its investments

evenly across all six sectors will have SIjmt = 1, 667.

A.7. Dominant Sectors

We classify a firm as having a dominant sector if it makes more than 75 (90) percent of its

investments (by $ volume) in one sector over the previous five-year period. Firms that do not

have a dominant sector (i.e., no one sector constitutes over 75 (90) percent of its investments in

a market) are considered generalists. At the market level, we consider a sector to be dominant if

it is the dominant sector for the greatest number of firms in a market that year. At the market

level, “generalist” could be the dominant sector, meaning that more firms in the market are

generalists than are specialized in any one sector.

A.8. MSA Sector Concentration

We calculate a market-level measure of the concentration of investments across sectors in a

market over a five-year rolling period. We compute this measure as Herfindahl-Hirschman Index

such that a market with J firms has a concentration level of SImt = 10, 000 ∗∑6

s=1(∑J

j=1 Ijsmt)2

(∑J

j=1∑6

s=1 Ijsmt)2

in year t, where Ijsmt represents the dollar amount of investments made by firm j in sector

s in market m over the five-year period prior to and including year t. Thus, a market that

has investments exclusively in one sector will have SImt = 10, 000, while a market that has

investments spread evenly across all six sectors will have SImt = 1, 667.

A.9. MSA Firm Concentration

We additionally calculate a market-level measure of the concentration of investments across

firms in a market over a five-year rolling period. We compute this measure as the Herfindahl-

Hirschman Index such that a market with J firms has a firm-concentration level of FCmt =

10, 000 ∗∑J

j=1 I2jmt

(∑J

j=1 Ijmt)2in year t, where Ijmt represents the dollar amount of investments made

by firm j in market m over the five-year period prior to and including year t. Thus, a market

that has investments made exclusively by one firm will have FCmt = 10, 000, while FCmt limits

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to 0 as the number of firms investing in a market becomes arbitrarily large.

A.10. VC Industry Inflows

Gompers & Lerner (2000) show that the prices VCs pay when investing in portfolio companies

increase as more money flows into the VC industry, holding investment opportunities constant.

They interpret this pattern as evidence that competition for scarce investment opportunities

drives up valuations. If so, it also seems plausible that competition for deal flow will affect VCs’

choices of which sectors to invest in. We therefore include in our models the aggregate VC fund

inflows in the year of observation, as calculated from the VE database.

Additionally, we compute a firm-weighted measure of capital availability (inflows) in the

VC industry by weighting sector-level inflows proportionally by the dollar amounts actually

invested by a firm across sectors over the prior five years in a market. That is, INweighted =∑6s=1 INst∗Ijsmt

(∑6

s=1 Ijsmt).

A.11. VC Entrants

We define a VC firm as a new entrant into a market when the firm appears for the first time

in that market. To allow for an investment history to calculate our specialization measure, we

wait until the entrant has accumulated five years of investment activity in the market before

calculating the sector HHI and including the firm as an entrant in our models.

We compute the total number of new entrants into the market as a market-level measure of

new entry. Additionally, we compute a firm-weighted measure of overall new entry into a market

by weighting entry proportionally at the sector level by the dollar amounts actually invested by

a firm across sectors over the prior five years in a market. That is, Eweighted =∑6

s=1 Est∗Ijsmt

(∑6

s=1 Ijsmt).

A.12. Investment Opportunities

To control for the investment opportunities open to a VC, we follow the intuition presented in

Gompers & Lerner (2000), who propose public-market pricing multiples as indirect measures of

the investment climate in the private markets. There is a long tradition in corporate finance,

based on Tobin (1969), that views low book-to-market (B/M) ratios in an industry as an indi-

cation of favorable investment opportunities. Price-earnings (P/E) ratios are also used for the

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same purpose. By definition, private companies lack market value data, so we must rely on

multiples from publicly traded companies. Following Hochberg et al. (2007), to allow for inter-

industry differences in investment opportunities, we map all COMPUSTAT companies into the

six broad Venture Economics industries. We begin with VC-backed companies that Venture

Economics identifies as having gone public, and for which therefore SIC codes are available. We

then identify which Venture Economics industry each available four-digit SIC code is linked to

most often. We compute the pricing multiple for each of the six Venture Economics industries

in year t as the value-weighted average multiple of all COMPUSTAT companies in the relevant

four-digit SIC industries.2

We then compute a firm-weighted measure of investment opportunities, weighted propor-

tionally by the dollar amounts actually invested by a firm across sectors over the prior five years

in a market. That is, PEweighted =∑6

s=1 PEst∗Ijsmt

(∑6

s=1 Ijsmt).

B. A Macro View of Specialization by Sector

Tables I and II provide a macro view of dominant sectors of specialization for VC firms on

an annual basis. The dominant sector of specialization for a VC firm in year t is determined

based on investments made over the previous five years. We present the data both by number

of firms per sector (Table I) and by percentage of firms (Table II). Both tables employ a cutoff

of 75% of investment volume to define a firm as a specialist in a given sector. From the tables,

it is clear that prevalence of a given sector as dominant for the population of VC firms varies

considerably over time. For example, 20.61% of firms specialize in computer related investments

in 1979 (the first year of data availability allowing five years of investment from 1975-1979 to

construct the specialization measure), during the beginning of the microprocessor boom. This

share rises over time, reaching 39.14% in 2003, at the end of our sample period. In contrast,

2We define a public companys P/E ratio as the ratio of stock price (COMPUSTAT data item #199) to earnings-per-share excluding extraordinary items (#58). We define the B/M ratio as the ratio of book equity to market equity,where book equity is defined as total assets (#6) minus liabilities (#181) minus preferred stock (#10, #56, or #130,in order of availability) plus deferred tax and investment tax credit (#35), and market equity is defined as stock price(#199) multiplied by shares outstanding (#25). To control for outliers, we follow standard convention and winsorizethe P/E and B/M ratios at the 5th and 95th percentiles for the universe of firms in COMPUSTAT in that year.(The results are robust to other winsorization cutoffs.) To calculate a value-weighted average, we consider as weightsboth the firms market value (market value of equity plus liabilities minus deferred tax and investment tax credit pluspreferred stock) and the dollar amount of investment in each four-digit SIC code each year (as calculated from theVenture Economics database).

