Vol. 6, No. 1, January 2025
E-ISSN: 2723 - 6692
P-ISSN: 2723 - 6595
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Jurnal Indonesia Sosial Sains, Vol. 6, No. 1, January 2025 82
KEYWORDS
ABSTRACT
Macroeconomic Indicators;
Non-Performing Financing;
Islamic Banking; Mudharabah;
Musyarakah; Murabahah
This research aims to analyze the influence of macroeconomic indicators
on non-performing financing in Islamic banking with Mudharabah,
Musyarakah, and Murabahah contracts. This research uses a quantitative
approach with the VAR/VECM method. The research data covers the
years 2015 - 2018 and includes all Islamic banks in Indonesia. The
research results indicate that the macroeconomic indicators that
influence and determine non-performing financing, based on Impulse
Response Function (IRF) and Forecast Error Variance Decomposition
(FEVD) tests, are as follows: for Mudharabah financing, Inflation,
Industrial Production Index, and Exchange Rate according to IRF, and
Inflation and Exchange Rate according to FEVD; for Musyarakah
financing, Money Supply according to IRF, and Inflation according to
FEVD; and for Murabahah financing, Inflation and Money Supply
according to IRF, and Industrial Production Index, Exchange Rate, and
Money Supply according to FEVD.
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Introduction
Islamic banking in its activities provides services in payment traffic (Law No. 21 of 2008)
(Pemerintah Pusat, 2008). Islamic banks function to raise public funds, both in the form of deposits and
loans where each product has been reviewed and supervised by the Sharia Supervisory Board (DPS) so as
not to deviate from sharia principles.
The distribution of mudharabah financing contracts from 2015 to 2017 has increased by an average of 4.29%
with a financing distribution of IDR 630 billion. However, from 2018 to 2021, the distribution of financing
experienced an average negative growth of 12.22% with a nominal value of IDR 1,632 trillion. There are
several things that cause the distribution of mudharabah contract financing to decline, including:
First, the decline in Indonesia's economic growth in 2018-2022 from 5.2% (2018) to 5% (2019), 4.1%
(2020), and 3.69% (2021). This decline in economic growth has an impact on the decline in people's
purchasing power and demand for credit. Second, the increase in Bank Indonesia's benchmark interest rate
in 2018-2022, from 4.75% in 2018 to 5.25% in 2019, 6% in 2020, and 6.25% in 2021 (Priyadi et al., 2021).
The increase in interest rates is one of the causes of Islamic banking financing margins increasing, so that
Islamic banks become more conservative in lending. Third, government policies that have an impact on the
decline in Islamic bank lending, such as the banking credit relaxation policy and the policy of postponing
credit installments. These policies make Islamic banks prefer to extend credit to debtors who have low risk,
so that lending to debtors who have high risk is reduced. Fourth, competition with conventional banks that
The Influence of Macroeconomic Indicators on Non-Performing Financing
in Islamic Banking Using Mudharabah, Musharakah, and Murabahah
Contracts
Fandi Achmad Syarif
1*
, Rifki Ismail
2
, Zulkarnain Muhammad Ali
3
Institut Agama Islam Tazkia, Indonesia
Email: fandiachmadsyarif@gmail.com
Correspondence: fandiachmadsyarif@gmail.com
*
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are increasingly aggressive in offering credit to customers, so that Islamic banks become less competitive.
Islamic banking needs to offer lower margins and easier terms, even though the profit margins of Islamic
banks are reduced. Fifth, the development of financial technology (fintech) has made it easier for people to
get access to loans, making Islamic banks less attractive. Fintech offers loans with a faster, easier, and
cheaper process, making it difficult for Islamic banks to compete. After the decline in financing caused by
several things that have been explained above, in 2022 the distribution of financing with the Mudahrabah
contract increased again by 1.13% with a nominal distribution of Rp 107 billion.
The distribution of musyarakah financing contracts from 2015 to 2022 has a growth trend every year
which is always positive where during the eight-year period the average growth is 21.22% with a nominal
value of Rp 20,642 trillion. the performance of the distribution of murabaha financing contracts in Indonesia
is always positive from 2015 - 2022, this can be seen from the average growth of 9.80% with a nominal
value that has been distributed to the public amounting to Rp 15,177 trillion.
