Competition in the venture capital market and the success of startup companies: Theory and evidence

• Author: Konstantinos Serfes,Veikko Thiele,Suting Hong
• Published date: 01 October 2020
• Date: 01 October 2020
• DOI: http://doi.org/10.1111/jems.12394

J Econ Manage Strat. 2020;29:741–791. wileyonlinelibrary.com/journal/jems © 2020 Wiley Periodicals LLC

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Received: 11 March 2019

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Revised: 14 February 2020

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Accepted: 2 July 2020

DOI: 10.1111/jems.12394

ORIGINAL ARTICLE

Competition in the venture capital market and the success

of startup companies: Theory and evidence

Suting Hong

1

|Konstantinos Serfes

2

|Veikko Thiele

3

1

School of Entrepreneurship and

Management, ShanghaiTech University,

Shanghai, China

2

School of Economics, LeBow College of

Business, Drexel University, Philadelphia,

Pennsylvania

3

Smith School of Business, Queen's

University, Kingston, Ontario, Canada

Correspondence

Veikko Thiele, Smith School of Business,

Queen's University, 143 Union Street,

Kingston, Ontario, K7L 3N6, Canada.

Email: thiele@queensu.ca

Abstract

We examine the effect of a competitive supply of venture capital (VC) on the

exits (initial public offering or mergers and acquisitions) of startups. We de-

velop a matching model with double‐sided moral hazard, and identify a novel

differential effect of VC competition on the success of startups. Using VC data,

we find evidence for this differential effect. For example, when the VC market

becomes more competitive (Herfindahl–Hirschman Index decreases by 50%

from its mean of 0.08), the absolute likelihood of success increases by 2.8% for

startups backed by less experienced VC firms, but it decreases by 3.6% for

startups backed by the most experienced VC firms.

1|INTRODUCTION

Over the last three decades, venture capital (VC) has become an increasingly important source of startup funding for

entrepreneurs (ENs) with innovative products. For example, the number of VC firms has more than quadrupled in the

United States: While 408 VC firms actively invested in startup companies in 1991, this number rose to 1,639 in 2015

(Thomson One). At the same time a substantially larger number of startup companies received VC financing: 970

companies in 1991 versus 3,743 in 2015 (Thomson One). The list of successful and well‐known companies that received

VC at some point during their startup phase includes Google, Facebook, Airbnb, and Uber. Other companies, such as

Pets.com, eToys, Jawbone (and many others), received VC funding but eventually failed.

Governments around the globe have implemented various policies to spur VC investments (e.g., capital gains

holidays, R&D subsidies, etc.), which has contributed significantly to the rise of VC (e.g., P. A. Gompers &

Lerner, 1998). Clearly, startup companies that would have otherwise not received VC funding benefit from a more

competitive VC supply. However, in this paper we pursue a more nuanced approach, and examine, both theoretically

and empirically, whether a more competitive supply of VC has a differential impact on the funded companies,

depending on the ‘quality’of their investors. For this, we focus on the likelihood to experience a successful exit, which

is critical for ENs, investors, and policy makers alike.

To analyze the relationship between the supply of VC and the rate of successful exits for startup companies, we first

develop an equilibrium model of the VC market with two‐sided heterogeneity, matching, and double‐sided moral

hazard.

1

In our model, ENs and VC firms are vertically heterogeneous with respect to the quality of their business ideas

(ENs), and their experience or management expertise (VC firms). In equilibrium, ENs with high‐(low‐)quality projects

match with high‐(low‐)quality VC firms (positive assortative matching [PAM]). Each VC firm then provides capital in

exchange for an equity stake, which in turn determines the valuation of the startup company. Moreover, for a given

match, both the EN and the VC firm (as an active investor) need to exert private effort to bring the entrepreneurial

project to fruition.

2

The joint effort then determines the probability for the venture to generate a positive payoff (double‐

sided moral hazard).

The main insight from our theory is that a more competitive supply of VC (through the entry of new VC firms in a

market) has a differential effect on startup companies: It improves the success rate of lower quality entrepreneurial

projects (backed by less experienced VC firms), while it diminishes the success rate of high‐quality projects (backed by

more experienced VC firms). Despite this differential effect on success rates, the model shows that a more competitive

VC supply improves the equilibrium valuation of all startup companies.

