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THE INFLUENCE OF SME FUNDING AND NON-PERFORMING FINANCING
ON INDONESIA'S ECONOMIC GROWTH IN THE PERIOD 2015-2022
Fadly Yashari Soumena
1*
, Umaima
2
, Nurwahida
3
, Dwi Resky Yulianti Syam
4
Institut Parahikma Indonesia, Indonesia
1,3,4
Intitut Agama Islam Negeri Parepare, Indonesia
2
Email: fadly.yashar[email protected]
1
, jalanlurus7[email protected]
2
,
nurwahida.yusuf@parahikma.ac.id
3
, dwirezky[email protected]om
4
ABSTRACT
This research aims to analyze the effect of MSME fund disbursement and non-performing financing
(NPF) on Indonesia's economic growth in the 2015-2022 period. The research method used is
quantitative with a descriptive approach. The data used comes from secondary data sources, such as the
financial statements of the Financial Services Authority (OJK) and the Central Bureau of Statistics. The
results showed that partially, the distribution of MSME funds had a positive and significant effect on
Indonesia's economic growth. This shows that financial support provided to MSMEs through the
distribution of funds makes an important contribution in encouraging better economic growth. However,
partially, non-performing financing (NPF) has an insignificant negative effect on economic growth.
Although the NPF problem may affect the financial sector, the results of this research show that its
impact on overall economic growth is not significant. Simultaneously, MSME disbursement and NPF
have a positive and significant influence on Indonesia's economic growth. This result confirms the
importance of good management of MSME disbursement and NPF control in supporting sustainable
economic growth. The implication of this research is that proper allocation of funds to MSMEs can
improve business efficiency and development. In addition, effective handling of the NPF problem can
minimize its negative impact on economic growth. Thus, policies that support good disbursement of
MSME funds and effective NPF control can contribute to stronger and more sustainable economic
growth in Indonesia.
Keywords : Islamic Banking; MSME Funds; NPF; Economic Growth
INTRODUCTION
Economic conditions change from an irrational state to a better state over a period of time
during the process of economic growth. When economic activity is higher than before, an
economy is said to be growing. (Amalia et al., 2021). Economic development is the work to work
on a country's way of life many times estimated by the actual per capita salary. The goal of
economic development is to increase productivity and national income (Hariyanto, 2019).
According to Sukirno (2012) stated in (Mashuri, 2018) the physical expansion of
production, goods, and services of the country shows economic growth. Similarly, the increase in
the production of goods and industries, the enhancement of infrastructure, the growth in the
number of educational institutions, the expansion of production in the humanitarian sector, and
the development of capital goods all contribute to the economic progress of a region. The pace of
economic growth serves as an indicator of local advancement.
The interaction of various groups of variables, such as human resources, natural resources,
capital, technology, and others which are essentially the drivers of economic development of a
nation or region. Indonesia is a country whose main goal of national development is to improve
general welfare (Halim, 2020). There is a positive impact of population growth on economic
development where the conditions and progress of the population are closely related to the growth
and development of economic businesses. Economic development is a way for a country to
improve the standard of living and welfare of its people. Economic development is an ongoing
and strategic process aimed at creating improved conditions compared to previous circumstances.
Economic growth is also due to the increase in society in making goods and services and
increasing the number and factors of production (Firmansyah, 2018).
One sector that can encourage and support the national economy as well as play a very
important role in national development is the banking sector. Banking is one of the institutions
that carry out the function of financial intermediation, namely an establishment that collects
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support as a reserve fund from people in general and then adjusts it back to the local area as credit
or funding (Simatupang, 2019).
According to Saputra (2020) there are several types of banking in Indonesia that are
regulated in financial regulations. Different types of banking can be seen from various angles
such as how banks operate and how they set selling and buying prices. When viewed in terms of
function, commercial banks are banks that carry out conventional business activities and are based
on sharia principles which in their activities provide payment traffic services, the nature of the
services provided is general in the sense that it can provide all banking services and people's credit
banks (BPR) are banks that carry out "Conventional" or "Sharia Principles" business activities
whose implementation does not offer any type of assistance in installment traffic. When compared
to general banking activities, BPR activities are more restricted.
