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E.J. Jeon, J.-D. Lee, and Y.-H. Kim
Technology Management, Economics, and Policy Program, Seoul National University,
Seoul, South Korea
J.-D. Lee, A. Heshmati(eds.) Productivity, Efficiency, and Economic Growth 123
in the Asia-Pacific Region,
© Springer-Verlag Berlin Heidelberg 2009
Chapter 6
The Effect of Asset Composition Strategy
on Venture Capital Firm Efficiency:
An Application of Data Envelopment Analysis
E.J. Jeon, J.-D. Lee, and Y.-H. Kim
6.1 Introduction
The Korean government has driven the venture capital market since KTB Network was
created in 1981 to provide capital to the high tech firms. Thanks to the venture policy, the
venture capital market has undergone a compressed growth in such a short period of time. In
1986, the government enacted the “Small and Medium Business Start-up Support Act” and
“Finance Act to Support New Technology Businesses” to provide legal bases to establish
venture capital(VC) firms. The government pushed the VC firms to carry out equity
investments on small and medium businesses within the age of seven years. Hence, the
Korea Development Bank Capital and TG Venture, the archetypes of today’s VC firms,
have been established to finance high tech firms such as Medison, Mirae, and Sambo
Computer(Lee 2003). In spite of the efforts made by the government, until the mid 1990s,
there were problems in constructing the venture capital market, due to poor system to
finance technology and lack of policy measures to support the high tech firms. There was no
exit system to liquidize the equity investments, the most of the investment targets were from
mature industries which brought low returns, and debt financing was preferred to equity
investment because of the low risk and high interest rate.
In 1996, the object oriented economy started moving since the internet rapidly spread out
the entire nation. The Kim Dae Jung Government(1998-2003) enacted the “Special Act to
Foster High Tech Firms” in 1997 to overcome the financial crisis(1997-1998) by promoting
market efficiency, industrial restructuring, research and development, and job creations.
This in effect induced enormous number of start-ups of high tech firms. The KOSDAQ
boomed and there has been a tremendous growth in the information technology industry and
the venture capital market in 1999. The venture policy took a dominant role in creating the
venture capital market during the introduction stage in 1981-1986 and market formation
124 E.J. Jeon et al.
stage in 1986-1995. During and after the financial crisis(1996-2000), venture policy induced
the rapid growth of the venture capital market.
However, the success of the venture policy was only temporary and backfired by
inducing high tech firms to devote their resources rather on rent seeking than R&D
investment. Moral hazard problems such as, illegal lobbying, window dressing, solicitation
to the media for advertisement, and cozy relations between politics and business permeated
the venture business society(Ji 2006). When the market crashed due to the dot com crises
and the venture gates, the government decided to continuously provide public funds to the
venture capital market and focused to amend the fundamentals by increasing the
transparency and improving the exit system.
The Korean government has been successful in creating a venture capital market and
substantially financing the equity gaps
1
. However, the venture capital market settled in an
anomalous form with the characteristic of low risk and low return. Park(1997) showed that
during 1994-1996, VC firms had lower return on equity than the local banks and lease
companies. Kwak(2001) figured that during 1991-1998, the VC firms, compared to the
market portfolio and the stock beneficiary certificates, focused on low risk investments and
produced relatively low returns. Chung and Ryu(2004) compared the performances of
venture capital funds of Korea to those of the United States and suggested that the Korean
venture capital had relatively low-risk and low-return.
The questions are continuously raised whether the venture policy induced effective
financing to the equity gaps and bore successful high tech firms. Obviously, high tech firms
were directly financed by government loans and the problem of screening and monitoring of
these firms has been overlooked. In specific, the venture policy failed to notice the
important role of the VC firms as ‘risk controllers’ and ‘high tech firm managers’. Even
though VC firm is the key solution to the innate problems of information asymmetry,
uncertainty, and moral hazard, it has not been the interest of the venture policy. To answer
the question of why is the venture capital market showing the characteristics of low risk and
low return and why are there so few successful high tech firms, the role of the VC firm in
attaining the venture policy goal should be studied. This study raises the question of by what
asset composition strategy does the VC firm raise its operating efficiency and whether these
profit maximizing strategies are meeting the policy demands of maximizing the social
benefit. The purpose of this study is to figure out the efficiency maximizing strategies of the
VC firms in respect to asset composition and configure them with the venture policy in
Korea.
This is the first paper to study the efficiency of the VC firms in Korea and to focus on the
features of asset composition strategies. Not only is the data envelopment analysis(DEA)
applied on the venture capital but also the strategic variations causing such results are
analyzed. Furthermore, whether the efficient VC firms are fulfilling the social expectations
are examined. To sum up, two research questions are raised: How should a VC firm
compose its investment assets to raise its operating efficiency? Are the strategies of the
efficient VC firms fulfilling the social expectations?
Studying the efficiency of the VC firms has two implications. First, the absolute measures
of performance such as, revenue, profit, level of investment have its limitations because it
1
Policy makers seize upon the equity gap issue as a justification for the provision of public finance to the small
and medium businesses(Lawton, 2002; OECD, 1997).
