Evaluation of Credit Guarantee Policy using
Propensity Score Matching
Inha Oh a, Jeong-Dong Lee a, Gyoung-Gyu Choi b and Almas Heshmati c
a
Techno-Economics and Policy Program,
College of Engineering, Seoul National University,
San 56-1, Shillim-Dong, Gwanak-Gu,
Seoul 151-742, KOREA
b
C
Department of Business Administration,
Dongguk University,
26, 3-Ga, Pil-Dong, Chung-Gu,
Seoul 100-715, Korea
Seoul National University and University of Kurdistan - Hawler
Last Update: September 10, 2006
Abstract
In the aftermath of the Asian financial crisis in Korea, the credit guarantee policy was
used as an instrument by the government to support small and medium enterprises
(SMEs). However, the effect of the credit guarantee has not been carefully studied and
the policy has been criticized for impairing development of innovative private financial
sector and for making SMEs highly dependent on government policy. In this paper, we
evaluate the effect of the credit guarantee policy by comparing a large sample of
guaranteed firms and matched non-guaranteed firms from 2000 to 2003. The sample
firms are compared with respect to growth rates of different performance indicators
including: productivity, sales, employment, investment, R&D, wage level and survival
of firms in the post crisis period. To avoid the selectivity problem, recently developed
propensity score matching methodologies are adopted. Results suggest that the
guarantee provision influenced significantly for supported firms to maintain their size in
terms of sales and employment, and increase their survival rate, but credit guarantees
did not help firms to increase their R&D and investment and hence, growth in
productivity. Moreover, the selection of firms to receive guarantee funds was not linked
to the productivity of the supported firms.
Keywords: credit guarantee, selection bias, propensity score matching, SME.
JEL Classification Numbers: C40, H43, H81, L25, L53
1
1. Introduction
The credit guarantee was used as a policy instrument by the Korean government in its
support towards small and medium enterprises (SMEs) on the afterward of the Asian
financial crisis. However, the credit guarantee programs has been criticized by many
domestic and foreign scholars for its negative effects by impairing development of
innovative private financial sector and for making SMEs highly dependent to
government policy measures (Kang, 2005; Lee, 2006; IMF, 2005). Even though there
have been some qualitative remarks on the effectiveness of the policy, evaluation of
credit guarantee policy has not been conducted systematically in terms of methodology
and data.
This study aims at filling the gap in the existing literatures. In that, we evaluate the
effect of the credit guarantee policy in terms of growth of productivity, sales,
employment, investment, R&D, wage level of the supported firms and their survival
rates in the post crisis period. The reliability of measures of effectiveness of the policy
is hampered by the ‘selectivity’ problem, which implies that public funding goes to
firms having a priori favorable conditions to correspond to the policy objectives (Jaffe,
2002; Blundell and Costa Dias, 2000). In this paper, we deal with the ‘selectivity’ issues
by employing recently developed propensity score matching methodologies.
1.1 Impact of Asian financial crisis on Korean manufacturing sector
Manufacturing is a major contributor to the Korean Economy. It is highly export
oriented and as such subject to both domestic and international competition and various
market related risks. The Asian financial crisis, which occurred from mid-1997 to 1998,
can be a representative example of such external risk. In 1998, the gross domestic
product (GDP) level decreased by 6.7% and fixed investment decline was as much as
40% (Borensztein and Lee, 2002). Although the Korean economy severely suffered
from the Asian financial crisis, it is well known for its rapid recovery from the crisis
(Koo and Kiser, 2001). During the process of recovery, the transparency and the general
market confidence for the Korean economy has significantly improved (Hong and Lee,
2000). However, one can expect that the impact of crisis on firms would be different
2
depending on the firms’ characteristics, e.g. size classes.
During and after the crisis, the International Monetary Fund (IMF) set a large number of
guidelines mainly regarding restructuring and downsizing the conglomerates labeled as
Chaebols1 and large scale enterprises (LSEs). Revision of bankruptcy-related laws and
implementation of the outside courts workout programs mainly for Chaebols were also
executed. As a result, some notable changes happens; for example, a number of
Chaebols became bankrupt and average debt/equity ratio of LSEs has gone down
dramatically.
The impact of the economic crisis on SMEs was more disastrous than on the LSEs
counterpart. For example, the number of SME bankruptcies in 1998 reached 22,800,
while its corresponding number was only 11,600 in 1996 (Gregory et al., 2002). After
the Asian financial crisis, SMEs in Korea faced great difficulties to regain its position
due to decreased productivity and profitability, inefficient restructuring plans, slowdown
in demand and the rise of the Chinese manufacturing sectors (Kim and Lee, 2002).
Table 1 compares status of LSEs and SMEs throughout the crisis periods and confirms
arguments discussed above. Especially, the profit ratios and debt equity ratios clearly
indicate the gap between the two size classes after the crisis; i.e. increased profit ratio
and stabilized debt equity ratio for LSEs which are in concordance with IMF
restructuring plan. The difference in R&D intensity for LSEs and SMEs persisted,
which implies that performance gap between the two groups is rather difficult to be
further reduced.
LSEs succeeded in the required restructuring during the aftermath of the financial crisis
and grew rapidly in terms of profitability and output, particularly in the export market.
However, SMEs were still suffering from the effects of the recession and, in particular,
in the domestic market. The proportion of SMEs making loss increased from 17.7
percent in 2002 to 21.3 percent in 2003 (IMF, 2005). It seems that the restructuring of
SME industries was less successful and insufficient. This led to the fact that many
SMEs managed to survive with the financial support program provided by the
government.
Oh et al. (2006), by comparing plant turnover and productivity dynamics of Korean
3
manufacturing industry by size in pre- and post-crisis periods, observed the undesirable
exit of SMEs with higher productivity, especially pronounced in the post crisis period.
The study confirmed the existence of ‘zombie firms’ which were kept alive by support
from the public financial sector as one of the sources of the negative exit effect.
1. 2. The credit guarantee policy
SMEs face great difficulties to finance investment due to asymmetric information which
arises from the lack of financial information and standardized financial statements.
Stiglitz and Weiss (1981) showed that in equilibrium a loan market may be
characterized by credit rationing. In perfect economic system, if prices do their duty,
rationing should not exist. However, credit rationing does in fact exist due to an excess
demand for loanable funds. In loan markets, there exists residual imperfect information
after the evaluation of loan applications. Due to imperfect information and the adverse
selection, it is often difficult for SMEs to borrow funds even at higher interest rates.
The belief that capital markets do not provide adequate funds for new businesses is also
one of the rationales for government loan assistance programs to SMEs (Evans and
Jovanovic, 1989). Many governments provide subsidized loans and loan guarantees to
SMEs for start-up and expansion. The USA, UK, France, Belgium, the Netherlands and
others have adopted financial assistance programs for unemployed workers who start
businesses, let alone the efficiency of the programs (Bendick and Egan, 1987).
The Korean government in its efforts tried to avoid systemic risk bringing about a
contagious failure of solvent but temporarily illiquid SMEs in the event of a credit
crunch. In order to prevent the decline in bank loans to SMEs immediately after the
crisis, the government sharply expanded its credit guarantees schemes. The purpose of
credit guarantee was to provide financial support to SMEs suffering from insufficient
investment from private financial institutions due to market failures, to enhance
competitiveness of SMEs and finally to increase SMEs’ accessibility to private
financing. The credit guarantee gives warranty to private investors by reliving the risks
of lending to SMEs.
The credit guarantee scheme was one of the most influential public support policies
4
aimed at SME sector for the case of Korean economy. The amount of government
contribution to credit guarantee funds was about 1 trillion KRW2 in 2003, which will
increase total guarantee balances by nearly 11 trillion KRW, considering the operation
multiple3 which lies between 9.7 and 16. And large portion of SMEs lending money
from banks are utilizing the credit guarantee scheme (1/4 for the case of deposit banks
and 1/3 for the case of commercial banks). On the other hand the amount of other public
supporting policy measures to SMEs was about 6 trillion KRW in the same period (Lee,
2006).
There exist two major public credit guarantee institutions in Korea4; the Korea Credit
Guarantee Fund (KCGF) and the Korea Technology Credit Guarantee Fund (KOTEC).
KCGF is a pubic financial institution established in 1976 under the provision of the
Korea Credit Guarantee Fund Act. The objective of KCGF is to lead the balanced
development of the national economy by extending credit guarantee services for the
liabilities of promising enterprises which lack tangible collateral and stimulating sound
credit transactions through the efficient management and use of the credit information.
KOTEC was founded in 1989 under the Financial Assistance to New Technology
Businesses Act which went through a full-scale revision and was newly titled Korea
Technology Credit Guarantee Fund Act in 2002. The mission of KOTEC was to
contribute to the national economy by providing credit guarantees to facilitate financing
for new technology-based enterprises while promoting the growth of technologically
advanced SMEs and venture businesses.
