Rob Dixon John Ritchie Di Guo - Cass Business School · Di Guo Economics, Finance and Business...

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The Impact of Governance Structure and Financial Constraints on

Risk Tolerance of VCs: An Empirical Work on China’s Venture Capital

Industry

Rob Dixon Economics, Finance and Business School,

University of Durham E-mail: [email protected]

John Ritchie Economics, Finance and Business School,


Di Guo Economics, Finance and Business School,


Appreciation is expressed to Maozu Lu, Guy Liu, and Yan Guo for comments

and advices on earlier version of the manuscript.

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The Impact of Governance Structure and Financial Constraints on Risk

Tolerance of VCs: An Empirical Work on China’s Venture Capital Industry

Abstract

This paper examines the impact of governance structures and soft budget

constraints on the risk tolerance of venture capitalist institutions (hereinafter as VCs)

under Chinese institutional environments. Using the financing stage and technological

preference as proxy risk tolerance scales, VCs with different governance structure and

budget constraints demonstrate various risk taking. The findings from 648 projects

invested by 56 VCs in China and selected unstructured interviews are consistent with

those of Qian and Xu (1999) and Mayer (2002) regarding the significant difference

between hierarchic VCs with harder budget constraints compared with VCs with

softer budget constraints with respect to risk tolerance.

Key words: risk tolerance, venture capital institutions, governance structures,

financial constraints,

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Section 1 introduction

The development of hi-technology sectors has for long been seen as important for

improving the prosperity and vitality of the national economy (Marx, 1978;

Schumpeter, 1950). However, traditional financial institutions were reluctant to invest

in risky hi-tech sectors, especially newly established hi-tech firms. Starting from

1950s in the U.S., venture capital investment has shown striking capability in

nurturing new established hi-tech firms with its special risk return profile.

With unique designs of monitoring mechanism and incentive schemes, venture

capitalists demonstrate a higher degree of risk tolerance than traditional financing

institutions. Nonetheless, studies show that VCs’ risk tolerance degree is far from

homonymous (Elango et al., 1995; Mani et al., 2002; Robinson, 1987, etc). Some VCs

show more risk taking with preference to earlier staged firms with higher

technological intensity while others prefer later staged firms with lower technological

intensity (Wang, 2002; Mayer, 2002; Sahlman, 1990).

Why do VCs differ from each other in investment preference? Are there any

determining factors on the investment choice of VCs? Researchers have attempted to

explain it using both portfolio theories and organizational theories. In this study, we

aim to test the relationship between risk taking degree of VCs and their governance

structures with the perspectives of Soft Budget Constraints (hereinafter SBC) under

China’s institutional arrangments.

As a developing Asian country that is experiencing the transition from planned

economy to market economy system, China has long seen science and technology as a

critical part of its search for economic development and national competitive

capability. As a result, venture capital in the Chinese context has been officially

promoted as a critical mechanism for linking scientific and technological capabilities

and outputs on one hand, with national and regional economic and social development

on the other. Since 1985, when the first venture capital institution was built, China’s

venture capital industry has developed dramatically in terms of the amount and

sources of funds, the types of venture capital institutions and the diversity of

investment preference of VCs.

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According to the theory of SBC, hierarchic organizations with softer budget

constraints are generally less risk taking than independent organizations with harder

budget constraints (Qian & Xu, 1999). Taking financing stages and technological

intensity as risk tolerance scales, and classifying VCs by their governance structures

based on different degree of budget constraints, our study explores whether there is a

statistically significant difference in risk taking between foreign limited partnership

VCs (hereinafter FLPVCs) and domestic hierarchic VCs (hereinafter DVCs), while

the former is more risk taking than the latter in China.

By exploring company documents from various sources, we build up a dataset

covering 648 projects invested by 56 VCs in China, with unstructured interviews with

VCs and entrepreneurs backed by a foreign limited partnership VC, as well as using

further documentations.

This is among the first empirical works on this subject with such a large dataset.

This study could provide a basis for further research in cross country comparative

work in the venture capital industry, with significance for understanding how soft

budget constraints impact upon innovation financing in contemporary transitional

economies, with implications for policy makers concerned with adjusting legal

frameworks and economic strategies and encouraging VCs to meet different hi-tech

development objectives.

The rest of this paper is organized as follows: Section 2 introduces the research

background. Section 3 clarifies the research questions and measurement definitions.

Section 4 introduces the methodology employed. Section 5 discusses the findings

Section 6 is provides the conclusion with future implications. Appendix 1 is the

bibliography, Appendixes 2-4, statistical analyses, Appendix 5, the case study

profiles.

Section 2 Research background

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1. China’s venture capital industry

Starting from mid 1980s, China’s venture capital industry has experienced a

dramatic transformation. Early in 1984, the concept of venture capital was officially

brought forward by National Research Centre of Science and Technology for

Development (under State Science and Technology Commission, SSTC) with the

suggestion that a new venture capital system should be established to promote the

technological development. Consequently, China New Technology Venture

Investment Corporation was formed in 1986 as the first venture capital institution by

the State Science and Technology Commission and the Ministry of Finance.

Operating as a central government agency, its major mission was to support national

technology venture rather than financial organization. The performance of China New

Technology Venture Investment Corporation was not satisfactory and suffered

investment losses amounting to 30m RMB till 1996 when it closed.

Hence, in 1989, China’s first Sino-foreign joint venture in venture capital was

founded with the approval of the State Council and Ministry of Foreign Trade and

Economic Cooperation (MOFTEC). This VC was established with the mission of

funding the industrialization of R&D projects supported by national high-tech

promoting programmes. (863, Torch)1. Chinese government would expand funding

sources by setting up more commercialized hi-tech financing organizations. In 1991,

the State Council announced the “Authorization of National High-Tech Zones and

Related Policies”; allowing relevant departments to set up VC funds in high-tech

zones to support risky high-tech industry development, and mature high-tech zones

could set up VC corporations. Consequently, a series of VCs were established by

local governments in Shenyang, Shanxi, Guangdong, Shanghai and Zhejiang in 1992.

This led to a wave of VC organizational establishments. However, the industry

encountered problem with the experience of domestic venture capitalists drawn from

governmental sectors, the lack of surplus funds for its further investment prior to the

exit of the first invested projects, and thus the constraints of early institutional 1 863 and torch programmes are two important projects in supporting certain promising new hi-tech enterprises

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environment compelled the introduction of a more feasible mechanism for developing

venture capital.