6 The Effect of Asset Composition Strategy on Venture Capital Firm Efficiency 125
only expresses one dimension of the object in analysis. Also, the management index, which
is a comparative measure of performance, such as the return on equity, return on asset is
limited to the analysis of one output over one input. These simplistic measures can not
evaluate multiple conditions and ignores relationships. Thus, the traditional measure of
performance has its limitations in explaining the complex nature of the VC firms in the real
world. On the other hand, the performance of putting in multiple inputs and producing
multiple outputs can be quantified by using DEA and the complicated nature of the VC firm
is well reflected in the derived technical efficiency. Second, DEA which was used to derive
the ‘efficiency’ is a powerful benchmarking tool. DEA sorts out the efficient firms from the
inefficient firms. Comparing these two groups of firm conveys some insight in formulating
strategies and deriving policy implications.
This paper is constructed as follows. In chapter 2, literatures are reviewed and hypotheses
are proposed. In chapter 3, methodologies are presented. In chapter 4, data and model is
reviewed. In chapter 5, the effect of asset composition strategies on operating efficiency is
estimated and analyzed. In chapter 6, the estimation results are reviewed and policy
implications are addressed.
6.2 Strategies of Venture Capital Firms
Many studies suggest that firm performance is affected by strategy (Wernerfelt 1984; Teece
et al. 1997; Boeker 1997; Zahra et al. 2000; Canals 2000). According to the resource-based
theory
2
, resources play a pivotal role in strategy formulation. In particular, among these
resources, financial resource is the critical strategic dimension sought by VC
firms(Robinson 1987). Two strategic dimensions of the VC firm are studied in this paper:
(1) stage of investment and (2) investment horizon.
Much scholarly work has been done on the strategic behavior of the VC firms according
to the different focus on stages of investments(Gorman and Sahlman 1989; Gupta and
Sapienza 1992; Rosenstein et al. 1990; Carter and Auken 1994). Different from the early-
stage investments, VC firms are motivated to focus their investments on late-stage because
it requires less risk and yields moderate return. Timmons and Sapienza(1992) suggested that
the VC firms shift their investment capital to later stages because the high tech firms require
less general partner’s assistance. Gifford(1997) theoretically proved that given a choice
among ventures of varying maturity, but equal compensation, the general partner will
choose the more mature ventures if time is a binding constraint.
As spelled out in the law
3
, the Korean VC firm has a limited role in participating as board
members and providing managerial assistance to the high tech firms. Thus, the venture
capitals are not able to control and manage the risk that occurs in early-stage investments.
2
The resource-based theory argues that firms possess resources, a subset of which enables them to achieve
competitive advantage, and a subset of those that lead to superior long-term performance(Penrose, 1959).
Resources that are valuable and rare can lead to the creation of competitive advantage. That advantage can be
sustained over longer time periods to the extent that the firm is able to protect against resource imitation, transfer,
or substitution. In general, empirical studies have strongly supported the resource-based theory.
3
Even though this law was abolished in June, 2006, it was spelled out in section 2, article 8 of Small and
Medium Business Start-up Support Act and had influenced the role of the VC firms.
126 E.J. Jeon et al.
As there are high costs to pay for taking risky investments, expectations on high risk
investments are lower than low risk investments. As seen in Figure 6.1, the Korean VC
firms have been changing its investment focus from early-stage to late-stage since year 2001
and it can be presumed that the return may have decreased continuously.
Thus, the following hypothesis can be formulated.
Hypothesis 1: Venture capital firms that focus on early-stage investment tend to have
lower efficiency than the late-stage focused firms.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
01' 02' 03' 04' 05'
Over 14 Y ear
Under 14 year
Under 7 year
Under 5 year
Under 3 year
Under 1 year
Fig. 6.1 Investment rate of venture capital on high tech firms by age
Investment horizon is one of the key factors that affect the asset performance. In spite of
the scarce literature on VC firms’ strategy formulation in regards to investment horizon,
empirical evidence suggests that VC firms tend to aim for short-term profit than the long-
term. As the length of the investment horizon increases, it becomes increasingly difficult for
venture capital investors to maintain high rates of return(Petty et al. 1994). This is because,
as high tech firms become more seasoned, required rate of return falls to reflect the lower
risk and the greater prospect of liquidity.
Insights could be shed by looking at the VC firms’ focus of investment on certain
industries. Figure 6.2 shows the investment focus of the VC firms in various industries.
While the Korean VC firms focuses on the short-term investments such as, information
technology(IT), entertainment, and manufacturing, the long-term investments such as the
biotechnology(BT) and environmental technology(ET) are neglected. This may be because
the government has not been successful in bridging the return gap between the short-term
and long-term investments.
Hypothesis 2: Venture capital firms that aim for short-term profit have higher efficiencies
than the ones with long-term objective.
In short, the hypotheses can be reviewed by Figure 6.3. The investment asset of a VC
firm is composed of current asset, venture capital asset, and operation asset. Hypothesis one
can be tested by comparing the effect of current asset and the non-current asset. Hypothesis
two can be tested by comparing the effect of venture capital asset and operation asset.
6 The Effect of Asset Composition Strategy on Venture Capital Firm Efficiency 127
0%
20%
40%
60%
80%
100%
01' 02' 03' 04'
Etc.