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more cyclical growth is evident in the Biotech and Medical sectors. While only 1.53% of firms

specialize in Biotech in 1979, while 9.18% of firms specialize in Biotech in 1996, and 4.00%

specialize in Biotech in 2003. The percentage of firms specializing in the Medical and Health

sector similalry rise from a mere 1.53% in 1979 to a peak of 15.63% in 1996, falling to 7.77% in

2003. The Semi-Conductor sector shows similar cyclicality, though with lower volatility, while

the specialization in communications and media has generally increased over time, from a low

of 7.83% in 1980 to roughly 16% at the end of the sample. Over time, it is apparent that

specialization in technology and science sectors has become more prevalent–the share of firms

specializing in Non-high-technology investments has fallen steadily over time, from a high of

41.74% in 1980 to 6.44% by the end of the sample period. The relative share of generalists in

the population holds fairly steady over time, in the low twenty percent range.

Tables III and IV provide a similar macro view of specialization over time, employing a

90% investment volume cutoff as the criteria for being specialized, as compared to 75% in the

previous tables. Overall, the patterns of specialization over time for the various sectors are

similar to the previous table under this higher cutoff, though more variation is apparent over

time in the overall share of generalist VC firms.

Tables V and VI provide a breakdown of VC firm sectors of specialization by investment

volume over the five year preceding period. Table V employs an investment volume share cutoff

of 75% to determine specialization sector, while Table VI employs a cutoff of 90%. From both

tables, it appears that firms with greater investment volume (typically larger, more established

firms) are more likely to be generalist investors. At the 75% cutoff, only 3.79% of the smallest

quartile firms by investment volume are generalists, while 50.81% of the largest quartile of firms

by investment volume are generalists. At the 90% cutoff, the corresponding shares are 5.47%

for the smallest quartile of firms, and 64.53% for the largest quartile.

III. Market-Level Specialization

We begin our analysis by examining specialization at the market level. We are interested in

determining how competition affects the overall sector specialization of firms in a specific market.

We relate market-level specialization, as measured by MSA sector concentration (HHI index),

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to competition, as measured by the number of firms investing in the market over the preceding

five year period. The unit of observation is a market-year. We control for the size of the market,

defined as the total $ volume of VC investments in the market over the preceding five years. We

also further control for competition by including the number of new entrants into the market

over the preceding five years, MSA firm concentration, total $ inflows into the VC industry

over the preceding five years, and the total number of new entrants into the industry over the

preceding five years. We also include market and year fixed effects, to control for any unobserved

market-specific factors and influences on specialization related to the time period in question.

Summary statistics for our dependent and independent variables are presented in Table VII.

The results from our regression of market-level sector concentration are presented in Table

VIII. Most notably, competition in a market – here, measured by the number of firms investing

in the market over a five-year period – has a positive and statistically significant effect on sector

concentration in that market. For context, we note that a one-standard-deviation increase in

the number of firms investing in a market increases sector concentration by 433.9, or 7.4% in the

average market. In our specification, we have included market and year fixed effects to control

for unobservable market-level and time characteristics that might influence a firm’s decision to

specialize in a sector.

Our control variables also provide some insights into the effects of competition on specializa-

tion at the market level. While market size does not have an effect on market-level specialization,

higher firm concentration in the market is positively and significantly related to specialization,

as is the number of new VC entrants into the industry as a whole (though not into the specific

MSA in particular). In contrast, higher inflows of money into the VC industry as a whole leads

to a lower level of market specialization, perhaps as firms are forced to diversify portfolios in

order to find ways to deploy the funds.

IV. Firm-Level Specialization

To better understand how and why market-level specialization is positively related to compe-

tition, we turn to examining the specialization choices of individual firms given the level of

competition in the market.

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We relate firm-level specialization (firm-level, five-year sector concentration) to competition

(measured as the number of firms investing in the market over the preceding five year period)

controlling for a variety of factors that may be expected to influence firm-level specialization.

The unit of observation is a VC firm-year. As in the market-level models presented in Section

III., we control for the total dollar inflows into the overall VC industry as well as the total number

of new entrants into the VC industry over the preceding five years. We also control for both

MSA sector concentration (the overall level of specialization in this specific market) and MSA

firm concentration. In addition, we control for a number of VC firm-specific characteristics. We

include the total dollar investments made by the VC firm over the preceding five year period,

as well as the total number of investments made by the VC firm over that period. We also

include the firm-weighted measure of investment opportunities (PE ratios) facing the VC firm,

the firm weighted measure of sector inflows, and the firm weighted measure of new entrants.

We thus control for the specific investment opportunities and competition facing the VC firm

based on on its history of investing across sectors. Finally, we include an indicator variable if

the firm is a new entrant, as described in Section II., and interact this variable with both our

measure of competition and the square of this measure. Summary statistics for the dependent

and independent variables are presented in Table IX. We include market and year fixed effects

to control for unobservable market-level and time characteristics that might influence a firm’s

decision to specialize in a sector. The model is estimated via OLS with robust standard errors

clustered by MSA.

The estimates from our regression analysis are presented in Table X. Notably, being a new en-

trant to a market has a positive and significant effect on a firm’s sector concentration, increasing

a firm’s concentration by 204.4, or 2.4% on average. Additionally, the interaction term between

competition and new entry is positive and significant. Economically speaking, the magnitude is

relatively large, with a one-standard-deviation increase in the average number of competitors in

a new entrant’s market leading to a increase in firm-level sector concentration of 255.9, or 3%.