In 1997, Deng Nan (Vice Minister of SSTC), when overseeing VC system,

directed a group from the School of Economics and Management of Tsinghua

University to report about a better VC structure, the relationship between VC and

capital markets, and plan for developing the VC system. That was the first time

central government formally sought academic support for venture capital

development. In the same year, Zindart, a toy manufacturer that had received

investment from ChinaVest in 1993, listed ADRs on NASDAQ, was one of the first

examples of venture capital investment exit.

At the same time, a new tech-based venture AsiaInfo received US$18 million

investment from 3 foreign VC institutions. This was the largest investment from

foreign VCs. ‘Sohu.com’ also received US$6.5 million from foreign VCs as the first

new venture in China’s IT industry to receive such investment. Later, both companies

were listed on NASDAQ, and, since both were foreign investments, researchers

question that whether the foreign venture capitalist practices were more effective.

In March of 1998, the Central Committee of Chinese National Democratic

Constructive Association presented ‘Proposal for developing China’s VC Industry’ at

the Ninth Conference of the NPC. This proposal attracted the serious attention of both

the academics and policy makers. Having been clarified the legitimacy of corporate

VC institutions, venture capital shifted from being a solitary topic of policy research,

discussion and experimentation, or a form of government subsidization of new

technology ventures, to being a rapidly growing segment of China’s commercial

financial system (White, Gao, Zhang, 2002).

2. Data concerning China’s venture capital industry

China’s venture capital industry shows a successive increase in the number of

VC institutions and funds under management from 1994 to 2001. According to the

with the low-interest rate loans.

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China Venture Capital Yearbook 2002, the number of VC institutions increased from

17 in 19942 to 246 by the end of 2001, these VCs have supported over 800 projects

among which over 80% are categorized as hi-tech sector (CVCY, 2002). The

cumulative VC funds under management increased from 2 billion RMB in 1994 to 40

billion RMB till 2001, which shows a 20 times of increase in investment within 7

years (Chart 2.1). However, the rate of increase was not even. From 2.2 billion RMB

under management in 1994, the two sharpest increases occurred in 1997 and 1999

respectively.

Chart 2.1 Increase of venture capital under management 1994-2002

0500000

100000015000002000000250000030000003500000400000045000005000000

1 2 3 4 5 6 7 8 91994-2002

Am

ount

of i

nves

tmen

t un

it: 1

0000

RM

B

0

0.2

0.4

0.6

0.8

1

1.2

Incr

emen

t rat

etotal amount new investment Increment rate

Source: China’s Venture Capital Yearbook 2002

The three major sources of venture capital investment in China are: central and

local government, domestic corporations and foreign venture capital funds, which

contribute 34%, 37%, 21.9% respectively to total VC funds in 2001. The three

sources account for about 92% of the total funds as in chart 2.2-a. VC fund sources

have diversified during the years as shown in chart 2.2-b. While the government has

long been a major capital provider, its percentage contribution has been successively

reduced. The role of foreign VC funds has fluctuated, from being the second funds

2 The reliable statistical data concerning venture capital were collected from 1994. Therefore, in this paper, all statistical data are counted from 1994.

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provider before 1999, it fell to third important with 22% overall. Funds from domestic

corporations have grown considerably to 37% of total capital by 2001.

Chart2.2-a Source of China's venture capital investment in 2001

34. 3%

37. 0%

21. 9%

4. 1% 2. 7%

Governments Domestic corporationsForeign VC firms Domestic Financial institutionsOther institutions and individuals


Chart 2.2-b Source of China's venture capital investment 1994-2001

0%

10%

20%

30%

40%

50%

60%

70%

1 2 3 4 5 6 7 81994- 2001

proportion

Governments

Domestic corporations

Foreign VC firms

Domestic FinancialinstitutionsOther institutions andindividuals


According to CVCY 2002, China’s venture capital industry shows distinct

diversity in terms of the capital flow in financing stages, industrial sectors, and

regional allocations.

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First, the venture capital market is segmented by financing stages. Of the

number of projects, over 71% of VC backed (803 cases) are at the early stage of

development (11.6% at seed stage; 30.5% at start-up stage and 33.4% at growing

stage). Only 16.3% of total are at expansion stage and 8.2% are at mature stage. (See

Chart 2.3-c).

Chart 2.3-c Distribution of venture capital investment by stages (counted by cases)

16. 3%

8. 2%

11. 6%

30. 5%

33. 4% Expanding stageMature stageSeed stageStartup stageGrowing stage


Moreover, as shown in chart 2.3-e, most investments concentrate on special

industrial sectors3. About 45% VC backed projects are in the IT sector and less than

10% in traditional sectors.

3 Distribution of venture capital investment by sector is accounted for by cases but not by the capital invested until the end of 2001.

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Chart 2.3-e Distribution of VC backed projects by industry

7.47%20.55%

15.44%

2.74%

9.47%

44.33%

IT

Communication

Pharmacy and bio-techNew material

O ther Hi-tech

Traditionalindustry


3. Risk tolerance and VCs

Risk aversion is the fundamental investment principle. Concerns about risks are

related to the high degree of uncertainty. Previous work on the screening process of

VCs suggest the particular concerns about the uncertainty of future market,

management team, products and service, business model, and cash out potential

(Macmillan, Zemann & Subbanarasimha, 1987; Tybee & Bruno, 1984; Quindlen,

2000; Kaplan & Stromberg, 2002). Risk factors are often related to the growth stage

of firms and the technological intensity of the projects (Churchill & Lewis, 1983;

Ruhnka & Young, 1987; Wang, 2002).

Ruhnka and Young (1987) created a model consisting five sequential stages in

the development process based on the views of VCs. They found a strong census on

key development goals or benchmarks in various stages, and developmental risks

associated with each stage. It is commonly believed that early stage ventures face

considerable management, market, and technological uncertainty. Empirical research

shows that VCs believe the risk of loss of their investment is much higher for early-

stage investments (Elango et al., 1995; Ruhnka and Young (1991). In addition,

researchers argue that different technological intensity determines uncertainty about

projects. More intensive technology imposes higher risks due to the more serious

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issues of informational asymmetry and the market uncertainty. Thus, VCs’ preference

about financing stages and technological sectors is an important scale in testing the

risk tolerance of VCs (e.g. Wetzel, 1981; Wang, 2002; Mayer, 2002, etc.).

