Buybacks as an Efficient Strategy for

Venture Capital in Emerging Markets

Lanfang Wang1 and Susheng Wang2

July 2016

Abstract: Special circumstances in emerging markets call for special strategies for venture

capitalists. In emerging markets, venture capitalists are often observed to exit through buy-

backs. This paper develops a theory and provides empirical evidence on such joint ventures.

We show that buybacks can be an efficient solution in emerging markets. We also find sup-

porting evidence from the China market.

Keywords: buybacks, emerging markets, venture capitalists

JEL Classification: G24, G34, G11

1 Shanghai University of Finance and Economics. Email: wang.lanfang@mail.shufe.edu.cn.

2 Hong Kong University of Science and Technology. Email: s.wang@ust.hk.

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1. Introduction

Venture Capital plays an important role in a nation’s innovation and economic growth

(Schumpeter 1934; Lerner 2010). The venture capital industry in developed countries, espe-

cially in the U.S., has been hugely successful and many emerging countries around the world

are trying to develop their own venture capital industry and entrepreneurship. But whereas

initial public offerings (IPOs) and third-party buyouts are popular forms of venture capital

exits in developed economies, buybacks are preferred among venture capitalists (VCs) in

emerging markets. No one has ever seriously investigated these buybacks, besides the com-

mon assumption that buybacks are inferior to IPOs. Only Cumming et al. (2006) and Cum-

ming & Johan (2008) provide empirical evidence that VCs with different experiences and in

countries with different traditions and institutional factors may exit differently. Cumming et al.

show that different ways of exit can be optimal in different environments. In this paper, we

provide a theory and some evidence on buybacks. We show that, in a typical emerging market,

a buyback can be an efficient solution.

In a buyback deal, the firm repurchases shares from investors that have bought shares

from it before. In practice, the share price in a buyback is typically mentioned in the initial

agreement and later determined by negotiation/bargaining at the time of exit. The existing

literature has focused on venture capital exits through IPOs. However, buybacks or manage-

ment buyouts (MBOs) are more popular than IPOs in emerging markets. For example, buy-

backs are the dominant form of venture capital exits in China. This phenomenon is also in-

creasingly widespread in some Western countries. For example, as pointed out by Murray &

Lott (1995), “the UK industry has increasingly come to be dominated by management buy-

outs/buyins,” and “in 1992, 64% of total UK investment by members of the British Venture

Capital Association was directed exclusively to management buyouts and buyins.” Our paper

investigates the possibility of an efficient joint venture between a domestic firm and an inves-

tor in an emerging market when their joint venture is expected to cease following a buyback.

Many of the existing studies on VCs focus on developed economies, especially the U.S.,

where VCs play an important role beyond providing finance (Sahlman, 1990; Kortum and

Lerner, 2000; Gompers and Lerner, 2004). In emerging markets, domestic firms rely on

venture capital investment for financing, experience and technology. In recent years, many

VCs from developed countries have found opportunities in emerging markets. In such a case,

the two parties tend to form a joint venture that is defined by equity holdings of the firm.

However, in a typical equity joint venture, a major concern is the agency problem. Wang &

Zhou (2004) focus on equity sharing in venture capital investment and show that the agency

problem cannot be completely eliminated by a popular venture capital investment strategy

called staged financing. However, Wang & Zhou (2004) implicitly assume that the venture

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capital will exit through an IPO or a third-party buyout. In this paper, we explicitly allow

buyback as a way of exit. A distinct feature of our buyback contract is that it is an incomplete

contract. It is the incompleteness of our contract that results in an efficient joint venture.

Having an exit strategy is crucial in venture capital investment (Sahlman, 1990). The ex-

isting studies have focused on the IPO exit. Barry et al. (1990) find that the value that VCs add

to the firms “appears to be recognized by markets through lower underpricing for IPOs.”

Megginson & Weiss (1991) find the same evidence. Brav & Gompers (1997) also find that

“venture-backed IPOs outperform non-venture-backed IPOs using equal weighted returns.

Value weighting significantly reduces performance differences and substantially reduces

underperformance for nonventure-backed IPOs.” However, Lee & Wahal (2004) find opposite

evidence. There are also a few studies on the full set of exits, including IPOs, acquisitions,

secondary sales, buybacks and write-offs. MacIntosh (1997) and Cumming & MacIntosh (2003)

analyze the factors affecting the choice of venture capital exit from the full set for VCs in Can-

ada and the U.S.; Schwienbacher (2002) and Cumming (2008) study venture capital exits in

Europe; Fleming (2002) studies venture capital exits in Australia; Petty et al. (1999) and

Cochrane (2005) study venture capital exits in the U.S. In particular, Cumming et al. (2006)

find a close connection between the quality of legal environments and venture capital exits

through IPOs. Smith and Smith (2000) discuss aspects of IPOs, acquisitions and buybacks.

Also, Black & Gilson (1998) introduce implicit contracting over venture capital exits.

There are also many studies on buyouts. Buyouts have been shown to improve perfor-

mance. Lichtenberg & Siegel (1990) investigate the total factor productivity (TFP) of leveraged

buyouts (LBOs). LBOs, particularly MBOs, are shown to have a strong positive effect on TFP

in the first three post-buyout years. Smith (1990) finds that “operating returns increase signif-

icantly from the year before to the year after buyouts.” The operating returns are also found to

improve for the long term. Wright et al. (1996) investigate the long-term effect of buyouts.

They show that “a variety of financial ratios buyouts significantly outperform a matched sam-

ple of non-buyouts, especially from year 3 onwards.” See also Demski & Sappington (1991),

Chaplinsky et al. (1998), Bruton et al. (1999) and Bruining et al. (2004) on buyouts.

However, buyouts are different from buybacks. A buyout is a takeover strategy which is

generally not planned in advance, while a buyback contract generally includes an explicit

statement allowing the investor to sell its shares back to the founders or managers ex post. In

our model, the buyback contract is an investment strategy and the investor fully expects to exit

through buyback when it enters the agreement. To our knowledge, not one study in the litera-

ture investigates the effect of such buybacks on firm performance. Our theory and empirical

evidence suggest that when VCs have strong bargaining power, a buyback can be efficient.