The way of pricing deposit products (current accounts, savings, and deposits) and loan
products (credit) can also be seen in terms of banking operations. Conventional banks use interest
as the price of deposit products (current accounts, savings, and deposits) that are not fully
regulated by certain loan fees in determining costs and making profits for customers. Some other
bank services banks apply various fees with a percentage called fee-based. In contrast to banks
that operate sharia, when determining prices and seeking profits to customers based on sharia
principles, namely financing based on the principle of profit sharing (musyarakah), namely
financing based on the principle of equity participation, murabahah, namely financing based on
the principle of buying and selling goods with profit and ijarah, namely financing capital goods
based on pure rent without options (Saputra, 2020).
Islamic banking products are more varied than products in conventional banks. According
to Islamic banking law number 21 of 2008, one of the functions of Islamic banking is to channel
financing to the wider community. The intermediary function is also a concern for Islamic
banking in terms of its business or business activities (Kholidah, 2018). With this financing
distribution, Islamic bank products can offer more opportunities to meet customer needs.
Theoretically, the main contracts in the profit and loss sharing system are still called
mudharabah and musyarakah financing in Islamic banking financing products. Based on the
possibility of profit and loss, the ratio or profit sharing is planned at the time of the contract
(Agusriansyah, 2018). When running a project implemented by the customer, if there is a loss, it
will be borne together. This can provide opportunities for MSME players to develop their
businesses based on the principle of partnership supported by Islamic banking, it is proven that
MSMEs are the engine of the economy so that their existence cannot be doubted.
MSMEs are able to manage all the factors that influence economic growth. MSMEs are
considered an important sector in driving economic growth. MSMEs have activities that have the
potential to expand employment opportunities, increase economic stability by equalizing and
increasing community income, and contribute to economic growth (Hamza & Agustien, 2019). This
involves MSMEs as the main actors in economic activities so that the growth of MSMEs can
drive economic growth. The Indonesian economy has benefited greatly from MSMEs, as has been
shown (Jannah, 2022).
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Chart 1
Graph of MSME contribution to GDP in 2010-2020
(Source: BPS Indonesia 2023)
Based on Chart 1 above data from ministry of cooperatives and MSMEs (Peraturan Menteri
Koperasi Dan Usaha Kecil Dan Menengah Nomor 5 Tahun 2020 Tentang Rencana Strategis
Kementerian Koperasi Dan Usaha Kecil Dan Menengah Tahun 2020-2024, 2020). Since 2018 the
MSME sector has contributed 61.07% to the Gross Domestic Product (GDP). Meanwhile, in
2020, MSMEs have a major contribution to GDP, namely 61.97% of the total national GDP or
comparable to Rp. 8,573.89 trillion. In addition, in 2020 MSMEs also absorbed a workforce of
97% of the capacity of the business world. This shows that MSMEs play an important role in
national economic growth. MSMEs also play a role in equitable distribution of development
results, in addition to contributing to employment and economic growth. MSMEs have also
demonstrated that they are significant contributors to GDP growth and job creation.
The progress of MSMEs in the midst of globalization and high competition requires
MSMEs to be able to face global challenges. These improvements include product improvement,
administrative progress, expanding marketing areas and creating human resources and innovation.
The main problem faced by MSMEs is capital. The existence of easy access to capital can create
the success of MSME development in a country. Supporting the financing aspect is one way to
overcome these problems and increase the number of MSME actors (Sedyastuti, 2018). One area
that is anticipated to make a significant contribution to the financing aspect is the existence of
Islamic banking.