Empirical research (Wetzel, 1981; Elango et al., 1995) suggests that there is a

significant difference in risks profiles by stages and technologies in venture capital

investment. Hence, questions raised here are: Why do VCs differ regarding

investment preference? Are there any particular determinants influencing on

investment choice of VCs?

Previous literature has explained the reason for the investment choice of VCs by

employing Markotwiz’s portfolio methodology. It attributes the results to resource

constraints (Elango et al., 1995), cost considerations (Lerner, 1995) and

macroeconomic environments. These studies mainly focus on explaining why VCs

diversify their investment at different stages and in technological sectors, but they

cannot explain why the venture capital market is divided into different segments as

preferred by different forms of VCs (Wang, 2002).

Recently, researchers (Mayer, 2002; Sahlman, 1990; Wang, 2002) tend to

emphasize the impact of governance structures on the risk tolerance of VCs. Sahlman

(1990) and Bleicher & Paul (1987) state that the hierarchic VCs typically lack an

internal incentive arrangement based on the performance and thus conduct financing

activities on an ad hoc basis. Such a governance structure would discourage hierarchic

VCs to take risks. Wang (2002) tested the risk tolerance degree of three different

types of VCs (i.e. non-organizational VCs, independent VCs and hierarchic VCs

including strategic VCs and financial VCs) and found that there is relationship

between the governance structures to the risk tolerance of VCs.

Moreover, researchers begin to employ the theories of soft budget constraints

(hereinafter as SBC) to explain financing intermediaries (Dewatripont & Maskin,

1995; Qian & Xu, 1999; Dewatripont & Rolland, 1999). Qian and Xu’s work (1999)

is the first one, which sets up a model explaining innovation project financing with

the perspective of SBC. They argue that the lack of commitment to terminate bad

investment projects due to the soft budget constraints of hierarchic investment

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institutions lead to reduced risk taking in investing projects of higher uncertainty. The

findings of Mayer’s empirical work (2002), which examines the degree of risk

tolerance of VCs in UK and Germany, are consistent with Qian and Xu (1999).

SBC has emerged as a very suitable approach in researches in transitional

economies and financial intermediaries. However, empirical work in venture capital

under this framework is still scarce. Our research aims to fill this gap by testing the

relationship between degrees in risk taking and VCs with a different scale of budget

constraints in China.

Section 3 Research questions 1. Research questions statements

As we have stated in previous sections, there are three major types of VC

institutions operating in China, i.e. foreign fund backed VCs (FVCs), government

backed VCs (GVCs) and corporate backed VCs (CVCs). Most FVCs employ limited

partnership structure except for certain strategic VCs under management of large

foreign corporations. GVCs and CVCs are all generally limited companies, which are

under control of their parent company or higher-level supervisional organizations;

Thus, they are of a typical hierarchical governance structure.

Limited partnership VCs base their governance structure on agency contracts

with investors (Sahlman, 1990). Under such a legal structure, VCs serve as an agent

to gain financial revenues for their principals. In order to avoid a general partner

taking undue risks, the contracts are designed to limit life time (Sahlman, 1990).

Thus, limited partners have a very hard budget constraints based on their unlimited

responsibility and limited fund contracts with backers.

Our initial research found that even though GVCs and CVCs are all under

hierarchical governance structure, GVCs do differ from CVCs in a number of

organizational factors, especially the intensity of budget constraints. GVCs operate

with much softer financial constraints. Most of the leaders are former governmental

officers or appointed by related bureaus. Thus, governments not only act as fund

providers, but also fund managers. This suggests that GVCs maintain relatively soft

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budget constraints. Our interviewee ---Dr. X (an executive officer of a GVC) stated

that soft budget constraints cannot be avoided by saying that: [‘All of our

shareholders are governments or government tied, so most of time our work is to

support their policies. But testing policies are risky, and it’s costly, right? So it is

reasonable that we get compensations (refinancing) for the costs.’] This shows that,

acting as political agent, GVCs are generally ‘relaxed’ about budget constraints.

However, CVCs funds are generally provided by cash-rich listed companies in

China. Due to the supervision policy of listed corporations, information disclosure

requirements and diversified shareholders, listed corporations must be very cautious

about their financial statement and may not afford continuous refinancing. Hence, we

propose that the budget constraints of corporate VCs are harder than government

backed VCs.

Therefore, based on the different budget constraints of VCs, we classify VCs into

two groups: 1. Domestic VCs (hierarchic structure, hereinafter DVCs); 2. Limited

partnership FVCs 4 (market oriented structure, hereinafter FLPVCs). We divide

domestic hierarchic VCs into two subgroups: GVCs and CVCs. We then examine

each group in terms of their investment preferences in financing stages and

technology intensity respectively. Based on the literature and our initial studies on

China’s venture capital industry, the research propositions are raised as follows:

Proposition 1: There is a clear difference between FLPVCs and DVCs in risk

taking. DVCs take less risk compared with FLPVCs by investing in more later staged

projects in hi-tech, while FLPVCs are more inclined to expansion staged hi-tech

projects.

Proposition 2: There is a clear difference between CVCs and GVCs in terms of

their risk taking. GVCs take less risk-return projects compared with CVCs by

investing in more later-staged projects.

Proposition 3: There is a clear difference among FLPVCs, CVCs and GVCs in

terms of their risk taking. GVCs take less risk-return projects compared with CVCs

4 Here we do not take foreign hierarchic VCs into our model since from our initial studies, foreign corporate VCs differ a lot from domestic VCs in governance structure.

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and FLPVCs by investing in more later-staged projects than the other two groups

while FLPVCs take more expansion staged projects than other groups.

2. Measurement definitions

Most of our measurements are defined according to existing literature i.e. Mayer

(2002,2001) and Wang (2002). First of all, we use Wang (2002)’s ‘risk return

investment across stages and technology matrix’ to set up the risk tolerant degree

measures. (See table 4.1)

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Table 4.1 Risk return investment across stages and technology

Risk return Early stages Expansion stage Later stage

Hi-tech High Modest Lower

Traditional industry Low Lower Lowest

1.Early stages: include Seed stage, Start-up stage and Growing stage.