The remainder of this paper is organized as follows. Section 2 presents our theory on buy-

back contracts. Section 3 presents empirical analysis of buybacks in the Chinese venture capi-

tal market. Section 4 concludes the paper. All proofs are given in the Appendix.

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2. Theoretical Analysis

2.1. The Model

The Project

Consider a domestic firm that relies on a venture capitalist (VC) for financing in a project.

The project lasts two periods. The firm is initially owned and managed by a single entrepre-

neur. The entrepreneur (EN) provides efforts and in two periods after accepting the

contract, with costs and respectively. The VC provides a total amount of necessary

funding. Given efforts and from the EN and financial capital from the VC, an output

is produced at the end of the second period. At time the VC can choose to exit.

0 1 2

Output yNegotiationfor VC Exit

Ex ante Ex post End

1,x k

Equity Sharing Agreement

2x

Figure 1. A Buyback Model

The production function is With the EN’s total effort and the VC’s fi-

nancial investment the output is We assume a concave production function.

The timing of events is as follows. At the two parties negotiate a contract. If the con-

tract is accepted by the two parties, the VC invests and the EN invests and incurs cost

At the VC considers making an exit which requires the EN to buy back the VC’s shares.

The selling price of the shares is based on ex post negotiation. In the second period, the EN

makes a second effort At the project is finished and payments are made according to

the existing contract.

A Buyback Contract

The EN’s efforts and are not contractible, but the VC’s financial investment is con-

tractible. The output is also contractible. The initial agreement at is that the VC will

invest an amount in exchange for a portion of the firm’s equity. Or equivalently, the two

parties sign an equity-sharing contract at according to which the VC will invest an

agreed amount. At the VC will consider making an exit. The exit will be made through a

buyback agreement, i.e., the VC will sell her shares back to the EN. The selling price of shares

when the VC exits will be determined through ex post negotiation. Hence, a buyback contract

can be denoted by where is the VC’s equity share and is the VC’s capital investment.

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This buyback contract allows the VC to exit at by selling her shares back to the EN

through ex post negotiation.3

Technically speaking, the admissible contracts in our model are equity-sharing contracts

only. Our contracts are incomplete in the sense that, although an exit through a buyback is

implicitly understood by both parties, it is not specified in the contract.

The First-Best Solution

The first-best problem is

,

which implies the first-order conditions (FOCs):

∗ ∗ ∗ ∗ (1)

We will assume that the project is profitable, i.e., ∗ ∗ ∗ ∗

Remark 1. We do not restrict ourselves to a principal-agent setup, in which one of the parties

is given the full bargaining power ex ante. In our model, the two parties negotiate ex ante for

an equity-sharing contract; the two parties negotiate again ex post for a buyback agreement.

Remark 2. Our model is not a standard contract model. In the standard contract model, the

admissible contracts are output-sharing contracts and they are treated as complete contracts.

Our model has an incomplete contract, in that the buyback is completely left out, even though

it is assumed by both parties. This is consistent with our casual observation of the reality

about buybacks in emerging markets. Kaplan & Strömberg (2003) also observe inherently

incomplete contracts in the U.S. venture capital industry.

2.2. The Solution

In this section, we identify a buyback contract that yields the first-best solution ∗ ∗ Since is contractible, the contract can specify ∗ The question is, what is We

now discuss the two parties’ relationship in steps.

At the two parties negotiate the VC’s exit through buyback. We use the Nash bar-

gaining solution to define the bargaining outcome. Given and if bargaining on buyback

fails (the VC keeps her shares), the ex post problem (the EN’s investment problem) is

3 A real buyback contract would typically state explicitly the possibility of a buyback and a possible share price

for the buyback. However, this price is negotiable ex post. Hence, this selling price is typically decided through ex

post negotiation at the time of exit.

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Then, the EN chooses his second-period investment by the following first-order condition

(FOC):

If bargaining succeeds (the VC sells her shares back to the EN), the ex post problem is

Then, the EN chooses his second-period investment ∗ by the following FOC:

∗ (2)

In other words, given and if the VC does not exit at then the EN invests in the

second period; if the VC exits at then the EN invests ∗ in the second period. Therefore,

given the VC’s bargaining power as measured by the Nash bargaining solution at is ∗ ∗ ∗ ∗These two formulas define the two parties’ ex post payoffs and Here, is the VC’s

equity holding and is the VC’s bargaining power. These payoffs defined by the Nash bargain-

ing solution implicitly imply the selling price for the VC’s shares.

Hence, at the two parties’ payoffs are ∗ ∗ ∗ ∗where and are the ex ante payoffs to the VC and the EN, respectively. Then, the EN

chooses based on the following FOC (where and ∗ are dependent on ):

(3)

In equilibrium, ex post bargaining at will lead to a solution by which the EN buys back

all the shares. Hence, the two parties will negotiate an agreement at as a solution of the

following ex ante social welfare maximization problem:

, , , (4)

In this problem, the incentive compatibility (IC) condition ensures that the EN will invest

and no less as is stipulated in their contractual agreement. The two individual rationality

(IR) conditions ensure that the agreement is acceptable to both parties.

7/26

Proposition 1. If ∗ and is sufficiently large such that ∗( ∗, ∗) ∗ an optimal

buyback contract ∗ ∗ ∗ exists, where ∗ and ∗ is the first-best capital in-

vestment. This buyback contract is efficient (the first best).

The condition of a sufficiently large means that the VC has strong bargaining power.

This indeed fits the reality in an emerging market. Since the project is profitable, with ∗ ∗ ∗ ∗ we have ∗( ∗, ∗) ∗ Hence, we can always find a such

that ∗( ∗, ∗) ∗ That is, the existence of such a is technically guaranteed in our model.

The condition ∗ means that the EN’s investment is essential. That is, the pro-

ject can never be efficient without the EN’s investment.

Remark 3. We can allow uncertainty in our model. For example, we can allow a random

shock such that the production function is This random shock is realized at

Our conclusion remains unchanged. The reason why uncertainty does not affect our conclu-

sion is because our contract is incomplete. The ex post negotiation of buyback can accommo-

date a realized random event at

Remark 4. We can relax our assumption that inputs and are perfect substitutes in

production. We can assume a production function of the form where

is the EN’s total contribution to the project. We find that, if is increasing in

and separately and ( , )

our conclusion remains unchanged.

Remark 5. We have assumed that the cost of the EN’s effort is We may also use a more

general convex cost function and our conclusion remains unchanged.

