Evaluation on the Effectiveness of the Credit Guarantee Scheme in
*
Thailand
*This dissertation is in the proposal stage
Nawapol Pinyoanantapong
Ph. D., Economics
ABSTRACT
Market failures, i.e. imperfect information and positive
externalities, rationalize the government intervention in financial
markets for small and medium enterprises (SMEs). Among many
tools of intervention, credit guarantee is viewed to be relatively
market-friendly as it can solve the right financial access barrier of
the SMEs, i.e. lack of collateral, without substituting the markets
like other vehicles do. However, the challenge for guarantee
schemes is that there is always a trade-off between financial
sustainability, and financial and economic additionalities. Certain
level of loss, covered by government subsidies, may be acceptable
if the scheme can generate additionalilies satisfactorily. My
dissertation, therefore, aims to evaluate the economic
addtionalities, in terms of improvement in sales, profit, and tax
payment, of the incorporated firms in Thailand after using the
credit guarantee by adopting the matching method to deal with the
selection bias.
Whether or not the guarantee scheme can lead to the financial and
economic additionalities is still controversial. Some empirical
studies find the positive addtitionalities of the schemes in Japan,
US, Canada, Colombia, and Korea (See 4-11), while other
literature points out the ineffectiveness of the scheme in Japan (See
4 and 5). Panyanukul, Promboon, and Vorranikulkij (2014), the
first and only one empirical study on the Thailand case, find the
positive effect of the guarantee in terms of financial additionalities
(See 12). However, There exists some limitation regarding the data
set, which is biased toward large SMEs and insufficient analysis on
the economic additionalies.
Recognizing the important role of the SMEs, difficulties in
financial access, and market failures, policy makers have been
introducing lots of measures to foster the SME activities. Among
many tools of the intervention, credit guarantee is viewed to be
relatively market-friendly, as it can solve the right financial access
barrier of the SMEs, i.e. lack of collateral, without substituting the
market like other vehicles do.
However, the challenge for guarantee schemes is that there is
always a trade-off between financial sustainability, and financial
and economic additionalities. Financial additionalities are defined
as improvement in the conditions of the guaranteed loans such as
lower collateral requirement, lower interest rates, or longer
repayment term, while economic additionalities are defined as
improvement in the performance of SMEs after receiving the
guarantee, for instance number of employment, profit, or export
volume (See 1, 2, 3). Certain level of loss, covered by the
government subsidies, may be acceptable if the schemes can
generate financial and economic additionalities satisfactorily
(given that the scheme condition is already designed
appropriately).
CONCLUSIONS AND EXPECTED RESULTS
To deal with this selection bias, Heckman-model control function,
instrumental variables, and matching method have been developed
(See 4, 10, 12, 13, and 14). Panyanukul, et al. (2014) introduce the
Heckman-model to evaluate the Thai guarantee scheme (See 12).
However, an efficient Heckman-model requires exclusion
restriction, which they have not taken into account. To avoid the
exclusion restriction in the Heckman model, matching method can
be substituted (See 13 and 14).
Certain level of the scheme loss may be acceptable if the scheme
can generate additionalilies satisfactorily (given that the scheme is
already designed properly). This dissertation, therefore, aims to
evaluate the economic addtitionalities of the incorporated firms
that use the guarantee in Thailand by adopting the matching
method to deal with the selection bias problem.
This dissertation, as the second attempt on the Thailand case,
therefore, aims to evaluate the effectiveness of the guarantee
scheme in Thailand in terms of economic addtionalities by using
the dataset from the Business Data Warehouse, which consisted of
all active incorporated firms in Thailand.
With the matching-method estimation, we can overcome the
selection bias by creating the “counterfactual” group of nonguarantee users who have “statistically” identical characteristics as
the guaranteed firms based on the propensity score of the guarantee
participation estimated from the probit model. The dependent
variable is a binary variable representing whether or not the firms
participate the program. The independent variables are a set of
factors Xt-1 that determine the program participation such as timeinvariant characteristics In (age, size, sector, and location), and
time-variant financial performances Pn (fixed asset ratio, current
asset ratio, return on asset ratio, and debt to equity ratio).
WHY WE NEED BIG DATA?
Pr(Guaranteet = 1| Xt-1) = Φ (γnIn,i + λnPn,i,t-1)
Most importantly, besides the controversy of the findings in the
previous studies, to evaluate the contribution of the guarantee
scheme is not simple as it sounds like. Conventionally, most
research has been using the aggregate data or the firm-level dataset
that is consisted solely of the guaranteed firms.
Then, we match the guaranteed firms with the nearest neighboring
non-guaranteed firms in each calendar year using the propensity
score as a distance measurement. Next, we construct the outcome
equation as follows:
INTRODUCTION
SMEs play an important role in the economy as they provide the
majority of job opportunities and are the key players for economic
recovery during the recession. However, the SME sector is highly
volatile as despite emerging of numerous new small businesses,
enormous SMEs also collapse in the same time. Moreover, the
SMEs generate lower productivity growth than the large firms.
Due to their lack of collateral, financial access is one of the
obstacles for the SMEs to improve their productivity and/or to
retain their business. In addition, There exist the market failures,
i.e. imperfect information and positive externalities, in the SMEfinancing market.