Even though both institutions are providing public guarantee service to mainly SMEs,
but the missions are different. KCGF was aimed to provide credit guarantee to firms
with competitive power, but lack of credit ratings or tangible collateral to pull
investment, while KOTEC was developed to provide guarantee funds to technology
oriented firms in SME sectors. Figure 1 shows the development pattern of credit
guarantee balance for the two guarantee institutions. As can be seen in the Figure 1, the
remaining balance of credit guarantee increased steeply after the crisis periods (after
1998), which implies that government used these two credit guarantee institutions as a
policy instrument to give support to the SME sectors during and after the financial crisis.
The amount of credit guaranteed by government soared and it reached almost 6 to 8
5
percent of GDP, which is higher than other countries, e.g. 0.1 percent in USA, 0.02
percent in UK, 0.2 percent in Germany in 2004. Even for the case of Taiwan, where
SMEs play an important role in the overall national economy, the ratio of credit
guarantee balance to GDP is only less than 3 percent (Kang, 2005).
Right after the crisis, government prepared large amount of public funds and a major
portion of which was given as a public support to temporary illiquid SMEs in broad
sense. One can easily imagine that the two guarantee institutions might lack experience
or capacity of conducting detailed investigation for all applications and of selecting
eligible firms. KCGF has adopted the sophisticated corporate credit rating system
(CCRS) only after 1999. Based on the accumulated financial data of firms and
considering potential risks on management and operation, CCRS mechanically calculate
credit rating of each firm which will be used for screening process to select firms to be
supported. However, after the crisis the workload for employees of KCGF who are in
charge of screening process increased significantly due to soaring number of applicants
received by the guarantee funds and increased amount of guarantee supply. Compared
to 1997, the amount of guarantee supply per examiner was 3.63 times higher and the
number of firms per examiner was 2.53 times higher in 2001 (Lee, 2006). Besides
KCGF has utilized circulation of job mission of employees inside the institution for
every 2 or 3 years, which seemed not to be very suitable to train examiners, specialized
in evaluation and screening processes. On the other hand, KOTEC aimed to give credit
primarily to technology based newly founded firms, but as pointed out in Kang et al.
(2006) and shown in Table 2, the amounts of credit guarantee supply based on the
detailed technology assessment was less than 4 percent in 2001. Thus, we can infer that
most of screening process to pick firms to receive support was insufficient.
Figure 3 shows the increasing trend of guarantee default in 2003 for both KCGF and
KOTEC. The average length of guarantees provided by both of institutions was around
five years, implying that most of firms subject to guarantee default are the ones
originally selected in 1998 when guarantees were expanding in the wake of the crisis.
That is to say, the increasing default rates can be attributable to the termination of
guarantees which were assigned right after the crisis. This fact partly explains that, right
after the Asian financial crisis, lots of marginal firms were selected in the guarantee
6
policy, due to lack of capacity to evaluate credit condition of firms, and sudden increase
in demand for both institutions.
There exist few recently published studies analyzing credit guarantee policy, which are
mostly qualitative critics about the policy scheme. According to Lee (2006), the object
of government intervention in SME loan market was to compensate for market failure
due to information asymmetry and external effects. However, as an agency of
government, guarantee institution had its own objective to maintain operational
efficiency and long-term sustainability. Moreover the incentive structure among the
three key players, guarantee institutions, banks, and SMEs, is inappropriately
coordinated, since most of risk should be handled by the guarantee institutions. And no
one was really taking responsibility for losses from the guarantee defaults. The study
indicated that the divergence of objectives between government and institutions and
inappropriately coordinated incentive structure lead to insufficient policy performance,
and eventually to financial burden for the government. Kang (2005) pointed that the
non-selective government support of SMEs was one of the key sources of the sluggish
SME restructuring process after the financial crisis. It worsened the SME market
environment in two ways: the first was the effect of crowding out the private financial.
The second and long-term negative effect was to make SMEs become more dependent
on public support. The survival of uncompetitive SMEs with the help of government
support might result in a decrease in market share and profits of competitive firms. Thus,
uncompetitive firm might replace competitive one with the public intervention.
IMF (2005) described the behavior of government intervention in Korea as ‘the
ubiquitous hand’. In the review of IMF, the policy intervention after the crisis brought
about financial difficulties of SMEs and provided negative incentive larger corporations
for investment. IMF shares the point raised by Kang (2005) that inappropriate public
intervention toward SMEs through non-selective guarantee support hinder the voluntary
market based restructuring of SMEs. Based on the observations, IMF suggested
reducing the credit balance ratio to GDP by 1 percent per annum for the next 5 years.
We know that several researches agree the common point that the public intervention
including credit guarantee program has had negative effect. However, not many
quantitative researches have been done especially for the effectiveness of the credit
7
guarantee policy mainly due to the unavailability of consistent data and appropriate
methodological framework.
In this research, we evaluate the effect of the credit guarantee policy in terms of
employment, investment, survival and productivity in the post crisis period. The amount
of funds allocated to the credit guarantee is huge and the number of targeted firms is
large, then we need to investigate its effectiveness and to provide background
information for further evolution of the policy.
Methodologically, reliable policy evaluation should solve the ‘selectivity’ problem,
which implies that public funding may be allocated to proposals judged in advance with
high probability to succeed. If we cannot control selectivity problem, we might over- or
under-estimate the true effect (Jaffe, 2002; Blundell and Costa Dias, 2000). To deal with
the selectivity issue, we adopt propensity score matching techniques, which has been
developed in labor economics (Dehejia and Wahba, 1999, 2002; Frölich et al., 2004;
Heckman et al., 1997; Smith, 2000), and recently has been applied to firm-level studies
(Arnold and Hussinger, 2005; Yasar and Rejesus, 2005; Lööf and Heshmati, 2005).
This study has a number of contributions to the existing literature. First, it utilizes
original dataset that covers all manufacturing firms with 5 more employees in Korea
and all the guaranteed firms. Especially the data set for guaranteed firms are unique
since all the guaranteed firms are included and non-financial characteristics, in addition
to financial ones, are covered. Second, we analyze the effectiveness of the public policy
in terms of survival of firms as well as traditional growth measures. Third, we adopt the
most up-to-date statistical methodology in the field of propensity score matching.
The remainder of this study is organized as follows. Section 2 reviews the
methodologies of propensity score matching. Section 3 describes the data and presents
key descriptive statistics. The empirical results are discussed in Section 4. The final
Section 5 summarizes and concludes this study.
2. Propensity Score Matching
In this section we introduce the propensity score matching technique. The method has
been developed in the field of labor economics and adopted in many field for policy
8
evaluation. We review the most frequently used matching methods and briefly argue for
factors in the support of their use, the strengths and weaknesses of such methods in
evaluation of public support programs. We also elaborate with properties of the
estimates of treatment effects.
2.1 The difficulties in evaluation studies - selection bias
Since Rubin (1974), the effect of a program or a support is defined as a created value
added by participating in a program. In other words, the effect of program can be
defined as ‘what would have happened to those who, in fact, did receive treatment, if
they had not received treatment (or vice versa)?’ (Rubin, 1974). Thus, we have to
compare the factual and (hypothetical) counterfactual situations. Unfortunately, we
cannot observe this counterfactual in real situation, since participants are observed in
only one factual state. Mere comparison between supported and non-supported groups
can not identify the exact additional effect of the support program, since their
characteristics before participation in the supporting program were different already.
The concept of treatment effect defined by the additional value added should, thus, be
based on the appropriate construction of a counterfactual. In other words, the policy
treatment effect can be defined as the difference between real outcome and hypothetical
outcome represented by the counterfactual. Modern evaluation methods are focusing on
estimating this counterfactual (Blundell and Costa Dias, 2000).
One other issue, except for the construction of counterfactual, is the well-known
‘selectivity’ problem, which implies the recipient firms are eligible before the
implementation of the policy program. Jaffe (2002) expressed this issue that the projects
that are the best candidates to be funded - in the sense of maximizing the impact of
public support - are also the projects that would have the highest expected output in the
absence of funding (Jaffe, 2002).
Given the counterfactual and selection problems, the most appropriate measure of the
effectiveness of government support might be comparing the performances of two firms
with same characteristics, assuming that one received support (or treatment) and the
other did not. However, it is hard to find appropriate comparison groups which can
9
represent non-supported firms in evaluating the program.
In this study we apply the recently developed propensity score matching (PSM
hereinafter) methodology (Rosenbaum and Rubin, 1983; Heckman et al., 1998; Dehejia
and Wahba, 2002), which allows to construct a comparison group by matching twin
units based on the propensity score in the population of untreated groups. With this
modern approach, we expect to solve the selectivity problem and to compare the factual
and counterfactual as a result.
2.2 PSM methodologies
PSM methodology was first introduced by Rosenbaum and Rubin (1983). The concept
of PSM is based on the strongly ignorable treatment assignment assumption. It means
that conditioned on the observable characteristics (X variables) of possible participants,
the decision for participation of the program should be independent of the outcome
measures. This is so-called conditional-independence assumption (CIA). CIA in this
respect can be written as following:
(1)
(Y0 , Y1 ) ⊥ T X
where Y1 means the outcome in the treated state and Y0 denotes the outcome in the
untreated state. T is an indicator variable denoting participation in the program.
Another condition is that the probability to participate in the program for program group
and comparison group should lie in the same domain, which is called common support
condition.