3. Empirical Analysis

Our theory suggests that a buyback contract can be efficient in emerging markets espe-

cially when the VC has strong bargaining power. In this section, we identify some empirical

evidence for our theory. We focus on China, which is the largest and the most important

emerging market in the world. Regional differences in institutional strength across China

provide a good scenario to investigate the role of institutions.

3.1. Research Design, Variables and Sample

We focus on three types of venture capital exits in China: (i) successful exits, including

IPOs and third-party buyouts, which are widely investigated in the literature on U.S. venture

capital; (ii) buyback exits, which occur frequently in emerging markets and are indicated by

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our theory to be efficient under some circumstances; and (iii) unsuccessful exits, including

liquidation and secondary sales, where a VC sells its interest but the entrepreneur does not.

As suggested by our theory, if a VC has strong bargaining power, buybacks can be efficient.

We use the following multinomial logit model to compare the exit methods adopted among

VCs wielding different levels of bargaining power:

<Insert Table 1>

See Table 1 for the definition of all variables. The unit of observation is an entrepreneurial

company. We collect the data on VCs and their funded companies from CVSOURCE, which is

a leading venture capital and private equity database with an exclusive focus on China.4 We

also rely on a comprehensive Chinese company registration database developed by China’s

State Administration for Industry and Commerce (SAIC) to identify the type of venture capital

exit an entrepreneurial company made up to the end of 2014. To make sure that a VC had

sufficient time to exit, we retain only those entrepreneurial companies whose first venture

capital investment occurred no later than 2011, because the typical cycle of Chinese venture

capital investments is 2-3 years.

An entrepreneurial company may receive venture capital financing from multiple VCs.

These VCs form a syndicate, and are typically led by a lead VC that plays a dominant role in

mentoring and monitoring the entrepreneurial company. Following prior research, we focus

on the lead VC for each entrepreneurial company (Hochberg et al., 2007; Nahata, 2008), but

we control for the effect of venture capital syndicates in our analysis. For each entrepreneurial

company, we define the lead VC as the one that contributes the most to the initial venture

capital investment. If an entrepreneurial company has more than one lead VC, we take the

average over the lead VCs for each variable concerned.

VCs’ bargaining power is largely determined by their resource and human capital, which

are generally proxied by their experience, networks and reputation in prior literature. These

factors are normally found to be positively associated with successful venture capital exits in

4 Because venture capital firms are not required to publicly disclose their investment portfolio data, the quali-

ty of data on venture capital-funded companies from existing commercial databases commonly used in the extant

academic research has always been a concern. Our research indicates that CVSOURCE’s coverage of venture

capital-funded companies is more or less complete. According to The Annual Report on China’s VC Industry

Development, an authoritative year book jointly published by China’s National Development and Reform Com-

mission (NDRC), the government regulator of Chinese venture capital/private equity firms during our sample

period, and China Venture Capital & Private Equity Association, the total number of venture capital investment

deals over the period 2006-2012 was 9,596. The corresponding figure in CVSOURCE is 10,128.

9/26

the literature on developed markets. We hence make use of the three variables experience,

networks and reputation to measure a VC’s bargaining power.

As shown in Bottazzi et al. (2008) and Gompers et al. (2008), the more experience a VC

has, the more it is able to monitor its portfolio companies, and the better the investment per-

formance will be. Following Lee & Wahal (2004) and Nahata (2008), we use a VC’s age as a

proxy for its experience, which is available in the CVSOURCE database. Based on Sorensen

(2007) and Nahata (2008), we also use the cumulative number of investment rounds that a

VC had made as an alternative proxy for experience, and the results are qualitatively similar.

Hochberg et al. (2007) suggest that VCs with better networks have better access to future

deal flows, investment opportunities and information. Hochberg et al. (2010) further conclud-

ed that VC networks can be used by current VCs to prevent new entry and demand better deals

for their investments. Following Hochberg et al. (2007) and Nahata (2008), we measure a

VC’s network by the number of other VCs it had syndicated with within the previous 5 years on

a rolling basis and normalize it by the number of active VCs participating in at least one in-

vestment deal during the 5-year span. Considering the constantly changing investment envi-

ronment and the short VC investment cycle in emerging markets, we also measure a VC’s

network on a 3-year basis, and the results are qualitatively similar.

Nahata (2008) highlights the importance of reputation in the venture capital industry us-

ing the cumulative market capitalization of IPOs backed by VCs in the IPO market. A good

reputation attracts potential investors and helps in developing useful working relationships

with entrepreneurs, lawyers, investment bankers, auditors, and others that provide useful

services to entrepreneurial companies. Reputable VCs can also benefit from less costly and

larger fundraising exercises for future partnerships. Due to the immature capital markets in

China, many venture capital-backed entrepreneurial companies prefer to be listed on overseas

exchanges. Hence, it is difficult to measure the market capitalization of IPOs across the

world’s capital markets. We make use of the annual VC rank developed by the Zero2IPO group

to measure VC reputation. The Zero2IPO’s VC rank carries a lot of weight in China’s venture

capital industry. It takes into account each VC’s capital under management, new fundraising,

investment deals, investment amount, exit deals and investment return.

The regression model includes the following control variables: (a) characteristics of the

entrepreneurial companies (early, priorpatent);5 (b) syndicate size (synsize) (Casamatta &

Haritchabalet, 2007; Brander et al., 2002); (c) initial venture capital investment year fixed

5 Prior research suggests that a larger venture capital investment is associated with better entrepreneurial

company quality (Gompers, 1995; Mäkelä & Maula, 2006). Since VCs’ investment amounts are missing for a signif-

icant number of our sample entrepreneurial companies, we do not consider this effect in our reported results.

However, our results remain qualitatively the same if we control for the total venture capital investment in the first

round (unreported).

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effects to control for the influence of market conditions, such as competition among VCs for

investment opportunities, in the year of a lead VC’s first investment in an entrepreneurial

company (Hochberg et al., 2007; Nahata, 2008); and (d) the market condition in the year of a

VC’s exit (exit condition) (Lerner, 1994; Hochberg et al., 2007; Nahata, 2008). We also in-

clude the standard industry fixed effects.