(Seed stage: the stage when a concept has still to be proven and developed. Start-up

stage: the stage when products are developed and initial marketing takes place. The

firm may be a year old or younger at this stage. Growing stage: the stage when the

firm is expanding and producing but may remain unprofitable. It is often less than 5

years old at this stage.)

2. Expansion stage: the stage when the sales are close to the aimed amount, and the

firm might go public after 6 months or a year.

3. Later stage: the stage when the firm is gaining profits. Generally, VCs consider exit

at this stage.

Section 4 Methodology justification

1. Data collection

Data in this paper come from two basic sources----secondary documents and

unstructured interviews.

First, much of the data in this paper are drawn from secondary document

analysis. The aggregate data are mainly from China’s Venture Capital Yearbook 2002

and the survey report of Zero2IPO (a leading survey and research company which

focuses on Hi-tech and venture capital industry surveys), while the detailed data about

individual VC’s investment preference in financing stage and sector intensity are

collected from wide range of published sources including websites of venture capital

institutions and their portfolio companies, industrial and financial magazines,

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newspapers as well as related industrial reports. Certain cases in this paper are derived

from the documents and organization profile analysis.

The second major source is our unstructured interviews with venture capitalists

conducted in August 2003. We undertook unstructured interviews with key staff from

two different venture capital institutions (i.e. one is from one of top ten GVCs, the

other one is from CVCs) and with one entrepreneur who was funded by one of top ten

FVCs.

2. Sampling

Our samples are taken from the VC list in China’s VC Survey Report 2002

published by Zero2IPO. 78 venture capital institutions, which have more than 4

portfolio companies, are selected for our investigation, while 22 VCs were screened

out due to the lack of access to gain information of their portfolio companies. The

final sample pool covers 56 VCs with 648 invested projects. Among the sample, 21

are limited partnership VCs, 17 are GVCs and 18 are domestic CVCs.

3. Reliability of the data

Much data is drawn from secondary documents and websites, which could have

some impact on reliability, except the major aggregate figures are from an officially

published venture capital yearbook, may be presumed to be accurate. However, the

resources from website are also mainly from official sites of the venture capital

institutions and their portfolio companies, and are thus probably accountable. So, we

suggest that the data in this study is reliable.

4. Data analysis

Quantitative data in this paper are analyzed with simple descriptive statistical

approach i.e. AVOVA since the number of factors in this research is small and this

study is a preparatory one. Qualitative data are generated and analyzed based on

interview transcripts and company history analysis. Case1-4 are the results of our

company history analysis: case1 is a CVC while case 2 and case 3 are GVCs, case 4 is

a FLPVC.

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Section 4 Findings and Discussion

1. Overview of the investigation results

There is a clear market segmentation of VCs in stages and technological sectors

in China. 60% of projects were invested at their early stages, 31% at expansion stage,

and 10% at a later stage (see table 5.1. -a). A significant concentration on IT and

electronic sectors: about 87% of total projects are from hi-tech industry in which over

50% are IT and electronic projects, while only 13% of total are from traditional

sectors (see table 5.1-b).

Table 4-1-a Descriptive results of VCs’ preference in financing stage in China

Descriptive Statistics

56 .20 1.00 .5824 .0234 .1751356 .00 .63 .3130 .0218 .1635156 .00 .25 .1047 .0114 .0854956

EARLY STAGESEXPANSION STAGELATER STAGEValid N (listwise)

Statistic Statistic Statistic Statistic Std. Error StatisticN Minimum Maximum Mean Std.

Table 4-1-b Descriptive results of VCs’ preference in financing sectors in China

Descriptive Statistics

56 .00 1.00 .5057 .0386 .2887856 .00 .80 .1263 .0210 .15702

56 .00 .33 .0541 .0125 .09355

56 .00 .60 .1068 .0179 .1335956 .00 .50 .0739 .0151 .1130856 .00 1.00 .1331 .0297 .2221056

IT & ELECTRONICBIO-TECH&PHARMNEW MATERIAL &ENERGYCOMMUNICATIONOTHER HI-TECHTRADITIONAL INDUSTRYValid N (listwise)

Statistic Statistic Statistic Statistic Std. Error StatisticN Minimum Maximum Mean Std.

Basically, our results are similar to data from CVCY 2002. However, there are

some small differences in proportion of investment in the seed stage and start-up

stage, our data show about 10% less projects funded in their early stages than that in

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CVCY 2002. We argue that data from CVCY 2002 were collected by the end of 2001,

and VCs became more sensitive to investment risk after the crash of ‘internet bubble’

in 2001. Thus, projects invested after 2001 are mainly from expansion stage and later

stage. This is consistent both with the findings of our interviews with VCs and

secondary document analysis.

2. Findings of differences between FLPVCs and DVCs

The findings show that the risk tolerance degree of FLPVCs and DVCs is

significantly different, while FLPVCs are more risk taking than DVCs (see appendix

2-1). There is a statistically significant difference between FLPVCs and DVCs in

investments to projects at expansion stage and later stage. FLPVCs are more

interested in expansion-staged projects than DVCs, while DVCs are more interested

in the later stages than FLPVCs. There is almost no difference between the two

groups in choice of investing in hi-tech or traditional sectors (see appendix 2-2). Both

show strong interest in hi-tech industries though they are significantly different in hi-

tech sectors they choose (see appendix 2-3). FLPVCs are more significantly interested

in IT/Electronic and communication industry compared with DVCs, while DVCs

invest in new material /new energy and bio-tech/pharmacy significantly more than

FLPVCs. The results are consistent with the propostition1.

Qualitative data also support such statistics. For instance, VC3 (Appendix 5-3),

which is backed by both central and local governments, show a clear orientation

towards investing in expansion and later stages. According to its investment strategy,

this VC concentrates mainly on pre-investment screening processes. We also find that

the decision-making processes of hierarchic VCs are more complex than independent

VCs while the average time of pre-screening of hierarchic VCs are longer than

independent VCs.

In terms of the financing stage preference, Dr. X, an executive officer of one of

top ten GVCs stated that: [‘ Of course we prefer later staged firms which could be

more secure. Our project managers could not make final decisions. All the final

decisions are made by executive officers and based on the screening reports and the

advice from internal and external experts. We are responsible for all the losses and

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revenues. So, we have to be very cautious… a loss of one project could be millions of

RMB. Earning less is acceptable, losing is much more troublesome. And, you know,

we don’t earn more for more profits, but are blamed for losses…’] The interview

transcripts reveal the impact of DVCs’ hierarchic structure on their risk tolerance ia

related to the lack of sound incentive mechanisms.