If these assumptions and conditions are satisfied and when there exist sufficient number
of observable variables related to the characteristics of participants to a program, it is
theoretically possible to obtain unbiased estimation of effect of a program.
Propensity score indicates a conditional probability of applicants to participate in a
program when observable characteristics of applicants are given. In other words,
(2)
Propensity score = P = P ( X ) = Pr(T = 1 X )
10
.
Roesenbaum and Rubin (1983) proved the following two lemmas under CIA and the
common support condition:
Lemma 1: If P(X) is the propensity score, then
(3)
X ⊥ T P( X )
.
Lemma 2: Under CIA and Lemma 1, then the conditional independence result extends
to the use of the propensity score as:
(4)
(Y0 , Y1 ) ⊥ T P ( X ) .
Based on the above lemmas, for a population of units denoted by i, we can define the
policy impact, which is defined as the difference between real and counterfactual
outcomes, as the average effect of treatment on the treated (ATT) as follows:
ATT = E {Y1i - Y0i Ti = 1}
(5)
{
}
= E E (Y1i - Y0i Ti = 1, P ( X i ) )
CIA
{
}
= EP ( X i ) E (Y1i Ti = 1, P( X i ) ) - E (Y0i Ti = 0, P( X i ) ) Ti = 1
where the outer expectation is taken over the distribution of P(Xi) in the population of
participants, Ti=1.
We can estimate the propensity score and test the balancing hypothesis (3), known as
balancing test, according to the iterative algorithm suggested by Dehejia and Wahba
(2002) and Becker and Ichino (2002)5.
Once the balancing test is satisfied, we can confirm that the selected observed variables
and their interaction and square terms reflect the assignment mechanism sufficiently and
the use of calculated propensity score as a conditioning variable is acceptable.
However, estimation of the propensity score only, is not enough to estimate the ATT of
interest using equation (5). This is because the probability of observing two units with
exactly the same value of the propensity score is in principle zero, since P(X) is a
continuous variable. Various methods have been proposed in the literature to overcome
this problem, and three of the most widely used are (i) nearest neighbor matching, (ii)
radius matching and (iii) kernel matching, which are summarized as follows6.
11
2.2.a. The Nearest Neighbor Matching
The nearest neighbor matching method is the most common form of matching in the
statistics literature. In this method, each treated unit is matched to an untreated unit with
the nearest propensity score. Let T be the set of treated units and C the set of control
units, then:
(7)
C (i ) = min Pi − Pj
j
Here Pi and Pj are the propensity scores of treated and untreated units, respectively.
Once each treated unit is matched with an untreated unit, the difference between the
outcome of the treated units and the outcome of the matched untreated units can be
computed. The ATT is then obtained by averaging these differences as follows:
ATT =
=
(8)
1
NT
⎡
∑ ⎢Y
i∈T
⎣
i
T
−
⎤
wijY j C ⎥
j∈C ( i )
⎦
∑
1 ⎡
1
T
C⎤
Y
w
Y
−
⎢
⎥= T
∑
∑
∑
i
ij
j
T
N ⎣ i∈T
i∈T j∈C ( i )
⎦ N
∑Y
i∈T
i
T
−
1
NT
∑
j∈C ( i )
wijY j C
.
In the case of the nearest neighbor matching method all treated units can find a match of
their own. However, it is obvious that some of these matches are fairly poor because for
some treated units the nearest neighbor may have a very different propensity score.
Nevertheless this seemingly inappropriately matched pair would contribute to the
estimation of the treatment effect, which results in unreliable estimate of ATT (Becker
and Ichino, 2002).
2.2.b. The Radius Matching
With radius matching each treated unit is matched only with the untreated units whose
propensity score falls within a pre-specified range of neighborhood of the propensity
score of the treated unit. If the range of the neighborhood, i.e. the radius, is set to be
very small it is possible that some treated units are not matched because their
neighborhood does not contain any untreated units. On the while, the smaller the size of
the neighborhood the better is the quality of the matches. However, by using more
12
untreated observations, one can increase the precision of the estimates, but at the cost of
increased bias (Caliendo and Kopenig, 2005).
In radius matching the untreated unit is defined as:
(9)
{
C (i ) = Pj Pi − Pj < r
}
that is, all the untreated units with estimated propensity scores falling within a radius of
r from Pi are matched to the treated unit i. The radius matching shares the attractive
feature of over sampling mentioned above in the nearest matching, but avoids the risk of
attaining bad matches. We can also obtain ATT from (8).
2.2.c. The Kernel Matching
With the kernel matching all treated units are matched with a weighted average of all
untreated units with weights that are inversely proportional to the distance between the
propensity scores of treated and untreated units such that:
(10)
⎧
⎛ P − P ⎞⎫
YjCG ⎜ j i ⎟ ⎪
⎪
∑
1
⎪
j∈C
⎝ hn ⎠ ⎪
ATT = T ∑ ⎨YiT −
⎬
N i∈T ⎪
⎛ Pk − Pi ⎞ ⎪
G⎜
⎟
∑
⎪
k
C
∈
⎝ hn ⎠ ⎪⎭
⎩
where G(·) is a Gaussian kernel function and hn is a bandwidth parameter. Under
standard conditions on the bandwidth and kernel, a consistent estimator of the
counterfactural outcome Y0 is given by:
⎛P −P ⎞
G⎜ j i ⎟
j∈C
⎝ hn ⎠
⎛ P − Pi ⎞
G⎜ k
∑
⎟
k∈C
⎝ hn ⎠
∑Y
(11)
C
j
2.3 Choice of methodology for empirical analysis
Under some conditions of large sample, similar population distribution, and so on, the
13
alternative algorithms listed above are expected to provide similar result. However, in
practice, the results seem to be sensitive to the matching estimators selected (Heckman
et al., 1997). It should be clear that there is no winner for all situations and the choice of
the estimator crucially depends on the situation at hand, especially data structure (Zhao,
2004). Few comparative researches exist which deals with the issue of choice among
matching algorithms. Caliendo and Kopenig (2005) reported that trade-off relation
exists among estimators in terms of efficiency and bias, and generally the more
untreated observations are used to make untreated groups for treated groups, the
estimator become more efficient in terms of variance but bias becomes larger. Frölich
(2004) reported that, one to one nearest neighbor matching is obviously inefficient and
suggested use of kernel matching (Heckman et al., 1997) or ridge matching which was
first suggested in the same paper. Frölich in his work conducted Monte-Carlo simulation
varying untreated to treated ratio and the linearity of distribution between propensity
score and actual selection to the program. The ridge matching dominated in terms of
efficiency measured by variance in most of cases. However, when the untreated-totreated ratio was high (more than four times), the kernel matching often resulted in the
best estimation of the results.
Testing the statistical significance of treatment effects and computing their standard
errors is not straightforward procedure to do. The simple, but incorrect way to do it
might be doing nearest neighbor matching with or without replacement and then to take
mean differences between the outcomes in the treatment sample and the matched
comparison group sample, using the usual t-test for the variance of a difference in
means. The problem is that the estimated variance of the treatment effect should also
include the variance due to the estimation of propensity score and the common support
imputation. Smith (2000) reported that, in the case of nearest neighbor matching with
one nearest neighbor, treating the matched observation as mentioned above will
understate the standard errors. The usual method employed in practice is bootstrapping,
though no published work demonstrates the validity of the bootstrap for matching.
Recently, Abadie and Imbens (2006) showed that bootstrapping is not valid for nonparametric matching estimators as nearest neighbor matching and radius matching due
to the lack of smoothness, although their criticism is not applied to smooth non-
14
parametric regression methods as kernel matching (Todd, 2006).
Based on the literatures on the selection and implementation of matching estimator that
we investigated above, we decided to analyze the effect of credit guarantee with kernel
matching which showed good precision when the untreated-to-treated ratio is high (in
our cases, it is about ten) and can avoid the criticism expressed by Abadie and Imbens
(2006), which pointed out the problem of non-smoothness in calculating standard errors
with bootstrapping. To show the robustness of the estimation, we also conducted the
same analyses with nearest neighbor matching with replacement and radius matching
differing the radius of caliper for comparison. The nearest neighbor matching with
replacement is known to have the lowest bias but efficiency is lacking and radius
matching has good properties of over sampling mentioned above, but on the other hand
it avoids the risk of using inappropriate matches.
3. Data and Variables
3.1 The data
The data used in this study was obtained from the unpublished plant-level data
assembled from the Annual Report on Mining and Manufacturing Survey in Korea. The
data covers all plants with five or more employees in 580 manufacturing industries at
the KSIC (Korean Standard Industrial Classification) five-digit level. It was an
unbalanced panel data with approximately 95,000 to 109,000 plants for each year from
2000 to 2003, which covers the post economic crisis period. We identified all
manufacturing firms which received credit guarantees supplied by KOTEC and KCGF
during the period from 2001 to 2002. During the whole process of research, the actual
names of firms were kept confidential. Public guarantee funds distributed to the
manufacturing industry covers 32 to 35 percent of total credit guarantee balance
throughout 2001 to 2002, according to the annual report form guarantee funds. The
entry and exit of plants are identified based on the plants appearance and disappearance
in the survey data. Entry and exit of plants due to spin-off, split, merger, and acquisition
could not be identified with available plant level data base. So, further analyses were
15
conducted with the firms which do not have multi-plants. This is to avoid confusion
between firm turnovers and behaviors of multi-plant firms, or between firm level
analyses and plant level analyses. Among firms which received credit guarantee from
KCGF, 5.3% of the firms are classified to have multi-plant, and the corresponding was
4.4% for KOTEC. The multi-plant firms mainly consisted of LSEs, so the mean level of
output and number of employee for the entire firm population decreased significantly as
a result of removal of multi-plant firms.