Prior VC literature generally suggests that VCs’ experience, networks and reputation are

positively associated with successful exits in the form of IPOs and third-party buyouts

(Hochberg et al., 2007; Nahata, 2008). But these studies focus on developed countries. Wang

& Wang (2011) find little correlation between successful exits and VCs’ experience, networks

and reputation using a sample of cross-border venture capital investment in China. But they

ignore buyback exits, which turn out to be very important in emerging markets. Based on our

theory and the current literature, we expect the coefficients on experience, networks and

reputation to be positive for buyback exits, but insignificant for the other exit methods. Due to

a lack of strong priors, we cannot predict the signs of coefficients on other regression variables

for buybacks and unsuccessful exits. With regard to successful exits, based on prior research,

we expect the coefficient on early to be negative because entrepreneurial companies in the

early stage of development are likely to be riskier and therefore less likely to succeed. We also

expect the coefficient on priorpatent to be positive, since entrepreneurial companies with

more patents prior to the first round of venture capital investment are likely to have better

quality. Based on the evidence from the literature, we predict a positive coefficient on synsize

(Casamatta and Haritchabalet, 2007; Brander et al., 2002). Following Lerner (1994),

Hochberg et al. (2007) and Nahata (2008), we also predict a positive coefficient on exit condi-

tion, because VCs are more likely to exit successfully during times of favorable market condi-

tions.

From CVSOURCE we identified a total of 5,843 entrepreneurial companies that received

first round of investment from 1,605 VCs during our sample period 2000-2011. Due to data

limitation, we do not consider those first-round investments that occurred prior to 2000. After

excluding 567 entrepreneurial companies whose lead VCs could not be identified because the

amount of venture capital investment made in the first around was missing, we have 5,276

entrepreneurial companies receiving investment from 1,182 lead VCs. After excluding those

observations for which we cannot identify the exit type by the end of 2014, we have 4,187

entrepreneurial companies receiving investment from 1,053 lead VCs. Finally, after further

excluding those data points with other missing information, our sample contains 3,602 entre-

preneurial companies associated with 810 lead VCs.6

6 Our final sample of venture capital-financed entrepreneurial companies is much larger than a typical China

sample used in many cross-country studies, reducing the common concern of potential sample selection bias in the

venture capital literature. For example, Brander et al.’s (2015) China sample contains 1,226 venture capital-funded

11/26

<Insert Table 2>

Panels A, B, C and D of Table 2 show the distribution of the 3,602 entrepreneurial com-

panies by initial investment year, industry, region in China and exit type, respectively. The

number of observations and the corresponding percentages are given. Panel A is consistent

with the rapid expansion of China’s venture capital industry in recent years. Those industries

that have over 5% of the total venture capital investment include Information Transmission,

Computer Services and Software (22.23%), Equipment and Instruments Manufacturing

(10.02%), Automobile Manufacturing Industry (6.39%), Raw Chemical Materials and Chemi-

cal Products (6.05%) and Medical and Pharmaceutical Products (5.55%). Not surprisingly the

entrepreneurial companies are concentrated in relatively developed regions of China: Beijing,

Guangdong, Shanghai and Jiangsu. Panel D reports the frequency of each exit type. There are

a total of 2,312 exit events, of which 16.6% (384) are successful IPOs and third-party buyouts,

and 59.1% (1,366) are buyback exits. The remaining 11.3% (262) are unsuccessful exits. The

other 1,290 entrepreneurial companies had not exited as of the end of 2014. Much prior re-

search on U.S. venture capital has only looked at IPOs and third-party buyouts while ignoring

all other exits types due to a lack of data, but our evidence above suggests that this omission

could be problematic. We find that buyback exits form the majority of all exits in China.

<Insert Table 3>

Table 3 reports the descriptive statistics for the regression variables. Panel A reports re-

sults for the whole sample, Panel B reports results for the subsamples across exit types, and

Panel C reports results of the univariate difference test. The univariate difference test suggests

that VCs’ bargaining power proxied by experience, networks and reputation is strongly and

positively related to buyback exits. We also find that the distributions of our main and control

variables are not severely skewed and the independent variables are not so highly correlated as

to cause serious multicollinearity (unreported).

3.2. Empirical Results

Table 4 shows the regression results of the multinomial logit model. With regard to suc-

cessful exits, we do not find statistically significant coefficients on experience, networks and

reputation, which is inconsistent with the U.S. venture capital literature (Hochberg et al.,

2007; Nahata, 2008) but consistent with the one on venture capital in emerging markets

(Wang & Wang, 2011). Similarly, the coefficients on the three variables are all statistically

companies over 2000-2008 while our China sample contains almost twice the number over the same time period

(unreported). Dai et al. (2009, Table 7) cover only 175 venture capital-funded companies from China, Nahata et al.

(2014) do not cover China at all, while Chemmanur et al. (2014, Table 1) have only 400 venture capital-backed

companies from China in their sample.

12/26

insignificant with regard to unsuccessful exits. Consistent with our theory, the coefficients on

the three variables are significantly positive with regard to buyback exits. Models (1), (2) and

(3) separately include experience, networks and reputation, respectively. The coefficients are

all positive at the 1% significance level. Considering these positive overlaps among these three

variables, we include them all in model (4), and find that the coefficients on experience and

reputation remain significantly positive with regard to buyback exits. Taken together, our

regression results clearly indicate that VCs’ bargaining power is positively associated with

buyback exits, but is of little relevance to other exit types, which is consistent with our theory

that when a VC has strong bargaining power, a buyback exit can be an efficient choice in

emerging markets.

<Insert Table 4>

Many of the coefficients on the control variables are consistent with our predictions. Spe-

cifically, the coefficient on early is significantly negative for successful exits and buyback exits,

suggesting that entrepreneurial companies that received the first round of VC investment in

their early stages are less likely to exit successfully or exit through buybacks, ceteris paribus.

The coefficient on priorpatent is significantly positive for successful exits, suggesting that

entrepreneurial companies measured to be of a lower quality prior to the first round of in-

vestment by the lead VCs are less likely to exit successfully. The coefficient on synsize is signif-

icantly positive for successful exits, suggesting that entrepreneurial companies funded by lead

VCs belonging to a larger syndicate in the first round of investment are more likely to exit

successfully, ceteris paribus. Finally, the coefficient on exit condition is significantly positive

for all three types of exits, suggesting that external market conditions matter in VC-funded

companies’ exits.

