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Optimal Credit Guarantee Scheme and SME Finance in Asia
Farhad Taghizadeh-Hesary, Ph.D.Assist. Professor, Waseda University, Tokyo, Japan
Visiting Professor, Keio University, Tokyo, Japan
Naoyuki Yoshino, Ph.D.Dean & CEO, ADBI
Professor Emeritus, Keio University, Japan
LEVERAGING SME FINANCE THROUGH VALUE CHAINSIN THE CAREC LANDLOCKED ECONOMIES,
ADB-ADBI-CAREC INSTITUTENazarbayev University, Astana, Kazakhstan
Nov 29, 2018
The views expressed in this presentation are the view s of the authors and do not necessarily reflect the views or policies of the AsianDevelopment Bank Institute (ADBI), the Asian Development Bank (ADB), its Board of Directors, or the governments they represent.ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of theiruse. Terminology used may not necessarily be consistent with ADB official terms.
2
https://www.adb.org/adbi/research/call-for-papers
Deadline for submission of abstract: Dec 30, 2018
Outline
I. SMEs’ Difficulties in Raising Money in Asia
II. Credit Guarantee Schemes and SME Finance
III. Optimal Credit Guarantee Ratio
IV. Conclusion and the Policy Recommendations
V. Next step project
3
4
I. SMEs’ Difficulties in Raising Money in Asia
I. SMEs’ Difficulties in Raising Money
5
Bank of Japan (2016) – TANKAN
Lending Attitude of Financial Institutions
Limited bank lending to SMEs in Central Asia is a Challenge
6
CAM
BAN
MONINO
PRC
KAZMAL
THA
KOR
ageAver
21.1
0
5
10
15
20
25
30
35
40
45
50
2007 2008 2009 2010 2011 2012 2014*2013
BAN = Bangladesh, PRC = People’s Republic of China, IND = India, INO = Indonesia, KAZ = Kazakhstan, KOR = Republic of Korea,MAL = Malaysia, MON = Mongolia, PHI = Philippines, SRI = Sri Lanka, and THA = Thailand.Source: Asia Finance Monitor (2015)
Bank loans to SMEs to total loans (%)
Credit to the private sector in Central Asia remains comparatively modest
7
Domestic credit to private sector in Central Asia
Source: (World Bank, 2017), (EBRD, 2017), (RAEX, 2017), (OECD, 2018)
34%
21%
58%
19% 15%
44%
148%
0
20
40
60
80
100
120
140
160
Kazakhstan Kyrgyzstan Mongolia Tajikistan Uzbekistan Lower middleincome
OECDmembers
Domestic credit to private sector (% of GDP)
Non-performing loans remain high in the region
8
20.4%
8.5%
6.7%
0.4%
9.4%
4.2%
2.7%
0.6%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Tajikistan* Kyrgyzstan Kazakhstan Uzbekistan RussianFederation
Lower middleincome
OECD members Canada
Bank nonperforming loans to total gross loans (%)
Note: * Data for Tajikistan is from 2014 Sources: (World Bank, 2017; Bank of Mongolia, 2016; OECD, 2018)
Bank nonperforming loans to total gross loans (%)
Credit conditions are tight with high interest rates in the region
9
Interest rates in Central Asia, 2016
22%
20%
26%
16% 16%
3%4%
5%
-5%
0%
5%
10%
15%
20%
25%
30%
Kyrgyzstan Mongolia Tajikistan* Kazakhstan* Uzbekistan Canada Czech Republic Poland
Lending interest rate (%) Inflation Rate (CPI)
Note: *lending interest rates for Kazakhstan and Tajikistan (2015)
Source: (World Bank, 2017; CIA, 2018; State Committee of Uzbekistan on Statistics, 2018; Ministry of National Economy of Kazakhstan, 2017; OECD, 2018)
Lending interest rate and inflation rate
More than a third of SMEs are discouraged from applying loans due to tight credit conditions in the region, compared to less than a fifth in selected OECD countries.