Distribution
Enterntainment
Manufacturing
Energy
Environment
BT
IT
Fig. 6.2 Investment rate of venture capital by industry
Fig. 6.3 Asset composition strategy and hypotheses testing
6.3 Methodologies
6.3.1 Research Design
While the venture capital organizations in the United States are mainly in the form of
limited liability partnership, the Korean VC firms are mainly stock companies(Lee et. al.
2003). Thus, the analysis on Korean VC firm should take a different approach. There are
two ways of raising capital—in the form of total asset—equity and debt— and in the form
of venture capital fund. Accordingly, there are two ways to analyze the Korean venture
capital, one focusing on the VC firm, the other, focusing on the venture capital fund. In this
study, the efficiency is estimated based on the operating profit of the VC firm and the
resulting efficiency is explained by focusing on the usage of the investment assets coming
from the total asset of the VC firm. In other words, the focus of analysis is on the “VC
firm.”
128 E.J. Jeon et al.
Two steps are taken to carry out the research. First, the operating efficiency of each VC
firm is measured by using DEA. This study estimates the efficiency of firms by using output
oriented multiple variables DEA which assumes a Variable Returns to Scale. Second, the
independent sample t-test is used to compare the efficient VC firms to in-efficient firms and
tobit estimation
4
is used to analyze the strategic factors affecting the operating efficiencies.
6.3.2 Output oriented VRS
The DEA model “Variable Returns to Scale(VRS)” proposed by Banker et al.(1984) is used
in this study in estimating the technical efficiency. In the venture capital market, the
decision making units(DMUs), VC firms, are given a fixed quantity of resources from the
investors and they are asked to produce as much output as possible. As the venture
capitalists have most control over the output rather than the input by means of incentive,
strategies, and shareholder influences, output-oriented VRS is adopted.
Output-oriented VRS model is specified as follows:
,
max
(6.1)
s.t.
0
i
y Y
0
i
x X
' 1
N
0
, where
1
1
is the proportional increase in outputs that could be achieved by the i-th DMU,
with input quantities held constant.
1
defines a technical efficiency score which varies
between zero and one.
6.3.3 Fixed effects panel tobit
As fixed effects is always consistent in panel estimation and the result of the Hausman Test
rejected the null hypothesis that the coefficients estimated by the efficient random effects
estimator are the same as the ones estimated by the consistent fixed effects estimator, fixed
effects estimation was adopted in the analysis.
Many studies applying DEA uses tobit estimation as a general tool for regression because
the efficiency score, the dependent variable is censored at the upper limit of one. In this
study, the efficient VC firms possess the latent technical efficiency of greater than or equal
to one and those of the inefficient VC firms are below one.
Fixed effects panel tobit model can be formulated as follows:
4
Tobit model was first proposed by Tobin(1958) to analyze the data with censored dependent
variable.
6 The Effect of Asset Composition Strategy on Venture Capital Firm Efficiency 129
*
it i it it
TE x u
, (6.2)
where
2
~ (0, )
u N
1
it
TE
if
* 1
it
TE
*
it it
TE TE
if
* 1
it
TE
The fixed effects model is estimated by maximum likelihood and assumes individual VC
firm effects,
i
. The likelihood function of the above standard tobit model is as follows:
1
0 1
' ( ' )
[1 ( )] [ ]
it it i it
x TE x
L
, (6.3)
where
and
are the distribution and density function, respectively, of the standard
normal variable.
6.4 The Data and Variables
Data were based on the VC firms’ balance sheet, income statement, and statement of cash
flows that were retrieved from the Financial Supervisory Commission. Approximately
hundreds to hundred forty VC firms were examined during each period from year 2000 to
2005. Total of 810 observations in the form of unbalanced panel data were the basic ground
of analysis. The asset compositions were retrieved from the balance sheet and operating
revenue and cost were retrieved from the income statement.
Super-efficiency
5
of the decision making units were measured to detect outliers that has
been contaminated with noise. Approximately 10~15% of the outliers which had super-
efficiency values much greater than one were removed and the efficiency of the remaining
observations were re-estimated. As a result, a normal distribution of the VRS efficiency was
obtained. (See Banker and Gifford 1988 for the specific procedures)
In case the key variables had zero values, it was excluded from the analysis to prevent the
distortion of the DEA results by producing extremely high efficiency score or inefficient
values.
The primary purpose of this study is to investigate the effect of financial asset
composition on operating efficiency. The empirical model is constructed by the dependent
variable, efficiency derived from DEA and the independent variables, strategic asset
composition of the venture capital firms.
6.4.1 The Dependent Variable
In deriving the efficiency of the VC firm, viewpoint of banks in DEA literature is reviewed
because it has been extensively studied in the past decades and it will shed some insight in
applying the methodology in the new area of VC firm in Korea.
5
Super-efficiency is a measure of the relative radial distance from the origin to the DMU in question, when the
frontier is estimated without this DMU included in the dataset. A super-efficiency value of 1.2 implies that the
DMU is positioned "20% outside" where the frontier would have been without this DMU (in a radial sense)
130 E.J. Jeon et al.
It is commonly acknowledged that the choice of variables in efficiency studies
significantly affects the results. The problem is compounded by the fact that variable
selection is often constrained by the paucity of data on relevant variables. The cost and
output measurements in banking are especially difficult because many of the financial
services are jointly produced and prices are typically assigned to a bundle of financial
services(Frexias and Rochet 1997). The most commonly presented approaches to bank
production could be summarized under the following three headings: the production
approach, the intermediation approach, and the modern approach.