Our theory suggests that buyback exits can be efficient under some circumstances in

emerging markets. The regional differences in the institutional environment within China

provide an opportunity to investigate model (1) across regions, which makes our conclusion

more convincing. We make use of the marketization index (Fan et al., 2006, 2007, 2008, 2009,

2010, 2011) to proxy for the local institutional environment for each province. We partition

our full sample into two subsamples, based on Fan et al.’s marketization index of the region

where an entrepreneurial company is headquartered. We predict that our theory would mainly

hold in the low marketization subsample. We conduct the same regressions as in Table 4 using

the high and low marketization subsamples. The results are reported in Table 5. Consistent

with our theory, the coefficients on experience, networks and reputation are all insignificant

for buyback exits for the high marketization subsample, but strongly significant for buyback

exits for the low marketization subsample.

<Insert Table 5>

13/26

To make our results more convincing, we conduct two separate multinomial logit regres-

sions. The first one tests successful exits versus other types of exits including buyback and

unsuccessful exits, and the second one tests buyback exits versus unsuccessful exits. Our

theory predicts the coefficients on VC’s bargaining power to be positive for the buyback exits

in the second multinomial logit regression and also positive for the other types of exits in the

first multinomial logit regression. Table 6 reports the results. Models (1) and (2) report the

first and second multinomial logit regressions respectively. The results are consistent with our

predictions. Specifically, the coefficients on experience and reputation are significantly posi-

tive for buyback exits in the second multinomial logit regression, and significantly positive for

the other types of exits in the first multinomial logit regression. Again, we conduct these two

multinomial logit regressions separately using the high and low marketization subsamples.

Models (3) and (4) report the results for the high marketization subsample, and models (5)

and (6) report the results for the low marketization subsample. Consistently, we find the above

results mainly apply in the low marketization subsample. We do not find consistent results in

the high marketization subsample.

Taken together, our empirical results are consistent with our theory, suggesting that when

a VC has strong bargaining power, buyback exits can be efficient in emerging markets.

4. Concluding Remarks

An interesting idea of our theory is that a simple and realistic equity-sharing contract with

an open option of buyback can lead to an efficient joint venture. The key to this result is the

incomplete-contract approach. It is the incompleteness of the contract that leads to an effi-

cient solution. Our solution closely resembles real-world buybacks in emerging markets.

Hence, our theory can explain the popularity of buybacks in emerging markets. We have also

conducted an empirical investigation on the venture capital market in China, the largest

emerging market in the world where there are many VCs. The empirical evidence lends sup-

port to our theory.

We did not investigate IPOs because IPOs in an emerging market are constrained by gov-

ernment policy, market conditions, institutional factors and investor experiences. With a

limited number of IPOs, data availability is a real constraint on our empirical analysis. Taking

the popularity of buybacks as given, we depart from the existing literature on IPOs and focus

instead on buybacks as a possible efficient solution in an emerging market.

14/26

Appendix

This appendix provides the proof of Proposition 1. This proof centers on the following two

conditions:

∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ (6)

∗ (7)

where equation (7) defines the variable and condition (6) imposes constraints on In the

following, we first provide two lemmas and then proceed to prove Proposition 1.

Lemma 1

Lemma 1. Suppose that ∗ ∗ is the first-best solution and ∗ If is sufficiently

large such that ∗( ∗, ∗) ∗ condition (6) can be satisfied by some

Proof: Since we have

→ ∗ → (8)

Then,

→ ∗

Hence, to satisfy (6) for some we only need ∗ ∗ ∗ ∗ ∗ ∗ ∗

which is satisfied if ∗( ∗, ∗) ∗ Hence, if is sufficiently large, there exists such

that (6) is satisfied. The proof is complete.

Lemma 2

Lemma 2. If ∗ and ∗ then there exists a solution to equation (7)

for any If further is strictly concave in then this solution is unique.

Proof: Given we have

∗ ∗

15/26

Hence, by the continuity of ∗ in there must exist such that

∗

Obviously, if ∗ is strictly concave in or ∗ is strictly monotonic (decreasing), this

must be unique. The proof is complete.

Given ∗ social welfare is ∗ and hence the welfare maximum solution ∗ (the

first-best solution) must satisfy ∗ ∗ Given a concave production function, if condi-

tion ∗ fails, then this first-best solution does not exist.

Proof of Proposition 1

Since in equilibrium the two parties will always come to an agreement ex post on buyback,

by condition (2), no matter what is, as long as the contract imposes ∗ the EN will always

invest such that ∗ That is, will be chosen such that ∗ Hence, we do

not need to know what is as long as the VC exits at (which happens in equilibrium).

This means that we do not need to consider condition (3). Hence, we only need to choose to

satisfy the two IR conditions.

Let us now find out how is determined by the two IR conditions. The first IR condition

means that ∗ ∗ ∗ ∗ ∗ ∗

which can be written as ∗ ∗ ∗ ∗ ∗

The second IR condition means that ∗ ∗ ∗ ∗

Hence, we need to show the existence of such that (6) is satisfied. This is done with Lemma 1.

The proof is complete.

16/26

Table 1. Variable definitions

Variable Definition Source

Dependent variable:

exit type Equals 1 for entrepreneurial companies with successful exits including IPOs and third-party buyouts, 2 for entrepreneurial companies with buyback exits, 3 for entrepreneurial companies with other unsuccessful exits includ-ing bankruptcy and secondary sale, 0 for all the other entrepreneurial companies with no exits up to the end of 2014.

CVSOURCE, SAIC

Independent variables:

experience The natural logarithm of one plus the lead VC’s age since its formation measured at the beginning of the initial investment year.

CVSOURCE

networks The lead VC’s network, measured as the number of VCs the lead VC had cooperated with over the past 3 years divided by the number of active VCs over the past 5 years minus one, measured at the beginning of the initial investment year.

CVSOURCE

reputation The lead VC’s reputation, proxied by a dummy variable indicating whether the lead VC was one of the top 50 VCs in China at the beginning of the initial investment year.

Zero2IPO

Control variables

early A dummy variable indicating whether the first round of venture capital investment occurred at the early stage of an entrepreneurial company’s development as defined by CVSOURCE.

CVSOURCE

pripatent The natural logarithm of one plus the number of patent applications filed by the entrepreneurial company prior to the first round of venture capital investment.

