Small Business Financing and Bank Performance : Empirical Study of Indonesian Publicly Banks.
THIS PAPER, PRESENTED ON:
THE 2ND INDONESIA INTERNATIONAL CONFERENCE ON INNOVATION, ENTREPRENEURSHIP, &
SMALL BUSINESS (IICIES 2010), SERPONG, TANGERANG, INDONESIA ON JULY 11-15, 2010,
ORGANIZED BY : SBM ITB & UNIVERSITAS MULTIMEDIA NUSANTARA
Small Business Financing and Bank Performance :
Empirical Study of Indonesian Publicly Banks
Mokhamad Anwar1
ABSTRACT
The aim of this paper is to investigate small businesses financing
in Indonesia. Many programs in financing small businesses have been
undertaken through some collaboration between banks, government, other
related parties.
The study employed descriptive and inferential statistics to explain
the data characteristics of small businesses financing from Indonesian
publicly banks and tried to determine whether those financing had the
association with and influenced the bank performance for the period of
2004-2007. The data were collected from Bank Indonesia and Indonesian
Capital Market.
The result of the study showed that the small business financing
significantly influenced the bank performance in Indonesia especially
within the study period.
Keywords :
Small Business, Financing, Bank Performance
1
Lecturer at the Dept. of Management and Business, Faculty of Economics, The University of Padjadjaran
(FE UNPAD). Researcher at Research Division, Laboratory of Management Faculty of Economics, The
University of Padjadjaran (LMFE UNPAD), Address Correspondence to : anwardade@gmail.com,
mokhamad.anwar@fe.unpad.ac.id.
1
BACKGROUND
Small Business Sector has a significant role in enhancing economic growth in
Indonesia. Their contribution has increased for three decades since the deregulation
package launched by the Government of Indonesia in 1983 and nowadays, Small
Business Sector has been the main actor in Indonesian economy.
Economic crisis has beaten many countries in East Asia since 1997 and Indonesia
got suffered more deeply than any other countries in South East Asia Nations. Some
research have been conducted in Indonesia especially in identifying which one of
business sectors were more stable in facing the crisis, and many results showed that small
business sectors were the most stable one.
The government of Indonesia, particularly Cooperative and Small-Medium
Enterprises Department, has significantly contributed to the development of small
business sectors with many various programs such as Small Medium Enterprises (SMEs)
training and development program, Bank and Financial Institution linkages, Partnership
Programs between Small business and Big Corporation, etc.
In financing side, Commercial Banks have held a very important role in providing
funds for financing Small Business Sectors in Indonesia with some financial schemes
until now. The scheme comprises of two alternative programs, one is financing by their
bank’s own funds and the other is financing by government’s funds. With the
combination between the two alternatives (credit program policy from government and
the internal policy from their own banks) The Banks have given a significant contribution
particularly in developing small business sector and generally developing Indonesia as a
whole since 1983.
Data from BPS (Statistics Indonesia, Republic of Indonesia, 2004) showed that in
2003 the numbers of people who played as Small Medium Enterprises in Indonesia were
42,4 millions of people. The contribution of Small Medium Enterprises to the Gross
Domestic Product was 56.7 percent of the Total GDP of Indonesia in 2003 (with level of
contribution from Small Business Sector was 41.1 percent). The data has also showed
that the numbers of employees who worked at SMEs was 79 millions of people. Those
data indicated that the role of Small Business Sector in development of Indonesia was
great.
On the other hand, commercial banks in Indonesia have been continuously
improving their performance since the government of Indonesia cut the numbers of
banks. As known, there were about 100 banks were closed and merged with the other
banks since 1997 and the merger waves will keep going by maintaining the higher
performance banks and closing or merging the lower performance banks with the higher
ones. Beside that, with the new architecture of banking Industry in Indonesia, Bank
Indonesia as a commercial banks regulator has given some rules to be obeyed by the
commercial banks, including the obligation for each banks management to improve their
performance.
2
In regards to those purposes, the investigation about small business financing
from commercial banks in Indonesia are important to be evaluated and the influence on
banking performance need also be assessed.
The study about the topic will be implemented in a this research report which will
be conducted by the writer with the title of “Small Business Financing and Bank
Performance : Empirical Study of Indonesian Publicly Banks”.
SHORT LITERATURE REVIEW
Some researches have been undertaken by researchers in related to the Small
Business Financing and Banking Performance :
Constantinos Stephanou, Camila Rodriguez (2008) studied to shed light on
current trends and policy challenges in the financing of small- and medium-sized
enterprises (SMEs) by banks in Colombia. Data was collected from the authorities, a
representative sample of banks, and other relevant entities. The result of the research :
Bank financing to SMEs was becoming the current business and risk management models
for SME lending. Important institutional and policy constraints to SME lending remain,
but are not yet binding.
Leora F. Klapper, Virginia Sarria-Allende, Victor Sulla (2002) investigated the
Small- and Medium-Size Enterprise Financing in Eastern Europe. The result showed the
size of the SME sector (as measured by the percentage of total employment) in Eastern
European countries is smaller than in most developed economies. However, SMEs seem
to constitute the most dynamic sector of the Eastern European economies, relative to
large firms. In general, the SME sector comprises relatively younger, more highly
leveraged, and more profitable and faster growing firms. But at the same time, these
firms appear to have financial constraints that impede their access to long-term financing
and ability to grow.
Mohammed N. Alam (2005) investigated by comparative study of financing small
and cottage industries (SCIs) by interest-free banks in different countries like Turkey,
Cyprus, Sudan and Bangladesh. The research result shows that the lender–borrower
network relationship, especially in case of financing rural-based SCIs by interest-free
banks, differ from one country to the other even though the basic principles of interestfree financing remains the same.