Under the production approach, banks are viewed as service providers to the
customers(Benston 1965). It sets physical variables such as labor, material, space,
information and their associated costs as inputs and services such as the number and type of
transactions, documents processed or specialized services provided over a given time period,
number of deposit and loan accounts as outputs. This approach has primarily been employed
in studying the efficiency of bank branches.
Under the intermediation approach, banks are viewed as intermediates of the funds
between the savers and the investors. The inputs are set as operating and interest expenses
while outputs are set as loans and other major assets. There are wide variations according to
how the deposit should be treated; asset approach(Sealy and Lindley 1997), user cost
approach(Hancock 1985), and value-added approach(Berger et al. 1987).
Under the modern approach, measures of risk, agency cost, and quality of bank services
are integrated. The ratio-based CAMEL
6
approach devises the financial data to measure the
performance of the bank. The operating approach(or income-based approach) views banks
as business units with the final objective of generating revenue from the total cost incurred
for running the business(Leightner and Lovell 1998). Accordingly, it defines banks’ output
as the total revenue(interest and non-interest) and inputs as the total expenses(interest and
operating expenses).Operating approach has been widely used recently. Jemric and
Vujcic(2002) adopted an operating approach to measure the banking efficiency in Croatia by
setting the inputs as interest and related costs, commissions for services and related costs,
labor-related administrative costs, capital-related administrative costs and the outputs as
interest and related revenues and non-interest revenues. Das and Ghosh(2006) measured the
performance of Indian commercial banks by setting the inputs as the interest expenses,
employee expenses, and capital related operating expenses and the outputs as the interest
income and non-interest income.
Nevertheless VC firm has similar functions of the banks, as financial intermediaries and
service providers, the relevant DEA approaches were not appropriate in this study, because
there were difficulties in retrieving the related data figures and limitations in analyzing the
results. On the other hand, VC firms in Korea can be viewed as a profit maximizing
organizations pursuing greater operating efficiencies. Thus, the operating approach is
adopted in this study. Operating expense and revenue are set as the inputs and outputs of the
DEA to calculate the operating efficiency. In specific, input is set as the selling, general and
administrative expenses and costs of investment and financing. Output is set as the revenue
generated from investments on venture capital funds
7
, high tech firms, and other assets.
6
CAMEL is the acronym for Capital adequacy, Asset quality, Management, Earnings, and Liquidity
7
The return of venture capital fund consists of the management fee, performance fee, and dividends.
6 The Effect of Asset Composition Strategy on Venture Capital Firm Efficiency 131
Most of the DEA literature has approached the problem of measuring the efficiency in the
perspective of labor and capital. However, in this study, the capital structure is viewed as the
main cause for the resulting operating efficiency and the efficiency itself is calculated from
capital figures from the financial statement. It is assumed that the efficiency itself is caused
by strategic variables of how the capital is structured and invested.
There are lots of constraints in estimating the efficiencies of the VC firms. The main
limitation is that VC firms invest on various kinds of assets which have different investment
horizons. However, the financial statements do not convey such specific information. This is
the reason why lagging the variables were not appropriate. Instead, to check the robustness
of the results, the VC firms which are older than 3 years are selected and analyzed. It is
supposed that these old firms have had enough investment horizons to realize the returns
and must have been reflected in the financial statements.
6.4.2 Independent Variables
The variables used in the research are defined in Table 6.1. The dependent variable is set as
the VRS efficiency derived from DEA and the independent variables are set as the asset
composition ratios and control variables.
Table 6.1 Variable definitions
Variable Definition
Dependent
VRS DEA efficiency derived by assuming variable returns to scale
Independent
Asset Composition
Current asset ratio Current asset
8
divided by total asset
Venture capital investment ratio VC investment asset
9
divided by total asset
Management support asset ratio Management support asset
10
divided by total asset
Operation asset ratio operation asset
11
divided by total asset
Current to non-current asset ratio
12
Current asset divided by non-current asset
13
Cash outflow from operation to investment
ratio
14
Cash outflow from operation
Cash outflow from investment
Controls
Age Number of months since start-up
Year Time dummy indicating the year from 2000-2005
As VC firm is defined in law as a public tool for technology-finance to induce innovation,
the asset structure is different from the general service industry. Refer to Table 6.2 for the
8
Asset with investment horizon under one year
9
Asset invested in entrepreneurs and high tech firm start-ups
10
VC Asset excluding the VC investment asset
11
Asset invested on late stage and other purposes
12
Proxy variable for current asset ratio
13
Non-current asset= total asset- current asset
14
Opposite proxy variable for operation asset ratio
132 E.J. Jeon et al.
asset structure of a VC firm. According to the accounting standards set by the SMBA(2002),
the asset of a VC firm mainly consists of current asset, venture capital asset, and fixed asset.