SIPO

synsize The natural logarithm of the number of VCs participating in the entrepre-neurial company’s first round of venture capital investment.

CVSOURCE

exit condition Condition of the venture capital industry upon exit, defined as the natural logarithm of the number of exits over the prior year if exit type equals 0, and as the natural logarithm of the number of exits over the 4 quarters prior to the exit date if exit type equals 1, 2 or 3.

CVSOURCE

Other controls:

Industry Firms’ industry fixed effects. CVSOURCE

Year Initial investment year fixed effects. CVSOURCE

Table 2. Sample distribution

Panel A. Sample distribution by year Initial investment year Obs. % 2000 85 2.360 2001 110 3.054 2002 104 2.887 2003 124 3.443 2004 165 4.581 2005 158 4.386 2006 212 5.886 2007 395 10.966 2008 397 11.022 2009 379 10.522 2010 608 16.880 2011 865 24.014 Total 3602 100

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Panel B. Sample distribution by industry

Industry Obs. %

Agriculture，Forestry，Animal Husbandry and Fishery 65 1.805 Mining Industry 29 0.805 Manufacturing

Farm Products Processing 36 0.999 Food Manufacturing 36 0.999 Beverage Manufacturing 26 0.722 Textile Industry 13 0.361 Textile Garments Manufacturing 10 0.278 Leather, Fur, Feather and its products and Footwear 6 0.167 Timber Processing, Bamboo, Cane, Palm Fiber and Straw Products 13 0.361 Furniture Manufacturing 9 0.250 Paper Making and Paper Products 12 0.333 Printing and Record Medium Reproduction 4 0.111 Cultural, Educational, Sports and Entertainment Products 9 0.250 Petroleum Processing, Coking and Nuclear Fuel Processing 9 0.250 Raw Chemical Materials and Chemical Products 218 6.052 Medical and Pharmaceutical Products 200 5.552 Chemical Fiber 9 0.250 Rubber and Plastic Products 7 0.194 Nonmetal Mineral Products 51 1.416 Smelting and Pressing of Ferrous Metals 66 1.832 Smelting and Pressing of Non-ferrous Metals 6 0.167 Metal Products 44 1.222 Ordinary Machinery Manufacturing 41 1.138 Special Purpose Equipment Manufacturing 135 3.748 Automobile Manufacturing Industry 230 6.385 Railroad, Marine, Aviation and Other Transport Equipment Manufacturing Industry

85 2.360

Telecommunication Equipment, Computer and Other Electronic Product 187 5.192 Equipment and Instruments Manufacturing 361 10.022 Other Manufacturing Industry 76 2.110 Comprehensive Utilization of Waste Resources 18 0.500 Metal products, Machinery and Equipment Repair Industry 5 0.139

Production and Supply of Electricity, Heat, Gas and Water 39 1.083 Construction 26 0.722 Transport，Storage and Post 26 0.722 Information Transmission，Computer Services and Software 802 22.265 Wholesale and Retail Trades 172 4.775 Hotels and Catering Services 24 0.666 Financial Intermediation 74 2.054 Real Estate 11 0.305 Leasing and Business Services 145 4.026 Scientific Research，Technical Services，and Geological Prospecting 63 1.749 Management of Water Conservancy，Environment and Public Facilities 46 1.277 Services to Households and Other Services 12 0.333 Education 48 1.333 Health，Social Securities and Social Welfare 20 0.555 Culture，Sports and Entertainment 78 2.165 Total 3,602 100

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Panel C. Sample distribution by province

Province Obs. %

Anhui 53 1.471 Beijing 844 23.431 Fujian 72 1.999 Gansu 8 0.222 Guangdong 536 14.881 Guangxi 14 0.389 Guizhou 9 0.250 Hainan 7 0.194 Hebei 41 1.138 Henan 49 1.360 Heilongjiang 24 0.666 Hubei 97 2.693 Hunan 120 3.331 Jilin 19 0.527 Jiangsu 415 11.521 Jiangxi 34 0.944 Liaoning 39 1.083 Inner Mongolia 10 0.278 Ningxia 2 0.056 Qinghai 3 0.083 Shandong 146 4.053 Shanxi 8 0.222 Shaanxi 94 2.610 Shanghai 466 12.937 Sichuan 98 2.721 Tianjin 43 1.194 Tibet 2 0.056 Xinjiang 24 0.666 Yunnan 19 0.527 Zhejiang 243 6.746 Chongqing 63 1.749 Total 3,602 100

Panel D. Sample distribution by exit type

Exit type Obs. %

Successful exits IPO 522 22.578 Third party buyouts 162 7.007

Buyback 1,366 59.083 Unsuccessful exits

Secondary sale 132 5.709 Liquidation 130 5.623

Total 2,312 100

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Table 3. Descriptive statistics of the regression variables

Panel A. The whole sample Variable mean sd. min 0.25 median 0.75 max obs.

exit type 1.1666 1.0004 0.0000 0.0000 1.0000 2.0000 3.0000 3602 experience 1.8471 0.7432 0.6931 1.0986 1.9459 2.3979 3.5553 3602 networks 0.0193 0.0275 0.0000 0.0000 0.0068 0.0252 0.1156 3602 reputation 0.4731 0.4993 0.0000 0.0000 0.0000 1.0000 1.0000 3602 Early 0.7193 0.4494 0.0000 0.0000 1.0000 1.0000 1.0000 3602 priorpatent 0.7944 1.1906 0.0000 0.0000 0.0000 1.3863 4.3175 3602 synsize 0.9765 0.3900 0.6931 0.6931 0.6931 1.0986 2.1972 3602 exit condition 5.6435 1.0435 2.0794 5.0173 5.7683 6.6657 6.7627 3602

Panel B. The subsamples across exit type

Variable Successful exit Buyback exit Unsuccessful exit No exit

mean medi-an

obs. mean median obs. mean median obs. mean median obs.