With the data set we had, we created two balanced panels. In order to measure
differences in performance of guaranteed and non-guaranteed firms, we excluded firms
which did not existed for four consecutive years (2000-2003) and computed the
difference of growth in performance of firms which received guarantee in 2001 and
2002. The number of observation was 42,213 firms. To investigate the effect of credit
guarantee on survival, we excluded firms which did not existed for three consecutive
years (2000-2002) and observed whether the firms survived in 2003. In this case, the
number of observation was 48,540. As the description of data in Table 3 indicates, some
firms received credit guarantee from both KCGF and KOTEC, which are indicated as
BOTH7. We intend to observe comparative performance of the two institutions and
investigate the substitutability or complementarities between them.
3.2 Definition of variables
We evaluated the effect of credit guarantees by observing various aspects of a firms
operation concerning changes in firm status and performances. The outcome variables
considered are: TFP growth, growth in employment, sales, wage level and investment
intensity, and the change in R&D status.
The variables as growth in sales and number of employee will indicate the overall
growth of the firm size. The growth in wage level will represent improvement in skill or
quality of employees. Variables indicating changes in R&D status and intensity in
investment in fixed capital are introduced to observe if guarantee funds are used to
enhance future productivity and expanded production facility. The growth of
productivity might be an ultimate goal of such government support policy. These
16
outcomes were related to the variable used in estimating propensity scores but not
exactly same, since the firm status changes and performances are calculated by
comparing data in 2000 and 2003. We also investigated the effect of credit guarantee on
the survival of firms. Here, the dataset are slightly different from the one used above to
see the differences in outcomes, as explained in section 3.1. For the firms existed
through 2000 to 2002, we gave zero value to the firms exited in 2003 and one to the
firms survived in 2003.
The chosen variables to be used to estimate propensity score should be related to the
outcomes as well as participation decision based on economic theory and previous
empirical findings (Caliendo and Kopeinig, 2005). Among the variables in the Survey,
we utilized sales, fixed capital, number of laborer, investment intensity in fixed capital,
age of firm, and dummy variable for existence of R&D expenditure. These variables
will surely affect their own growth rate and survival (Oh et al., 2006). The definition of
variables is explained in the Appendix A. We also added four dummy variables
according to the management structure of firms. Industrial sector was divided by four
categories based on OECD international standard industrial classification (2001) and
Kim and Lee (2002). Because the survey do not contain information related to
profitability or productivity of firms, we calculated total factor productivity (TFP) of
each firms using chained-multilateral index developed in Good (1985) and applied by
Aw et al. (2001). The methodology adopted is explained in the Appendix B.
4. Estimation Results
The characteristics of supported and non-supported groups, before they received
guarantees, are different as shown in Table 3, which implicates that we need
sophisticated matching technique. The table shows the data in 2000 for non-supported
firms and firms which received credit guarantee from one of the two guarantee funds or
both denoted as KOTEC, KCGF and BOTH during 2001 and 2002. The relative size of
supported firms is significantly larger than that of non-supported firms in terms of sales
and number of workers, but their fixed capital does not show significant difference,
except for firms which received guarantees from both institutions. The firm size of
17
KOTEC and KCGF supported firms seems to be similar but the size of firm receiving
guarantee from BOTH institutions was much larger. However the firms which received
guarantee from KOTEC and from BOTH seem to have lower TFP level then nonsupported firms which suggest that the guarantee fund was not provided such that the
decision being affected by the level of productivity. The wage level of guaranteed firms
was higher than the non-guaranteed firms and their investment intensity was slightly
higher only for KOTEC firms. However the ratio of firms investing in fixed assets is
higher for firms which receive guarantee. Younger firms are more guaranteed by
KOTEC while KCGF focused on slightly older firms. This may come from the fact that
the aim of KOTEC was to fund technology oriented newly founded firms. The
guaranteed firms are involved in R&D more aggressively than non-guaranteed firms but
the effect was higher for KOTEC firms. When we look at the management structure,
firms owned by a company corporation was much more involved in the guarantee funds
and when we look at the industries, firms in high technology industrial sectors received
guarantees more frequently.
The descriptive statistics of outcome variables before matching are presented in Table 4
and 5. The performance variables and survival rate were also statistically different
among the two groups before matching. However, these differences should not be taken
at face value due to problem of selection bias outlined earlier, which requires us to
apply advanced matching technique.
4.1 Estimation of propensity score and balancing test
The estimation of propensity score was conducted by applying the procedure explained
in Section 2. As we are dealing with two populations of firms and three alternative
treatment forms (KOTEC, KCGF and BOTH), we conducted probit estimation for six
cases and the variables included were slightly different for all six cases8. As in Dehejia
(2005), different specifications for variables are needed to be included in probit
estimation for each combination of untreated and treated groups. Estimation of the
probit model was conducted by using population of non-supported firms and each one
of supported firms by KOTEC, KCGF and BOTH. It is to be noted that each time, the
18
untreated groups are only made up of non-supported firms, and hence the number of
observation in regression also differ by the size of the treated group.
When we applied the balancing test described in Section 2, almost in all cases, the
means were equal at the 1% significant level (0.5% for few dummy variables), and none
of the covariates systematically failed the mean equality test in all the blocks. While this
is a high level of significance, implying differences would have to be quite large in
order to facilitate failing the balancing test, this is justified for two reasons (de Boer,
2003; Becker and Ichino, 2002). The first is that the observations involved tend to be
large (>1000) increasing the likelihood of insubstantial differences with statistically
significant difference. The second reason is that the test is applied to each variable,
which means that, if the balancing test is strictly adhered to, it would fall if only one of
these differences was significant. There is an increased statistical probability of this
occurring as the number of variables increase9.
The results are presented in Table 6 and 7. Results in Table 6 was estimated to
investigate the performance difference during 2000-2003 while those in Table 7 was
estimated to investigate the difference in survival rate in 2003 for firms survived during
2000-2002. Since the parameters in Table 6 and 7 showed similar results, we will
concentrate on interpretation of Table 6. When we look at Table 6, the parameter for
sales, number of employees and age showed positive signs but all squared terms showed
negative signs, which implies that there exist some optimal levels for these variables.
Firms with less fixed capital received credit guarantee, since credit guarantee funds
were aimed to help SMEs with small collateral. R&D Dummy was positive and
significant for assignment to KOTEC and BOTH but insignificant for KCGF, which
implies KCGF funds was not very sensitive to R&D status of firms to be supported.
Firms taking the form of company corporation which belong to technologically
advanced industrial sectors tended to receive guarantee funds with a higher probability.
However, we could observe significant and negative parameter for TFP which implies
that it is hard to find any evidence that productivity of firms was taken into account in
the selection procedure. The same pattern was also found for the descriptive statistics
reported in Table 3.
19
4.2 Estimation of treatment effect
To compute ATT accurately, one should match the treated and untreated groups
(supported and non-supported firms) precisely on the basis of the propensity score. In
practice, it is never possible to match the scores precisely and therefore in this study,
three alternative matching methods of nearest neighbor matching (NNM), radius and
kernel matching methods were used and compared. Here the radius matching estimator
was conducted with different radius (0.00005 and 0.00001). It should be noted that all
the analyses were based on implementation of common support, so that the distribution
of treated and untreated units were located in the same domain. However, only a few
observations were discarded, and given the large samples, the number of excluded
observations is relatively small. Standard errors for treatment effects for all cases were
calculated by the bootstrapping method by using 200 replications.
The effect of credit guarantee provided by the two institutions in terms of performance
(growth in TFP, number of employees, sales, R&D status, investment intensity and
wage level) are presented in Table 8.a, 8.b and 8.c. In these tables we report ATT for
firms guaranteed by KOTEC, KCGF or by BOTH institutions, respectively.
When we look at the ATT for KOTEC firms in Table 8.a, we can see the effect of
KOTEC guarantee on growth of sales, number of employees and wage level is large and
statistically significant. The difference of employee growth rate was about 8.5-9.4
percent and the difference of sales growth rate for guaranteed firms and non guaranteed
firms was about 26.7-28.8 percent. ATT for wage level was estimated to be in the
interval of 7.2 to 8.4%. Changes in R&D status were positive but only significant for
NNM and kernel matching methods. However these changes in R&D was also negative
for KOTEC firms, but compared to non-supported firms, the rate of decrease was lower.