This contrasts with the negative and statistically insignificant coefficient on competition for all

firms generally, which suggests that a market’s structure – at least as it relates to competition –

has a stronger influence on new entrants than incumbents. Furthermore, a firm that makes more

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investments tends to be less specialized, with a one-standard-deviation increase in the average

number of firm investments associated with a decrease of 1,138.0 in concentration, or 13.5%.

Given the findings regarding new entrants in Table X, it is reasonable to expect that spe-

cialization decisions might vary more generally with the experience of the VC firm. Table XI

presents the estimates for a regression similar in spirit to that described for Table X. Once again,

the dependent variable is firm-level, five-year sector concentration, but instead of contrasting

new entrants and incumbents, we now use a firm’s experience as the noteworthy variable in

our estimation. As before, less experienced firms tend to be more concentrated than more ex-

perienced ones. In this case, a firm in existence for 20 years would be less concentrated by

462.5 relative to a new entrant, which is 5.5% of the average level of concentration. (Note

that this is not a mechanical relationship resulting from changes in investment patterns over

longer periods of time for more experienced firms, as the specialization measure for all firms

is constructed using only the preceding five years of investments). Moreover, the interaction

between firm experience and competition is negative and statistically significant (at the 10%

level), again suggesting that a market’s structure affects less-experienced firms differently than

more experienced firms. Other controls remain similar compared to the previous regression and

we again include market and year fixed effects to control for unobservable market-level and time

characteristics that might influence a firm’s decision to specialize in a sector.

In our final set of firm-level tests, we examine a firm’s decision to specialize in the market’s

dominant sector (as defined in Section II.. We denote a VC firm as a specialist in the market’s

dominant sector if 75% (90%)or more of its investments by $ volume over the preceding five years

are made in portfolio companies operating in the dominant sector of the market in question.

We begin by contrasting new entrants versus incumbent firms.

In Table XII, we present the marginal effects after a probit estimation of a firm’s decision

to concentrate in a market’s dominant sector for a 75% threshold, while Table XIII presents

the results from the same estimation, but with a 90% threshold. While the inferences resulting

from both specifications are broadly the same, the variable of greatest interest – new entry –

becomes larger and more significant at the higher threshold. In Table XII, we see that a new

entrant is 1.8% more likely to be in its market’s dominant sector at a 75% threshold than an

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incumbent, while at a 90% threshold a new entrant is 3.8% more likely. Thus, under the stricter

threshold, new entrants seem to be heavily influenced by their markets’ most populated sectors.

Again, we include market and year fixed effects to control for unobservable market-level and

time characteristics that might influence a firm’s decision to specialize in a sector.

Next, we again substitute the experience level of the VC firm, rather than partitioning

entrants and incumbents. In Table XIV, we present the marginal effects after a probit estimation

of a firm’s decision to concentrate in a market’s dominant sector for a 75% threshold, while Table

XV presents the results from the same estimation, but with a 90% threshold. As in the previous

probit estimations, the 90% threshold specification indicates that less-experienced firms are

much more likely than more experienced ones to specialize in their markets’ dominant sector.

In Table XV, we see that a VC firm is, roughly speaking, 6% more likely to be in its market’s

dominant sector at a 90% threshold than a VC firm with 20 more years experience. Thus, under

the stricter threshold, less experienced VC firms, once again, seem to be heavily influenced by

their markets’ most populated sectors. Again, we have included market and year fixed effects

to control for unobservable market-level and time characteristics that might influence a firm’s

decision to specialize in a sector.

We conduct a number of robustness checks. First, we rerun our models excluding the MSAs

for Silicon Valley and the Rt. 128 / Boston area. Our results are robust to focusing only on

the smaller VC markets. Second, we rerun our models using the $ volume of investments in

the market over the preceding five years as the measure of competition (rather than number of

firms). Our results remain robust in terms of magnitudes of effects and statistical significance.

Finally, we rerun our models excluding the internet bubble and aftermath (1997-2003). Once

again, our results remain robust to limiting the time period. Indeed, when we exclude the bubble

and its aftermath, we find that the effects of competition on specialization appear to be stronger,

especially for new entrants versus incumbents, both with regards to firm-level specialization and

with regards to the choice of specializing in the market’s dominant sector.

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V. Conclusion

In this paper, we examine the product choice decision of venture capital firms: whether to

specialize in a particular industry sector, or whether to remain a generalist investor, diversify-

ing portfolio company investments across a variety of industries. we investigate the extent to

which market competition affects the specialization decision. While specialization may afford a

VC firm advantages when selecting or managing ventures, competition for similar investment

opportunities within a market may make investing in less-crowded sectors preferable, as less

competition will make its ventures relatively more attractive despite its relative lack of sector

expertise, all else equal. Conversely, continuing to specialize in a crowded sector may make the

VC firm more attractive to ventures seeking funding from firms with extensive experience in

their industry.

Using a sample of US venture investments spanning 1975-2003, we investigate sector special-

ization at the geographic market level as well as for individual VCs, focusing on how special-

ization is related to various measures of market competition. Increased competition tends to

correlate with more specialization in markets overall, but the specialization choice varies widely

with individual firm characteristics, in particular incumbency and experience. New entrants

into the VC industry or into a given geographic tend to make investments that brand them as

specialists when entering a competitive market. In contrast, incumbents VC firm in a given

market appear to respond to competition by diversifying their sector portfolio, thus decreasing

their overall level of specialization. Similarly, we find that less experienced VCs respond to

competition by increasing their level of specialization, while more experienced VCs respond to

increased competition by becoming more generalist.

The decision to specialize will also have dynamic effects for the firm. A firm’s degree of

specialization in the current period will influence its access to promising investments in future

periods, both because ventures prefer funds that have expertise in their sector and because a

venture capitalist’s network affects her access to deals, and thus a more specialized venture cap-

italist will potentially have a more specialized network (Hochberg et al. (2007)). As investment

competition increases, this tradeoff for investors can potentially intensify. In future work, we

15

will build on structural models of product differentiation (e.g. Mazzeo (2002), Seim (2005),

Vitorino (2007), Zhu & Singh (2007) and Chernozhukov, Hong & Tamer (2007)) to explore

the dynamic consideration that a firm’s investment in a sector in one period might influence

its profitability in future periods should ventures, in part, base their funding decisions on the

cumulative investment experience of a VC in their sectors.