Moreover, Mr. Z, a successful entrepreneur backed by an FVC, stated that some

owners of earlier staged hi-tech projects are reluctant to ask for venture capital from

hierarchic DVCs. Asked whether he has any preferences in choosing VC when he was

looking for investments, the current CEO said that:

‘I have been in business for years before taking the current business, and I am

familiar with domestic institutions. It is normally very time consuming and uncertain

for they have incredibly complex procedures and system in decision making.

Sometimes you wait for a long time for a seemingly positive decision, but lose at

finally for unclear reasons…Hi-tech projects are those you cannot afford long time

waiting …losing time means losing money…So, at the very beginning I told myself to

contact foreign VCs, which only focus on the profitability of the investments. The fact

shows that I made a right decision while I gained the first round money in two months

and I have got the second round investments now…’

Concerning the technological intensity preference, there is a statistically

significant difference between the two groups in the choice of technology sectors (see

appendix 2-3). FLPVCs are significantly interested in IT/Electronic and

communication industry compared with DVCs, while DVCs’ investment in new

material /new energy and bio-tech/pharmacy are more than FLPVCs’.

We cannot measure the technological intensity of various hi-tech sectors and

their risk degree in this paper. However, intuitively, as a series of new business modes,

E-commerce and some other Internet services appears to be much riskier for there is

no prior-knowledge in this area, and the crash of ‘dot com bubble’ in 2000 also

evidenced the higher risks of certain IT sectors than of others. Even though we did

not break down IT industry into subgroups, interviews and case studies show that

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FLPVCs invest more in these riskier projects such as ICP and E-commerce than

DVCs. Asked about their technology preference, one interviewee said:

‘Well, everyone knows IT is a promising industry. But what I am concerned with

is that it is overestimated now in the investment market. You know, most of them are

just a concept, for example, the so called B to C (on-line Business to Customer

commercial mode) sounds like a good idea, but we are quite far away from that era

both from technical aspect and psychological aspects...no way… Investments in

hardware, software are sound, but, in Internet, one should be more cautious. So, we

prefer to invest in the IT industry, while we pay more attention to hardware, software

or some other facility manufacturing rather than ICP or E-commerce… (Dr. X----an

executive officer of a GVC).

From the interview transcripts, DVCs were more sensitive to certain riskier areas

in hi-tech fields, especially those sectors that are lack of prior knowledge and

experiments in business practices. This finding is consistent with Qian and Xu (1999).

DVCs’ portfolio companies in new material/new energy and bio-tech/pharmacy

are statistically more than FLPVCs’. New materials and energy are generally seen to

be more technologically intensive (Wang, 2002). However, it is hard to estimate the

risk taking capability of DVCs in technological intensity. Rather, it may be more

related to governmental policies. Recently, MoST cites a list of technology area as

priorities for development and commercialization: electronics and IT; biotechnology;

new materials; integration of optical, mechanical and electronic components; new

energy, high-efficiency energy, energy saving technology; and environmental

protection (Chen 2002). As we have stated in earlier sections, generally, such a

governmental policy has more influence on DVCs rather than FVCs. So, the choice of

GVCs in sectors of investments is not only determined by risk concerns but also

political considerations.

To summarize: according to both our statistical analysis and the follow-up

interviews and case studies, our findings support the first proposition, which presume

that there is a clear difference in risk tolerance between FLPVCs and DVCs while

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DVCs are of less risk taking than FLPVCs. This is consistent with Qian and Xu

(1999), Mayer (2002) and Wang (2002).

3. Difference between GVCs and CVCs

Our data show that no statistically significant difference between GVCs and

CVCs in investment stage preference. However, we can see that CVCs show more

interest in early staged projects and less interest in later staged projects comparing

with GVCs. (see appendix 3-1). This is consistent with our case studies (Case 1vs.

case 2 and case 3, appendix 5-1, 5-2, 5-3).

For the technological intensity preference, our data shows that there is also no

difference between the two groups in terms of choice of investing in hi-tech or

traditional sectors (see appendix 3-2. 3-3). Both of them show strong interest in hi-

tech industries with about 90% of projects in hi-tech sectors and only 10% projects

invested in traditional technology. Breaking down the hi-tech industries, there is also

no statistically significant difference between the two groups.

In short, with no significant difference in both stage preference and technological

intensity, the two groups do not show statistical divergence in risk tolerance. So, the

findings are not consistent with proposition 2 though it is worth noting that CVCs

show more interest in riskier projects than GVCs in the descriptive data and case

studies.

4. Difference between FLPVCs, CVCs and GVCs

Our data show that there is a statistically significant difference in later staged projects

investments among the three groups. FLPVCs, CVCs and GVCs are in ascending

order in later staged projects investments (See appendix 4-1). There is no statistically

significant difference among the three groups. However, FLPVCs is outstanding for

its greater interest in expansion stage with 37.27% of projects, which is 10% more

than for the two other groups in our descriptive data.

Again, there is no difference in technological intensity among the groups by

comparing their choice between hi-tech and traditional sectors. (See appendix 4-2).

However, they differ from each other in choice of sectors within hi-tech industries.

This is shown in IT/Electronic, new material/new energy and bio-tech/pharmacy

of 38

industries. GVCs are most interested in new material/new energy and bio-

tech/pharmacy, while FLPVCs invest more in IT/Electronic industry. (See appendix

4-3)

To summarize, with significant difference in investment in later staged projects

and little difference in technological intensity preference, GVCs are less risk taking

while CVCs and FLPVCs are in ascending order. This is consistent with proposition

3. In addition, our findings show that each type of VCs has their own favourite

technological sectors.

5. Summary

Based on the statistical tests, case studies and interviews, China’s venture capital

market is found to be segmented by different types of VCs in terms of their risk taking

degree.

VCs with different degree of budget constraints show different investment

preference in terms of their financing stage and technological sectors. Segmentation

might be caused by many factors, such as external environments, incentive

mechanism, etc., and our study reveals that budget constraints have a strong effect on

risk tolerance of VCs. Our findings are consistent with Dewatripont & Maskin (1995)

and Qian & Xu (1999) by demonstrating that VCs under hierarchic structure with

softer budget constraints are less risk taking comparing with independent VCs under

decentralized structure with a harder budget constraints.