Difference in growth in the ratio of investment to sales was found to be negative and
significant for one radius and kernel matching. It may result from the rapid growth of
sales of KOTEC firms. It might also partly reflect the fact that KOTEC fund did not
effect positively firms’ investment in fixed capital. ATT for TFP growth was
insignificant for all matching estimators.
When we look at the ATT for KCGF firms in Table 8.b, we can find significant effects
20
of credit guarantee in terms of growth in number of employees, sales, wage level.
However, ATT for TFP growth, changes in R&D status and investment intensity were
found to be insignificant. Difference in growth of employee was 5.5-6.2 percent, sales
16.3-20.3 percent and wage level 3.3-4.2 percent, ignoring the insignificant ATT from
NNM for wage level. These growth rates are slightly lower compared to those of
KOTEC firms. Additionally, in the case of KCGF firms, changes in R&D status turned
to insignificant.
Comparing the ATT for KOTEC firms and KCGF firms, it is certain that differences in
growth rate for sales, employee and wage level is higher for KOTEC firms. As can be
seen in Table 3, KOTEC firms consist of firms that are younger and conduct more R&D
compared to KCGF firms. In Table 6, we can observe that the assignment to KOTEC
fund was highly influenced by R&D status of firms. Becchetti and Trovato (2002) in
their analysis on Italian SMEs argued that, small surviving firms are characterized to
have a higher growth potential than average firms and are not fully utilizing their
growth potential due to the scarce availability of external finance. The KOTEC funds,
which mainly aimed to provide financial aid to technology oriented start-ups, may have
helped to reduce the scarcity of external finance resulting in a high growth rate in their
size in terms of sales and employee, as well as maintaining their R&D status. Another
explanation for a higher performance of KOTEC firms can be found from Table 10. As
Table 10 indicates, an average amount of guarantees given to an average KOTEC
guaranteed firm was 0.185 to 0.2 billion KRW while it was only 0.086 and 0.096 for an
average KCGF guaranteed firm from 2001 to 2002.
Investment in R&D is very costly decisions, which requires hiring more skilled
employees and involves investment in research facilities. So there might be some level
of threshold in investment amount to start up R&D. If certain amount of investment is
collected to allow firms to overcome fixed R&D startup cost, then the firm will start to
invest or maintain their investment amount on R&D (David et al., 2000). It seems
probable that the guarantee amount for KCGF firms had been insufficient for firms to
exceed the limit of fixed R&D startup cost and the guarantee funds and investment were
mainly used as marketing cost to increase their sales, hiring more skilled employees and
to increase their wage level, hence also to promote welfare of employees. However for
21
KOTEC firms, though their investment intensity decreased, they managed to maintain
their spending on R&D. However, the returns from R&D are still not realized in terms
of increase TFP growth rate.
When we look at the ATT for firms receiving guarantee from both of the institutions in
Table 8.c, we can observe similar trend for growth in employees, sales, and wage level,
which reached 4.4-8.1 percent, 25.4-32.6 percent and 8.1-10.7 percent, respectively
except for the insignificant wage effect for the case of NNM. ATT for changes in R&D
status turned out to be positive by 4.5–5.7 percent. ATT for TFP growth was positive,
while investment intensity growth was insignificant in all cases. The amount of ATT in
this BOTH case seems to be larger or similar to KOTEC firms, but less than the simple
sum of ATTs from KOTEC and KCGF. Bearing in mind that these BOTH firms were
consisted of much larger firm compared to KOTEC firms and KCGF firms as shown in
Table 3, (the sales of BOTH firms was 1.74 times larger than KOTEC firms and 1.66
times larger than KCGF firms) not the growth rate but the absolute increase might be
bigger. According to Table 10, the average amount of guarantee given to BOTH firms
was from 0.246 to 0.267 billion KRW during 2001 to 2002, which is as large as the sum
of an average KOTEC guaranteed and average KCGF guaranteed firm. With these large
amounts of credit guarantees coming from overlapped support and followed investment
from private sector, it seems that BOTH firms succeeded to maintain or to increase their
R&D, which finally resulted in the growth in TFP.
The effects of credit guarantee of two institutions in terms of survival are presented in
Tables 9.a, 9.b and 9.c, for the case of KOTEC, KCGF, and BOTH, respectively.
The differences of survival rate 1.7-2.5 percent for KOTEC, 2.4-2.9 percent for KCGF
and 3.2–3.9 percent for BOTH. Not surprisingly, the firms guaranteed from both of
institutions showed highest ATT on survival and the credit guarantee showed a positive
effect for survival in all cases. Here, though effects on performance were superior for
KOTEC firms compared to KCGF firms, the ATT on survival of KCGF firms is better
than or as good as KOTEC firms. The aim of KCGF institutions was to provide firms
under a temporary credit crunch with cheap credit, hence to tide over the situation. It
seems that in terms of survival, KCGF worked well and in closer correspondence with
the mission of the guarantee policy.
22
5. Summary and Discussion of Results
This research evaluates the impact of public policy, i.e. credit guarantee policy, for
recipient firms by controlling the selection bias with the up-to-date propensity score
matching technique.
The results are based on investigation of the differences in the performance indicators
from 2000 to 2003, for the firms receiving credit guarantee in 2001 and 2002. The
empirical analysis for the Korean credit guarantee policy showed that the policy
affected positively the growth of sales, employment, wage level as well as survival rate
of supported firms. A positive effect is interpreted as the guarantee fund has helped
firms to increase or maintain their size in terms of sales and employment and to hire
more skilled employees or it has helped to promote welfare of the employees.
The size of the effects were different for the two credit guarantee institutions, however it
was more pronounced when the firm received guarantee from both of institutions.
KOTEC firms showed changes in R&D status and firms receiving guarantee from
BOTH institutions showed positive effects on their TFP growth rates. Considering the
amount of guarantee in each case, it seems that certain level of guarantee fund given to
a firm would be needed to stimulate investment in R&D, which will eventually result in
growth in productivity. The credit guarantee funds also prevented firms from
bankruptcies by providing financial aid.
However, we could not find evidences that the selection of firms to receive guarantees
were affected by their productivity considerations. The size, age, management structure
and industrial sectors of firms seemed to influence more on the selection firms to be
guaranteed.
We also could not find sufficient evidences that the funds have affected positively TFP
growth, R&D and investment intensity. The criticism of credit guarantee policies was
mainly about its lack of effective selection mechanism, interference in the market
mechanism and its disturbing impacts on the restructuring plans which resulted in
overcapacity of SME sectors in the aftermath of the financial crisis (IMF, 2005; Kang,
2005). The results obtained from our analysis are in correspondence with their criticism
23
and provide quantification of the effects of credit guarantees on various firm
performance and survival indicators.
Notes:
1
Chaebol is a Korean corporate form consisting of a pyramid of subsidiary firms
operated by a single family line.
2
Korean Won, US Dollar (USD) 1$=1037 KRW, in November 2005.
3
Operation multiple is defined as the outstanding guarantee balance divided by basic
funds of the guarantee institution.
4
Also there exist 14 regional credit guarantee institutions founded by local
governments, and their aggregated guarantee balance compared to the total guarantee
balance provision from all guarantee institutions was 3.1% and 3.7% for year 2001 and
2002, respectively. We could not identify firms supported from regional credit guarantee
institutions. However more than half of firms supported by regional credit guarantee
institutions were also beneficiaries of KOTEC and KCGF (Lee, 2006).
5
The detailed algorithms of balancing tests are well presented in the appendix of
Dehejia and Wahba (2002).
6
It is to be noted that, in this study, we do not consider an alternative stratification and
interval matching, which is also noted as blocking and sub classification in Rosenbaum
and Rubin (1983), since it is ‘group to group’ matching and different scheme with other
matching methodologies which are based on matching of pair of observation.
7
Hereafter the KOTEC firms, KCGF firms and BOTH firms means the firms received
credit guarantee from one of KOTEC, KCGF and both of institutions, respectively.
8
Matching with multiple treatments applied in Frölich et al. (2004) could be
implemented in this kind of evaluation that there exist more than two institutions
working under the same policy. However we are using seven performance variables
including survival rate, and comparing all these by institutions will result in too many
combinations, so here we concentrated about comparing supported firms with nonsupported firms by institutions.
9
For example, assume that the significance level is set to 0.01. Taking ten variables and
five intervals of the propensity score would require 50 mutually independent tests of
mean differences. For this balancing test, there is a probability of
50 × 0.011 × 0.9949 = 0.306 that one of these tests reports significant difference although
the balancing test is true.
24
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27
Appendix A. Definition of variables
· Sales: variable used as output in the TFP calculation, explained in Appendix B.
· Fixed capital: variable used as capital stock in the TFP calculation, explained in
Appendix B.
· Worker: is measured as number of employees.
· Wage level: is measured as spending on wages, retirement pays and welfare programs
divided by number of employees, again deflated with consumer price index.
· Investment/Sales (Investment intensity): is the amount of investment in fixed capital
(measured by annual growth of fixed capital) divided by sales.
· Age: is defined as years from the foundation of firms
· R&D dummy: dummy variables taking value 1 if a firm has positive R&D expenditure
and 0 if a firm does not have spending on R&D.