Saovanee Chantapong (2005) studied the performance of domestic and foreign
banks in Thailand in terms of profitability and other characteristics after the East Asian
financial crisis. The study is based on a micro bank-level panel data on financial
statements by pooling cross-bank time series data with the major balance sheet and
income statement ratios for domestic and foreign banks in Thailand for 1995–2000. All
banks were found to have reduced their credit exposure during the crisis years, and to
have gradually improved their profitability during the post-crisis years. The results
indicate that foreign bank profitability is higher than the average profitability of the
domestic banks although importantly, in the post-crisis period, the gap between foreign
and domestic profitability become closer. This shows some positive results of the
financial restructuring program.
M. Kabir Hassan in the 12th ERF Conference Paper (2005) investigated relative
efficiency of the Islamic banking industry in the world by employing a panel of banks
3
during 1995-2001. Both parametric (cost and profit efficiency) and nonparametric (data
envelopment analysis) techniques are used to examine efficiency of these banks. Five
DEA efficiency measures such as cost, allocative, technical, pure technical and scale
efficiency scores are calculated and have been correlated with conventional accounting
measures of performance. The results show that, on the average, the Islamic banking
industry is relatively less efficient compared to their conventional counterparts in other
parts of the world. The results also show that all five efficiency measures are highly
correlated with ROA and ROE, suggesting that these efficiency measures can be used
concurrently with conventional accounting ratios in determining Islamic bank
performance.
Fadzlan Sufian (2007) identified the efficiency of Islamic banking industry in
Malaysia, between Foreign vs domestic banks. The results with Data Envelopment
Analysis suggested that Malaysian Islamic banks efficiency declined in year 2002 to
recover slightly in years 2003 and 2004. The domestic Islamic banks were more efficient
compared to the foreign Islamic banks albeit marginally.
Philip Molyneux and John Thornton (1992) identified the Determinants of banks
performance in 18 European Countries for the period of 1986-1989. The result of the
research showed that based on Return on Equity as proxy for bank performance, the
significant determinants were Concentration Ratio, Interest Rate, and Government.
Whereas based on Return on Assets, the significant determinants were Capital Ratio,
Interest Rates, Government and Concentration Ratio.
Llyod-William and Phill Molyneux (1994) investigated the influence of market
structure and market share on the bank profitability in Spain. The significant Predictors of
the bank performance was Concentration Ratio, Capital Assets Ratio, and Assets Size
(positive significant) and owner (negative significant). The Result of the Research
showed that the research supported the Structure-Behavior-Performance which stated that
the more concentrated the banks, the less competitive the banks and finally will enhance
the performance.
Asli Demirguc-Kunt and Harry Huizinga (1998) also studied factors that
determined the bank performance. By using pooled data of 80 countries for the period of
1988-1995, the result of this research showed that the significant predictors were loan to
total assets, customer and short-terms funding to total assets, GDP per capita and Real
Interest.
Balachandher K. Guru, John Staunton and Balashanmugam (1999) explored the
Determinants of Commercial Bank Profitability in Malaysia. By using sample of 17
Commercial Banks for the period of 10 years (1986-1995). The Result of the Research
are : Based on ROA Measure, significant variables were Loan to Total Assets (Assets
Composition), Current Accounts to Total Deposits and Total Expenses to Total Assets. On
the other hand, based on ROE Measure, significant variables were Loan to Total Assets,
Inflation, Market Interest Rate, Total Expenses to Total Assets, Capital to Total Assets,
and Market Growth.
Margarida Abreu and Victor Mendes (2001) investigated the Determinants of 2151 Banks Profitability which was located in 4 countries in Europe (Portugal, Spain,
France and Germany) for the period of 1986-1999. The result of the Research were :
based on ROA Measure, the significant variables were Equity to Assets, Loan to Assets,
Bank Market Share, Inflation Rate and Dummy variables which showed significant in
4
Spain and France. Whereas based on ROE Measure, the significant variables were Equity
to Assets, Bank Market Share, Unemployment Rate, Inflation Rate, and Dummy Variable
which showed significant in Spain.
OBJECTIVES
The above discussion, lead to the following formulation of research objectives:
1. To identify the small business financing in Indonesia, especially in Indonesia
Publicly Banks.
2. To identify and analyze the commercial banks performance especially in
Indonesia Publicly Banks.
3. To identify the Influence of small business financing on commercial banks
performance especially in Indonesia Publicly Banks.
METHODOLOGY
The methodology of this research uses some approaches which are employed to
make this complete such as :
1. Literature Study
The study elaborate the topics of small business financing and banks performance
by finding some practical schemes of small business financing in Indonesia and
some theories about banks performance.
2. Statistical and Econometrics Analysis.
The study will employ statistical and econometrics analysis such as descriptive
and inferential statistics as well as econometrics analysis to test the hypotheses.
For the model, the study will use the multiple regression analysis to investigate
whether independent variables influenced dependent variables in the banks within
the period.
Some variables which are employed are as follows :
Variables
Small
Business
Financing
(SBF)
Other
Financing
(OF)
Inter Banks
Placement
(IBP)
Capital
Adequacy
Ratio (CAR)
Return On
Assets (ROA)
INDEPENDENT VARIABLES
Definition
The financing for small businesses in Commercial
Banks, particularly in Indonesian Publicly Banks.
The banks financing for other than small business.
The financing are for corporate sectors, financing for
consumer needs, etc. The data are the Indonesian
Publicly Banks.
Funds Placement in other banks. This is one of the
some instruments for generating profit beside
financing. The data are the Indonesian Publicly
Banks.
DEPENDENT VARIABLES
The Adequacy measurement of banks capital.
Counted by Capital divided by Risky-Weighted
Assets. The data are the Indonesian Publicly Banks.
The measurement of banks profitability. Counted by
profit divided by total assets. The data are the
Indonesian Publicly Banks.