In connection with the function of Islamic banks as intermediary institutions in relation to
the distribution of public funds or financing facilities based on sharia principles, credit risk or
financing risk will be borne by Islamic banks. As stated in the Law Article 37 paragraph 1 "The
distribution of funds by Islamic banks and Islamic business units based on sharia principles
contains the risk of failure or congestion in its payment so that it can affect the health of the
Islamic financial institution". Financing risk for Islamic banks arises when the quality of the
financing is categorized as group III (substandard), group IV doubtful (doubtful) and group V
(bad debt) or in practice it is called nonperforming financing (NPF) which means that there is a
risk arising from customers who are unable or have not been able to pay the principal financing
or pay the compensation agreed by the customer in the financing agreement (Ubaidillah, 2018).
RESEARCH METHOD
This research is a type of quantitative research that uses a positive philosophy approach.
The purpose of this research is to investigate selected populations and samples using data
collection tools in the form of numbers. The data is then analyzed using statistical formulas related
to operational variables and certain scales, such as nominal, interval, ordinal, and ratio scales
(Sugiyono, 2018, 2019). The data used in this research comes from the Financial Services
Authority (OJK) which includes the distribution of MSME funds and non-performing financing
(NPF), as well as economic growth obtained from the Central Statistics Agency (BPS). This
research was conducted at Islamic Commercial Banks and Islamic Business Units registered with
OJK, as well as gross domestic product (GDP) data recorded at BPS. Relevant secondary data
were collected through the official websites of the relevant entities. The research implementation
time lasted from June 25, 2023 to July 25, 2023. The method used in this research is a secondary
data collection method from reports of Islamic Commercial Banks, Islamic Business Units
registered with OJK, and gross domestic product (GDP) data registered with BPS. This research
approach and method is used to describe or explain research findings, not to reach conclusions
(Hanifah & Anwar, 2020).
This research focuses on the population of Islamic Commercial Banks and Islamic
Business Units registered with the Financial Services Authority (OJK), as well as gross domestic
product recorded at the Central Statistics Agency (BPS). Sampling was done using the saturated
sampling method, where all members of the population were sampled. A total of 32 samples were
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taken from quarterly time series data during the research period, including MSME fund
disbursement and NPF from OJK, as well as economic growth from BPS. In collecting data, this
research uses the observation method, documentation method, and descriptive method. The
observation method is used to make direct observations of the research object in the field. The
documentation method is used to obtain data and information through documents such as reports,
archives, and information published on the official website of Islamic banks registered with OJK.
The descriptive method is used in the research approach to explain and describe the research
findings without the intention to conclude. For example, relevant data was obtained through
statistical analysis reports published on the official websites of Islamic banks registered with OJK.
This research uses documentation instruments to collect data, namely by recording,
collecting, and assessing secondary data from quarterly financial reports of Islamic Commercial
Banks and Islamic Business Units registered with the Financial Services Authority (OJK), as well
as Gross Domestic Product (GDP) data registered with the Central Statistics Agency (BPS) for
the period 2015-2022. The data processing technique used in this research is multiple linear
regression analysis using the Statistical Product and Service Solution (SPSS) application. The
data analysis techniques carried out in this research are as follows:
1. Descriptive Statistics: Performed to collect, present, and organize data in order to provide a
clear picture of the variations in the nature of the data.
2. Classical Assumption Test:
a. Data Normality Test: Used to determine whether the data obtained has a normal
distribution. In this research, the normality test was carried out using the Kolmogorov-
Smirnov test, histogram test, and normal probability plot test. If the significance value is
greater than 0.05, then the data is considered normally distributed.
b. Multicollinearity Test: Conducted to check the existence of interrelationships between
independent variables in the regression model. This test uses the tolerance method with
VIF (variable inflation factor). If the VIF value < 10 and the tolerance value > 0.1, it can
be concluded that there is no multicollinearity.
c. Heteroscedasticity Test: Used to check for differences in residual variation between
observations. The scatterplot test is used to see if there is a pattern or dispersion that
indicates heteroscedasticity.