· Dor1-Dor4: dummy variables indicating the type of management structure: company
corporation (a joint-stock corporation; a limited corporation; a limited partnership; an
unlimited partnership) (Dor1), corporation other than company corporation (Dor2),
individual with financial statements (Dor3) and individual without financial statements
(Dor4).
· Tl1-Tl4: dummy variables indication industry sectors where firm belongs to. It is
divided by technology level based on OECD international standard industrial
classification (2001): Low-technology industries (Tl1) are Food and Beverage, Tobacco
Products, Textile, Apparel, Leather and Footwear, Wood products, Paper products, and
Publishing and Printing; Medium-low technology industries (Tl2) are Petroleum
Refineries and Coal Products, Rubber and Plastic Products, Non-metallic Mineral
Products, Basic Metals, Fabricated Metal Products, Furniture, and Recycling; Mediumhigh technology industries (Tl3) are Chemical Products, Machinery, Medical and
Precision Equipments, Motor Vehicles, and Other Transport Equipment; Hightechnology industries (Tl4) are Computers and Office Machinery, Electrical Machinery,
and Electronic Components and Communication Equipments.
· TFP growth: is measured as log(TFP2003) - log(TFP2000).
· Worker growth: is measured as log(Worker2003) – log(Worker2000).
· Sales growth: is measured as log(Sales2003)-log(Sales2000).
· Changes in R&D status: is computed as the difference between R&D dummy 2003 and
R&D dummy 2000. It is 0 for the firms who remained R&D firms or non-R&D firms,
and 1 when the firm started to R&D and -1 when the firm stopped R&D investment.
· Investment/Sales growth:
(investment2000/sales2000)).
is
measured
as
((Investment2003/sales2003)-
· Wage Level growth: is measured as log(Wage Level2003)-log(Wage Level2000) .
28
Appendix B. The measurement of TFP
The TFP of each firm is estimated using the chained-multilateral index number
approach (Good, 1985; Aw et al., 2001). It uses a separate reference point for each
cross-section of observations and then chain-wise links the reference points together
over time. The reference point for a given time period is constructed as a hypothetical
firm with input shares that equal the arithmetic mean input shares and input levels that
equal the geometric mean of the inputs over all cross-section observations. Thus, the
output, inputs, and productivity level of each firm in each year is measured relative to
the constructed hypothetical firm at the base time period. This approach allows us to
make transitive comparisons of productivity levels among observations in a panel data
framework.
Specifically, the productivity index for firm i at time t in our study using the nonparametric approach outlined above is measured in the following way:
t
ln TFPit = (ln Yit − ln Yt ) + ∑ (ln Yτ − ln Yτ −1 ) −
τ =2
t
N
1
⎧ 1
⎫
⎨∑ ( Snit + S nt )(ln X nit − ln X nt ) + ∑∑ ( Snτ + S nτ −1 )(ln X nτ − ln X nτ −1 ) ⎬
τ = 2 n =1 2
⎩ n =1 2
⎭
N
(B.1)
where Y , X , S , and TFP denote output, input, input share, and TFP level, respectively.
The symbols with upper bar are corresponding measures for hypothetical firms. The
subscripts k and n are indices for time and inputs, respectively.
A number of variables in the survey were used to compute TFP levels. These include
input and output variables. The gross production of each firm was used as a measure of
output, while capital, labor and materials were used as input variables. The cost shares
were obtained as the respective input factors share of the total cost of capital, labor and
materials.
An alternative measure to gross output production is the value added. Gross production
was used to utilize information on material input levels. Materials were proportional to
output. However, variations in material inputs affect the economies of scale. In addition,
the source can be different, representing different advantages or constraints to different
industries. Output was deflated by the producer price index at the three-digit industry
level.
As a measure of capital stock, the average book value of capital stocks was used at the
beginning and end of the year, deflated by the capital goods deflator. The capital in
Korean manufacturing has been traditionally used intensively with extremely little loss
in the rate of capacity utilization. Thus, the book value of capital stock is considered as
a good measure of capital input.
As a measure of labor input, we used the number of workers. It includes paid employees
(production and non-production workers), working proprietors and unpaid family
workers. Here, the qualitative differential between production workers and all the other
types of workers was accommodated. The labor variable is adjusted for differences in
quality of labor. The labor quality index of the latter was calculated as the ratio of nonproduction workers’ and production workers’ cumulative wage, divided by the number
29
of workers involved in non-production and production activities for each year.
Finally, as a measure of intermediate input, the “major production costs” plus “other
production cost” was used in the survey. Major production costs covered costs arising
from materials and parts, fuel, electricity, water, outsourced manufactured goods and
maintenance. Additional production costs covered outsourced services such as
advertising, transportation, communication and insurance expenses. The estimated
intermediate input was deflated by the intermediate input price index.
When computing TFP growth based on a non-parametric approach, constant returns of
scale were assumed. Therefore, the sum of factor input elasticity is assumed to equal to
one. Labor and intermediate input elasticity for each firm were measured as the average
cost shares within the same firm size class in the five-digit industry in a given year. In
calculating various input elasticity, firms are grouped into three size classes according to
the number of employees: 50 or less, 51-300, and over 300. Thus, factor elasticity of
firms was permitted to vary across industries and size classes, as well as over time.
30
Table 1. Comparison of LSEs and SMEs with respect to key indicators and their development over time.
Year
Sales
Growth (%)
1993
1994
1995
1996
1997
LSE 11.23
18.96 22.25 11.27
12.92
SME 7.13
16.47 15.9
7.82
7.02
R&D /
LSE Sales (%)
SME Net Profits /
LSE 1.24
2.2
3.46
0.51
-1.26
Sales (%)
SME 0.84
1.4
1.18
0.59
-0.52
Debt-to-Equity LSE 273.5 282.88 268.29 301.56 390
Ratio (%)
SME 388.13 394.18 380.6 387.43 418.4
a
Source: Bank of Korea.
b
LSEs and SMEs are divided based on a 300 employee threshold.
1998
1.97
-2.01
2.02
0.62
-6.08
-0.45
295.38
334.37
1999
6.58
10.79
1.77
0.47
-1.26
2.37
208.94
232.38
31
2000
16.68
12.45
1.47
0.71
-4.34
2.6
224.59
179.71
2001
0.84
3.38
1.52
0.99
-0.7
1.37
201.63
144.74
2002
7.21
10.21
1.72
0.85
8.37
2.48
128.88
152.08
2003
6.55
5.39
2.02
0.78
4.84
2.08
113.49
147.57
Table 2. Amounts of credit guarantee provided by KOTEC based on technology evaluation, (Unit: Billion
KRW, %).
2001
2002
2003
2004
The amount of guarantee
601
by technology evaluation (A)
Total guarantee amount (B)
16,160
Ratio (A/B)
3.7
a
Source: KOTEC annual reports.
1,006
1,251
2,048
16,523
6.1
16,746
7.5
13,472
15.2
32
Table 3. Descriptive statistics of Non-supported firms, firms guaranteed by one of KOTEC, KCGF or by BOTH of the institutions, in the year 2000.
Non-supported
KOTEC
t-test KCGF
t-test BOTH
t-test
firms
Mean
STD Err
Mean
STD Err
Mean
STD Err
Mean
STD Err
Sales
3369.772
29265.270 3766.329 9180.725 *
3969.543 8233.804 ***
6590.620 8469.808 ***
Fixed Capital
1354.139
21392.742 1233.972 3419.557
1249.120 4397.462
2139.562 3250.520 ***
Worker
24.430
89.739
30.406
44.447
***
29.762
43.310
TFP
1.236
0.701
1.207
0.654
***
1.237
0.669
Wage Level
15.158
6.902
15.771
6.729
***
16.602
6.824
Investment/Sales
0.077
0.971
0.110
0.577
***
0.063
0.404
Investment Dummy
0.452
0.498
0.580
0.494
***
0.574
0.495
Age
8.478
7.971
7.427
6.937
***
9.283
R&D Dummy
0.093
0.290
0.197
0.398
***
Dor1
0.361
0.480
0.565
0.496
Dor2
0.007
0.086
0.002
Dor3
0.496
0.500
Dor4
0.135
Tl1
46.422
49.668
***
1.188
0.483
***
17.170
6.801
***
0.089
0.312
***
0.690
0.463
***
6.936
***
9.379
7.503
***
0.141
0.349
***
0.256
0.436
***
***
0.542
0.498
***
0.746
0.436
***
0.042
***
0.001
0.036
***
0.002
0.047
***
0.382
0.486
***
0.409
0.492
***
0.222
0.416
***
0.342
0.051
0.220
***
0.048
0.214
***
0.030
0.171
***
0.376
0.484
0.223
0.416
***
0.285
0.451
***
0.219
0.414
***
Tl2
0.305
0.461
0.309
0.462
0.334
0.472
***
0.301
0.459
Tl3
0.228
0.420
0.329
0.470
***
0.261
0.439
***
0.342
0.475
***
Tl4
0.091
0.287
0.139
0.346
***
0.120
0.325
***
0.138
0.345
***
Number of Observations 35299
3996
a
The ***, **, and * indicate the 1%, 5% and 10% levels of significance.
b
The t-test is to test the difference between supported and non-supported firms.
c
Monetary units in million KRW.