experience 1.8056 1.7918 684 1.9453 1.9459 1366 1.7595 1.6094 262 1.7829 1.7918 1290 networks 0.0200 0.0066 684 0.0208 0.0088 1366 0.0209 0.0055 262 0.0169 0.0055 1290

reputation 0.4664 0.0000

684 0.5132 1.0000 1366 0.4771 0.0000 262 0.4333 0.0000 1290

early 0.5409 1.0000 684 0.7445 1.0000 1366 0.8664 1.0000 262 0.7574 1.0000 1290

priorpatent 0.8977 0.0000

684 0.6724 0.0000 1366 0.3894 0.0000 262 0.9512 0.0000 1290

synsize 1.1552 1.0986 684 0.9420 0.6931 1366 0.9508 0.6931 262 0.9236 0.6931 1290 exit condition 5.9715 6.2126 684 5.5172 5.7683 1366 5.8015 5.9989 262 5.5713 5.7683 1290

Panel C. The difference tests

Variables Successful exit vs. no exit Buyback vs. no exit Unsuccessful exit vs. no exit

t-test ranksum t-test ranksum t-test ranksum

experience 0.026** 0.018** 0.001*** 0.000*** 0.839 0.574 networks 0.074* 0.465 0.001*** 0.005*** 0.012** 0.608 reputation 0.431 0.431 0.001*** 0.001*** 0.078* 0.078* early 0.000*** 0.000*** 0.204 0.204 0.001*** 0.001*** priorpatent 0.771 0.147 0.000*** 0.000*** 0.000*** 0.000*** synsize 0.000*** 0.000*** 0.112 0.403 0.042** 0.295 exit condition 0.000*** 0.000*** 0.172 0.008*** 0.001*** 0.006***

Table 4. Results of the multinomial logit regression

Variable (1) (2) (3) (4) Successful exit

Buyback exit

Unsuc-cessful exit

Successful exit

Buyback exit

Unsuc-cessful exit

Successful exit

Buyback exit

Unsuc-cessful exit

Successful exit

Buyback exit

Unsuc-cessful exit

Constant -12.078*** -2.042*** -11.772*** -12.115*** -1.775*** -11.713*** -12.110*** -1.831*** -11.736*** -12.116*** -2.079*** -11.818*** (0.000) (0.002) (0.000) (0.000) (0.007) (0.000) (0.000) (0.005) (0.000) (0.000) (0.001) (0.000) experience 0.066 0.288*** 0.126 0.011 0.239*** 0.086 (0.458) (0.000) (0.288) (0.908) (0.000) (0.497) networks 3.786 4.459*** 3.274 2.827 -0.038 0.833 (0.120) (0.003) (0.280) (0.337) (0.983) (0.821) reputation 0.168 0.335*** 0.228 0.065 0.225** 0.162 (0.176) (0.000) (0.176) (0.666) (0.026) (0.428) early -1.106*** -0.197** 0.207 -1.105*** -0.207** 0.208 -1.100*** -0.199** 0.211 -1.097*** -0.188* 0.212 (0.000) (0.042) (0.360) (0.000) (0.032) (0.356) (0.000) (0.039) (0.348) (0.000) (0.052) (0.348) priorpatent 0.008* 0.003 -0.007 0.008* 0.003 -0.007 0.008* 0.003 -0.007 0.008* 0.003 -0.007 (0.069) (0.365) (0.550) (0.075) (0.413) (0.544) (0.078) (0.434) (0.547) (0.072) (0.403) (0.550) synsize 0.995*** -0.063 -0.330 0.988*** -0.050 -0.326 0.987*** -0.071 -0.339 0.974*** -0.085 -0.350 (0.000) (0.578) (0.188) (0.000) (0.659) (0.197) (0.000) (0.532) (0.180) (0.000) (0.456) (0.169) exit condition 4.335*** 0.575*** 4.380*** 4.345*** 0.577*** 4.387*** 4.328*** 0.556*** 4.369*** 4.336*** 0.566*** 4.379*** (0.000) (0.007) (0.000) (0.000) (0.007) (0.000) (0.000) (0.009) (0.000) (0.000) (0.008) (0.000) Industry Yes Yes Yes Yes Initial VC invest-ment year

Yes Yes Yes Yes

Observations 3,602 3,602 3,602 3,602 Pseudo R-squared 0.280 0.278 0.279 0.281

See Table 1 for all variable definitions. The p-values adjusted for heteroskedasticity are reported in parentheses. The significance levels at 1%, 5% and 10% are identi-fied by ***, ** and *, respectively.

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Table 5. Subsample analysis on marketization

Panel A. High marketization subsample

Variable (1) (2) (3) (4) Successful exit

Buyback exit

Unsuc-cessful exit

Successful exit

Buyback exit

Unsuc-cessful exit

Successful exit

Buyback exit

Unsuc-cessful exit

Successful exit

Buyback exit

Unsuc-cessful exit

Constant -14.888*** -2.467* -34.378*** -14.706*** -2.251* -34.081*** -14.951*** -2.362* -34.433*** -14.553*** -2.381* -34.575*** (0.000) (0.061) (0.000) (0.000) (0.089) (0.000) (0.000) (0.076) (0.000) (0.000) (0.075) (0.000) experience 0.015 0.143 0.247 -0.102 0.072 0.163 (0.943) (0.243) (0.319) (0.659) (0.607) (0.536) networks 10.860 6.908 4.585 12.188 2.852 -4.634 (0.142) (0.198) (0.658) (0.213) (0.671) (0.729) reputation 0.171 0.251 0.582 -0.088 0.157 0.624 (0.565) (0.167) (0.137) (0.825) (0.486) (0.198) early -1.590*** -0.515** -0.836 -1.590*** -0.518** -0.832 -1.579*** -0.519** -0.845 -1.592*** -0.511** -0.825 (0.000) (0.018) (0.123) (0.000) (0.017) (0.124) (0.000) (0.017) (0.113) (0.000) (0.019) (0.127) priorpatent 0.013 -0.002 -0.004 0.013 -0.002 -0.004 0.013 -0.002 -0.003 0.013 -0.002 -0.002 (0.165) (0.820) (0.895) (0.162) (0.811) (0.908) (0.184) (0.752) (0.913) (0.173) (0.774) (0.954) synsize 1.203*** -0.498* -0.189 1.123*** -0.514* -0.103 1.180*** -0.499* -0.191 1.141*** -0.521* -0.172 (0.001) (0.063) (0.761) (0.001) (0.060) (0.867) (0.001) (0.065) (0.753) (0.001) (0.054) (0.782) exit condition 5.040*** 0.630** 4.592*** 5.023*** 0.608* 4.547*** 5.037*** 0.617** 4.581*** 5.035*** 0.613* 4.533*** (0.000) (0.044) (0.000) (0.000) (0.052) (0.000) (0.000) (0.047) (0.000) (0.000) (0.051) (0.000) Industry Yes Yes Yes Yes Initial VC invest-ment year