MODEL SPECIFICATION
Yi,t = ai + δ1Pi,t + δ2Pi,t-1 + ut + vi,t ,
Using the aggregate data, even though the scheme demonstrates
the positive effect on the economy, we cannot conclude that it has
effectively been used. It could possibly be the case that commercial
banks force their former clients (which can get the loans even
without the guarantee) to use the guarantee so that the banks are
less exposed to the risks. Then, the contribution to the economy
would come from the larger profit of the commercial banks that
force the SMEs to use the guarantee, implying that the benefit of
the guarantee falls upon financial sector instead of SME sector,
which is supposed to be the real target of the scheme.
Therefore, to justify the effectiveness of the guarantee scheme, we
need to use the firm-level data to show that the guaranteed firms
access to better loan conditions or/and have better performance
than the non-guaranteed ones do. This also implies that we need to
have the dataset that consists of both guaranteed and nonguaranteed firms. Furthermore, we cannot just easily compare
those two cases using the actual data since there could be some
characteristics that determine the scheme participation and
outcome simultaneously, i.e. selection bias. Our evaluation could
be misestimated if we do not take care of this selection bias.
Yi,t represents firm outcomes (sales, profit, tax payment); ai is a
firm-level fixed effect to control unobserved characteristics; Pi,t and
Pi,t-1 are binary variables expressing whether or not firms
participate the guarantee program; ut is a year-fixed effect; and vi,t
represents a random shock.
DATA
Our sample is constructed from the Business Data Warehouse
(BDW) provided by Ministry of Commerce, Thailand, which is
consisted of approximately 600,000 active incorporated firms in
Thailand. The firm’s basic information, i.e. location, firm size, age,
and sector, as well as financial statement, i.e. the balance sheet and
income statement, are available. Since the current guarantee
scheme was first launched in 2009, the firm information from 2008
to 2013 will be used for our analysis.
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REFERENCES
1. Green, A. (2003). Credit guarantee schemes for small enterprises: An effective instrument
to promote private sector-led growth?. The United Nations Industrial Development
Organization (UNIDO) Working Paper, No. 10, August 2003.
2. Levitsky, J. (1997). Credit guarantee schemes for SMEs: An international review. Small
Enterprise Development, 8(2), 4-17
3. OECD. (2010). Facilitating access to finance: Discussion paper on credit guarantee
schemes. France: OECD
4. Uesugi, I., Sakai, K., & Guy, M. Y. (2006) Effectiveness of credit guarantees in the
Japanese loan market. Journal of the Japanese and International Economies, 24(4), 457–480
5. Wilcox, J., A. & Yasuda, Y. (2008) Do government loan guarantees lower or raise banks’
non- guaranteed lending? Evidence from Japanese banks. Paper presented at the World Bank
Conference on Partial Credit Guarantees, March 13-14, 2008.
6. Craig, B., R., Jackson, W., E., & Thomson, J., B. (2005) SBA-loan guarantees and local
economic growth. Federal Reserve Bank of Cleveland Working Paper 05-03.
7. Hancock, D., Peek, J., & Wilcox, J., A. (2007). The repercussions on small banks and
small business of bank capital and loan guarantees. AFA 2008 New Orleans Meeting Paper
8. Industry Canada. (2010). Study of the economic costs and benefits of the canada small
business financing program.
9. Riding, A., L., Madill, J., & Haines, G., Jr. (2006) Incrementality of SME loan guarantees.
Small Business Economics, 29(1-2) 47-61.
10. Arraiz, I., Melendez, M., & Stucchi, R. (2014). Partial credit guarantees and firm
performance: evidence from Colombia. Small Business Economics, 43, 711-724
11. Kang, J., W., & Heshmati, A. (2007) Effect of credit guarantee policy on survival and
performance of SMEs in Republic of Korea. Small Business Economics, Online publication.
12. Panyanukul, S., Promboon, W., & Vorranikulkuj, W. (2014). Role of government in
improving SME access to financing: credit guarantee schemes and the way forward. Bank of
Thailand Symposium 2014.
13. Wooldridge, J. ,M. (2001). Econometric analysis of cross section and panel data.
Cambridge, Massachusetts: MIT Press.
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CONTACTS
FIRM SIZE (THAI BAHT)
RESEARCH POSTER PRESENTATION DESIGN © 2012
The followings are some of the expected results. Firstly, in terms
of firm characteristics, we expect to see the guaranteed firms with
riskier characteristics (e.g. younger, lower income, lower collateral
etc.) than the firms that can access to the loans without the
guarantee. Secondly, since the service sector tends to be relatively
labor-intensive and, thus, have more constraint to the financial
access, we anticipate to see more contribution of the guarantee to
the economy particularly in this sector. Lastly, unfortunately, due
to the limitation of the loan data availability, we cannot evaluate
the financial additionalities. However, even though we fail to do
so, considering that the financial system in Thailand is relatively
competitive, we can expect to see financial addtionalities to some
extent, otherwise banks may lose their customers to other banks
that can provide more attractive loan conditions with the guarantee.
Additionally, even though the incentive misalignment (the increase
in non-performing guarantee) are not directly taken into account,
as long as the firms can make higher income, it implies that the
incentive misalignment is less likely to occur.
SECTOR
E-mail: ouunawapol@msn.com, or nawapol.pinyoanantapong@cgu.edu