Venture Capital and Capital Allocation

• Published date: 01 June 2019
• Date: 01 June 2019
• DOI: http://doi.org/10.1111/jofi.12756
• Author: GIORGIA PIACENTINO

THE JOURNAL OF FINANCE •VOL. LXXIV, NO. 3 •JUNE 2019

Venture Capital and Capital Allocation

GIORGIA PIACENTINO∗

ABSTRACT

I show that venture capitalists’ motivation to build reputation can have beneficial

effects in the primary market, mitigating information frictions and helping firms go

public. Because uninformed reputation-motivated venture capitalists want to appear

informed, they are biased against backing firms—by not backing firms, they avoid

taking low-value firms to market, which would ultimately reveal their lack of infor-

mation. In equilibrium, reputation-motivated venture capitalists back relatively few

bad firms, creating a certification effect that mitigates information frictions. However,

they also back relatively few good firms, and thus, reputation motivation decreases

welfare when good firms are abundant or profitable.

DELEGATED INVESTORS WISH TO BE perceived as skilled in order to generate

“flows,” that is, to attract new investors and retain existing ones. This motiva-

tion to build a reputation for being skilled can distort their trades in secondary

markets. For example, delegated investors may trade too much in an attempt

to appear informed, even when they are not. Such excessive portfolio churning

can decrease secondary market efficiency (Dow and Gorton (1997), Dasgupta

and Prat (2006,2008), Guerrieri and Kondor (2012)). However, delegated in-

vestors play an important role not only in secondary markets, where their

trading behavior affects market efficiency, but also in primary markets, where

their investment behavior affects real efficiency. In particular, venture capital

firms (VCs) decide which firms to back and thus which projects go ahead. In this

paper, I ask whether VCs’ reputation motivation leads to inefficiencies in pri-

mary markets. Specifically, I examine whether reputation motivation induces

VCs to back the wrong firms in an attempt to appear skilled.

∗Giorgia Piacentino is with Columbia University and CEPR. I thank three anonymous referees,

the Associate Editor,and the Editor, Bruno Biais. I am also grateful to Ulf Axelson; the late Sudipto

Bhattacharya; Sugato Bhattacharya; Philip Bond; Mike Burkart; Amil Dasgupta; James Dow; Ja-

son Roderick Donaldson; Alex Edmans; Daniel Ferreira; Itay Goldstein; Radhakrishnan Gopalan;

Denis Gromb; Christian Julliard; Ohad Kadan; Peter Kondor; Todd Milbourn; Daniel Paravisini;

Christopher Polk; Uday Rajan; Anjan Thakor; David Thesmar; Dimitri Vayanos; Michela Verardo;

Kathy Yuan; Konstantinos Zachariadis; Hongda Zhong; and audiences at Amsterdam Business

School, Arizona State University, Bocconi University, the 2013 EFA meeting, EIEF, Federal Re-

serve Board of Governors, the 2013 FIRS conference, HEC Paris, the 2017 Institutional Investors

and Corporate Governance conference at the Swedish House of Finance, UBC, UNC, University

of Maryland, University of Michigan, Stockholm School of Economics, University of Pennsylva-

nia, University of Warwick, and Washington University in St. Louis. I have read the Journal of

Finance’s disclosure policy and have no conflict of interest to disclose.

DOI: 10.1111/jofi.12756

1261

1262 The Journal of Finance R

To address these questions, I develop a model in which a VC may be

reputation-motivated, instead of purely profit motivated. Surprisingly, I find

that, in equilibrium, the VC’s profits can be higher than they would be if it

were just trying to maximize them. The reason is that reputation motivation

changes a VC’s behavior, leading it to reduce the number of firms it backs,

and this creates a certification effect that mitigates information frictions at the

time of IPO. Reputation motivation is not all good, however, as it can also lead

a VC not to back some relatively good firms. Thus, reputation motivation leads

a VC to back not only fewer bad firms, but also fewer good firms. The net effect

on total welfare and VC profits therefore depends on the proportion and quality

of good firms in the market.

Model Preview. A penniless start-up firm requires outside finance for an

investment that may be good or bad. It looks for a VC to back it, that is, to

provide capital and expertise. If the firm receives backing, it gives the VC an

equity stake and invests. Later, the firm raises capital from uninformed bidders

in an initial public offering (IPO), in which the VC retains its stake in the firm.

Finally, the long-run value of the investment is realized.

The VC can be skilled or unskilled. If it is skilled, it can observe whether the

firm (i.e., its investment) is good or bad; if it is unskilled, it observes nothing.

I consider two cases. First, the VC may be profit-motivated, that is, it wants

to back the firm whenever it expects to make a profit for its current investors.