References

Chernozhukov, V., Hong, H. & Tamer, E. (2007), ‘Estimation and Confidence Regions for

Parameter Sets in Econometric Models’, Econometrica 75(2), 1243–1284.

Gompers, P., Kovner, A., Lerner, J. & Scharfstein, D. (2005), Specialization and Success: Evi-

dence from Venture Capital. Harvard University.

Gompers, P. & Lerner, J. (1999), The Venture Capital Cycle, MIT Press.

Gompers, P. & Lerner, J. (2000), ‘ Money chasing deals? The impact of fund inflows on private

equity valuations ’, Journal of Financial Economics 55, 281–325.

Gorman, M. & Sahlman, W. (1989), ‘What do venture capitalists do?’, Journal of Business

Venturing .

Hellmann, T. & Puri, M. (2002), ‘Venture capital and the professionalization of start-up firms:

Empirical evidence’, Journal of Finance 57, 169–197.

Hochberg, Y. (2006), ‘Venture capital and corporate governance in the newly public firm’,

working paper .

Hochberg, Y., Ljungqvist, A. & Lu, Y. (2007), ‘Venture Capital Networks and Investment

Performance’, Journal of Finance 62(1).

Hochberg, Y., Ljungqvist, A. & Lu, Y. (2008), Networking as a Barrier to Entry and the

Competitive Supply of Venture Capital. Northwestern University.

Kaplan, S. & Schoar, A. (2005), ‘Private equity returns: Persistence and capital flows’, Journal

of Finance .

Kaplan, S. & Stromberg, P. (2004), ‘Characteristics, contracts and actions: Evidence from

venture capital analyses’, Journal of Finance 59, 2177–2210.

Ljungqvist, A. & Richardson, M. (2003), ‘The investment behavior of private equity fund man-

agers’, working paper .

16

Mazzeo, M. (2002), ‘Product Choice and Oligopoly Market Structure’, RAND Journal of Eco-

nomics 33(2), 1–22.

Sahlman, W. (1990), ‘The structure and governance of venture capital organizations’, Journal

of Financial Economics 27, 473–521.

Seim, K. (2005), ‘An Empirical Model of Firm Entry with Endogenous Product-Type Choices’,

forthcoming, RAND Journal of Economics .

Sorensen, M. (2008), ‘Learning by Investing: Evidence from Venture Capital’, Working Paper .

Sorenson, O. & Stuart, T. (2001), ‘Syndication networks and the spatial distribution of venture

capital investments’, The American Journal of Sociology 106, 1546–1588.

Tobin, J. (1969), ‘A general equilibrium approach to monetary theory’, Journal of Money, Credit

and Banking 1, 15–19.

Vitorino, M. (2007), ‘Empirical Entry Games with Complementarities: An Application to the

Shopping Center Industry’, Working Paper .

Zhu, T. & Singh, V. (2007), ‘Spatial Competition with Endogenous Location Choices: An

Application to Discount Retailing’, Working Paper .

17

Yea

rG

ener

alis

tB

iote

ch.

Com

m./

Med

iaC

omp.

Rel

ated

Med

ical

Non

-Hig

hT

ech

Sem

icon

d.T

otal

1979

242

2627

245

513

119

8050

218

4013

9611

230

1981

758

2656

2012

06

311

1982

133

1430

117

2915

822

503

1983

210

2349

178

3721

430

741

1984

298

2467

254

4925

565

1012

1985

435

4712

842

578

352

9615

6119

8656

993

173

607

106

414

118

2080

1987

738

126

264

827

149

484

165

2753

1988

804

148

304

930

225

540

232

3183

1989

785

147

333

898

306

567

266

3302

1990

781

205

302

854

356

668

239

3405

1991

757

214

302

802

391

680

223

3369

1992

685

229

320

697

394

587

202

3114

1993

599

227

273

639

414

575

194

2921

1994

520

180

240

551

379

450

144

2464

1995

497

194

233

527

347

399

126

2323

1996

509

231

316

554

393

396

116

2515

1997

525

221

292

574

354

393

105

2464

1998

598

199

314

625

346

386

107

2575

1999

770

185

394

809

363

428

104

3053

2000

948

179

497

1139

365

447

8636

6120

0111

7820

362

113

1243

349

415

143

9220

0212

2921

669

616

4449

644

616

548

9220

0314

7825

210

0924

6348

940

519

762

93T

otal

1519

535

6972

2717

549

6534

9999

3175

6324

8

Tab

leI:

Dom

inan

tse

ctor

sfo

rfir

ms

byye

arat

the

75%

thre

shol

d.

18

Yea

rG

ener

alis

tB

iote

ch.

Com

m./

Med

iaC

omp.

Rel

ated

Med

ical

Non

-Hig

hT

ech

Sem

icon

d.T

otal

1979

18.3

2%1.

53%

19.8

5%20

.61%

1.53

%34

.35%

3.82

%10

0.00

%19

8021

.74%

0.87

%7.

83%

17.3

9%5.

65%

41.7

4%4.

78%

100.

00%

1981

24.1

2%2.

57%

8.36

%18

.01%

6.43

%38

.59%

1.93

%10

0.00

%19

8226

.44%

2.78

%5.

96%

23.2

6%5.

77%

31.4

1%4.

37%

100.

00%

1983

28.3

4%3.

10%

6.61

%24

.02%

4.99

%28

.88%

4.05

%10

0.00

%19

8429

.45%

2.37

%6.

62%

25.1

0%4.

84%

25.2

0%6.

42%

100.

00%

1985

27.8

7%3.