d. Autocorrelation Test: Used to check for correlation between residual errors in the previous
period in the regression model. The Durbin-Watson (D-W) test is used with D-W values
below -2 indicating positive autocorrelation, D-W values between -2 and +2 indicating no
autocorrelation, and D-W values above +2 indicating negative autocorrelation.
e. Multiple Linear Regression Model: Used to measure the effect of independent variables
(MSME fund disbursement and non-performing financing) on the dependent variable
(economic growth). The multiple linear regression model used in this research is as
follows:
Y = α + β1X1 + β2X2 + ε
Description:
Y = Economic Growth
α = Constant
X1 = MSME Fund Disbursement
X2 = Non-Performing Financing
β1 = Regression Coefficient of MSME Fund Distribution
β2 = Regression Coefficient of Non-Performing Financing
ε = Confounding Variable
4. Hypothesis Test:
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170 Return: Research of Management Economic And Business, Vol 3 (2), February 2024
a. T-Test (Partial): Used to determine the effect of independent variables partially on the
dependent variable. The test decision is made by comparing the t value with the t table. If t
count> t table, then the null hypothesis is rejected and the alternative hypothesis is accepted.
b. F-test (Simultaneous): Used to test the significance of the regression model simultaneously.
The test decision is made by comparing the value of f count with f table. If f count> f table
and significance value <0.05, then the null hypothesis is rejected and the alternative
hypothesis is accepted.
c. Coefficient of Determination Analysis (R2): Used to measure how successful the regression
model is in explaining variations in the dependent variable. The R^2 value ranges between
0 and 1, and the higher the value, the better the regression model is in explaining the
variation in the data.
RESULT AND DISCUSSION
Descriptive Statistics
1. MSME Fund Disbursement
Chart 2
Quarterly financial statements of the Financial Services Authority (OJK) variable MSME fund
distribution
Source: Financial Services Authority (2022)
Based on Figure 4.1 above, the financial condition of the Financial Services Authority
(MSME Fund Disbursement) from 2015 to 2022 with the maximum value occurring in the fourth
quarter of 2022 amounting to 47,036, while the lowest number occurred in the third quarter of
2015 amounting to 31,359.
2. Non Performing Financing (NPF)
Chart 3
Quarterly financial statements of the Financial Services Authority (OJK) Non-Performing Financing
(NPF) variable
0
20000
40000
60000
2018 2019 2020 2021 2022
(in thousands rupiah)
I
II
III
IV
0
2000
4000
2015 2016 2017 2018 2019 2020 2021 2022
(in thousands rupiah)
I II III IV
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Source: Financial Services Authority (2022)
Based on Chart 3 above, nonperforming financing (NPF) contained in the Financial
Services Authority from 2015 to 2022 with a maximum value of 3,351, namely in the first quarter
of 2020 while the minimum value occurred in the second quarter of 2017, namely 1905.
3. Economic Growth
Chart 4
Quarterly financial reports of the Central Bureau of Statistics (BPS) Economic Growth variables
Source: Central Bureau of Statistics (2022)
Based on Chart 4 above, economic growth from 2015 to 2022 experienced fluctuations
with the maximum value occurring in 2022 in the fourth quarter of 5114910.6, while the minimum
value occurred in 2015 in the first quarter of 2728180.7. The distribution of descriptive statistics
that have been processed using SPSS can be explained in the following table:
Table 1 Descriptive Analysis Results
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
MSME fund
disbursement
Non-performing
financing
Economic Growth
Valid N (listwise)
32
32
32
32
31.359
1.905
2728180.70
47.036
3.351
5114910.60
39.56128
2.59766
3756180.9313
4.825446
.
431491
630828.62974
Source: spss 20 output results (2023)
Based on the table above, it can be explained as follows:
a. MSME Fund Distribution shows a minimum value of 31,359, a maximum value of 47,036
and an average value of 39,56128, while the standard deviation of MSME fund distribution
is 4,825446.
b. Non-performing financing shows a minimum value of 1,905, a maximum value of 3,351 and
an average value of 2,59766, while the standard deviation of non-performing financing is
.431491.
c. Economic growth can be seen that the minimum value is 2728180.70, the maximum value
is 5114910.60 and the average value is 3756180.9313, while the standard deviation is
630828.62974.