The key mechanism behind this differential effect is as follows. For each EN–VC pair there exists a specific

allocation of equity that harmonizes the effort incentives between the two sides, and therefore maximizes the prob-

ability of generating a positive payoff. However, we show that in the matching equilibrium, an EN backed by a more

experienced VC firm (i.e., an EN with a high‐quality project) has ‘too much’equity, which is driven by the competition

among VC firms for high‐quality projects. The VC firm then does not apply enough effort (or does not provide sufficient

value‐adding services), and this deficiency cannot be compensated by the EN (despite being motivated to exert more

effort). As a result the venture's probability of experiencing a successful exit is inefficiently low. In contrast, an EN

backed by a less experienced VC firm (i.e., with a low‐quality projects) retains ‘too little’equity in equilibrium, because

weak competition among VC firms for these lower quality startups allows investors to obtain higher equity stakes,

driving down company valuation. The EN's effort is then inefficiently low, and this cannot be fully compensated by the

VC firm's higher effort. Again we find that the likelihood of a successful exit is not maximized in equilibrium.

A more competitive supply of VC (i.e., when more VCs compete in a market) forces investors to provide funding in

exchange for less equity. This implies a higher valuation of all startup companies, regardless of whether they have high‐

or low‐quality projects. However, leaving ENs with high‐quality projects with more equity, exacerbates the inefficient

equity allocation (which is tilted in favor of ENs), and therefore further diminishes the joint efficiency of effort

incentives. As a result, ventures backed by more experienced VC firms (with high‐quality projects) are then less likely to

generate a positive payoff (or to have a successful exit). We find the opposite for ventures backed by less experienced VC

firms (with low‐quality projects): More equity for ENs partially offsets the initial inefficiency associated with

unbalanced effort incentives, and therefore improves the likelihood of success.

We then test our theoretical predictions using VC investment data from Thomson One, covering all investments in

the United States from 1991 to 2010.

3

We define VC markets based on the geographical locations of the portfolio

companies (using the metropolitan statistical areas [MSAs]), and industries. To measure VC market concentration (or

the degree of VC competition) we use (a) the Herfindahl–Hirschman Index (HHI), (b) the inverse number of VC firms

in a given market, (c) and the inverse number of VC funds in a given market. Variations in market concentration occur

either when VC firms make investments in a market for the first time (Hochberg, Ljungqvist, & Lu, 2007) or con-

centration measures change through changes in shares of investments (with a fixed number of VC firms). These

variations in concentration may be caused by changes in fixed costs associated with making a first time investment in a

market and/or variations of flows of capital into a VC market. VC experience is measured by the number of prior

investments, as suggested by prior literature (see, e.g., Nahata, 2008; Sørensen, 2007). Finally, we use initial public

offerings (IPOs) and mergers and acquisitions (M&A) to measure the success of startup companies. In our robustness

analysis, we also use an alternative measure for success, IPO.

For each market–year, we identify VC firms of different percentiles of experience, and then explore how a more

competitive supply of VC firms affects startups funded by VC firms of different experiences. We find evidence for a

cutoff at the 90th percentile of VC experience for the differential effect of competition: For VC firms below the 90th

percentile experience level, a more competitive supply of VC improves the success rates of their portfolio companies.

However, for VC firms in the top 10th percentile of experience, stronger competition has a negative effect on the success

of their portfolio companies. We therefore find empirical support for the differential effect of VC competition, as

identified by our theory model.

We control for potential bias from endogeneity of market competition and selection due to matching between VC

firms and ENs. Following Samila and Sorenson (2011), we build an instrument for market competition that utilizes the

investment returns received by limited partners (LPs; i.e., university endowments). The identification assumption

contends that, following higher investment returns, institutional investors adjust their investment portfolios, which

affects the supply of VC. Given that VC typically represents only a small share of an LP's investment portfolio,

variations in the returns are unlikely to be driven by VC fund performance. Furthermore, we follow Bottazzi et al.

(2008) and Tian (2011) and adopt two alternative approaches to control for selection from endogenous matching

between VC firms and ENs. In the first approach, we follow Ackerberg and Botticini (2002) and use interactions

between market dummies and company characteristics (i.e., development stages) to instrument for the matching

between VC firms and startups in a local market. In the second approach, we implement a two‐step estimation

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HONG ET AL.

procedure that estimates a selection equation in the first step to explain which pair of VC firm and EN matching is

actually formed. In the second step, we address endogeneity of market competition using the instrument of the returns

of LPs, and include the inverse Mills ratio derived from the first‐step estimation as a regressor. Finally, we conduct

several tests as proposed by Altonji, Elder, and Taber (2005) and Oster (2019) to evaluate the robustness of our results to

the selection on unobservables.