3818
33
***
***
900
Table 4. Descriptive statistics of performance variables of Non-supported firms, firms guaranteed by one of KOTEC, KCGF or by BOTH of the institutions.
Non-supported firms KOTEC
KCGF
BOTH
Mean
STD.
Mean
STD.
TFP Growth
-0.038
0.411
-0.025
0.386 *
(% Growth)
-3.729
Worker Growth
(% Growth)
-0.037
-3.632
0.448
0.040
4.081
0.490 ***
0.011
1.106
Sales Growth
(% Growth)
0.121
12.862
0.711
0.323
38.127
0.697 ***
0.215 0.644 ***
23.986
0.306 0.689 ***
35.798
Changes in R&D Status
(% Growth)
-0.014
-1.400
0.317
-0.012
-1.200
0.431
-0.006
-0.600
0.392
0.004
0.400
0.517
Investment/Sales Growth -0.011
(% Growth)
-1.100
1.178
-0.041
-4.100
0.577 ***
0.004
0.400
0.404
0.000
0.000
0.423
Wage Level Growth
(% Growth)
0.459
0.194
21.410
0.460 ***
0.139 0.423 ***
14.912
a
0.105
11.071
t-test Mean
-2.469
-0.043
STD.
t-test Mean
0.370
-0.007
-4.209
STD.
t-test
0.365 **
-0.698
0.450 ***
0.003
0.300
0.502 **
0.197 0.437 ***
21.774
The ***, **, and * indicate the 1%, 5% and 10% levels of significance. t-test concerns the difference between supported and non-supported firms.
34
Table 5. Descriptive statistics of survival rate of Non-supported firms, firms guaranteed by one of KOTEC, KCGF or by BOTH of the institutions.
Non-supported firms KOTEC
KCGF
Both
Mean
STD.
Mean STD. t-test Mean STD. t-test Mean STD. t-test
Survival Rate (%)
0.862
0.345
0.898 0.303 *** 0.910 0.286 *** 0.908 0.289 ***
Number of Observations 40773
4524
4265
1022
a
The ***, **, and * indicate the 1%, 5% and 10% levels of significance. t-test tested the difference with non-supported firms.
35
Table 6. Probit model parameter estimates based on population of firms consecutively observed during 2000-2003. Dependent variable is receipt of guarantees from
KOTEC, KCGF and BOTH institutions.
KOTEC
KCGF
BOTH
Variables
Constant
Coef. STD
Variables
-5.195 0.238 *** Constant
Salesa
0.843
Sales2a
-0.046 0.005 *** Sales2a
a
Fixed Capital
Worker
a
Worker2
TFP
a
Investment Dummy
a
0.076 *** Salesa
0.976
-0.034 0.013 *** Fixed Capital
0.068 *** Worker
a
-0.030 0.011 *** Worker2
-0.177 0.041 *** TFP
0.065
Coef. STD
-8.646 0.537 ***
0.081 *** Salesa
1.170
-0.046 0.005 *** Sales2a
a
0.243
a
Coef. STD
Variables
-6.343 0.265 *** Constant
a
0.019 *** Investment Dummy
a
0.033 *** Age
-0.053 0.010 ***
a
-0.055 0.013 *** Fixed Capital
0.210
a
0.070 *** Worker
a
-0.043 0.011 *** Worker2
-0.215 0.043 *** TFP
0.058
-0.035 0.024
0.462
a
a
0.020 *** Investment Dummy
a
-0.062 0.018 ***
-0.289 0.082 ***
0.051
0.035
0.721
0.073 ***
0.292
Age2a
-0.112 0.010 *** Age2a
-0.170 0.011 *** Age2a
-0.193 0.019 ***
R&D Dummy
0.247
0.027 *** R&D Dummy
0.026
0.135
0.042 ***
Dor1
0.200
0.040 *** Dor2
-1.022 0.192 *** Dor1
0.056
0.084
Dor2
-0.489 0.171 *** Dor3
-0.010 0.024
-0.586 0.284 **
Dor3
0.129
-0.197 0.042 *** Dor3
-0.040 0.081
Tl1
Tl2
Tl3
Number of Observations
-0.292 0.033 *** Tl1
-0.124 0.032 *** Tl2
0.005 0.032
Tl4
39295
Number of Observations
-0.044 0.025 *
Tl1
0.018 0.024
Tl2
0.104 0.034 *** Tl4
39117
Number of Observations
-0.223 0.043 ***
-0.111 0.040 ***
-0.031 0.055
36199
Pseudo-R2
0.0673
0.0742
Pseudo-R2
0.1501
Log-likelihood
Prob>Chi2
-3581.2326
0.0000
Pseudo-R2
0.040 *** Age
0.123 ***
Age
0.037 *** Dor4
0.707
0.157 ***
0.030
Log-likelihood
-12050.703
Log-likelihood
-11580.646
Prob>Chi2
0.0000
Prob>Chi2
0.0000
a
(a) in logarithmic form. Sales2, Worker2, Age2 indicates squared variables.
b
The ***, **, and * indicate the 1%, 5% and 10% levels of significance.
36
R&D Dummy
Dor2
Table 7. Probit parameter estimates based on population of firms consecutively observed during 2000-2002. Dependent variable is receipt of guarantee from
KOTEC, KCGF and BOTH institutions.
KOTEC
Variables
Constant
Salesa
KCGF
Coef. STD
Variables
-5.811 0.268 *** Constant
0.928 0.072 *** Investment/Sales
Sales2a
-0.051 0.005 *** Salesa
Fixed Capitala
-0.030 0.012
Workera
** Sales2a
0.221 0.063 *** Fixed Capitala
Worker2a
-0.026 0.010 *** Workera
TFPa
-0.183 0.038 *** Worker2a
BOTH
Coef. STD
Variables
-6.387 0.252 *** Constant
Coef. STD
-8.889 0.506 ***
0.335 0.075 *** Salesa
1.245 0.148 ***
1.064 0.077 *** Sales2a
-0.056 0.009 ***
-0.050 0.005 *** Fixed Capitala
-0.044 0.013 *** Workera
0.110 0.073
Worker2a
-0.048 0.012 *** TFPa
-0.051 0.023
**
0.443 0.114 ***
-0.059 0.016 ***
-0.338 0.077 ***
Investment Dummy
0.071 0.018 *** TFPa
Agea
0.231 0.032 *** Agea
0.566 0.050 *** Agea
0.659 0.071 ***
Age2a
-0.102 0.010 *** Age2a
-0.179 0.011 *** Age2a
-0.180 0.019 ***
-0.170 0.042 *** Investment Dummy
0.026 0.033
R&D Dummy
0.245 0.026 *** Workera*R&D Dummy
0.015 0.008
Dor1
0.531 0.142 *** Dor1
0.179 0.039 *** Dor1
0.092 0.081
Dor3
0.491 0.143 *** Dor2
-0.861 0.193 *** Dor2
-0.581 0.281
Dor4
0.352 0.146
0.188 0.035 *** Dor3
0.028 0.078
** Dor3
* R&D Dummy
0.151 0.040 ***
**
Tl1
-0.306 0.031 *** Tl1
0.074 0.022 *** Tl1
-0.212 0.040 ***
Tl2
-0.133 0.030 *** Tl2
0.076 0.024 *** Tl2
-0.094 0.038
Tl3
-0.004 0.030
0.147 0.032 *** Tl4
-0.006 0.051
Tl4
LSE Dummy
-0.278 0.178
37
**
Wage Levela
-0.139 0.027 ***
Workera*Investment/Sales -0.107 0.028 ***
Number of Observations 45297
Workera*Agea
Number of Observations
Pseudo-R2
0.0743
Pseudo-R2
Log-likelihood
-13619.314
Log-likelihood
0.062 0.014 ***
45038 Number of Observations 41795
0.0779 Pseudo-R2
-13010.482 Log-likelihood
Prob>Chi2
0.0000
Prob>Chi2
(a) in logarithmic form. Sales2, Worker2, Age2 indicates squared variables.
b
LSE Dummy indicates that the firm has more than 300 employers.
c
The ***, **, and * indicate the 1%, 5% and 10% levels of significance.
0.0000 Prob>Chi2
a
38
0.1563
-4051.6873
0.0000
Table 8.a. Average treatment effect on the treated estimated in terms of different performance measures for firms supported by KOTEC.