Variables
Independent Variables
(X1)
Independent Variables
(X2)
Independent Variables
(X3)
Dependent Variables
(Y1)
Dependent Variables
(Y2)
5
Non
Performing
Loan (NPL)
The measurement of Banks Non-Performing Loan.
Counted by Non-performing Loans by Total
Outstanding Loans. The data are the Indonesian
Publicly Banks
Dependent Variables
(Y3)
This research uses the data from the open source or downloadable data from
www.bi.go.id with the Criteria as follows :
The samples are all commercial banks, which were listed in Indonesia Stock
Exchange.
The data were collected in yearly basis for the period of 2004-2007.
After downloading the data from the website, it was known that the data which
were complete are the only 20 banks out of 24 banks, so that the 4 banks were not
included in the research.
RESEARCH RESULTS
Descriptive Statistics
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
SmallBusinessFin
80
12888
34272151
2893128.26
6496055.040
OtherFinancing
80
367271
102514179
21137014.61
2.378E7
Interbank Placement
80
12142
30953865
3738862.66
5714849.420
Capital Adequacy
80
9
37
17.76
5.786
Return on Assets
80
-1
6
1.96
1.208
Non Performing Loan
80
0
16
2.50
2.510
Valid N (listwise)
80
The data above are derived from the financial statement of almost all publicly
banks which were downloaded from Bank Indonesia website (www.bi.go.id) with annual
basis for the period of 2004-2007. The data are 20 commercial banks which were listed in
Indonesia Stock Exchange.
From the above table, we can see that the minimum of small business financing
from Indonesian Publicly Banks for the period was Rp.12,888 million, the maximum of
the financing was Rp. 34,272,151 million, with the average of Rp.2,893,128 million.
On the other hand, other financing which comprises of corporate sector financing
and consumer financing, had the minimum financing of Rp.367,271 million, the
maximum financing of Rp.102,514,179, and the average financing of Rp.21,137,014.61
million.
6
From the above data, we can identify that, most publicly banks in Indonesia
allocated their funds in other financing such as corporate financing, consumer financing
almost ten times higher than its allocation for small business financing. It can be
concluded that most publicly banks in Indonesia still assumed that corporate and
consumer financing are much better than that of small business financing. Most publicly
banks seems to see that the corporate and consumer financing are more secure in terms of
collateral than that of small business financing.
The minimum of interbank placement from those banks for the period was
Rp.12,142 million, the maximum was Rp.30,953,865 million and the average was
Rp.3,738,862.66 Million. We can observe that the average of interbank placement was
higher than that of small business financing (Rp.3.74 trillion Vs Rp.2.89 trillion). It can
be concluded that those banks would rather invest in interbank placement than invest in
small business financing.
Meanwhile, in terms of banks performance, we can see that, with the indicator of
CAR (capital adequacy ratio), those publicly banks had the average CAR of 17,76% for
the period. This number indicate that the capital adequacy of those banks were very good
and in accordance with the basel standard and Indonesian banking standard which stated
that the minimum CAR of each bank are 8%.
The average of ROA (return on assets) of those banks for the period was 1.96%
with the range of -1% to 6%. This number can only be evaluated better or not if we
compare with the banking industry data. Since the data was not available, we would not
state the evaluation result.
The average of NPL (non-performing loan) of those banks for the period was
2.5%. This indicated that the NPL of those banks was very good since the NPL still under
the NPL limit of 5%, which was determined by Bank Indonesia.
Inferential Statistics
In this research, the writer employed three models :
Model 1, using CAR (capital adequacy ratio) as dependent variable, with SBF (small
business financing), OF (other financing), and IBP (interbank placement) as independent
variables.
Model 2, using ROA (return on assets) as dependent variable, with SBF (small business
financing), OF (other financing), and IBP (interbank placement) as independent
variables.
Model 3, using NPL (non performing loan) as dependent variable, with SBF (small
business financing), OF (other financing), and IBP (interbank placement) as independent
variables.
These are the results of the inferential statistics test for those three models :
Model 1
7
ANOVAb
Model
1
Sum of Squares
Regression
df
Mean Square
191.602
3
63.867
Residual
2453.478
76
32.283
Total
2645.080
79
F
Sig.
.124a
1.978
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Capital Adequacy
From the table of ANOVA, we can see that the predictors which were Small
Business Financing, Other Financing, and Interbank Placement not significantly
influenced the dependent variable. We can see form sig. value of 0.124 which was higher
than α=0.05, which means that H0 (Hypothesis null) was accepted.
Model Summaryb
Std. Error of the
Model
1
R
R Square
.269a
Adjusted R Square
.072
Estimate
.036
Durbin-Watson
5.682
.877
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Capital Adequacy
We can see that those independent variables influenced the dependent variables
with the only 3,60%, with means that 96,4% variation of dependent variables was
explained by other factors not included in this model.
From the Durbin-Watson score of 0.877 which are in the range of -2 and 2 which
means that there was not any autocorrelation in this model.
Standardized
Unstandardized Coefficients
Model
B
1
16.836
.853
SmallBusinessFin
-2.113E-7
.000
OtherFinancing
3.231E-8
Interbank Placement
2.271E-7
(Constant)
Std. Error
Coefficients
Beta
Collinearity Statistics
t
Sig.
Tolerance
VIF
19.728
.000
-.237
-1.700
.093
.626
1.597
.000
.133
.747
.458
.386
2.591
.000
.224
1.217
.227
.359
2.782
From the above table, we can conclude that Small Business Financing was not
significant in predicting CAR with the alpha of 5%, but it significantly influenced CAR
with the alpha of 10%.
8
We can also see that VIF (variance inflation factor) was under 5, which means
that there is no multicollinearity in this model.
From the scatterplot we can see that the dots in the picture were spread randomly
around number zero. It means that there is no heteroscedasticity in this model.