Non-performing financing in Islamic banking is also inseparable from the influence of
macroeconomic variables, because macroeconomic variables can affect overall economic conditions.
Unstable economic conditions can make it difficult for customers to repay their financing to Islamic banks.
Macroeconomic variables that can affect problematic financing include:
The Industrial Production Index (IPI) is one of the indicators used to measure the performance of the
manufacturing industry. A high IPI indicates that the manufacturing industry is experiencing good growth.
This can increase people's purchasing power and increase demand for goods and services. This can increase
the risk of non-performing financing in Islamic banks because customers who have businesses in
manufacturing will be more likely to apply for loans to Islamic banks to develop their businesses. However,
if the IPI decreases, it can lead to a decrease in people's purchasing power and demand for goods and
services. This can cause customers who have businesses in manufacturing to be unable to repay their loans
to Islamic banks.
Inflation can also make people's purchasing power decrease. This can lead to customers being unable
to repay their loans to Islamic banks, even if they have the ability to do so. Exchange rate is the price of one
country's currency expressed in another country's currency. A low exchange rate can make goods and
services from other countries cheaper in that country. This can increase demand for goods and services from
other countries, and can reduce demand for goods and services from that country. In addition, this can cause
customers who have businesses in trade to be unable to repay their loans to Islamic banks.
Money supply is the amount of money circulating in the economy. A high money supply can cause
high inflation, which can make the value of customer loans higher, so that it can make it difficult for
customers to repay their loans to Islamic banks. In addition, a high money supply can lead to high economic
growth, which can make customers more consumptive, thus increasing the risk of non-performing financing
at Islamic banks.
For Musyarakah and Murabahah financing based on the data above, it can be seen that the distribution
of financing carried out by Islamic banking is increasing every year, but the increase in distribution should
not be followed by an increase in non-performing financing. The increase in non-performing financing in
Islamic banking financing using the Murabahah and Musayarakah contracts shows that Islamic banking risk
management has not anticipated the potential risks that arise.
This thesis focuses on examining the influence of macroeconomic indicators on the problematic
financing of Islamic banking with Mudharabah, Musyarakah and Murabahah contracts. Where the
macroeconomic indicators used are the Industrial Production Index (IPI), inflation, exchange rates and
money supply.
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Research Methods
This research design will use quantitative methods to analyze the effect of macroeconomic indicators
on Non Performing Financing of Murabahah, Mudharabah and Musyarakah Financing in Islamic banking
in Indonesia.
The data used in this study is entirely secondary data which the authors obtained through the official
website of each agency concerned, namely Bank Indonesia, the Indonesian Financial Services Authority
and the Central Bureau of Statistics. The data used is monthly or time series data from January 2015 to
December 2022 which is a combination of Islamic Commercial Banks (BUS) and Islamic Business Units
(UUS).
The analysis method used in this study is Vector Autoregression (VAR), a non-structural approach
developed by Sims (1980). VAR assumes that all variables in the system are endogenous, making it an a-
theoretical model, often used when economic theory alone cannot fully explain variable relationships
(Ascarya, 2009).
Model Testing Steps
1. Stationarity Test
Using the ADF (Augmented Dickey-Fuller) test with a real level of 5% (α=5%). If the ADF t-statistic
probability < 5%, the data is stationary; if > 5%, the data is not stationary.
a. Stationary data at level: use VAR method.
b. Data is not stationary at level: use VAR difference or VECM.
2. Correlation Test If the residual correlation between variables is >0.2 at more than 50%, a causality test
or variable order specification is required.
3. Optimum Lag Determination Test
Determine the optimum lag length using LR, FPE, AIC, SC, or HQ criteria. The model is selected
based on the smallest criterion value.
4. Cointegration Test
Performed on level data with the trace method. If the trace statistic value > critical value, there is
cointegration.
5. VAR Model Stability Test
The stability of the model is seen from the modulus value of the inverse roots AR polynomial. If the
modulus < 1, the model is stable.