Table 6.2 Asset structure of venture capital firm
I. Current Asset
II. Venture Capital Asset
(1)Venture Investment Asset
stock, convertible bond, project investment, venture capital fund,
public fund
(2) Management Support Asset
committed investment, loan, overseas investment, small and
medium business investment
III. Fixed Asset
(1) Operation Asset
(2) Tangible Asset
In generally accepted accounting principles, the current asset is defined as an asset on the
balance sheet which is expected to be sold or otherwise used up in the near future, usually
within one year, or one business cycle - whichever is longer. Typical current assets include
cash, cash equivalents, accounts receivable, inventory, the portion of prepaid accounts
which will be used within a year, and short-term investments.
Venture capital asset is the investment and subsidies carried out on entrepreneurs and
high tech firms. Venture capital asset is basically composed of venture capital investment
asset and management support asset. Venture capital investment asset is the actual
investment results approved by the investment companies’ regulations and this consists of
stock, convertible bond, project investment, fund disbursement, and public disbursement.
Management support asset is defined as the venture capital asset which is not included in
the venture capital investment asset. Management support asset is composed of committed
stock, start-up loan, overseas investment, and small and medium business investment.
Fixed asset consists of operation asset and tangible asset. Operation asset is defined as
investments that have not been committed to the venture capital asset. Thus, operation asset
is mainly focused on late-stage investments targeting high tech firms over 7 years old.
Tangible asset is an asset that has a physical form such as machinery, buildings and land.
6.4.2.1 Early-stage investments vs. late-stage investments
• Comparison of VC investment asset ratio with operation asset ratio
Literature has long suggested that the younger a business is, the more tenuous is its viability.
Stinchcombe’s(1965) proposition regarding the “liability of newness” has been upheld in
several empirical studies. Philips and Kirchoff(1988) reported that the probability of a new
venture’s survival was quite low in the first four years. Gupta and Sapienz(1992) suggested
some key reasons why early-stage ventures tend to be riskier investments than late-stage
ventures: fewer resolved demand uncertainties, technological uncertainties (in both product
and process design), resource uncertainties (in areas such as availability of skilled personnel,
raw materials, and channels of distribution), and management uncertainties (in areas such as
the leadership capabilities of the founder, compatibility and balance within the tope
6 The Effect of Asset Composition Strategy on Venture Capital Firm Efficiency 133
management team, etc.) Venture capital investment asset ratio has been set as the variable to
represent the degree of investment on early-stage and operation asset ratio has been set to
represent the degree of investment on late-stage.
• Cash outflow from operation to investment ratio
To check the robustness of the result on the previous variable, the ratio of cash outflow from
operation to cash outflow from investment is devised as the proxy to represent the
proportion of early-stage investments to late-stage investments. According to the accounting
standards for VC firms set by the SMBA(2002), the cash flow from operation is generated
from the investment activities of the venture capital asset and the cash flow from investment
is generated from the investment activities of the operation asset. As the venture capital
asset focuses on early-stage investments and the operation asset focuses on late-stage
investments, the ratio of the two figures imply the ratio of early-stage investments to late-
stage investments. This is an opposite proxy of the previously devised venture capital
investment ratio.
6.4.2.2 Short-term investments vs. long-term investments
• Current asset ratio
Current asset is defined as the asset managed to obtain profit within one year. It is the
variable to represent the degree of investment on pursuing short-term profit.
• Current to non-current asset ratio
To check the robustness of the result on the previous variable, another proxy variable to
represent the degree of short-term investments has been devised. This may be a more
detailed measure compared to previously set variable, the current asset ratio, because direct
comparison of the current asset with the non-current asset is possible.
6.4.2.3 Controls
• Age
Age of the VC firm was estimated from its start-up date and counted by months. This
variable controls the experience of the VC firms.
• Size
Size was represented by the total assets. Size was controlled in the econometric equation by
dividing the major asset composition variables with total assets.
• Year
The Korean venture capital market has undergone the venture boom (1999-2000) and
cooling (after year 2000). Thus, taking in the yearly effect would raise the accuracy of the
estimation.
134 E.J. Jeon et al.
6.5 Empirical Analysis
6.5.1 Compared Groups Analysis
Independent samples t-test was used to carry out the compared group analysis. Levine’s test
for equality of variances is rejected when the F-test is significant. Refer to Table 6.3 for the
comparison between the efficient frontier(VRS=1) and the non-efficient firms(VRS<1).
Even though the results are not statistically significant, the sign of the mean difference may
shed some insight to the differences between the two groups. It can be conjectured that
whereas the efficient firms tend to possess smaller venture capital investment asset and
management support asset, they tend to possess greater current asset and operation asset. It
is likely that the results were statistically insignificant because there were other various
factors affecting the two groups.