01%

8.20

%27

.23%

5.00

%22

.55%

6.15

%10

0.00

%19

8627

.36%

4.47

%8.

32%

29.1

8%5.

10%

19.9

0%5.

67%

100.

00%

1987

26.8

1%4.

58%

9.59

%30

.04%

5.41

%17

.58%

5.99

%10

0.00

%19

8825

.26%

4.65

%9.

55%

29.2

2%7.

07%

16.9

7%7.

29%

100.

00%

1989

23.7

7%4.

45%

10.0

8%27

.20%

9.27

%17

.17%

8.06

%10

0.00

%19

9022

.94%

6.02

%8.

87%

25.0

8%10

.46%

19.6

2%7.

02%

100.

00%

1991

22.4

7%6.

35%

8.96

%23

.81%

11.6

1%20

.18%

6.62

%10

0.00

%19

9222

.00%

7.35

%10

.28%

22.3

8%12

.65%

18.8

5%6.

49%

100.

00%

1993

20.5

1%7.

77%

9.35

%21

.88%

14.1

7%19

.69%

6.64

%10

0.00

%19

9421

.10%

7.31

%9.

74%

22.3

6%15

.38%

18.2

6%5.

84%

100.

00%

1995

21.3

9%8.

35%

10.0

3%22

.69%

14.9

4%17

.18%

5.42

%10

0.00

%19

9620

.24%

9.18

%12

.56%

22.0

3%15

.63%

15.7

5%4.

61%

100.

00%

1997

21.3

1%8.

97%

11.8

5%23

.30%

14.3

7%15

.95%

4.26

%10

0.00

%19

9823

.22%

7.73

%12

.19%

24.2

7%13

.44%

14.9

9%4.

16%

100.

00%

1999

25.2

2%6.

06%

12.9

1%26

.50%

11.8

9%14

.02%

3.41

%10

0.00

%20

0025

.89%

4.89

%13

.58%

31.1

1%9.

97%

12.2

1%2.

35%

100.

00%

2001

26.8

2%4.

62%

14.1

4%29

.87%

9.86

%11

.25%

3.44

%10

0.00

%20

0225

.12%

4.42

%14

.23%

33.6

1%10

.14%

9.12

%3.

37%

100.

00%

2003

23.4

9%4.

00%

16.0

3%39

.14%

7.77

%6.

44%

3.13

%10

0.00

%A

vera

ge24

.05%

5.10

%10

.63%

25.5

7%9.

33%

20.3

1%5.

01%

100.

00%

Tab

leII

:D

omin

ant

sect

ors

for

firm

sby

year

atth

e75

%th

resh

old,

perc

enta

geba

sis.

19

Yea

rG

ener

alis

tB

iote

ch.

Com

m./

Med

iaC

omp.

Rel

ated

Med

ical

Non

-Hig

hT

ech

Sem

icon

d.T

otal

1979

312

2425

242

513

119

8061

215

3812

9210

230

1981

957

2548

1911

25

311

1982

172

1328

104

2514

318

503

1983

272

2143

147

3119

829

741

1984

390

2359

211

4023

059

1012

1985

578

4211

135

868

320

8415

6119

8673

789

146

535

9737

898

2080

1987

937

120

244

740

135

433

144

2753

1988

1039

137

270

843

210

474

210

3183

1989

1033

132

304

818

279

495

241

3302

1990

1000

191

274

797

326

600

217

3405

1991

975

197

274

733

366

621

203

3369

1992

885

208

289

633

363

549

187

3114

1993

789

208

239

576

383

548

178

2921

1994

686

163

212

498

352

422

131

2464

1995

645

172

209

485

326

374

112

2323

1996

693

208

279

510

360

364

101

2515

1997

713

193

264

523

314

363

9424

6419

9878

917

928

356

230

435

999

2575

1999

1015

167

354

700

324

397

9630

5320

0012

9916

343

294

433

141

775

3661

2001

1567

181

539

1110

408

457

130

4392

2002

1672

198

598

1403

462

416

143

4892

2003

1978

228

891

2198

450

380

168

6293

Tot

al20

051

3244

6406

1553

959

8791

8428

3763

248

Tab

leII

I:D

omin

ant

sect

ors

for

firm

sby

year

atth

e90

%th

resh

old.

20

Yea

rG

ener

alis

tB

iote

ch.

Com

m./

Med

iaC

omp.

Rel

ated

Med

ical

Non

-Hig

hT

ech

Sem

icon

d.T

otal

1979

23.6

6%1.

53%

18.3

2%19

.08%

1.53

%32

.06%

3.82

%10

0.00

%19

8026

.52%

0.87

%6.

52%

16.5

2%5.

22%

40.0

0%4.

35%

100.

00%

1981

30.5

5%2.

25%

8.04

%15

.43%

6.11

%36

.01%

1.61

%10

0.00

%19

8234

.19%

2.58

%5.

57%

20.6

8%4.

97%

28.4

3%3.

58%

100.

00%

1983

36.7

1%2.

83%

5.80

%19

.84%

4.18

%26

.72%

3.91

%10

0.00

%19

8438

.54%

2.27

%5.

83%

20.8

5%3.

95%

22.7

3%5.

83%

100.

00%

1985

37.0

3%2.

69%

7.11

%22

.93%

4.36

%20

.50%

5.38

%10

0.00

%19

8635

.43%

4.28

%7.

02%

25.7

2%4.

66%

18.1

7%4.

71%

100.

00%

1987

34.0

4%4.

36%

8.86

%26

.88%

4.90

%15

.73%

5.23

%10

0.00

%19

8832

.64%

4.30

%8.

48%

26.4

8%6.

60%

14.8

9%6.

60%

100.

00%

1989

31.2

8%4.

00%

9.21

%24

.77%

8.45

%14

.99%

7.30

%10

0.00

%19

9029

.37%

5.61

%8.

05%

23.4

1%9.

57%

17.6

2%6.

37%

100.

00%

1991

28.9

4%5.