We cannot conclude that the degree of budget constraints has the single

determining impact on stage preference and technological choice based on our

premature tests, but, we propose that the findings of this research do imply that the

impact of budget constraints needs to be examined.

Section 6 Conclusion and implication

of 38

By demonstrating the market segmentation of China’s venture capital industry in

terms of financing stages and technological sectors, this study has implications in both

policymaking and further research.

First, this study provides a brief view on China’s venture capital industry in

terms of investment choice of different VCs according to their different degree of

budget constraints. This provides some useful data for further comparative research.

In addition, the framework employed in this research could be extended to studies on

other aspects of financial intermediaries, for example, the relationship between budget

constraints and the financial contracting, monitoring activities. Further, studies would

appear to reveal differences of investment choice, contracting and the controlling

mode of the same financial institution under different institutional environments----

decentralized and centralized (e.g. FVC in China and in their own land).

Moreover, we suggest that our study has implications for policymaking. In recent

years, many developing nations have promoted venture capital industry to accelerate

commercialization of hi-technology and support newly established small hi-tech

firms. However, this research reveals that not all VCs are willing to nurture projects at

earlier stages. Therefore, we suggest that government could either adjust their policies

to encourage certain types of VCs, under different circumstance to change budget

constraints for some VCs.

of 38

Appendix 1

Reference

1. Arrow, K. J. (1971). Essays in the Theory of Risk Bearing. Chicago: Markham.

2. Bell, D.E. (1995). A contextual uncertainty condition for behaviour under risk.

Management Science, 41(7): 1145—1150.

3. Bleicher, K., & Paul, H. (1987). The external corporate venture capital fund---- A

valuable vehicle for growth. Long Rang Planning, 20 (6): 64—70

4. Bryman, A. (1988). Social Research Methods. Oxford: Oxford University

5. Churchill, N.C., & Lewis, V.L. (1983). The five stages of small business growth.

Harvard Business Review 3, 30—50

6. Cornrath D. W. (1967). Organizational decision making behaviour under varying

conditions of uncertainty. Management Science, 13 (8): B575---B590.

7. Dewatripont, M, & Tirole, J. (1994). A Theory of Debt and Equity: Diversity of

Securities and Manager Shareholder Congruence. Quarterly Journal of

Economics, No. 109: 1027-1054

8. Dewatripont, M & Maskin, E. (1995) Credit and efficiency in centralized and

decentralized economies, Review of Economic Studies, 62, 541—555.

9. Elango B., Fried V. H., Hisrich R.D. & Polonchek, A. (1995). How venture

capital firms differ. Journal of Business Venturing, 10, 157—179.

10. Fiet, J.O. (1995) Risk avoidance strategies in venture capital markets. Journal of

Management Studies, 32 (4): 551—574.

11. Greiner, L. (1972). Evolution and revolution as organizations grow. Harvard

Business Review, July—August 37—46.

12. Guiso, L. Jappeli, T.,& Terlizzese, D (1996). Income risk, borrowing constraints

and portfolio choice. The American Economic Review, 86(1): 158---172.

13. Jemison D. B (1987). Risk and the relationship among strategy, organizational

processes, and performance. Management Science, 33 (9) :1087—1101.

of 38

14. Kaplan, S.& Stromberg, P. (2001). Financial Contracting Theory meets the Real

World: an Empirical Analysis of Venture Capital Contracts. Working paper,

University of Chicago

15. Kaplan, S.& Stromberg, P. (2002). Characteristics, Contracts, and Actions:

Evidence From Venture Capitalist Analyses. Working paper, University of

Chicago.

16. Kimberly, J. R., & Miles, R. H. (1980). The organizational life cycle. San

Francisco: Jossey—Bass.

17. Kim D. C. (1992). Risk preferences in participative budgeting. The accounting

Review, 67 (2): 303—318.

18. Kornai, J. (1980) Economics of Shortage. Amsterdam: North Holland.

19. Lerner, J. (1998) ‘Angle’ financing and public policy: An overview. Journal of

banking and Finance, 22, 773---783.

20. MacMillan, I.C., Siegel, R. & SubbaNarasimha, P.N. (1985), ‘Criteria used by

venture capitalists to evaluate new venture proposals’, Journal of Business

venturing, 1(1) 119–128.

21. MacMillan, I.C., and SubbaNarasimha, P.N. (1987). Characteristics distinguishing

funded from unfunded business plans evaluated by venture capitalists. Strategic

Management Journal, 8(6) 579–585

22. Mani, S. (2000). Exports of High Technology products From Developing

Countries: Is it Real or a Statistical Artefact? UNU/INTECH Discussion Paper

2000-1.

23. Marx, K. (1978) Capital. London: Penguim.

24. Mayer, C. (2002) Financing the new economy: financial institutions and corporate

governance. Information Economics and policy, 14 (2): 311-326.

25. Qian, .Y, & Xu, C. (1998) Innovation and Bureaucracy under soft and hard budget

constraints. The review of Economic studies, 65 (1): 151—164.

26. Robinson. R. B, (1987) Emerging Strategies In the Venture Capital Industry,

Journal of Business Venturing 2, 53-57.

of 38

27. Ruhnka, J. C. & Yong, J. E. (1991). Some hypotheses about risk in venture capital

investing. Journal of Business Venturing, 6, 115—133.

28. Sahlman, W. A. (1988). Aspects of financial contracting in venture capital,

Journal of Applied Corporate Finance, 1, 23—36.

29. Sahlman, W. A. (1990). The structure and governance of venture capital

organizations. Journal of Financial Economics, 27, 473---521.

30. Siegel, R., Siegel, E & Macmillan, I. C. (1988). Corporate Venture Capitalists:

autonomy, obstacles, and performance. Journal of Business Venturing, 3, 233—

247.

31. Sykes, H. B (1990). Corporate Venture capital: strategies for success. Journal of

Business Venturing, 5, 37---47.

32. Tyebjee, T.T. & Bruno, A.V. (1984). A model of venture capital investment

activity. Management Science, 30(9) 1051–1066.

33. Wang. C. K, (2002), Differences in the Governance Structure of Venture Capital:

The Singapore Venture Capital Industry. UNU/INTECH Discussion Paper 2002.