10
Source: (EBRD, 2017; OECD, 2018)
Percentage of SMEs that are discouraged to apply for a loan by credit conditions
14%
18%
42%
35%
37%
37%
44%
56%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Czech Republic
Poland
Russia
Tajikistan
Uzbekistan
Kazakhstan
Kyrgyzstan
Mongolia
Discouraged by credit conditions No need
High and systematic collateral requirements limit access to finance for SMEs
11
140%
150%
160%
170%
180%
190%
200%
210%
220%
230%
60% 65% 70% 75% 80% 85% 90% 95% 100% 105%
Val
ue
of
colla
tera
l nee
ded
fo
r a
loan
(%
of
th
e lo
an a
mo
un
t)
Proportion of loans requiring collateral (%)
Mongolia
Lower middle income
Kyrgyzstan
Kazakhstan
Tajikistan
Uzbekistan
OECD average
High and systematic collateral requirements
Average but systematic collateral requirements
Source: (EBRD, 2017; World Bank, 2017, OECD, 2018)
Figure 5. Collateral requirements in Central Asia
II. Credit Guarantee Schemes and SME finance
12
Source: Yoshino and Taghizadeh-Hesary (2015)
SMEs, CRD, CGCs and Banks
13
Example: Credit Guarantee Scheme of Japan
14
LocalGovernments
National GovernmentMinistry of Finance
Ministry of Economy, Trade and Industry(Small and Medium Enterprise Agency)
Private Financial
Institutions
SMEs and Micro-
enterprises
Japan Finance Corporation (JFC)
Japan Federation of Credit Guarantee Corporations (JFG)
Credit Guarantee Corporations (CGCs):
51 CGCs
Contributions/ Loans
Supervision
Subsidies forCGC’s fund
Supervision Supervision Subsidies for compensation assets
Compensation for the loss Fund for Credit Insurance
Supervision
Insurancecontracts
Credit Guarantee Consignment Contract
Loans
Repayments
GuaranteeContract
Source: Japan Federation of Credit Guarantee Corporations (JFG 2014)Note: above figure is reproduced by the authors
CGS reduces the expected default lose of banks on SME
loan and increase bank lending to SMEs
15
credit guarantee scheme and SME loan supply𝑟𝑆𝑀𝐸
𝐿𝑆𝑀𝐸
Normal loan supply curve to SMEs with existence ofcredit guarantee scheme
Backward bending loan supply curve
Source: Yoshino and Taghizadeh-Hesary (2018)Note: rSME = lending interest rate to SMEs; LSME= amount of loan to SMEsSME= small and medium-sized enterprises;
16
SMEs Access to Credit Guarantees
Source: ACSIC (2012), The 25th Anniversary Publication of ACSIC – The 25-year History of ACSIC
1.0
5.3
1.90.0
36.7
7.4
26.7
1.7
8.9
0.1
10.9
1.5
0
5
10
15
20
25
30
35
40
0
1
2
3
4
5
6
7
8 Outstanding Guaranteed Liabilities (% of GDP) [left]
SME Access to Guarantee (% of total SMEs) [right]
( )% (%)
Japan
Source: Japan Federation of Credit Guarantee Corporations (JFG)
1. Following the introduction of credit guarantee scheme (CGS) in Japan in 1937, their use spread first throughout Europe and the Americas in the 1950s, and then to Africa, Asia and Oceania in the 1960s and 1970s.
2. At present, there are 51 CGCs, one for each prefecture and one in each of the cities of Nagoya, Yokohama, Kawasaki, and Gifu.
3. At the end of 2013, their total liabilities stood at approximately 30 trillion yen.
17Copyright: Yoshino & Taghizadeh-Hesary (2017)
Eligible SMEs for the Credit Guarantee in Japan
CGCs define the scope of MSMEs eligible to receive credit guarantees asfollows. MSMEs which either meet the requirements in terms of number ofregular employees or paid-up capital as given in the table below are eligible forcredit guarantees (excluding some special industries).