Table 6.3 Independent samples t-test for efficient frontier vs. non-efficient
assumption
15
of variances
t-value p-value
Mean
difference
std. error
difference
current asset ratio Inequality 0.9946 0.3272 0.0839 0.0844
VC investment asset ratio Equality -0.9335 0.3511 -0.1093 0.1171
management support asset ratio
Equality -0.0087 0.9930 -0.0002 0.0249
operation asset ratio Inequality 0.9159 0.3669 0.0581 0.0635
* Significant at 10% confidence level; ** Significant at 5% confidence level; *** Significant at 1% confidence level
6.5.2 Tobit Estimation
The operating efficiency of the VC firm is mainly caused by the firm’s strategic alternatives
in respect to asset composition. In particular, four asset composition variables— current
asset, venture capital investment asset, management support asset, operation asset—are
devised, controls are set by age, and year is set as dummy variables. Size is controlled by
dividing each asset by total asset. Age is a proxy for experience, total asset is the proxy for
size, and year is the proxy for the trend effects. Following Eq. 6.4 is the regression devised.
0 1 2 3
*
it it it it
VRS Current VentureCapital ManageSupport
4 5 6
it it it it
Operation Age Year u
(6.4)
Taking out all the zeros from the unbalanced panel data set, there were 361 observations
and the following Table 6.4 shows the descriptive statistics.
Even though the average value of the current asset ratio is low as 0.24, there exist VC
firms that have the current asset ratio up to 0.93 and these can not be distinguished from the
general financial institutions.
The mean of the venture capital investment asset ratio is approximately 0.45 which
indicates as the law
16
spells out, the VC firms operates the venture capital investment asset
15
The assumptions were held according to the Levene's test of equality of variances
16
According to Section 2, Article 8 from the Small and Medium Business Start-up Support Act,
since 3 years
6 The Effect of Asset Composition Strategy on Venture Capital Firm Efficiency 135
up to 50%. Further, the statistics show a wide variation with a minimum of 1.08E-11 to a
maximum of 0.99. Compared to the other asset ratios, venture capital investment asset ratio
has the largest standard deviation of 0.25. It is obvious that there are large variations among
the VC firms, from risk-averse VC firms to risk-loving ones in respect to venture capital
investment asset ratio.
Table 6.4 Descriptive statistics (all samples)
No.
Mean Std.Dev. Minimum
Maximum
Current asset ratio 361
0.24 0.19 0.003 0.93
Venture capital investment asset ratio 361
0.45 0.25 1.08E-11
0.99
Management support asset ratio 361
0.07 0.12 1.25E-11
0.69
Operation asset ratio 361
0.08 0.12 8.90E-12
0.93
Current to non-current asset ratio 361
0.55 1.29 0.003 12.49
Cash outflow from operation to
investment ratio
361
2.30E+07 3.17E+08
9.27E-11
5.27E+09
Age 361
85.38 64.99 2 228
The VC firms have the mean age of 85 months, which indicates that the Korean venture
capital market is in its early-stage since its formation. In spite of its youth, the venture
capital market has its dynamic feature because there are a wide variety of firms from the
ones which just entered the market with the age of 2 months to the ones that have been in
the market with the age of 228 months.
Table 6.5 shows the correlation coefficients of the variables. The result verifies that there
is no problem of multicollinearity among the variables. From the correlation coefficients, it
can be predicted that the technical efficiency of the VC firm may increase in line with the
current asset ratio, operation asset ratio, and age and on the other hand, decrease in line with
the venture capital investment asset ratio.
Table 6.5 Correlation Coefficients
VRS
Current
asset
ratio
VC
investment
asset ratio
management
support
asset ratio
operation
asset
ratio
current to
non-current
asset ratio
cash
outflow
ratio
age
VRS 1.00
0.14 -0.04 0.01 0.10 0.03 -0.08 0.10
current asset ratio 0.14
1.00 0.45 0.15 0.17 0.38 0.02 -0.08
VC investment asset
ratio
-0.04
0.45 1.00 0.38 -0.02 0.10 0.01 -0.15
management
support asset ratio
0.01
0.15 0.38 1.00 0.02 -0.07 0.04 0.06
operation asset ratio
0.10
0.17 -0.02 0.02 1.00 0.13 -0.03 0.03
current to non-
current asset ratio
0.03
0.38 0.10 -0.07 0.13 1.00 0.00 -0.03
cash outflow from
operation to
investment ratio
-0.08
0.02 0.01 0.04 -0.03 0.00 1.00 0.01
Age 0.10
-0.08 -0.15 0.06 0.03 -0.03 0.01
1.00
from its start-up, VC firms should invest at least 50% of their capital to early-stage asset defined by
the Presidential decree. These investments are targeted for entrepreneurs(start-ups under 7 years old),
high tech firms, and overseas investment.
136 E.J. Jeon et al.
Four different models were estimated by fixed effects censored panel tobit estimation and
the results are shown in Table 6.6. Model III and IV include the substitute variables for the
current asset ratio and operation asset ratio. The limits of the efficiency scores in the
censored tobit model were set from zero to one. The independent variables are logged for
the sake of clear explanation. This implies that one percentage change in the independent
variable will cause the dependent variable to change by one hundredth of the estimated
coefficients.
The estimation results are consistent among the different model settings. Current asset
ratio was positively significant, venture capital investment asset ratio was negatively
significant, and the operational asset ratio was positively significant, and all year effects
were negatively significant.
Taking only the significant results from all models, the average effect of the variables on
raising the operating efficiencies are as follows. In average, increasing 1% of the current
asset ratio resulted in raising the efficiency by 0.00037. Increasing 1% of the venture capital
investment asset ratio resulted in decreasing the efficiency by 0.00019. 1% increase in
operational asset ratio contributed to raising the efficiency by 0.00005.