85%

8.13

%21

.76%

10.8

6%18

.43%

6.03

%10

0.00

%19

9228

.42%

6.68

%9.

28%

20.3

3%11

.66%

17.6

3%6.

01%

100.

00%

1993

27.0

1%7.

12%

8.18

%19

.72%

13.1

1%18

.76%

6.09

%10

0.00

%19

9427

.84%

6.62

%8.

60%

20.2

1%14

.29%

17.1

3%5.

32%

100.

00%

1995

27.7

7%7.

40%

9.00

%20

.88%

14.0

3%16

.10%

4.82

%10

0.00

%19

9627

.55%

8.27

%11

.09%

20.2

8%14

.31%

14.4

7%4.

02%

100.

00%

1997

28.9

4%7.

83%

10.7

1%21

.23%

12.7

4%14

.73%

3.81

%10

0.00

%19

9830

.64%

6.95

%10

.99%

21.8

3%11

.81%

13.9

4%3.

84%

100.

00%

1999

33.2

5%5.

47%

11.6

0%22

.93%

10.6

1%13

.00%

3.14

%10

0.00

%20

0035

.48%

4.45

%11

.80%

25.7

9%9.

04%

11.3

9%2.

05%

100.

00%

2001

35.6

8%4.

12%

12.2

7%25

.27%

9.29

%10

.41%

2.96

%10

0.00

%20

0234

.18%

4.05

%12

.22%

28.6

8%9.

44%

8.50

%2.

92%

100.

00%

2003

31.4

3%3.

62%

14.1

6%34

.93%

7.15

%6.

04%

2.67

%10

0.00

%A

vera

ge31

.48%

4.64

%9.

47%

22.6

6%8.

51%

18.7

4%4.

49%

100.

00%

Tab

leIV

:D

omin

ant

sect

ors

for

firm

sby

year

atth

e90

%th

resh

old,

perc

enta

geba

sis.

21

Fund

Size

Gen

eral

ist

Bio

tech

.C

omm

./

Med

iaC

omp.

Rel

ated

Med

ical

Non

-Hig

hT

ech

Sem

icon

d.T

otal

1st

Qua

rtile

600

1034

1911

5144

2081

3705

1337

1581

22n

dQ

uart

ile22

3311

4518

2046

6320

5029

4795

215

810

3rd

Qua

rtile

4327

964

1851

4341

1647

2091

590

1581

14t

hQ

uart

ile80

3542

616

4534

0175

612

5629

615

815

Tot

al15

195

3569

7227

1754

965

3499

9931

7563

248

1st

Qua

rtile

3.79

%6.

54%

12.0

9%32

.53%

13.1

6%23

.43%

8.46

%10

0.00

%2n

dQ

uart

ile14

.12%

7.24

%11

.51%

29.4

9%12

.97%

18.6

4%6.

02%

100.

00%

3rd

Qua

rtile

27.3

7%6.

10%

11.7

1%27

.46%

10.4

2%13

.22%

3.73

%10

0.00

%4t

hQ

uart

ile50

.81%

2.69

%10

.40%

21.5

0%4.

78%

7.94

%1.

87%

100.

00%

Tot

al24

.02%

5.64

%11

.43%

27.7

5%10

.33%

15.8

1%5.

02%

100.

00%

Tab

leV

:D

omin

ant

sect

ors

for

firm

sby

($)

inve

stm

ent

volu

me

over

prec

edin

gfiv

eye

ars

atth

e75

%th

resh

old.

22

Fund

Size

Gen

eral

ist

Bio

tech

.C

omm

./

Med

iaC

omp.

Rel

ated

Med

ical

Non

-Hig

hT

ech

Sem

icon

d.T

otal

1st

Qua

rtile

865

1027

1873

5051

2048

3655

1293

1581

22n

dQ

uart

ile30

9610

7517

0943

7919

3327

4287

615

810

3rd

Qua

rtile

5884

831

1575

3797

1431

1817

476

1581

14t

hQ

uart

ile10

206

311

1249

2312

575

970

192

1581

5T

otal

2005

132

4464

0615

539

5987

9184

2837

6324

8

1st

Qua

rtile

5.47

%6.

50%

11.8

5%31

.94%

12.9

5%23

.12%

8.18

%10

0.00

%2n

dQ

uart

ile19

.58%

6.80

%10

.81%

27.7

0%12

.23%

17.3

4%5.

54%

100.

00%

3rd

Qua

rtile

37.2

1%5.

26%

9.96

%24

.01%

9.05

%11

.49%

3.01

%10

0.00

%4t

hQ

uart

ile64

.53%

1.97

%7.

90%

14.6

2%3.

64%

6.13

%1.

21%

100.

00%

Tot

al31

.70%

5.13

%10

.13%

24.5

7%9.

47%

14.5

2%4.

49%

100.

00%

Tab

leV

I:D

omin

ant

sect

ors

for

firm

sby

($)

inve

stm

ent

volu

me

over

prec

edin

gfiv

eye

ars

atth

e90

%th

resh

old.

(Uni

tof

obse

rvat

ion

=fir

m-y

ear)

.

23

Variable Mean Std. Dev.MSA Sector Concentration 5837.297 2740.588Comp. Firms 49.227 90.985Total Investments in MSA ($) 285751.881 1323317.919MSA Firm Concentration 2933.503 3095.436Total VC Inflows 23161.258 34074.379Total New VC Entrants 263.496 235.277

N 3287

Table VII: Summary statistics for market-level regressions.