Second, the VC may be reputation-motivated, that is, it wants to maximize the

market’s belief that it is skilled, so as to maximize the capital it can raise from

future investors.

Results Preview. I first characterize the equilibrium with a profit-motivated

VC. A skilled VC knows the quality of the firm. As a result, it does not back bad

firms, while it backs good firms as long as it anticipates being able to take them

to IPO. Hence, a skilled VC “filters out” bad firms. In contrast, an unskilled VC

does not know whether the firm is good or bad but it knows that the market

believes that VC-backed firms are better than average, due to the skilled VC’s

filtering. Thus, if the unskilled VC backs the firm, it raises cheap capital at the

time of the IPO, that is, it is subsidized by pooling with the positively informed

skilled VC. As a result, the unskilled VC may also back the firm, despite its lack

of information, as doing so maximizes its expected profits. However, this may

be socially inefficient. Indeed, the unskilled VC overinvests, that is, it may still

back a firm with negative net present value (NPV). When the average NPV is

low enough, the unskilled VC’s overinvestment can reduce the average quality

of VC-backed firms so much that IPO bidders are unwilling to provide capital.

Thus, in anticipation of being unable to IPO, skilled VCs do not back firms even

when they know they are good, that is, the collapse of the IPO market causes

the VC market to break down.

What changes if the VC is reputation-motivated? In this case, an unskilled

VC is averse to backing a firm that might end up being bad, but less averse

to not backing a firm that might end up being good. Since the firm’s value

is revealed only if the VC backs it, the market can determine whether the VC

wrongly backed a bad firm but not whether the VC wrongly rejected a good firm.

Venture Capital and Capital Allocation 1263

Put differently, the market can distinguish between false positives and true

positives, but cannot distinguish between false negatives and true negatives.

This biases the unskilled VC toward “negatives,” that is, toward not backing

the firm, which makes the unskilled reputation-motivated VC conservative—it

backs firms less frequently than an unskilled profit-motivated VC.

When the unskilled reputation-motivated VC rejects a firm, it is doing some-

thing that decreases its profits—if it just maximized its profits, it would act

like the unskilled profit-motivated VC and overinvest to get the IPO subsidy

from the skilled VC. However, even though it is not trying to maximize them,

these profits can still be higher than those of the unskilled profit-motivated

VC in equilibrium. The reason is that its conservatism leads to a certification

effect that can increase efficiency in two ways. First, it can prevent market

breakdowns: since unskilled reputation-motivated VCs back firms relatively

rarely, many VC-backed firms are backed by positively informed skilled VCs.

The market therefore infers that VC-backed firms are probably good. This effect

mitigates information frictions and helps firms go public. Second, even absent

market breakdowns, a VC’s conservatism can increase aggregate efficiency.

Relative to the profit-motivated VC, the reputation-motivated VC backs fewer

good firms, but also fewer bad firms. The net effect of this behavior for social

efficiency depends on firms’ average NPV. If it is negative, aggregate efficiency

is higher with reputation-motivated VCs, as they back firms less often than

profit-motivated VCs. In contrast, if firms’ average NPV is positive, aggregate

efficiency is lower with reputation-motivated VCs, as in this case, the costs of

backing fewer good firms outweigh the benefits of backing fewer bad firms.

Below, I place a bit more emphasis on the negative-NPV case, and hence on

the positive effects of reputation motivation because I think this case may be

more important in practice. VC partners “source a few thousand opportunities,

invest in a handful, and get returns from a few”; indeed, “almost 80 percent of

all investments fail” (Ramsinghani (2014, p. 69 and p. 6)). That said, although

the total population of firms that VCs could possibly back may have negative

average NPV, the set of firms in my model could represent a subset of this pop-

ulation, with the worst already partially filtered out. Under this interpretation,

the positive average NPV case becomes important too.

Extensions and Robustness. I show that the positive side of reputation mo-

tivation is robust to a number of extensions, two of which are particularly

important. First, I include adverse selection among IPO bidders, so that the

firm must issue shares at a discount to induce uninformed bidders to par-

ticipate. I show that the results are robust to this more realistic model of

an IPO. More importantly, I show that VCs’ reputation motivation can im-

prove welfare even if the average NPV is positive. Indeed, by not backing firms

with low but positive NPV—something that would typically be inefficient—the

certification effect of reputation-motivated VCs is strong enough to overcome

adverse-selection-induced market breakdowns.

Second, I allow the unskilled VC to be partially informed. In particular,

I assume that it gets a continuous signal about the quality of the firm, so

it backs firms only if its signal is above a threshold. As in the baseline, an