Model 2
ANOVAb
Model
1
Sum of Squares
df
Mean Square
Regression
29.932
3
9.977
Residual
85.354
76
1.123
115.286
79
Total
F
Sig.
8.884
.000a
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Return on Assets
From the table of ANOVA, we can see that the predictors which were Small
Business Financing, Other Financing, and Interbank Placement significantly influenced
the dependent variable. We can see form sig. value of 0.000 which was lower than
α=0.05, which means that H0 (Hypothesis null) was rejected.
Model Summaryb
Std. Error of the
Model
1
R
R Square
.510a
Adjusted R Square
.260
.230
Estimate
1.060
Durbin-Watson
1.614
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Return on Assets
9
We can see that those independent variables influenced the dependent variables
with the 23%, with means that 23% variation of dependent variable was explained by the
independent variables and the rest are 77% variation of dependent variable are explained
by other factors not included in this model.
From the Durbin-Watson score of 1.614 which are in the range of -2 and 2 which
means that there was not any autocorrelation in this model
Standardized
Unstandardized Coefficients
Model
1
(Constant)
B
Std. Error
Collinearity Statistics
Beta
1.653
.159
SmallBusinessFin
9.546E-8
.000
OtherFinancing
1.101E-8
-5.276E-8
Interbank Placement
Coefficients
t
Sig.
Tolerance
VIF
10.384
.000
.513
4.116
.000
.626
1.597
.000
.217
1.365
.176
.386
2.591
.000
-.250
-1.516
.134
.359
2.782
We can see from the above table that Small Business Financing was the only
independent variable which significantly influenced the ROA, since the sig.value of
0.000 which was lower than alpha 0.05. This number also showed that Small Business
Financing also significantly influenced the ROA with the alpha of 0.01.
From the above table, we can also see that VIF (variance inflation factor) was
under 5, which means that there is no multicollinearity in this model.
From the scatterplot we can see that the dots in the picture were spread randomly
around number zero. It means that there is no heteroscedasticity in this model.
Model 3
10
ANOVAb
Model
1
Sum of Squares
Regression
df
Mean Square
F
99.844
3
33.281
Residual
397.843
76
5.235
Total
497.688
79
Sig.
.001a
6.358
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Non Performing Loan
From the table of ANOVA, we can see that the predictors which were Small
Business Financing, Other Financing, and Interbank Placement significantly influenced
the dependent variable. We can see form sig. value of 0.001 which was lower than
α=0.05, which means that H0 (Hypothesis null) was rejected.
Model Summaryb
Std. Error of the
Model
R
R Square
.448a
1
Adjusted R Square
.201
Estimate
.169
Durbin-Watson
2.288
1.557
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Non Performing Loan
Standardized
Unstandardized Coefficients
Model
1
(Constant)
B
Std. Error
Coefficients
Collinearity Statistics
Beta
t
Sig.
Tolerance
VIF
2.235
.344
6.502 .000
SmallBusinessFin
-8.959E-8
.000
-.232 -1.789 .078
.626
1.597
OtherFinancing
-3.026E-8
.000
-.287 -1.736 .087
.386
2.591
3.108E-7
.000
.359
2.782
Interbank Placement
.708
4.136 .000
We can see that those independent variables influenced the dependent variables
with the 16.9%, with means that 16.9% variation of dependent variable was explained by
the independent variables and the rest are 83.1% variation of dependent variable are
explained by other factors not included in this model.
11
From the Durbin-Watson score of 1.614 which are in the range of -2 and 2 which
means that there was not any autocorrelation in this model
From the above tabel, it can be seen that Small Business Financing can influence
the NPL with the alpha of 10% but it failed to determine the NPL in the alpha of 5%.
From the above table, we can see that VIF (variance inflation factor) was under 5,
which means that there is no multicollinearity in this model.
From the scatterplot we can see that the dots in the picture were spread randomly around
number zero. It means that there is no heteroscedasticity in this model
CONCLUSION & REMARKS
The average of small business financing from Indonesian Publicly Banks for the
period of 2004-2007 was Rp.12,888 million, and the average of other financing was
Rp.21,137,014.61 million. It means that most publicly banks in Indonesia allocated their
funds in other financing such as corporate financing, consumer financing almost ten
times higher than its allocation for small business financing.
T
The average of interbank placement was higher than that of small business
financing (Rp.3.74 trillion Vs Rp.2.89 trillion). It can be concluded that those banks
would rather invest in interbank placement than invest in small business financing.
From the result of statistical computation, we can conclude that out of the three model,
The model 2 and model 3 were significant. The independent variables which were SBF
(small business financing), OF (other financing), and IBP (interbank placement)
influenced the dependent variables which were ROA (return on assets) and NPL (non
performing loan). Whereas the model 1, with CAR (capital adequacy ratio) as the
12
dependent variable, the independent variables did not significantly influence the
dependent variables.
In partially, Small Business Financing significantly influenced CAR at the alpha
0f 10%, influenced ROA at the alpha of 5%, and influenced NPL at the alpha of 10%.
For the next researcher, it is advisable to add the samples by adding the number of banks,
other variables, and the period to make the result of the research much better.
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Note”. Journal of Banking and Finance, No. 16, Page 1173-1178.
Pyndick, Robert S., and Rubinfeld, Daniel L. “Econometric Models and Economic Forecasts,
Fourth Edition, Mc-Graw-Hill, 1998.
14
Ramaswamy, Kannan, 1997. “The Performance Impact of Strategic Similarity in Horizontal
Merger : Evidence from the US Banking Industry; Academy of Management Journal, Vol.
40, P. 697-715.
Ross, Stephen A, R.W. Westerfield, Jeffrey Jaffe, 1999. “Corporate Finance”, International
Edition, 5th Edition, The Irwin-McGraw Hill, 1999.
Whalen, Gary, 1987. “Concentration and Profitability in Non MSA Banking Market”, Economic
Review.