Table 6.6 Fixed effects tobit estimation on VC firm efficiency (all samples)
I II III IV
Log current asset ratio 0.035** 0.039**
(0.018) (0.019)
Log current to non-current asset
ratio
0.025* 0.032**
(0.014) (0.015)
Log VC investment asset ratio -0.017*** -0.022*** -0.018*** -0.022***
(0.004) (0.004) (0.004) (0.004)
Log management support asset
ratio
0.001 0.001 0.001 0.002
(0.001) (0.002) (0.002) (0.002)
Log operation asset ratio 0.007*** 0.004**
(0.002) (0.002)
Log cash outflow from operation
to investment ratio
-0.014** -0.020***
(0.006) (0.007)
Log age 0.016 0.022
(0.022) (0.022)
Year 2001 -0.327*** -0.321***
(0.076) (0.076)
Year 2002 -0.574*** -0.539***
(0.081) (0.081)
Year 2003 -0.511*** -0.462***
(0.080) (0.079)
Year 2004 -0.491*** -0.438***
(0.081) (0.080)
Year 2005 -0.276*** -0.234***
(0.082) (0.082)
Log likelihood -348.75 -362.77 -350.50 -362.33
no. of observations 361 361 361 361
* Significant at 10% confidence level; ** Significant at 5% confidence level; *** Significant at 1% confidence level
Several implications are conveyed from these results. First, short-term investments raise
the efficiency of the VC firm in larger degree than the long-term investments. 1% increase
6 The Effect of Asset Composition Strategy on Venture Capital Firm Efficiency 137
in current asset ratio caused the efficiency to rise seven times more than the case of
increasing 1% of operation asset ratio. Also, 1% raise of current to non-current asset ratio
raised the efficiency by 0.00028. This backs the previous analysis that the VC firms
focusing on short-term investments had greater efficiencies than those focusing on long-
term investments.
Second, the early-stage investments via the venture capital investment assets tend to
decrease the operating efficiencies. This result is interesting because investment focused on
early-stage is what makes the VC firm a VC firm. This implies that the VC firms are far
from showing the innate investment behavior of taking high-risk and earning high-return.
Rather, the VC firms that take high risks are likely to show lower operating efficiencies. On
the other hand, the late-stage focused asset tends to increase the operating efficiencies.
Efficient VC firms tend to find profit from rather on late-stage investments than on early-
stage investments.
There may be questions of whether the previous analysis is reliable because the young
VC firms have been included in the analysis and these firms may not have had enough time
to reap returns. To check the robustness of the previous estimation result, estimation on the
VC firms older than three years was carried out. These firms had enough investment periods
to achieve modest returns. The total observation was 259 and the following Table 6.7 shows
the descriptive statistics.
Table 6.7 Descriptive statistics (older than 3 years sample)
No.
Mean Std.Dev.
Minimum
Maximum
Current asset ratio 259
0.23
0.19
0.005
0.93
Venture capital investment ratio 259
0.43
0.25
1.08E-11
0.99
Management support asset ratio 259
0.08
0.13
1.25E-11
0.69
Operation asset ratio 259
0.09
0.11
8.90E-12
0.70
Current to non-current asset ratio
259
0.58
1.47
0.005
12.49
Cash outflow from operation to
investment ratio
259
3.15E+07
3.71E+08
0.006
5.27E+09
Age 259
108.98
60.63
37
228
Similar to the descriptive statistics of the all sample firms, the above result can be
interpreted as follows.
First, there exist VC firms with large current asset ratio up to 0.93 and these firms can not
be differentiated from the general financial institutions.
Second, older age did not affect the wide variation of risk-taking behaviors in respect to
venture capital investment ratios. It had the largest standard deviation of 0.25 among the
variables. There were risk-averse firms with the minimum venture capital investment ratio
of 1.08E-11 to risk-loving firms with the maximum ratio of 0.99. It can be noticed that the
venture capital investment ratio is highly maintained due to the restraints by the law.
However, the VC investment asset ratio of the older than 3 years sample is smaller than
those of the all sample because VC firms gradually seek profit by investing in other assets.
The estimation results of the VC firms older than 3 years are shown in Table 6.8. Along
with the all samples case, the samples including the firms older than three years have shown
statistically significant results. Most of the findings are consistent with the previous results.
138 E.J. Jeon et al.
While the venture capital investment ratio tends to decrease the operating efficiency, the
operation asset ratio and current asset ratio tends to increase the efficiency. It can be
concluded that the VC firms focusing on early-stage investments have lower efficiencies
than those focusing on late-stage investments. Also, the results from model II and IV
concludes that the VC firms aiming for short-term profit tend to have greater efficiencies
than those pursuing long-term profit. Based on the previous estimation results and empirical
analyses, both hypotheses one and two are accepted.