0
2000000
4000000
6000000
2015 2016 2017 2018 2019 2020 2021 2022
(in million rupiah)
I II III IV
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Classical Assumption Test
1. Data Normalization Test
Chart 5
Histogram and Normal P-P Plot Test Results
The graph above shows that the histogram pattern follows a normal hill-shaped curve. It
can be concluded that the regression model in this research is normally distributed. In addition,
that the points spread parallel to following the diagonal line, it can be concluded that the
regression model in this research is normally distributed. The results of the data normality test are
intended if the significance value <0.05 then the data is not normally distributed and if the
significance value> 0.05 then the data is normally distributed.
Table 2 Results of the Kolmogorov-Smirnov Normality Test
One-Sample Kolmogrov-Smirnov Test
Unstandardized
Residual
N
Normal Parameters
a,b
Most Extreme Differences
Kolmogrov-Smirnov Z
Asymp. Sig. (2-tailed)
Mean
Std. Deviation
Absolute
Positive
Negative
32
0E-7
262859.99642621
0,081
0,081
-0,073
0,460
0,984
a. Test distribution is normal.
b. Calculated from data.
Looking at table 2 above shows One Sample Kolmogorov Smirnov obtained from the
Asym.Sig value. (2-tailed) of 0.984 which means greater than 0.05 (sig or α = 5%). The residual
data can be concluded to be normally distributed because it meets the requirements for a sig value
greater than 0.05.
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2. Multicollinearity Test
If no relationship is found between the variables, then the regression mode is declared
correct. VIF value < 10 and tolerance value > 0.1, then it can be determined that there is no
multicollinearity (Marranitha & Suardana, 2020).
Table 3 Multicollinearity Test Results
Coefficients
a
Collinearity Statistics
Model
Tolerance
VIF
1
(Constans)
1.863
1.863
MSME Fund Disbursement
0,537
Non-performing financing (NPF)
0,537
a. Dependent Variable: Economic Growth
Source: spss 20 output results (2023)
Based on table 3 above, it shows that the distribution of MSME funds obtained a VIF value
of 1,863 < 10 and a tolerance value of 0.537 > 0.1 and for the nonperforming financing (NPF)
variable with a VIF value of 1,863 < 10 and a tolerance value of 0.537 > 0.1. So, from these
results it states that the independent variables do not have multicollinearity symptoms seen from
the results of VIF < 10 and tolerance > 0.1 for each variable.
3. Heteroscedasticity Test
This test is carried out to determine the relationship between values, predicted values and
residual studentized delete values (Ghozali, 2018).
Figure 1 Heteroscedasticity test results
Source: spss 20 output results (2023)
Based on the scatterplot image above, it shows that the points spread randomly not at and
below the number 0 on the Y axis and do not form a clear pattern. This shows that there is no
heteroscedasticity, which means that the regression model can be used to predict the increase in
economic growth on the independent variable.
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4. Autocorrelation Test
A well-specified model means a regression without autocorrelation. This test is evaluated
in terms of value:
Table 4 Autocorrelation Test Results
Model Summary
b
Model
R
R Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-
Watson
1
0,983
a
0,967
0,963
113400,50826
1,799
a. Predictors: (Constant), Non-Performing Financing (NPF), MSME Fund
Disbursement
b. Dependent Variable: Economic Growth
Source: spss 20 output results (2023)
The results of table 4 above show that the calculated DW value is 1.799. if seen from the
DW table with a significance of 0.05 obtained from n = 32, k = 2 with the DW equation < (4-DU)
obtained DU = 1.5736 and 4-DU (4-1.5736) is worth . So, (4-DW) > DU < DW = 2.4264 > 1.5736
< 1.799 so it is said that the regression model does not experience autocorrelation symptoms.