24

Variable Coefficient (Std. Err.)Comp. Firms 4.769∗∗ (1.155)Total Investments in MSA ($) 0.000 (0.000)New Entrants in MSA -2.826 (4.601)MSA Firm Concentration 0.409∗∗ (0.015)Total VC Inflows -0.029∗∗ (0.005)Total New VC Entrants 4.050∗∗ (0.680)Intercept 8486.170∗∗ (1349.600)

N 3287R2 0.784F (303,2983) 35.649Significance levels : † : 10% ∗ : 5% ∗∗ : 1%

Table VIII: The sample pools 3,287 market-year observations, which is an aggregate of 63,248 firm-market-year observations. A market enters the sample in the fifth year after the first investmentmade in that market, and all included variables are constructed on a five-year moving basis. In thispanel structure, the dependent variable is a market’s level of sector concentration, as defined inSection II.. This variable ranges from 1,667 to 10,000. The model is estimated via an OLS regressionwith market and year fixed effects (output omitted). All independent variables are constructed inthe manner described in Section II..

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Variable Mean Std. Dev. NComp. Firms 238.037 217.159 63248Log Firms in MSA 4.947 1.095 63248Log Total Investments in MSA ($) 13.158 1.819 63237Change in Firm-Level Sector Concentration 70.715 1199.326 61382Firm Investments ($) 9115.791 27416.636 63248Firm Investments (#) 6.079 11.012 63248Firm HHI 8455.785 2323.824 632481[In MSA Dom. Ind.] 0.388 0.487 63248Firm Age 5.54 5.082 63248MSA Sector Concentration 3491.51 1293.067 63248MSA Firm Concentration 1301.731 1970.191 63248Total VC Inflows 25240.189 35746.735 63248Total New VC Entrants 259.053 252.665 63248PE Ratio (Firm Weighted) 5.497 5.929 63248Inflows (Firm Weighted) 6176.949 11919.771 63248New VC Entrants in Sector (Firm Weighted) 61.583 85.953 63248

Table IX: Summary statistics for firm-level regressions.

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Variable Coefficient (Std. Err.)New Entrant 204.410∗∗ (43.993)New Entrant * Comp. 1.075∗∗ (0.375)New Entrant * Comp. Sq. -0.002∗∗ (0.000)Firm Investments ($) 0.005∗∗ (0.002)Firm Investments (#) -103.339∗∗ (19.841)Comp. Firms -0.790 (0.823)Comp. Firms Sq. 0.001 (0.001)MSA Sector Concentration 0.065∗∗ (0.017)MSA Firm Concentration 0.011∗ (0.005)Total VC Inflows 0.025∗∗ (0.004)Total New VC Entrants -3.851∗∗ (0.632)PE Ratio (Firm Weighted) 3.463 (3.140)Sector Inflows (Firm Weighted) -0.041∗∗ (0.007)New VC Entrants in Sector (Firm Weighted) 6.945∗∗ (1.297)Intercept 7282.631∗∗ (551.858)

N 63248R2 0.347Significance levels : † : 10% ∗ : 5% ∗∗ : 1%

Table X: The sample pools 63,248 firm-market-year observations. A firm enters the sample in thefifth year after its first investment in a market. Thus, we define the “New Entrant” regressor asan indicator that takes a value of 1 in the fifth year after a firm’s first investment in a market.All included variables are constructed on a five-year moving basis. In this panel structure, thedependent variable is a firm’s level of sector concentration, as defined in Section II.. This variableranges from 1,667 to 10,000. The model is estimated via an OLS regression with market andyear fixed effects (output omitted) and robust standard errors clustered by MSA. All independentvariables are constructed in the manner described in Section II..

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Variable Coefficient (Std. Err.)Firm Age -23.125∗∗ (7.446)Firm Age * Comp. -0.096† (0.052)Firm Age * Comp. Sq. 0.000∗∗ (0.000)Comp. Firms 0.670 (1.047)Comp. Firms Sq. -0.001 (0.001)Firm Investments ($) 0.005∗∗ (0.002)Firm Investments (#) -103.620∗∗ (20.053)MSA Sector Concentration 0.068∗∗ (0.017)MSA Firm Concentration 0.012∗ (0.005)Total VC Inflows 0.026∗∗ (0.004)Total New VC Entrants -3.777∗∗ (0.649)PE Ratio (Firm Weighted) 3.640 (3.176)Sector Inflows (Firm Weighted) -0.040∗∗ (0.007)New VC Entrants in Sector (Firm Weighted) 6.833∗∗ (1.288)Intercept 7389.373∗∗ (571.987)

N 63248R2 0.348Significance levels : † : 10% ∗ : 5% ∗∗ : 1%

Table XI: The sample pools 63,248 firm-market-year observations. A firm enters the sample in thefifth year after its first investment in a market. Thus, we define the “Firm Age” regressor as adiscrete variable that takes a value of 1 in the fifth year after a firm’s first investment in a marketand increases by one for each subsequent year a firm invests in that market. All included variablesare constructed on a five-year moving basis. In this panel structure, the dependent variable is afirm’s level of sector concentration, as defined in Section II.. This variable ranges from 1,667 to10,000. The model is estimated via an OLS regression with market and year fixed effects (outputomitted) and robust standard errors clustered by MSA. All independent variables are constructedin the manner described in Section II..

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Variable Coefficient (Std. Err.)1[New Entrant] 0.018† (0.009)New Entrant * Comp. 0.000 (0.000)New Entrant * Comp. Sq. 0.000 (0.000)Firm Investments ($) 0.000∗∗ (0.000)Firm Investments (#) 0.006∗∗ (0.000)Comp. Firms 0.001∗∗ (0.000)Comp. Firms Sq. 0.000∗∗ (0.000)MSA Sector Concentration 0.000∗∗ (0.000)MSA Firm Concentration 0.000 (0.000)Total VC Inflows 0.000∗∗ (0.000)Total New VC Entrants -0.001∗∗ (0.000)PE Ratio (Firm Weighted) -0.003∗∗ (0.001)Inflows (Firm Weighted) 0.000∗∗ (0.000)New VC Entrants in Sector (Firm Weighted) 0.006∗∗ (0.000)

N 63060Log-likelihood -36800.571χ2

(182) 10534.478Pseudo R2 0.1252Pred. Prob. 0.3708Significance levels : † : 10% ∗ : 5% ∗∗ : 1%

Table XII: The sample pools 63,060 firm-market-year observations. A firm enters the sample inthe fifth year after its first investment in a market. Thus, we define the “New Entrant” regressoras an indicator that takes a value of 1 in the fifth year after a firm’s first investment in a market.All included variables are constructed on a five-year moving basis. In this panel structure, thedependent variable is an indicator variable that takes a value of 1 if a firm is concentrated at the75% threshold in its market’s dominant sector, as defined in Section II.. The model is estimatedusing a panel probit estimator with market and year fixed effects (output omitted). The tablepresents the marginal effects following this estimation. All independent variables are constructedin the manner described in Section II..