34. Wetzel, W. E. (1981). Informal risk capital in New England. In Frontiers of

Entrepreneurship Research, Edited by Vesper K. H., Wellesley, MA: Babson

College.

35. White. S, Gao. J & Zhang.W (2002), China’s venture capital industry:

Institutional trajectories and system structure. UNU/INTECH Discussion Paper

2002.

36. Wright, P, Ferries, S. P. Sarin, A, & Awaysyhi, V. (1996). Impact of Corporate

Insiders Blockholder, and Institutional equity ownership on firms’ risk-taking.

Academy of Management Journal, 39 (2) 441---463.

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Appendix 2-1 Differences in financing stage between DVCs and FLPVCs

Descriptives

21 .5623 .17041 .03719 .4847 .6399 .25 .9135 .5944 .17925 .03030 .5329 .6560 .20 1.0056 .5824 .17513 .02340 .5355 .6293 .20 1.0021 .3727 .15781 .03444 .3009 .4445 .05 .6335 .2771 .15834 .02676 .2227 .3315 .00 .6056 .3130 .16351 .02185 .2692 .3567 .00 .6321 .0650 .08929 .01948 .0243 .1056 .00 .2535 .1285 .07468 .01262 .1028 .1541 .00 .2556 .1047 .08549 .01142 .0818 .1276 .00 .25

LPFVCDVCTotalLPFVCDVCTotalLPFVCDVCTotal

EARLY STAGES

EXPANSION STAGE

LATER STAGE

N Mean Std. Deviation Std. Error Lower Bound Upper Bound

95% Confidence Interval forMean

Minimum Maximum

ANOVA

.014 1 .014 .437 .5111.673 54 .0311.687 55

.120 1 .120 4.798 .0331.350 54 .0251.470 55

.053 1 .053 8.181 .006

.349 54 .006

.402 55

Between GroupsWithin GroupsTotalBetween GroupsWithin GroupsTotalBetween GroupsWithin GroupsTotal

EARLY STAGES

EXPANSION STAGE

LATER STAGE

Sum ofSquares df Mean Square F Sig.

of 38

Appendix 2-2 Differences in technological choice (traditional /hi-tech) between DVCs and FLPVCs

Descriptives

21 .1003 .16420 .03583 .0255 .1750 .00 .5935 .1056 .13214 .02234 .0602 .1510 .00 .5756 .1036 .14355 .01918 .0652 .1421 .00 .5921 .8997 .16420 .03583 .8250 .9745 .41 1.0035 .8944 .13214 .02234 .8490 .9398 .43 1.0056 .8964 .14355 .01918 .8579 .9348 .41 1.00

LPFVCDVCTotalLPFVCDVCTotal

TRADITIONAL INDUSTRY

HI-TECH



Minimum Maximum

ANOVA

.000 1 .000 .018 .8941.133 54 .0211.133 55

.000 1 .000 .018 .8941.133 54 .0211.133 55

Between GroupsWithin GroupsTotalBetween GroupsWithin GroupsTotal


HI-TECH


of 38

Appendix 2-3 Differences in hi-tech sector preference between DVCs and FLPVCs

Descriptives

21 .6378 .25784 .05627 .5204 .7552 .20 1.0035 .4533 .25212 .04262 .3667 .5399 .00 1.0056 .5225 .26757 .03576 .4508 .5941 .00 1.0021 .0268 .05439 .01187 .0020 .0516 .00 .1735 .1907 .16533 .02795 .1339 .2475 .00 .8056 .1293 .15616 .02087 .0874 .1711 .00 .8021 .0157 .04341 .00947 -.0040 .0355 .00 .1735 .0803 .10696 .01808 .0436 .1171 .00 .33

56 .0561 .09356 .01250 .0311 .0812 .00 .33

21 .1672 .15805 .03449 .0953 .2392 .00 .6035 .0831 .10667 .01803 .0465 .1198 .00 .4056 .1147 .13345 .01783 .0789 .1504 .00 .6021 .0521 .08254 .01801 .0146 .0897 .00 .2535 .0869 .12732 .02152 .0432 .1306 .00 .5056 .0739 .11308 .01511 .0436 .1042 .00 .5021 .1003 .16420 .03583 .0255 .1750 .00 .5935 .1056 .13214 .02234 .0602 .1510 .00 .5756 .1036 .14355 .01918 .0652 .1421 .00 .59



IT & ELECTRONIC

BIO-TECH&PHARM

NEW MATERIAL &ENERGY

COMMUNICATION

OTHER HI-TECH




Minimum Maximum

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ANOVA

.447 1 .447 6.913 .0113.491 54 .0653.938 55

.353 1 .353 19.267 .000

.989 54 .0181.341 55

.055 1 .055 6.933 .011

.427 54 .008

.481 55

.093 1 .093 5.660 .021

.887 54 .016

.979 55

.016 1 .016 1.246 .269

.687 54 .013

.703 55

.000 1 .000 .018 .8941.133 54 .0211.133 55



IT & ELECTRONIC

BIO-TECH&PHARM


COMMUNICATION

OTHER HI-TECH



of 38

Appendix 3-1 Differences in financing stage between GVCs and CVCs

Descriptives

17 .5693 .13565 .03290 .4995 .6390 .33 .8018 .6182 .21381 .05040 .5119 .7245 .20 1.0035 .5944 .17925 .03030 .5329 .6560 .20 1.0017 .2825 .15615 .03787 .2023 .3628 .00 .5018 .2720 .16473 .03883 .1900 .3539 .00 .6035 .2771 .15834 .02676 .2227 .3315 .00 .6017 .1482 .05158 .01251 .1216 .1747 .08 .2518 .1099 .08885 .02094 .0657 .1540 .00 .2535 .1285 .07468 .01262 .1028 .1541 .00 .25

2.003.00Total2.003.00Total2.003.00Total

EARLY STAGES

EXPANSION STAGE

LATER STAGE



Minimum Maximum

ANOVA

.021 1 .021 .644 .4281.072 33 .0321.092 34

.001 1 .001 .038 .847

.851 33 .026

.852 34

.013 1 .013 2.395 .131

.177 33 .005

.190 34


EARLY STAGES

EXPANSION STAGE

LATER STAGE


of 38

Appendix 3-2 Differences in technological choice (traditional /hi-tech) between GVCs and CVCs