Multiple Linear Regression Model
Table 5 Multiple Linear Regression Test Results
Coefficients
a
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
MSME Fund
Disbursement
Non-performing
financing (NPF)
-766909,212
139448,962
-382533,093
404121,262
13807,196
154408,410
1,067
-0,262
-1,898
10,100
-2,477
0,068
0,000
0,019
a. Dependent Variable: Economic Growth
Source: spss 20 output results (2023)
Based on table 5 above, it can be seen that the constant value (α value/economic growth)
is -766909.212 and for the distribution of MSME funds (β1 value) is 139448.962 while (β2 value)
for nonperforming financing is -382533.093. so that the multiple linear regression equation
model can be obtained as follows:
Y= -766909.212 + 139448.962 X
1 -
-382533.093 + ε.
The results of multiple linear regression analysis show that the distribution of MSME
funds and non-performing financing have a significant influence on economic growth. The
constant value of -766909.212 indicates that if the two independent variables are at the value of
0, economic growth remains -766909.212. The coefficient of MSME fund disbursement
139448.962 shows that every 1% increase in MSME fund disbursement will increase economic
growth by 139448.962, and vice versa. The coefficient of non-performing financing -382533.093
shows that every 1% increase in non-performing financing will decrease economic growth by -
382533.093, and vice versa. Thus, higher disbursement of MSME funds and effective control of
non-performing financing can contribute positively to economic growth.
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Hypothesis Test
1. T Test (Partial)
Table 6 T test results (partial)
Coefficients
a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
-766909,212
139448,962
-382533,093
404121,26
2
13807,196
154408,41
0
1,067
-0,262
-
1,89
8
5,87
5
-
2,47
7
0,068
0,000
0,019
MSME Fund
Disbursement
Non-performing
financing (NPF)
a. Dependent Variable: Economic Growth
Source: spss 20 output results (2023)
Based on table 6 above, to determine the value of t
tabel
indicated by the formula df = n-k-1
(df = 32-2-1), the value of df = 29 with a value of t
tabel
1.699. The regression analysis shows that
the distribution of MSME funds (X1) has a positive and significant influence on economic growth
(Y), with a calculated t value greater than the t table and a significance smaller than the specified
significance level. In contrast, non-performing financing (X2) has a negative but insignificant
effect on economic growth, as indicated by a t value that is smaller than the t table and a
significance that is greater than the specified significance level. Thus, it can be concluded that
MSME disbursement has a positive and significant impact on economic growth, while non-
performing financing has no significant impact.
2. F Test (Simultaneous)
Table 7 F Test Results (simultaneous)
ANNOVA
a
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
Residuals
Total
10083376911139,980
347211232391,096
10430588143531,076
3
27
31
3361125637046,660
12859675273,744
261,369
0,000
b
a. Dependent Variable: Economic Growth
b. Predictors: (Constant), Non-performing financing, distribution of MSME funds
Source: spss 20 output results (2023)
Based on the value of
fcount
according to table 4.6 above is 261.369 and the significance
value is 0.000 to find the value of f
tabel
can be found through the statistical table with a
significance level of 0.05 using the formula dfl = k-1 meaning dfl = (3-1) so 2 and df2 = n-k-1
meaning df2 = 32-2-1 so 29. This is the value of f
tabel
3.328 so it can be concluded based on the
decision making
fhitung
261.369 > f
tabel
3.328 with a significance level of 0.000 <0.05, then the
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distribution of MSME funds and non perfoming financing has an effect and is significant on
economic growth.
3. Coefficient of Determination
The coefficient of determination (R
2
) is used in order to calculate and estimate how far the
simultaneous influence of the independent variables on the dependent variable in this research,
the coefficient of determination uses the adjusted R square value using the formula KD = R
2
x
100%.