Wild, John J., Leopold A. B., K. R. Subramanyam, 2001. “Financial Statement Analysis”. 7 th
Edition, The Irwin-McGraw Hill, 2001.
Karen C. Bigler, “The Environment for Partnership of SME and Large Business in Indonesia”.
Prepared and Submitted to USAID/ECG, Indonesia, May 2003.
Robert C. Rice, Herustiati, and A. Junaidi, “Factors Affecting the Competitiveness of, and Impact
of the 1997-99 Monetary Crisis on, Selected Small and Medium Manufacturing
Industries in Central and West Java”, The Event in collaboration between the State
Minister of Cooperative, Small and Medium Enterprises-LPM UGM with financial
support from GOI and USAID- Funded by PEG Project, Januari 2004.
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Assessment Series. Research School of Pasific And Asian Studies.
15
THE 2ND INDONESIA INTERNATIONAL CONFERENCE ON INNOVATION, ENTREPRENEURSHIP, &
SMALL BUSINESS (IICIES 2010), SERPONG, TANGERANG, INDONESIA ON JULY 11-15, 2010,
ORGANIZED BY : SBM ITB & UNIVERSITAS MULTIMEDIA NUSANTARA
Small Business Financing and Bank Performance :
Empirical Study of Indonesian Publicly Banks
Mokhamad Anwar1
ABSTRACT
The aim of this paper is to investigate small businesses financing
in Indonesia. Many programs in financing small businesses have been
undertaken through some collaboration between banks, government, other
related parties.
The study employed descriptive and inferential statistics to explain
the data characteristics of small businesses financing from Indonesian
publicly banks and tried to determine whether those financing had the
association with and influenced the bank performance for the period of
2004-2007. The data were collected from Bank Indonesia and Indonesian
Capital Market.
The result of the study showed that the small business financing
significantly influenced the bank performance in Indonesia especially
within the study period.
Keywords :
Small Business, Financing, Bank Performance
1
Lecturer at the Dept. of Management and Business, Faculty of Economics, The University of Padjadjaran
(FE UNPAD). Researcher at Research Division, Laboratory of Management Faculty of Economics, The
University of Padjadjaran (LMFE UNPAD), Address Correspondence to : anwardade@gmail.com,
mokhamad.anwar@fe.unpad.ac.id.
1
BACKGROUND
Small Business Sector has a significant role in enhancing economic growth in
Indonesia. Their contribution has increased for three decades since the deregulation
package launched by the Government of Indonesia in 1983 and nowadays, Small
Business Sector has been the main actor in Indonesian economy.
Economic crisis has beaten many countries in East Asia since 1997 and Indonesia
got suffered more deeply than any other countries in South East Asia Nations. Some
research have been conducted in Indonesia especially in identifying which one of
business sectors were more stable in facing the crisis, and many results showed that small
business sectors were the most stable one.
The government of Indonesia, particularly Cooperative and Small-Medium
Enterprises Department, has significantly contributed to the development of small
business sectors with many various programs such as Small Medium Enterprises (SMEs)
training and development program, Bank and Financial Institution linkages, Partnership
Programs between Small business and Big Corporation, etc.
In financing side, Commercial Banks have held a very important role in providing
funds for financing Small Business Sectors in Indonesia with some financial schemes
until now. The scheme comprises of two alternative programs, one is financing by their
bank’s own funds and the other is financing by government’s funds. With the
combination between the two alternatives (credit program policy from government and
the internal policy from their own banks) The Banks have given a significant contribution
particularly in developing small business sector and generally developing Indonesia as a
whole since 1983.
Data from BPS (Statistics Indonesia, Republic of Indonesia, 2004) showed that in
2003 the numbers of people who played as Small Medium Enterprises in Indonesia were
42,4 millions of people. The contribution of Small Medium Enterprises to the Gross
Domestic Product was 56.7 percent of the Total GDP of Indonesia in 2003 (with level of
contribution from Small Business Sector was 41.1 percent). The data has also showed
that the numbers of employees who worked at SMEs was 79 millions of people. Those
data indicated that the role of Small Business Sector in development of Indonesia was
great.
On the other hand, commercial banks in Indonesia have been continuously
improving their performance since the government of Indonesia cut the numbers of
banks. As known, there were about 100 banks were closed and merged with the other
banks since 1997 and the merger waves will keep going by maintaining the higher
performance banks and closing or merging the lower performance banks with the higher
ones. Beside that, with the new architecture of banking Industry in Indonesia, Bank
Indonesia as a commercial banks regulator has given some rules to be obeyed by the
commercial banks, including the obligation for each banks management to improve their
performance.
2
In regards to those purposes, the investigation about small business financing
from commercial banks in Indonesia are important to be evaluated and the influence on
banking performance need also be assessed.
The study about the topic will be implemented in a this research report which will
be conducted by the writer with the title of “Small Business Financing and Bank
Performance : Empirical Study of Indonesian Publicly Banks”.
SHORT LITERATURE REVIEW
Some researches have been undertaken by researchers in related to the Small
Business Financing and Banking Performance :
Constantinos Stephanou, Camila Rodriguez (2008) studied to shed light on
current trends and policy challenges in the financing of small- and medium-sized
enterprises (SMEs) by banks in Colombia. Data was collected from the authorities, a
representative sample of banks, and other relevant entities. The result of the research :
Bank financing to SMEs was becoming the current business and risk management models
for SME lending. Important institutional and policy constraints to SME lending remain,
but are not yet binding.
Leora F. Klapper, Virginia Sarria-Allende, Victor Sulla (2002) investigated the
Small- and Medium-Size Enterprise Financing in Eastern Europe. The result showed the
size of the SME sector (as measured by the percentage of total employment) in Eastern
European countries is smaller than in most developed economies. However, SMEs seem
to constitute the most dynamic sector of the Eastern European economies, relative to
large firms. In general, the SME sector comprises relatively younger, more highly
leveraged, and more profitable and faster growing firms. But at the same time, these
firms appear to have financial constraints that impede their access to long-term financing
and ability to grow.