Industries covered by the credit guarantee system are based on the industries designated bythe enforcement regulation under the Small and Medium-sized Enterprise Credit InsuranceAct. Agriculture, forestry, fisheries, financial industry are excluded.
Source: Japan Federation of Credit Guarantee Corporations (JFG)
18Copyright: Yoshino & Taghizadeh-Hesary (2017)
Ceiling on Guarantee in Japan
Source: Japan Federation of Credit Guarantee Corporations (JFG)
Credit Guarantee fee rate classification
19Copyright: Yoshino & Taghizadeh-Hesary (2017)
Partial Guarantee
Source: Japan Federation of Credit Guarantee Corporations (JFG)20
Copyright: Yoshino & Taghizadeh-Hesary (2017)
21
III. Optimal credit guarantee ratio
22
SME1
CREDIT GUARANTEE
CORPORATION (CGC)
lending
Optimal Credit
Guarantee Scheme
(BO
RR
OW
ER
s)
SME2
A
B
(Le
nd
ers
)
Copyright: Yoshino & Taghizadeh-Hesary (2017)
In the literature on loan guarantees has left three
important questions unanswered:
(i) What is the optimal credit guarantee ratio to fulfillgovernment’s goal for minimizing banks’nonperforming loans to SMEs while at the same timefulfilling the government policies for supporting SMEs?
(ii) Should this rate be constant regardless of themacroeconomic status?
(iii)Should this rate be same for all banks, or should it varybased on a bank’s soundness?
23
Research Questions
Copyright: Yoshino & Taghizadeh-Hesary (2017)
24
Models for the Optimal Credit Guarantee Ratio
Policy Objective Function
2*2
2*1 wLLwU
eLo YlrllL 21
Loan Demand Function
Max. DLDZMPPYgLL CDrLLr SL ,,,, ,,
Banks Profit Maximization
ADLL 1Subject to: Banks’s Balance sheet
25
LDZMPPYg
e rYll
lllL SL ,,,
1
2
1
01,,
2
Amount of loan in equilibrium
Optimal Credit Guarantee ratio: g
Depends on:
• Actual SME loans• The desired SME loans• The desired default risk ratio of loans• Fixed demand for loan• Deposit interest rate• Expected GDP• The weight for stabilizing the SME loans • The weight for reducing the non-performing loan ratio• Marginal increase of non-performing loans by increase of additional loans• Price of Land, Price of stock, GDP, money supply, • Financial profile of banks
26
ZMPPYwLlryll
lllw
wlwg SLLD
e
1
6
1
5
1
4
1
3
1
2*
1
2*
1
1
1
2
1
02
11
2
211
1
24.
4
1
Empirical Survey
No. Symbol Definition
1 L–D Total loans/total deposits
2 PR–L Properties/total loans
3 (SD+LD)–D (Saving deposits + long-term deposits)/total deposits
4 A–L Total assets/total loans
5 SC–L Securities/total loans
6 CA–D Cash/total deposits
7 CBR–D Accounts receivable from central bank/total deposits
8 OBR–D Accounts receivable from other banks/total deposits
Note: Properties are land, buildings, and other hard assets owned by banks. Securities include shares of corporate stock or mutual funds, bonds issued by corporations or governmental agencies, limited partnership units, and various other formal investment instruments that are negotiable and fungible. Accounts receivable from the central banks includes reserve requirement (or cash reserve ratio) and other sums that are normally in the form of cash stored physically in a bank vault (vault cash) or deposits made with a central bank. Accounts receivable from other banks are sums loaned to other banks.Source: Yoshino, Taghizadeh-Hesary, Nili (2015)
Variables Examined for Bank’s Soundness
27
Statistical Analysis of banks’ balance sheet data
Variables
(Financial Ratios of Banks)
Component
Z1 Z2 Z3
L–D (0.238) (0.912) (0.143)
PR–L 0.042 0.190 0.780
(SD+LD)–D (0.287) 0.819 (0.123)
A–L 0.987 0.083 0.130
SC–L (0.096) (0.140) 0.875
CA–D 0.379 (0.536) 0.039
CBR–D 0.954 (0.104) (0.102)
OBR–D 0.981 (0.011) (0.117)
( ) = negative.Note: The extraction method is principal component analysis. The rotation method is direct oblimin with Kaiser normalization.