Table 8 Test Results of the Coefficient of Determination R
2
Model Summary
b
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
0,983
a
0,967
0,963
113400,50826
a. Predictors: (Constant), Non-performing financing, distribution of MSME funds
b. Dependent Variable: Economic Growth
Source: spss 20 output results (2023)
The table of R analysis test results
2
above, shows that the coefficient of determination is
0.963 so that the dependent variable of economic growth is 96.3% influenced by the independent
variables while the remaining 3.7% is influenced by variables outside the variables used in this
research.
Discussion
The distribution of MSME funds affects economic growth.
The channeling of funds to Micro, Small, and Medium Enterprises (MSMEs) has a positive
and significant impact on economic growth. The MSME industry has long-term growth potential,
and proper allocation of funds to MSMEs can accelerate the development of this industry. The
disbursement of funds to MSMEs enables the adoption of new technologies, improved quality of
products and services, increased production capacity, and improved infrastructure. Financial,
technical, and training support to MSMEs can improve productivity, quality, and profits for
MSME owners and employees. Public authorities and financial institutions need to provide
adequate attention and support to increase commitment to financial development and improve
overall community welfare.
According to Schumpeter's theory, financial institutions can support Micro, Small, and
Medium Enterprises (MSMEs) by providing various financial instruments such as business loans,
investment loans, or special financing plans that encourage MSME innovation and growth.
Special funding programs and business incubators also provide financial support as well as
guidance, training, and mentoring to promote MSME business growth and innovation. In the
Solow-Swan theory, the direction of asset channeling to MSMEs can increase capital pooling in
the economy. MSMEs have the potential to take investment and use it for business development,
increasing production capacity, and improving operational efficiency. Higher capital channeling
to the MSME sector can encourage long-term financial development. In addition, funding
allocated to MSMEs can encourage technological innovation, productivity improvements, and
efficiency, contributing to long-term economic growth. MSMEs also play an important role in
employment growth, where funds obtained by MSMEs can be used to provide training and
financial assistance, thereby increasing employment opportunities and overall national income.
Non-performing financing affects economic growth
The results show that although non-performing financing (NPF) has a negative influence
on economic growth, the impact is not statistically significant. Internal factors such as insufficient
supervision, inadequate financial evaluation, and inappropriate working capital can affect NPF,
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while external factors such as business market saturation and natural disasters also contribute to
the condition. A high NPF rate affects investors' and lenders' confidence in the financial sector
and the economy as a whole, resulting in lenders being more cautious in granting new loans. In
addition, a high NPF rate may also indicate sluggish economic growth or unstable economic
conditions. Although the negative effect of NPF on economic growth has been proven, the lack
of statistical significance in this research could be due to other factors such as global conditions,
political stability, and government policies that also affect overall economic growth.
This research is also supported by previous researchers related to existing problems,
namely research conducted by Nikmah Safriani in the year (2022) conducted by Helly Avoza
Siregar and Febdwi Suryani in the year (2022) entitled "The effect of Islamic banking financial
performance on MSME productivity and its impact on Indonesia's economic growth". The method
used is purposive sampling. The data used is secondary data obtained from Islamic banking
statistical data by OJK. The results showed that all Islamic banking financial performance, namely
CAR, ROE, NPF, FDR and BOPO, had no effect on economic growth in Indonesia through the
role of MSME productivity.
MSME fund disbursement and nonperforming financing on economic growth
In the simultaneous test results, it can be seen that the distribution of MSME funds and
non-performing financing (NPF) simultaneously have a positive and significant effect on
economic growth. The expansion of MSME disbursement has a positive impact on economic
growth, as well as changes in NPF indicators that can affect economic growth. Signal theory
supports this research, where MSMEs can use the right signals to increase their chances of getting
financing, overcoming information and resource access difficulties. In addition, signal theory is
also related to NPF, where financial institutions can receive signals of high credit risk through
NPF, which indicates loans that cannot be repaid as agreed.
In economic growth, the role of Islamic banking is very important to fulfill several
components supporting economic growth. Increasing the supply of goods continuously is a
component needed for economic growth. In increasing the continuous supply of goods, Islamic
banking can work with MSMEs to partner and increase the supply of goods on an ongoing basis.