Matching Estimator
NNM
Radius (.00005)
Radius (.00001)
Kernel
ATT T value Treated
UnATT T value Treated
UnATT T value Treated
UnATT T value Treated
Untreated
treated
treated
treated
TFP Growth
0.007
0.714
-0.025 -0.032 0.011
1.579
-0.030 -0.041 0.001
0.113
-0.042 -0.043 0.008
1.590
-0.025 -0.033
(%)
0.7
-2.5
-3.1
1.0
-3.0
-4.0
0.1
-4.1
-4.2
0.8
-2.5
-3.2
Worker Growth
0.088
6.176
0.040 -0.048 0.087
9.763
0.041 -0.046 0.094
6.854
0.047 -0.047 0.085 11.143
0.040 -0.045
(%)
8.8
4.0
-4.7
8.7
4.2
-4.5
9.4
4.8
-4.6
8.5
4.0
-4.4
Sales Growth
0.215 10.537
0.323
0.108 0.229 18.569
0.323
0.094 0.234 12.928
0.323
0.089 0.217 19.515
0.323
0.106
(%)
26.7
38.1
11.4
28.2
38.1
9.9
28.8
38.1
9.3
26.9
38.1
11.2
Changes in R&D Status 0.031
2.828
-0.012 -0.043 0.000
0.063
0.000
0.000 0.015
1.576
0.016
0.001 0.013
2.142
-0.012 -0.025
(%)
3.1
-1.2
-4.3
0.0
0.0
0.0
1.5
1.6
0.1
1.3
-1.2
-2.5
Investment/Sales Growth -0.035 -1.433 -0.041 -0.006 -0.040 -2.765 -0.040 -0.001 -0.034 -1.302 -0.038 -0.005 -0.035 -3.359 -0.041 -0.006
(%)
-3.5
-4.1
-0.6
-4.0
-4.0
-0.1
-3.4
-3.8
-0.5
-3.5
-4.1
-0.6
Wage Level Growth
0.061
5.158
0.194
0.133 0.072
8.687
0.189
0.117 0.065
5.317
0.178
0.113 0.070
8.897
0.194
0.124
(%)
7.2
21.4
14.2
8.4
20.8
12.4
7.5
19.5
12.0
8.2
21.4
13.2
Number of observations
(3996:3543)
(3832:25681)
(3309:9311)
(3996:35226)
(Treated:Untreated)
39
Table 8.b. Average treatment effect on the treated estimated in terms of different performance measures for firms supported by KCGF.
Matching Estimator
NNM
Radius (.00005)
Radius (.00001)
ATT T value Treated
UnATT T value Treated
UnATT T value Treated
Untreated
treated
treated
TFP Growth
0.010 0.971
-0.043 -0.053 0.004 0.635
-0.044 -0.048 0.009
0.938
-0.043 -0.052
(%)
1.0
-4.2
-5.2
0.4
-4.3
-4.7
0.9
-4.2
-5.1
Worker Growth
0.063 4.966
0.011 -0.052 0.059 6.993
0.010 -0.049 0.056
4.142
0.008 -0.048
(%)
6.2
1.1
-5.0
5.8
1.0
-4.7
5.5
0.8
-4.7
Sales Growth
0.179 9.874
0.215
0.036 0.152 11.664
0.216
0.064 0.172
8.792
0.228
0.056
(%)
20.3
24.0
3.7
17.5
24.0
6.6
19.8
25.6
5.8
Changes in R&D Status 0.009 0.794
-0.006 -0.015 0.013 1.832
-0.004 -0.017 0.009
0.954
-0.002 -0.011
(%)
0.9
-0.6
-1.5
1.3
-0.4
-1.7
0.9
-0.2
-1.1
Investment/Sales Growth 0.014 0.878
0.004 -0.010 0.001 0.048
0.004
0.004 -0.005 -0.314
0.004
0.010
(%)
1.4
0.4
-1.0
0.1
0.4
0.4
-0.5
0.4
1.0
Wage Level Growth
0.021 1.791
0.139
0.118 0.033 3.949
0.138
0.105 0.037
3.183
0.142
0.105
(%)
2.4
14.9
12.6
3.7
14.8
11.1
4.2
15.2
11.1
Number of observations
(3813:3399)
(3753:24898)
(3309:8731)
(Treated:Untreated)
40
ATT
0.001
0.1
0.057
5.6
0.141
16.3
0.011
1.1
0.006
0.6
0.029
3.3
Kernel
T value Treated
0.239
-0.043
-4.2
8.636
0.011
1.1
14.602
0.215
24.0
1.697
-0.006
-0.6
0.704
0.004
0.4
4.044
0.139
14.9
(3818:35298)
Untreated
-0.044
-4.3
-0.046
-4.5
0.074
7.7
-0.017
-1.7
-0.002
-0.2
0.110
11.7
Table 8.c. Average treatment effect on the treated estimated in terms of different performance measures for firms supported by BOTH of the institutions
Matching Estimator
NNM
Radius (.00005)
Radius (.00001)
Kernel
ATT T value Treated
UnATT T value Treated
UnATT T value Treated
UnATT T value Treated
Untreated
treated
treated
treated
TFP Growth
0.003 0.132
-0.007 -0.010 0.031 2.467
-0.011 -0.043 0.017 0.881
-0.029 -0.046 0.031 2.849
-0.007 -0.038
(%)
0.3
-0.7
-1.0
3.0
-1.1
-4.2
1.6
-2.8
-4.5
3.0
-0.7
-3.8
Worker Growth
0.084 2.986
0.003 -0.081 0.045 2.374
0.002 -0.043 0.064 3.033
0.025 -0.039 0.049 2.905
0.003 -0.046
(%)
8.1
0.3
-7.8
4.4
0.2
-4.3
6.3
2.5
-3.9
4.8
0.3
-4.5
Sales Growth
0.275 7.300
0.306
0.031 0.208 7.764
0.304
0.096 0.232 6.056
0.322
0.090 0.210 8.781
0.306
0.096
(%)
32.6
35.8
3.1
25.4
35.5
10.1
28.5
38.0
9.4
25.7
35.8
10.1
Changes in R&D Status 0.057 2.270
0.004 -0.053 0.027 1.558
0.012 -0.015 0.045 2.193
0.031 -0.015 0.022 1.379
0.004 -0.018
(%)
5.7
0.4
-5.3
2.7
1.2
-1.5
4.5
3.1
-1.5
2.2
0.4
-1.8
Investment/Sales Growth 0.012 0.243
0.000 -0.012 0.012 0.524
0.004 -0.008 0.014 0.371
0.007 -0.007 0.004 0.278
0.000 -0.004
(%)
1.2
0.0
-1.2
1.2
0.4
-0.8
1.4
0.7
-0.7
0.4
0.0
-0.4
Wage Level Growth
0.032 1.454
0.197
0.165 0.092 5.832
0.192
0.100 0.070 3.011
0.177
0.107 0.086 5.855
0.197
0.111
(%)
3.8
21.8
17.9
10.7
21.2
10.5
8.1
19.4
11.3
10.0
21.8
11.7
Number of observations
(900:847)
(861:16414)
(686:4217)
(900:34923)
(Treated:Untreated)
41
Table 9.a. Average treatment effect on the treated estimated based on survival for KOTEC supported firms.
Matching Estimator
NNM
Radius (.00005)
Radius (.00001)
Kernel
ATT T value Treated
UnATT T value Treated
UnATT T value Treated
UnATT T value Treated
Untreated
treated
treated
treated
Survival Rate (%)
0.7
0.900
89.8
89.1
2.4
4.162
90.1
87.6
2.5
3.263
90.0
87.5
1.7
3.542
89.8
88.1
Number of observations
(4524:4020)
(4367:30160)
(3791:11445)
(4524:40349)
(Treated:Untreated)
Table 9.b. Average treatment effect on the treated estimated based on survival for KCGF supported firms.
Matching Estimator
NNM
Radius (.00005)
Radius (.00001)
Kernel
ATT T value Treated
UnATT T value Treated
UnATT T value Treated
UnATT T value Treated
Untreated
treated
treated
treated
Survival Rate (%)
1.2
1.516
91.0
89.8
2.9
4.819
91.0
88.2
2.4
3.116
90.7
88.3
2.8
5.595
91.0
88.2
Number of observations
(4265:3806)
(4212:30226)
(3763:10826)
(4265:40752)
(Treated:Untreated)
Table 9.c. Average treatment effect on the treated estimated based on survival for firms supported by BOTH institutions.
Matching Estimator
NNM
Radius (.00005)
Radius (.00001)
Kernel
ATT T value Treated
UnATT T value Treated
UnATT T value Treated
UnATT T value Treated
Untreated
treated
treated
treated
Survival Rate (%)
-0.7 -0.470
90.8
91.5
3.9
3.146
90.7
86.8
2.3
1.356
89.5
87.2
3.2
3.173
90.8
87.6
Number of observations
(1022:955)
(975:20809)
(791:5316)
(1022:40460)
(Treated:Untreated)
42
Table 10. Average amount of guarantee given to a firm from: KOTEC, KCGF or by BOTH of the
institutions.
Average amount of guarantee given to a
firm from:
Both
KOTEC
KCGF
Institutions
2001
0.200
0.0855
0.246
2002
0.185
0.096
0.267
a
Unit in billion KRW.
b
Source: Parliamentary inspection of the administration, 2004.
43
Figure 1. Development of credit guarantee balance for KCGF and KOTEC.
a
Source: Annual reports from KCGF and KOTEC.
Figure 2. International comparison of credit guarantee balance to GDP ratio.
a
Source: Kang (2005)
44
Figure 3. Guarantee default ratio for KCGF and KOTEC supported firms.
a
Source: Annual reports for KCGF and KOTEC,
b
Guarantee default ratio (%) = guarantee default amount to the total balance of guarantee * 100.
45