Mohammed N. Alam (2005) investigated by comparative study of financing small
and cottage industries (SCIs) by interest-free banks in different countries like Turkey,
Cyprus, Sudan and Bangladesh. The research result shows that the lender–borrower
network relationship, especially in case of financing rural-based SCIs by interest-free
banks, differ from one country to the other even though the basic principles of interestfree financing remains the same.
Saovanee Chantapong (2005) studied the performance of domestic and foreign
banks in Thailand in terms of profitability and other characteristics after the East Asian
financial crisis. The study is based on a micro bank-level panel data on financial
statements by pooling cross-bank time series data with the major balance sheet and
income statement ratios for domestic and foreign banks in Thailand for 1995–2000. All
banks were found to have reduced their credit exposure during the crisis years, and to
have gradually improved their profitability during the post-crisis years. The results
indicate that foreign bank profitability is higher than the average profitability of the
domestic banks although importantly, in the post-crisis period, the gap between foreign
and domestic profitability become closer. This shows some positive results of the
financial restructuring program.
M. Kabir Hassan in the 12th ERF Conference Paper (2005) investigated relative
efficiency of the Islamic banking industry in the world by employing a panel of banks
3
during 1995-2001. Both parametric (cost and profit efficiency) and nonparametric (data
envelopment analysis) techniques are used to examine efficiency of these banks. Five
DEA efficiency measures such as cost, allocative, technical, pure technical and scale
efficiency scores are calculated and have been correlated with conventional accounting
measures of performance. The results show that, on the average, the Islamic banking
industry is relatively less efficient compared to their conventional counterparts in other
parts of the world. The results also show that all five efficiency measures are highly
correlated with ROA and ROE, suggesting that these efficiency measures can be used
concurrently with conventional accounting ratios in determining Islamic bank
performance.
Fadzlan Sufian (2007) identified the efficiency of Islamic banking industry in
Malaysia, between Foreign vs domestic banks. The results with Data Envelopment
Analysis suggested that Malaysian Islamic banks efficiency declined in year 2002 to
recover slightly in years 2003 and 2004. The domestic Islamic banks were more efficient
compared to the foreign Islamic banks albeit marginally.
Philip Molyneux and John Thornton (1992) identified the Determinants of banks
performance in 18 European Countries for the period of 1986-1989. The result of the
research showed that based on Return on Equity as proxy for bank performance, the
significant determinants were Concentration Ratio, Interest Rate, and Government.
Whereas based on Return on Assets, the significant determinants were Capital Ratio,
Interest Rates, Government and Concentration Ratio.
Llyod-William and Phill Molyneux (1994) investigated the influence of market
structure and market share on the bank profitability in Spain. The significant Predictors of
the bank performance was Concentration Ratio, Capital Assets Ratio, and Assets Size
(positive significant) and owner (negative significant). The Result of the Research
showed that the research supported the Structure-Behavior-Performance which stated that
the more concentrated the banks, the less competitive the banks and finally will enhance
the performance.
Asli Demirguc-Kunt and Harry Huizinga (1998) also studied factors that
determined the bank performance. By using pooled data of 80 countries for the period of
1988-1995, the result of this research showed that the significant predictors were loan to
total assets, customer and short-terms funding to total assets, GDP per capita and Real
Interest.
Balachandher K. Guru, John Staunton and Balashanmugam (1999) explored the
Determinants of Commercial Bank Profitability in Malaysia. By using sample of 17
Commercial Banks for the period of 10 years (1986-1995). The Result of the Research
are : Based on ROA Measure, significant variables were Loan to Total Assets (Assets
Composition), Current Accounts to Total Deposits and Total Expenses to Total Assets. On
the other hand, based on ROE Measure, significant variables were Loan to Total Assets,
Inflation, Market Interest Rate, Total Expenses to Total Assets, Capital to Total Assets,
and Market Growth.
Margarida Abreu and Victor Mendes (2001) investigated the Determinants of 2151 Banks Profitability which was located in 4 countries in Europe (Portugal, Spain,
France and Germany) for the period of 1986-1999. The result of the Research were :
based on ROA Measure, the significant variables were Equity to Assets, Loan to Assets,
Bank Market Share, Inflation Rate and Dummy variables which showed significant in
4
Spain and France. Whereas based on ROE Measure, the significant variables were Equity
to Assets, Bank Market Share, Unemployment Rate, Inflation Rate, and Dummy Variable
which showed significant in Spain.
OBJECTIVES
The above discussion, lead to the following formulation of research objectives:
1. To identify the small business financing in Indonesia, especially in Indonesia
Publicly Banks.
2. To identify and analyze the commercial banks performance especially in
Indonesia Publicly Banks.
3. To identify the Influence of small business financing on commercial banks
performance especially in Indonesia Publicly Banks.
METHODOLOGY
The methodology of this research uses some approaches which are employed to
make this complete such as :
1. Literature Study
The study elaborate the topics of small business financing and banks performance
by finding some practical schemes of small business financing in Indonesia and
some theories about banks performance.
2. Statistical and Econometrics Analysis.
The study will employ statistical and econometrics analysis such as descriptive
and inferential statistics as well as econometrics analysis to test the hypotheses.
For the model, the study will use the multiple regression analysis to investigate
whether independent variables influenced dependent variables in the banks within
the period.
Some variables which are employed are as follows :
Variables
Small
Business
Financing
(SBF)
Other
Financing
(OF)
Inter Banks
Placement
(IBP)
Capital
Adequacy
Ratio (CAR)
Return On
Assets (ROA)
INDEPENDENT VARIABLES
Definition
The financing for small businesses in Commercial
Banks, particularly in Indonesian Publicly Banks.