Table 6.8 Fixed effects tobit estimation on VC firm efficiency (older than 3 yrs)
I
II
III
IV
Log current asset ratio 0.013 0.033**
(0.016) (0.017)
Log current to non-current
asset ratio
0.012 0.025**
0.012 (0.012)
Log venture capital asset
ratio
-0.005* -0.011*** -0.006* -0.011***
(0.003) (0.003) (0.003) (0.003)
Log management support
asset ratio
-0.001 0.002 -0.00005 0.003
(0.002) (0.002) (0.002) (0.002)
Log operation asset ratio 0.004** 0.003*
(0.002) (0.002)
Log cash outflow from
operation to investment ratio
-0.012** -0.016***
(0.005) (0.006)
Log age 0.038 0.037
(0.028) (0.028)
Year 2001 -0.216*** -0.232***
(0.072) (0.071)
Year 2002 -0.470*** -0.465***
(0.070) (0.070)
Year 2003 -0.375*** -0.361***
(0.065) (0.065)
Year 2004 -0.383*** -0.363***
(0.064) (0.064)
Year 2005 -0.263*** -0.250***
(0.067) (0.067)
Log likelihood 0.943 -26.923 1.099 -24.585
no. of observations 259 259 259 259
* Significant at 10% confidence level; ** Significant at 5% confidence level;
*** Significant at 1% confidence level
6 The Effect of Asset Composition Strategy on Venture Capital Firm Efficiency 139
6.6 Conclusion
VC firms that tend to focus on early-stage investments and long-term investments show
relatively low efficiency than the firms focusing on late-stage investments and short-term
investments. This may not be a problem to the VC firms themselves because their goal of
profit maximization is achieved anyway.
However, in the perspective of technology policy, this result is gloomy because the
efficient VC firms are taking exactly opposite strategies from the social expectation. The VC
firms are neither showing the characteristics of ‘high risk and high efficiency’ nor meeting
the policy demand of maximizing the social benefit. The VC firms are supposed to finance
high tech firms with relatively low marginal cost of capital, sort out the potentially
successful ones by screening, and add value on them by monitoring. The problem can be
summed up in two dimensions—difficulty in inducing risky venture capitals and VC firms
themselves being inefficient in managing capitals, especially those focused on early-stage
and long-term investments.
The venture capital market in Korea has been created by inducing the cash from the loan
market to the venture investments. The underground capital has been transformed in to
technology capital empowered by the law. These capitals originally had their focus on short-
term investments and late-stage investments as moneylenders. The policy failure of
financing high tech firms with the objective of inducing investments on early-stage high
tech firms and pursuing long-term profit was rooted from its creation. And this paper has
confirmed the failure of technology-finance policy via VC firms.
Additionally, several policy implications are suggested. First, the legal institution should
be spelled out to provide VC firms with incentives to specialize in early-stage and long-term
investments. Current legal system prevents the VC firms from managing the basic problems
of uncertainty, information asymmetry, and moral hazard in regards to financing high tech
firms. If the VC firms are provided with the necessary bells and whistles, the supportive
legal institution which lets them to fully function as VC firms by enabling them to carry out
effective functions of screening and monitoring, their operating efficiencies in regards to
early-stage and long-term investments may be raised. Thus, to maximize the social benefit,
VC firms which specializes in early-stage and long-term investments and raises substantial
profit via such investments should be brought up by supportive legal institution.
Second, public capital should be provided to support the VC firms to concentrate their
assets on early-stage and long-term investments. The VC firms were not able to accumulate
appropriate knowledge and experiences in screening and monitoring, especially in the areas
of early-stage and long-term investments, due to lack of substantial capital providers such as
the government. In case of the United States, the pool of money managed by VC firms grew
dramatically over the past 20 years as pension funds became active investors, following the
U.S. Department of Labor's clarification of the "prudent man" rule
17
in 1979. In fact,
pension funds became the single largest supplier of new funds and during 1990–2002,
pension funds supplied about 44% of all new capital(OECD 2006). Likewise, the Korean
venture capital market may need a public investor to provide risky venture capitals. Public
17
Under the Department of Labor "Prudent Person" standard, "A fiduciary must discharge his or her
duties in a prudent fashion." For pension fund managers, the standard emphasizes how prudent
investors balance both income and safety as they choose investments.
140 E.J. Jeon et al.
capital should be provided to support investment activities on early-stage investments and
long-term profit making.
There are several limitations in this study. First, nevertheless the results convey the nature
of VC firms in Korea, the data retrieved from the Financial Supervisory Service maybe
imperfect. The Korean accounting standards leave VC firms a room for misreporting and
"window dressing." Furthermore, the supervisory capacity of the Audit Institution is in
question to prevent those practices. In particular, venture capitals have more tricks to inflate
capital figures, manipulate book profits, etc. In this aspect, capital inflows and outflows
from the statement of cash flow may be used to make a better estimation on the VC firm
efficiencies.
Second, one of the significant factors affecting the operating efficiency, the human factor
has been excluded from the analysis due to difficulties of obtaining such data. Further
studies are recommended to include the quality of human resources in to the econometric
equation.
Third, venture capital fund is a significant part of the venture capital market. Though it is
a separate entity from the VC firm, it explains approximately 50% of the profit generated
and thus, it should not be omitted when studying the venture capital in Korea.
This paper focused on examining the effects of different asset compositions on the VC
firms’ efficiency. The methodologies and ideas may be applied to the studies on venture
capital funds and private equity.
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