Yes Yes Yes Yes

Observations 842 842 842 842 Pseudo R-squared 0.352 0.352 0.352 0.354

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Panel B. Low marketization subsample

Variable (1) (2) (3) (4) Successful exit

Buyback exit

Unsuc-cessful exit

Successful exit

Buyback exit

Unsuc-cessful exit

Successful exit

Buyback exit

Unsuc-cessful exit

Successful exit

Buyback exit

Unsuc-cessful exit

Constant -11.565*** -1.835** -12.102*** -11.596*** -1.508** -12.097*** -11.588*** -1.580** -12.082*** -11.624*** -1.886** -12.154*** (0.000) (0.016) (0.000) (0.000) (0.046) (0.000) (0.000) (0.034) (0.000) (0.000) (0.013) (0.000) experience 0.079 0.328*** 0.099 0.036 0.290*** 0.051 (0.436) (0.000) (0.478) (0.746) (0.000) (0.730) networks 3.000 3.769** 3.100 2.055 -0.937 2.906 (0.254) (0.018) (0.340) (0.517) (0.635) (0.455) reputation 0.159 0.335*** 0.096 0.067 0.241** -0.030 (0.251) (0.000) (0.616) (0.688) (0.038) (0.897) early -0.984*** -0.085 0.537** -0.984*** -0.100 0.540** -0.978*** -0.091 0.547** -0.975*** -0.077 0.549** (0.000) (0.437) (0.037) (0.000) (0.359) (0.036) (0.000) (0.403) (0.033) (0.000) (0.482) (0.033) priorpatent 0.008 0.005 -0.006 0.007 0.005 -0.006 0.008 0.005 -0.006 0.008 0.005 -0.006 (0.128) (0.224) (0.611) (0.142) (0.257) (0.592) (0.139) (0.258) (0.595) (0.131) (0.238) (0.592) synsize 0.928*** 0.039 -0.333 0.926*** 0.052 -0.335 0.920*** 0.025 -0.333 0.912*** 0.016 -0.345 (0.000) (0.759) (0.243) (0.000) (0.682) (0.243) (0.000) (0.845) (0.245) (0.000) (0.898) (0.231) exit condition 4.257*** 0.500** 4.454*** 4.269*** 0.500** 4.463*** 4.253*** 0.479* 4.450*** 4.263*** 0.489** 4.463*** (0.000) (0.046) (0.000) (0.000) (0.046) (0.000) (0.000) (0.052) (0.000) (0.000) (0.049) (0.000) Industry Yes Yes Yes Yes Initial VC invest-ment year

Yes Yes Yes Yes

Observations 2,760 2,760 2,760 2,760 Pseudo R-squared 0.274 0.271 0.272 0.275

See Table 1 for all variable definitions. The p-values adjusted for heteroskedasticity are reported in parentheses. The significance levels at 1%, 5% and 10% are identi-fied by ***, ** and *, respectively.

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Table 6. Results of an alternative multinomial logit regression

Variable

(1) (2) (3) (4) (5) (6) Whole sample Other exits High marketization subsample Low marketization subsample Successful exit

Other exit Buyback exit

Unsuccess-ful exit

Successful exit

Other exit Buyback exit

Unsuccess-ful exit

Successful exit

Other exit Buyback exit

Unsuccess-ful exit

Constant -11.859*** -4.377*** -2.719*** -12.089*** -13.862*** -5.144*** -3.777** -37.736*** -11.254*** -4.202*** -2.353** -12.699*** (0.000) (0.000) (0.003) (0.000) (0.000) (0.000) (0.021) (0.000) (0.000) (0.000) (0.020) (0.000) experience 0.054 0.227*** 0.240*** 0.199 -0.154 0.091 0.090 0.099 0.093 0.271*** 0.295*** 0.184 (0.546) (0.000) (0.000) (0.157) (0.479) (0.504) (0.527) (0.762) (0.361) (0.000) (0.000) (0.266) networks 2.007 0.147 -0.082 -2.746 17.463* 1.220 3.025 -9.526 0.362 -0.472 -0.857 -0.648 (0.479) (0.936) (0.965) (0.573) (0.055) (0.850) (0.655) (0.662) (0.906) (0.809) (0.669) (0.899) reputation 0.069 0.219** 0.219** 0.156 -0.197 0.217 0.148 0.991 0.105 0.215* 0.222* -0.053 (0.638) (0.029) (0.031) (0.541) (0.575) (0.327) (0.512) (0.104) (0.527) (0.062) (0.057) (0.855) early -1.190*** -0.131 -0.209** 0.051 -1.563*** -0.523** -0.521** -1.062 -1.103*** 0.005 -0.104 0.400 (0.000) (0.180) (0.033) (0.838) (0.000) (0.019) (0.018) (0.111) (0.000) (0.963) (0.349) (0.148) priorpatent 0.010** 0.002 0.003 0.004 0.011 -0.002 -0.001 0.007 0.010** 0.004 0.005 0.005 (0.022) (0.624) (0.431) (0.759) (0.179) (0.769) (0.837) (0.818) (0.042) (0.400) (0.259) (0.677) synsize 1.016*** -0.114 -0.081 -0.246 1.094*** -0.467* -0.530* -0.020 0.968*** -0.020 0.020 -0.215 (0.000) (0.324) (0.477) (0.422) (0.001) (0.081) (0.053) (0.980) (0.000) (0.878) (0.874) (0.522) exit condition 3.359*** 1.814*** 0.895** 4.387*** 4.085*** 2.046*** 1.290** 5.035*** 3.216*** 1.746*** 0.732* 4.530*** (0.000) (0.000) (0.016) (0.000) (0.000) (0.000) (0.022) (0.000) (0.000) (0.000) (0.074) (0.000) Industry Yes Yes Yes Yes Initial VC invest-ment year

Yes Yes Yes Yes

Observations 3,602 2,918 842 676 2760 2,242 Pseudo R-squared 0.216 0.212 0.301 0.250 0.204 0.220

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