The banks financing for other than small business.
The financing are for corporate sectors, financing for
consumer needs, etc. The data are the Indonesian
Publicly Banks.
Funds Placement in other banks. This is one of the
some instruments for generating profit beside
financing. The data are the Indonesian Publicly
Banks.
DEPENDENT VARIABLES
The Adequacy measurement of banks capital.
Counted by Capital divided by Risky-Weighted
Assets. The data are the Indonesian Publicly Banks.
The measurement of banks profitability. Counted by
profit divided by total assets. The data are the
Indonesian Publicly Banks.
Variables
Independent Variables
(X1)
Independent Variables
(X2)
Independent Variables
(X3)
Dependent Variables
(Y1)
Dependent Variables
(Y2)
5
Non
Performing
Loan (NPL)
The measurement of Banks Non-Performing Loan.
Counted by Non-performing Loans by Total
Outstanding Loans. The data are the Indonesian
Publicly Banks
Dependent Variables
(Y3)
This research uses the data from the open source or downloadable data from
www.bi.go.id with the Criteria as follows :
The samples are all commercial banks, which were listed in Indonesia Stock
Exchange.
The data were collected in yearly basis for the period of 2004-2007.
After downloading the data from the website, it was known that the data which
were complete are the only 20 banks out of 24 banks, so that the 4 banks were not
included in the research.
RESEARCH RESULTS
Descriptive Statistics
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
SmallBusinessFin
80
12888
34272151
2893128.26
6496055.040
OtherFinancing
80
367271
102514179
21137014.61
2.378E7
Interbank Placement
80
12142
30953865
3738862.66
5714849.420
Capital Adequacy
80
9
37
17.76
5.786
Return on Assets
80
-1
6
1.96
1.208
Non Performing Loan
80
0
16
2.50
2.510
Valid N (listwise)
80
The data above are derived from the financial statement of almost all publicly
banks which were downloaded from Bank Indonesia website (www.bi.go.id) with annual
basis for the period of 2004-2007. The data are 20 commercial banks which were listed in
Indonesia Stock Exchange.
From the above table, we can see that the minimum of small business financing
from Indonesian Publicly Banks for the period was Rp.12,888 million, the maximum of
the financing was Rp. 34,272,151 million, with the average of Rp.2,893,128 million.
On the other hand, other financing which comprises of corporate sector financing
and consumer financing, had the minimum financing of Rp.367,271 million, the
maximum financing of Rp.102,514,179, and the average financing of Rp.21,137,014.61
million.
6
From the above data, we can identify that, most publicly banks in Indonesia
allocated their funds in other financing such as corporate financing, consumer financing
almost ten times higher than its allocation for small business financing. It can be
concluded that most publicly banks in Indonesia still assumed that corporate and
consumer financing are much better than that of small business financing. Most publicly
banks seems to see that the corporate and consumer financing are more secure in terms of
collateral than that of small business financing.
The minimum of interbank placement from those banks for the period was
Rp.12,142 million, the maximum was Rp.30,953,865 million and the average was
Rp.3,738,862.66 Million. We can observe that the average of interbank placement was
higher than that of small business financing (Rp.3.74 trillion Vs Rp.2.89 trillion). It can
be concluded that those banks would rather invest in interbank placement than invest in
small business financing.
Meanwhile, in terms of banks performance, we can see that, with the indicator of
CAR (capital adequacy ratio), those publicly banks had the average CAR of 17,76% for
the period. This number indicate that the capital adequacy of those banks were very good
and in accordance with the basel standard and Indonesian banking standard which stated
that the minimum CAR of each bank are 8%.
The average of ROA (return on assets) of those banks for the period was 1.96%
with the range of -1% to 6%. This number can only be evaluated better or not if we
compare with the banking industry data. Since the data was not available, we would not
state the evaluation result.
The average of NPL (non-performing loan) of those banks for the period was
2.5%. This indicated that the NPL of those banks was very good since the NPL still under
the NPL limit of 5%, which was determined by Bank Indonesia.
Inferential Statistics
In this research, the writer employed three models :
Model 1, using CAR (capital adequacy ratio) as dependent variable, with SBF (small
business financing), OF (other financing), and IBP (interbank placement) as independent
variables.
Model 2, using ROA (return on assets) as dependent variable, with SBF (small business
financing), OF (other financing), and IBP (interbank placement) as independent
variables.
Model 3, using NPL (non performing loan) as dependent variable, with SBF (small
business financing), OF (other financing), and IBP (interbank placement) as independent
variables.
These are the results of the inferential statistics test for those three models :
Model 1
7
ANOVAb
Model
1
Sum of Squares
Regression
df
Mean Square
191.602
3
63.867
Residual
2453.478
76
32.283
Total
2645.080
79
F
Sig.
.124a
1.978
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Capital Adequacy
From the table of ANOVA, we can see that the predictors which were Small
Business Financing, Other Financing, and Interbank Placement not significantly
influenced the dependent variable. We can see form sig. value of 0.124 which was higher
than α=0.05, which means that H0 (Hypothesis null) was accepted.
Model Summaryb
Std. Error of the
Model
1
R
R Square
.269a
Adjusted R Square
.072
Estimate
.036
Durbin-Watson
5.682
.877
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Capital Adequacy
We can see that those independent variables influenced the dependent variables
with the only 3,60%, with means that 96,4% variation of dependent variables was
explained by other factors not included in this model.
From the Durbin-Watson score of 0.877 which are in the range of -2 and 2 which
means that there was not any autocorrelation in this model.
Standardized
Unstandardized Coefficients
Model
B
1
16.836
.853
SmallBusinessFin
-2.113E-7
.000
OtherFinancing
3.231E-8
Interbank Placement
2.271E-7
(Constant)
Std. Error
Coefficients
Beta
Collinearity Statistics
t
Sig.