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Variable Coefficient (Std. Err.)1[New Entrant] 0.038∗∗ (0.009)New Entrant * Comp. 0.000∗∗ (0.000)New Entrant * Comp. Sq. 0.000∗∗ (0.000)Firm Investments ($) 0.000∗∗ (0.000)Firm Investments (#) 0.009∗∗ (0.000)Comp. Firms 0.001∗∗ (0.000)Comp. Firms Sq. 0.000∗∗ (0.000)MSA Sector Concentration 0.000∗∗ (0.000)MSA Firm Concentration 0.000∗∗ (0.000)Total VC Inflows 0.000 (0.000)Total New VC Entrants -0.001∗∗ (0.000)PE Ratio (Firm Weighted) -0.006∗∗ (0.001)Inflows (Firm Weighted) 0.000∗∗ (0.000)New VC Entrants in Sector (Firm Weighted) 0.004∗∗ (0.000)

N 63060Log-likelihood -37866.531χ2

(182) 9068.710Pseudo R2 0.1069Pred. Prob. 0.3882Significance levels : † : 10% ∗ : 5% ∗∗ : 1%

Table XIII: The sample pools 63,060 firm-market-year observations. A firm enters the sample inthe fifth year after its first investment in a market. Thus, we define the “New Entrant” regressoras an indicator that takes a value of 1 in the fifth year after a firm’s first investment in a market.All included variables are constructed on a five-year moving basis. In this panel structure, thedependent variable is an indicator variable that takes a value of 1 if a firm is concentrated at the90% threshold in its market’s dominant sector, as defined in Section II.. The model is estimatedusing a panel probit estimator with market and year fixed effects (output omitted). The tablepresents the marginal effects following this estimation. All independent variables are constructedin the manner described in Section II..

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Variable Coefficient (Std. Err.)Firm Age 0.001 (0.001)Firm Age * Comp. 0.000 (0.000)Firm Age * Comp. Sq. 0.000 (0.000)Firm Investments ($) 0.000∗∗ (0.000)Firm Investments (#) 0.006∗∗ (0.000)Comp. Firms 0.001∗∗ (0.000)Comp. Firms Sq. 0.000∗∗ (0.000)MSA Sector Concentration 0.000∗∗ (0.000)MSA Firm Concentration 0.000 (0.000)Total VC Inflows 0.000∗∗ (0.000)Total New VC Entrants -0.001∗∗ (0.000)PE Ratio (Firm Weighted) -0.003∗∗ (0.001)Inflows (Firm Weighted) 0.000∗∗ (0.000)New VC Entrants in Sector (Firm Weighted) 0.006∗∗ (0.000)

N 63060Log-likelihood -36795.295χ2

(182) 10545.031Pseudo R2 0.1253Pred. Prob. 0.3708Significance levels : † : 10% ∗ : 5% ∗∗ : 1%

Table XIV: The sample pools 63,060 firm-market-year observations. A firm enters the sample inthe fifth year after its first investment in a market. Thus, we define the “Firm Age” regressor as adiscrete variable that takes a value of 1 in the fifth year after a firm’s first investment in a marketand increases by one for each subsequent year a firm invests in that market. All included variablesare constructed on a five-year moving basis. In this panel structure, the dependent variable isan indicator variable that takes a value of 1 if a firm is concentrated at the 75% threshold in itsmarket’s dominant sector, as defined in Section II.. The model is estimated using a panel probitestimator with market and year fixed effects (output omitted). The table presents the marginaleffects following this estimation. All independent variables are constructed in the manner describedin Section II..

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Variable Coefficient (Std. Err.)Firm Age -0.003∗∗ (0.001)Firm Age * Comp. 0.000∗∗ (0.000)Firm Age * Comp. Sq. 0.000∗∗ (0.000)Firm Investments ($) 0.000∗∗ (0.000)Firm Investments (#) 0.009∗∗ (0.000)Comp. Firms 0.000 (0.000)Comp. Firms Sq. 0.000 (0.000)MSA Sector Concentration 0.000∗∗ (0.000)MSA Firm Concentration 0.000∗∗ (0.000)Total VC Inflows 0.000 (0.000)Total New VC Entrants -0.001∗∗ (0.000)PE Ratio (Firm Weighted) -0.006∗∗ (0.001)Inflows (Firm Weighted) 0.000∗∗ (0.000)New VC Entrants in Sector (Firm Weighted) 0.004∗∗ (0.000)

N 63060Log-likelihood -37857.654χ2

(182) 9086.466Pseudo R2 0.1071Pred. Prob. 0.3882Significance levels : † : 10% ∗ : 5% ∗∗ : 1%

Table XV: The sample pools 63,060 firm-market-year observations. A firm enters the sample inthe fifth year after its first investment in a market. Thus, we define the “Firm Age” regressor as adiscrete variable that takes a value of 1 in the fifth year after a firm’s first investment in a marketand increases by one for each subsequent year a firm invests in that market. All included variablesare constructed on a five-year moving basis. In this panel structure, the dependent variable isan indicator variable that takes a value of 1 if a firm is concentrated at the 90% threshold in itsmarket’s dominant sector, as defined in Section II.. The model is estimated using a panel probitestimator with market and year fixed effects (output omitted). The table presents the marginaleffects following this estimation. All independent variables are constructed in the manner describedin Section II..

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