Factor Loadings of Financial Variables after Direct Oblimin Rotation
28
Distribution of factors
FFEE DD
CC
BB
AA
Z
YXWVU
TSRQ PO
N
L K
J
IF
E
DC
A
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
0 1 2 3 4 5 6
Co
mp
on
ent
2 (Z
2)
Component 1 (Z1)
FFEE
DD
CCBB
AAZ
YXWV
U TS
RQPO
N
L K
J
I F
E
D
C
A
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
-2.5 -2 -1.5 -1 -0.5 0 0.5 1
Co
mp
on
ent
3 (Z
3)
Component 2 (Z2)
FFEE
DD
CCBB
AAZ
YXWVU T
SRQ
PON
L K
J
I F
E
D
C
A
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
0 1 2 3 4 5 6
Co
mp
on
ent
3 (Z
3)
Component 1 (Z1)
29
Clustering
Dendrogram
Bank Credit
rank
Rank
of L–D
Rank of
PR–L
Rank of
(SD+LD)
–D
Rank of
A–L
Rank of
SC–L
Rank of
CA–D
Rank of
CBR–D
Rank of
OBR–D
I 2 24 1 16 3 5 8 21 2
R 14 14 17 12 15 9 11 9 7
W 28 11 20 22 20 6 10 3 18
Robustness Check for Three Sample Banks
30
Calculated Optimal Credit Guarantee ratios
31
Group 1 of banks: 0.775Group 2 of banks: 0.683
the optimal credit guarantee ratio in our model depends on three groups of factors:
1. macroeconomic variables
2. government policies,
3. banking profile.
These three groups consist of various variables including:
price of land, price of stock, gross domestic product (GDP), money supply,actual SME loans, fixed demand for loans, deposit interest rate, expectedGDP, marginal increase of nonperforming loans by increase of additionalloans, desired SME loans, desired default risk ratio of loan, weight forstabilizing the SME loans, weight for reducing the nonperforming loan ratio,and financial profile of banks.
Robustness Check of the Optimal Credit Guarantee Model
• Stationarity test• Co-integration analysis• VECM
32
321 ,,,1,,, ZZZmcpigdpV
Impulse Response Analysis: Group 1 of banks
33
Impulse Response Analysis: Group 2 of banks
-.03
-.02
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10
Accumulated Response of NPL2/L2 to Z2,1
-.03
-.02
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10
Accumulated Response of NPL2/L2 to Z2,2
-.03
-.02
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10
Accumulated Response of NPL2/L2 to Z2,3
-.03
-.02
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10
Accumulated Response of NPL2/L2 to M
-.03
-.02
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10
Accumulated Response of NPL2/L2 to NPL2/L2
-.03
-.02
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10
Accumulated Response of NPL2/L2 to P
-.03
-.02
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10
Accumulated Response of NPL2/L2 to Y
Accumulated Response to Cholesky One S.D. Innovations
34
35
IV. Conclusion and Policy Recommendations
Conclusion and Policy recommendations (1)
1. The public credit guarantee scheme is a tool toreduce the supply–demand gap in SME finance.
2. In order to avoid moral hazard and for increasingthe effectiveness and sustainability of the CGS,adoption of the optimal credit guarantee ratio isneeded
3. Optimal credit guarantee ratio is determined bythree groups of variables: (i) government policiesfor NPL reduction and SME support, (ii)macroeconomic variables, and (iii) bank-levelvariables or banking behavior
36
Conclusion and Policy recommendations (2)
4. The optimal credit guarantee ratio should vary foreach bank, or for each group of banks, based ontheir financial soundness.