Tolerance
VIF
19.728
.000
-.237
-1.700
.093
.626
1.597
.000
.133
.747
.458
.386
2.591
.000
.224
1.217
.227
.359
2.782
From the above table, we can conclude that Small Business Financing was not
significant in predicting CAR with the alpha of 5%, but it significantly influenced CAR
with the alpha of 10%.
8
We can also see that VIF (variance inflation factor) was under 5, which means
that there is no multicollinearity in this model.
From the scatterplot we can see that the dots in the picture were spread randomly
around number zero. It means that there is no heteroscedasticity in this model.
Model 2
ANOVAb
Model
1
Sum of Squares
df
Mean Square
Regression
29.932
3
9.977
Residual
85.354
76
1.123
115.286
79
Total
F
Sig.
8.884
.000a
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Return on Assets
From the table of ANOVA, we can see that the predictors which were Small
Business Financing, Other Financing, and Interbank Placement significantly influenced
the dependent variable. We can see form sig. value of 0.000 which was lower than
α=0.05, which means that H0 (Hypothesis null) was rejected.
Model Summaryb
Std. Error of the
Model
1
R
R Square
.510a
Adjusted R Square
.260
.230
Estimate
1.060
Durbin-Watson
1.614
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Return on Assets
9
We can see that those independent variables influenced the dependent variables
with the 23%, with means that 23% variation of dependent variable was explained by the
independent variables and the rest are 77% variation of dependent variable are explained
by other factors not included in this model.
From the Durbin-Watson score of 1.614 which are in the range of -2 and 2 which
means that there was not any autocorrelation in this model
Standardized
Unstandardized Coefficients
Model
1
(Constant)
B
Std. Error
Collinearity Statistics
Beta
1.653
.159
SmallBusinessFin
9.546E-8
.000
OtherFinancing
1.101E-8
-5.276E-8
Interbank Placement
Coefficients
t
Sig.
Tolerance
VIF
10.384
.000
.513
4.116
.000
.626
1.597
.000
.217
1.365
.176
.386
2.591
.000
-.250
-1.516
.134
.359
2.782
We can see from the above table that Small Business Financing was the only
independent variable which significantly influenced the ROA, since the sig.value of
0.000 which was lower than alpha 0.05. This number also showed that Small Business
Financing also significantly influenced the ROA with the alpha of 0.01.
From the above table, we can also see that VIF (variance inflation factor) was
under 5, which means that there is no multicollinearity in this model.
From the scatterplot we can see that the dots in the picture were spread randomly
around number zero. It means that there is no heteroscedasticity in this model.
Model 3
10
ANOVAb
Model
1
Sum of Squares
Regression
df
Mean Square
F
99.844
3
33.281
Residual
397.843
76
5.235
Total
497.688
79
Sig.
.001a
6.358
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Non Performing Loan
From the table of ANOVA, we can see that the predictors which were Small
Business Financing, Other Financing, and Interbank Placement significantly influenced
the dependent variable. We can see form sig. value of 0.001 which was lower than
α=0.05, which means that H0 (Hypothesis null) was rejected.
Model Summaryb
Std. Error of the
Model
R
R Square
.448a
1
Adjusted R Square
.201
Estimate
.169
Durbin-Watson
2.288
1.557
a. Predictors: (Constant), Interbank Placement, SmallBusinessFin, OtherFinancing
b. Dependent Variable: Non Performing Loan
Standardized
Unstandardized Coefficients
Model
1
(Constant)
B
Std. Error
Coefficients
Collinearity Statistics
Beta
t
Sig.
Tolerance
VIF
2.235
.344
6.502 .000
SmallBusinessFin
-8.959E-8
.000
-.232 -1.789 .078
.626
1.597
OtherFinancing
-3.026E-8
.000
-.287 -1.736 .087
.386
2.591
3.108E-7
.000
.359
2.782
Interbank Placement
.708
4.136 .000
We can see that those independent variables influenced the dependent variables
with the 16.9%, with means that 16.9% variation of dependent variable was explained by
the independent variables and the rest are 83.1% variation of dependent variable are
explained by other factors not included in this model.
11
From the Durbin-Watson score of 1.614 which are in the range of -2 and 2 which
means that there was not any autocorrelation in this model
From the above tabel, it can be seen that Small Business Financing can influence
the NPL with the alpha of 10% but it failed to determine the NPL in the alpha of 5%.
From the above table, we can see that VIF (variance inflation factor) was under 5,
which means that there is no multicollinearity in this model.
From the scatterplot we can see that the dots in the picture were spread randomly around
number zero. It means that there is no heteroscedasticity in this model
CONCLUSION & REMARKS
The average of small business financing from Indonesian Publicly Banks for the
period of 2004-2007 was Rp.12,888 million, and the average of other financing was
Rp.21,137,014.61 million. It means that most publicly banks in Indonesia allocated their
funds in other financing such as corporate financing, consumer financing almost ten
times higher than its allocation for small business financing.
T
The average of interbank placement was higher than that of small business
financing (Rp.3.74 trillion Vs Rp.2.89 trillion). It can be concluded that those banks
would rather invest in interbank placement than invest in small business financing.
From the result of statistical computation, we can conclude that out of the three model,
The model 2 and model 3 were significant. The independent variables which were SBF
(small business financing), OF (other financing), and IBP (interbank placement)
influenced the dependent variables which were ROA (return on assets) and NPL (non
performing loan). Whereas the model 1, with CAR (capital adequacy ratio) as the
12
dependent variable, the independent variables did not significantly influence the
dependent variables.
In partially, Small Business Financing significantly influenced CAR at the alpha
0f 10%, influenced ROA at the alpha of 5%, and influenced NPL at the alpha of 10%.
For the next researcher, it is advisable to add the samples by adding the number of banks,
other variables, and the period to make the result of the research much better.
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