For example, Islamic banks can assist MSMEs in distinguishing providers who can provide
substances or raw goods at a low price. MSMEs can also collaborate with Islamic banks to
improve their competitiveness by pooling inventory or collaborating on procurement.
In supporting economic growth, it is important for MSMEs to utilize technology and
innovation supported by Islamic banking. Islamic banks can advise MSMEs on the appropriate
use of technology to improve business productivity and efficiency, and encourage innovation
among MSMEs. By utilizing innovation and technology, MSMEs can expand their markets
through online platforms, establish international business partnerships, and provide international
payment options. In addition, to encourage economic expansion, factors such as human resources,
natural resources, capital, and technology must also be considered. Islamic banking human
resources need to continuously develop products and services that are relevant to MSMEs and
have a deep understanding of the needs and challenges faced by MSMEs. They must also have
strong risk management capabilities to reduce the risk of non-performing financing and ensure
the sustainability of MSME growth, and take important steps to limit default gambling from
MSMEs.
It is important to remember that human resource development (HRD) in Islamic banking
must keep up with industry developments and improve skills through continuous training and
development. By having qualified and skilled human resources, Islamic banking can effectively
support the economic growth of MSMEs and manage the risk of non-performing financing wel
(Irma, 2019). Economic growth is also influenced by the availability of natural resources.
Sustainable utilization of natural resources and economic diversification are important keys in
improving a country's economy.
Islamic banking can support this expansion by providing financing to non-traditional
sectors that have development potential, such as the assembly industry, administration, tourism,
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and creative sectors. Traditional asset-based financing, such as mining, agriculture, plantations,
renewable energy, is also a focus for Islamic banking in supporting MSMEs and managing NPF
risk. It is important to ensure that the management of natural resources is done responsibly and
sustainably. Islamic banking can encourage MSMEs and NPFs to adopt environmentally friendly
approaches and support ecosystem sustainability.
Islamic banking plays an important role in economic growth by providing financial access
to MSMEs in accordance with sharia principles. Such support includes funding product
development, business expansion, working capital, speculation, and trade. Islamic banking also
helps reduce financial disparities and create employment opportunities. However, supervision is
needed to reduce the risk of non-performing financing. Shariah principles are the cornerstone in
resolving non-performing financing, with fair transactions and arrangements. Islamic banking
must adhere to sharia principles and sound banking practices to maintain stability and sustainable
economic growth.
Technological advances have had a positive impact on the management of Non-Performing
Financing (NPF) and MSME access in Islamic banking. Islamic banking innovations through
applications and digitalization solutions have strengthened banking processes. Computerized
platforms and streamlined frameworks ease interaction with supporting applications. This helps
MSMEs in getting support for business development. In addition, technological innovations have
also improved NPF screening tools, enabling more accurate risk identification and appropriate
preventive measures. Research shows that Islamic bank financing of MSMEs plays an important
role in boosting the real sector and national economic growth. Overall, technological innovation
contributes positively to NPF management and MSME access in Islamic banking.
CONCLUSION
The partial hypothesis test results show that the distribution of MSME funds has a positive
and significant effect on Indonesia's economic growth. The higher the disbursement of MSME
funds, the more economic growth tends to increase. This shows that financial support provided to
MSMEs through disbursement of funds can be an important factor in encouraging better economic
growth. On the other hand, the non-performing financing variable has a negative but insignificant
effect on economic growth. This suggests that non-performing financing does not have a
significant impact on overall economic growth. Simultaneously, both variables, namely
disbursement of MSME funds and non-performing financing, have a significant effect on
economic growth. This confirms the importance of good management of MSME disbursement
and non-performing financing control in supporting sustainable economic growth. The rise and
fall of MSME disbursement and control of non-performing financing will have implications for
overall economic growth, so attention to these two factors is very important in an effort to
strengthen Indonesia's economic growth.
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