Descriptives

17 .1050 .10479 .02541 .0511 .1588 .00 .4218 .1063 .15680 .03696 .0283 .1843 .00 .5735 .1056 .13214 .02234 .0602 .1510 .00 .5717 .8950 .10479 .02541 .8412 .9489 .58 1.0018 .8937 .15680 .03696 .8157 .9717 .43 1.0035 .8944 .13214 .02234 .8490 .9398 .43 1.00

GVCCVCTotalGVCCVCTotal


HI-TECH



Minimum Maximum

ANOVA

.000 1 .000 .001 .977

.594 33 .018

.594 34

.000 1 .000 .001 .977

.594 33 .018

.594 34



HI-TECH


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Appendix 3-3 Differences in hi-tech sector preference between GVCs and CVCs

ANOVA

.077 1 .077 1.224 .2772.084 33 .0632.161 34

.014 1 .014 .501 .484

.915 33 .028

.929 34

.004 1 .004 .307 .583

.385 33 .012

.389 34

.003 1 .003 .263 .612

.384 33 .012

.387 34

.025 1 .025 1.596 .215

.526 33 .016

.551 34

.000 1 .000 .001 .977

.594 33 .018

.594 34



IT & ELECTRONIC

BIO-TECH&PHARM


COMMUNICATION

OTHER HI-TECH



of 38

Appendix 4-1 Differences in financing stage among FLPVCs, GVCs and CVCs

Descriptives

21 .5623 .17041 .03719 .4847 .6399 .25 .9117 .5693 .13565 .03290 .4995 .6390 .33 .8018 .6182 .21381 .05040 .5119 .7245 .20 1.0056 .5824 .17513 .02340 .5355 .6293 .20 1.0021 .3727 .15781 .03444 .3009 .4445 .05 .6317 .2825 .15615 .03787 .2023 .3628 .00 .5018 .2720 .16473 .03883 .1900 .3539 .00 .6056 .3130 .16351 .02185 .2692 .3567 .00 .6321 .0650 .08929 .01948 .0243 .1056 .00 .2517 .1482 .05158 .01251 .1216 .1747 .08 .2518 .1099 .08885 .02094 .0657 .1540 .00 .2556 .1047 .08549 .01142 .0818 .1276 .00 .25

LPFVCGVCCVCTotalLPFVCGVCCVCTotalLPFVCGVCCVCTotal

EARLY STAGES

EXPANSION STAGE

LATER STAGE



Minimum Maximum

ANOVA

.034 2 .017 .553 .5791.652 53 .0311.687 55

.121 2 .060 2.375 .1031.349 53 .0251.470 55

.066 2 .033 5.179 .009

.336 53 .006

.402 55


EARLY STAGES

EXPANSION STAGE

LATER STAGE


of 38

Appendix 4-2 Differences in technological choice (traditional /hi-tech) among FLPVCs, GVCs and CVCs

Descriptives

21 .1003 .16420 .03583 .0255 .1750 .00 .5917 .1050 .10479 .02541 .0511 .1588 .00 .4218 .1063 .15680 .03696 .0283 .1843 .00 .5756 .1036 .14355 .01918 .0652 .1421 .00 .5921 .8997 .16420 .03583 .8250 .9745 .41 1.0017 .8950 .10479 .02541 .8412 .9489 .58 1.0018 .8937 .15680 .03696 .8157 .9717 .43 1.0056 .8964 .14355 .01918 .8579 .9348 .41 1.00

LPFVCGVCCVCTotalLPFVCGVCCVCTotal


HI-TECH



Minimum Maximum

ANOVA

.000 2 .000 .009 .9911.133 53 .0211.133 55

.000 2 .000 .009 .9911.133 53 .0211.133 55



HI-TECH


of 38

Appendix 4-3 Differences in hi-tech sector preference among FLPVCs, GVCs and CVCs

Descriptives

21 .6378 .25784 .05627 .5204 .7552 .20 1.0017 .4049 .24715 .05994 .2778 .5320 .00 1.0018 .4989 .25513 .06013 .3721 .6258 .00 1.0056 .5225 .26757 .03576 .4508 .5941 .00 1.0021 .0268 .05439 .01187 .0020 .0516 .00 .1717 .2112 .13227 .03208 .1432 .2792 .00 .5018 .1714 .19335 .04557 .0752 .2675 .00 .8056 .1293 .15616 .02087 .0874 .1711 .00 .8021 .0157 .04341 .00947 -.0040 .0355 .00 .1717 .0907 .10618 .02575 .0362 .1453 .00 .3318 .0705 .10980 .02588 .0159 .1251 .00 .3356 .0561 .09356 .01250 .0311 .0812 .00 .3321 .1672 .15805 .03449 .0953 .2392 .00 .6017 .0735 .08977 .02177 .0273 .1196 .00 .2918 .0922 .12245 .02886 .0313 .1531 .00 .4056 .1147 .13345 .01783 .0789 .1504 .00 .6021 .0521 .08254 .01801 .0146 .0897 .00 .2517 .1146 .12424 .03013 .0508 .1785 .00 .4018 .0607 .12806 .03018 -.0030 .1244 .00 .5056 .0739 .11308 .01511 .0436 .1042 .00 .5021 .1003 .16420 .03583 .0255 .1750 .00 .5917 .1050 .10479 .02541 .0511 .1588 .00 .4218 .1063 .15680 .03696 .0283 .1843 .00 .5756 .1036 .14355 .01918 .0652 .1421 .00 .59

LPFVCGVCCVCTotalLPFVCGVCCVCTotalLPFVCGVCCVCTotalLPFVCGVCCVCTotalLPFVCGVCCVCTotalLPFVCGVCCVCTotal

IT & ELECTRONIC

BIO-TECH&PHARM


COMMUNICATION

OTHER HI-TECH




Minimum Maximum

of 38

ANOVA

.524 2 .262 4.070 .0233.413 53 .0643.938 55

.367 2 .183 9.968 .000

.975 53 .0181.341 55

.058 2 .029 3.656 .033

.423 53 .008

.481 55

.096 2 .048 2.879 .065

.883 53 .017

.979 55

.041 2 .021 1.653 .201

.662 53 .012

.703 55

.000 2 .000 .009 .9911.133 53 .0211.133 55



IT & ELECTRONIC

BIO-TECH&PHARM


COMMUNICATION

OTHER HI-TECH



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