The significance of our findings can be summarized as follows. First, there is prior empirical evidence that the

matching in the VC market is positive assortative (see Fu, Yang, & An, 2019; Sørensen, 2007): high‐(low‐)experience VC

firms fund high‐(low‐)quality startups. Our data also support this finding. For example, companies funded by VC firms

in the top 10% in terms of investment experience have, on average, a 24% likelihood of a successful exit. In contrast,

only 17% of companies funded by VC firms below the top 10% experience a successful exit; see Table 2. Second, and

given PAM, we show that a more competitive supply of VC has a negative effect on the success rate of the highest

quality startups, while it has a positive effect on the success of lower quality companies (differential effect). Specifically,

we find that if the HHI decreases from its mean (0.08) by 50% (i.e., the market becomes more competitive), the

likelihood of a successful exit (IPO or acquisition) decreases by 3.6% for companies backed by the most experienced VC

firms (the ones in the top 10th percentile).

4

In contrast, the success rate of companies receiving funding from less

experienced VC firms (the ones below the 90th percentile) increases by 2.8%. We obtain a similar result when using the

number of VC firms in a market as an alternative measure of VC market competition. For our third alternative measure

for market concentration—the number of VC funds in a market—we again find qualitatively similar results, although

with lower magnitudes. This could be explained by the fact that in 30% of the cases in our data, a single VC firm

manages more than one investment fund in a given market–year. Changes in the fund number therefore represent a

higher‐bound measure for the real changes in market concentration.

Overall we find that VC competition has a negative effect on the success rates of the most innovative startups, which

tend to receive funding from the most experienced VC firms. Clearly, these ‘star companies’are fewer in numbers to

begin with but have much higher valuations than the companies funded by lower experience VC firms (see Table 2).

Therefore, they are much more likely to generate innovation (Kortum & Lerner, 2000), and economic growth and

employment (Samila & Sorenson, 2011) in a region. Our research therefore identifies a significant economic and

societal cost of promoting VC investments (e.g., through public programs, such as capital gains holidays, tax credits,

etc.), if this results in a more competitive VC market: It diminishes the number of such successful ‘star companies,’with

all the implications for innovation and economic growth.

5

We first develop a theory model which illustrates the differential effect of VC competition on successful exits of

portfolio companies. Naturally we need to make several simplifying assumptions for our matching model with en-

dogenous VC contracts to keep it tractable—we now briefly explain them.

First, we assume that the quality of each project and the experience of each VC firm in the market is common

knowledge. We are not the first to make this assumption. In fact, many matching and search models within the context

of VC assume complete information at the time of contracting (see, e.g., Ewens et al., 2019; Hellmann & Thiele, 2015;

Inderst & Müller, 2004; Silviera & Wright, 2016; Sørensen, 2007). For our model one could introduce uncertainty about

the project quality. This would not affect our analysis as long as information is symmetric. However, with asymmetric

information we would also need to account for signaling and/or screening, which is clearly beyond the scope of our

paper. Instead, in this paper we focus on the information asymmetry problem concerning the efforts of the EN and the

VC firm (double‐sided moral hazard).

Second, we focus on the allocation of equity as the contracting tool, similar to Keuschnigg and Nielsen (2004). This

allows us to derive the so‐called postmoney valuation, which is commonly used in the empirical VC literature to assess

and compare investment deals.

6

Naturally VC contracts are more complex in practice. In addition to cash flow rights

(e.g., equity), they also specify various controls rights. First and foremost, VC firms obtain some control over the

company through their board seats, but they also include various covenants in the venture contract. Such covenants

typically give VC firms certain veto rights, for example, for the issuance of new equity, asset acquisitions, and the hiring

of executives (see, e.g., Bengtsson, 2011).

7

For our theory, however, we note that it is in fact the allocation of utility that

matters. In our model with double‐sided moral hazard and a binary payoff structure (success vs. failure), the utility

levels determine effort incentives, and therefore the success rates of startup companies. Beyond double‐sided moral

hazard, there is no other inefficiency in our model, so control rights do not play any additional role for a venture's

success; they would simply affect the utility allocation between a VC and an EN. In a richer model with unverifiable

profits, different states of nature and multiple milestones that a venture needs to achieve, optimal contracts would

naturally be more complex (compared with a simple equity contract).

8

Our goal in this paper is to keep the VC–EN

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