5. Sound banks should receive a higher guaranteeratio from the government, and less healthy banksshould receive a lower guarantee to avoid a moralhazard problem.
6. Moreover, this rate should vary based on economicconditions. Governments should lower theguarantee ratio in good economic conditions wherethe default risk of SME loans is reduced, and raiseit in bad economic conditions to protect the SMEfinancing and economic growth.
37
Next Step:
Measuring the optimal credit guarantee ratio andoptimal credit guarantee fee for CAREC membercountries (First case: Kazakhstan)
In Recent years DAMU fund in Kazakhstan hadsignificant impact in increasing share ofguaranteed loans to SMEs.
The share of “Damu” Fund credit in priorityindustries have been steadily increasing over theyears to KZT574 bln in 2016 or 17% of the totalcredit. (DAMU, 2017).
38
Dosmagambet, Oskenbayev, Taghizadeh-Hesaryand Mukan (Forthcoming) found that the publiccredit guarantee scheme and financial systemcreditworthiness in Kazakhstan is vulnerable to oilprice movements.
In order to have a sustainable credit guaranteescheme for Kazakhstan for securing the SMEsaccess to finance, its important to adopt theoptimal credit guarantee ratio and optimal creditguarantee fee.
39
Reference:
40
1. Yoshino, N. (2012). Global Imbalances and the Development of Capital Flows among AsianCountries. OECD Journal: Financial Market Trends. Vol. 2012/1.
2. Yoshino, N. and F. Taghizadeh-Hesary (2014). An Analysis of Challenges Faced by Japan’sEconomy and Abenomics. The Japanese Political Economy 40: 1–26. DOI:10.1080/2329194X.2014.998591
3. Yoshino, N. and F. Taghizadeh-Hesary (2014). Analytical Framework on Credit Risks forFinancing SMEs in Asia. Asia-Pacific Development Journal. United Nations Economic andSocial Commission for Asia and the Pacific (UN-ESCAP). 21(2): 1-21.
4. Yoshino, N., Taghizadeh-Hesary, F., Nili, F. (2015), ‘Estimating Dual Deposit InsurancePremium Rates and Forecasting Non-performing Loans: Two New Models’. ADBI WorkingPaper 510. Asian Development Bank Institute: Tokyo
5. Kuwahara, S., N. Yoshino, M. Sagara, and F. Taghizadeh-Hesary. (2015). Role of the CreditRisk Database in Developing SMEs in Japan: Lessons for the Rest of Asia. ADBI Working Paper547. Tokyo: Asian Development Bank Institute.
6. Yoshino, N. and F. Taghizadeh-Hesary. (2015). Analysis of Credit Risk for Small and Medium-Sized Enterprises: Evidence from Asia. Asian Development Review (ADR). Vol. 32 No. 2.: 18-37, MIT Press.
7. Yoshino N., and Taghizadeh-Hesary, F. (2018). Optimal credit guarantee ratio for small andmedium-sized enterprises’ financing: Evidence from Asia. Economic Analysis and Policyhttps://doi.org/10.1016/j.eap.2018.09.011
8. OECD (2018). Enhancing Access to Finance for SMEs: The case of central Asia. UnlockingAccess to Finance for SMEs in Asia. Eds. N. Yoshino and F. Taghizadeh-Hesary. London:Routledge
Thank you for your attention!nyoshino@adbi.org
farhad@aoni.waseda.jp
www.linkedin.com/in/farhadth41
Forthcoming book, 2019
Unlocking SME Finance in Asia: Roles of credit rating and credit guarantee scheme
Farhad Taghizadeh-HesaryAssist. Professor, Waseda University, Japan
Visiting Professor, Keio University, Japan
Naoyuki YoshinoDean & CEO, ADBI
Professor Emeritus, Keio University, Japan
Editors:
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