6. Impulse Response Function (IRF)
Analyze the response of variables to shocks from other variables in the short and long term.
7. Forecast Error Variance Decomposition (FEVD)
Estimating the contribution of variables to changes in other variables. In this study, it is used to see the
effect of macroeconomic variables on NPF in Islamic banking in Indonesia.
Results and Discussion
Stationarity Test Results
Table 1 shows the results of the Stationarity Test of Islamic Banking Problem Financing Research
Data with Mudharabah, Musyarakah and Murabahah Akad.
Table 1. Stationarity test results
Variable
Argumented Dicky Fuller (ADF)
MCKinnon Critical Value 5%
First Difference
MUD
-3.458326
MUS
-3.45995
MUR
-3.460516
INF
-3.458326
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IPI
-3.458856
NT
-3.458326
M2
-3.458326
Ket:* bold means stationary at 5% real level (attachment)
The stationary test results show that four variables are not stationary at the 5% real level (MUD: -
2.048087, MUS: -2.851454, INF: -0.624084, M2: -2.042927). The data must be transformed to first
derivative form. If cointegrated, the VECM (Vector Eror Corection Model) method is used for short and
long run analysis. Otherwise, First Difference VAR is used for short-term analysis.
Correlation Test Results
The results of the correlation test for the research model of Conflict Financing in Islamic Banking
with Mudharabah, Musyarakah and Murabahah Agreements can be seen in table 2 where the results of the
majority of correlation values in the system are below 0.2.
Table 2. Correlation test results
MUD
MUS
MUR
INF
IPI
NT
M2
MUD
1.000000
MUS
-0.007622
1.000000
MUR
-0.031917
0.806571
1.000000
INF
-0.077206
0.4693
-0.500179
1.000000
IPI
0.077692
-0.60948
-0.517135
-0.261513
1.000000
NT
-0.074119
-0.663964
-0.751419
-0.263853
0.436717
1.000000
M2
-0.164257
-0.80304
-0.919517
-0.446191
0.505994
0.74936
1.000000
Optimum Lag Test Results
In table 3 the information criterion used is Akaike Information Criterion (AIC). Based on the Akaike
Information Criterion (AIC) criteria, the optimum lag result is obtained at lag one.
Table 3. Optimal lag test results of mudharabah contracts
Lag
AIC
SC
HQ
0
-10.96664
-10.82195*
-10.90848*
1
-10.74596
-9.877812
-10.39697
2
-10.71658
-9.124973
-10.07677
3
-10.66535
-8.350288
-9.734716
4
-10.40071
-7.362192
-9.179253
5
-10.29869
-6.536715
-8.786410
6
-10.21331
-5.727875
-8.410203
7
-10.30786
-5.098965
-8.213927
8
-10.20144
-4.269094
-7.816690
9
-10.56618
-3.910368
-7.890597
10
-10.71245
-3.333188
-7.746051
11
-11.36231*
-3.259589
-8.105085
Notes: Asterisks (*) and bold print indicate the optimum lag level.
Table 3. Optimal lag test results of contracts
Lag
AIC
SC
HQ
0
-12.08221
-11.93752*
-12.02404*
1
-11.94397
-11.07582
-11.59498
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2
-12.04444
-10.45283
-11.40463
3
-11.86787
-9.552809
-10.93724
4
-11.58114
-8.542621
-10.35968
5
-11.39879
-7.636815
-9.886510
6
-11.33564
-6.850207
-9.532536
7
-11.45183
-6.242934
-9.357896
8
-11.43063
-5.498283
-9.045879
9
-11.81909
-5.163279
-9.143508
10
-11.97578
-4.596519
-9.009381
11
-12.59793*
-4.495207
-9.340704
Notes: Asterisks (*) and bold print indicate the optimum lag level.
Table 3. Optimal lag test results of murabaha contracts
Lag
AIC
SC
HQ
0
-12.83435
-12.68966*
-12.77619*
1
-12.62103
-11.75288
-12.27204
2
-12.64574
-11.05413
-12.00593
3
-12.45282
-10.13775
-11.52218
4
-12.16350
-9.124981
-10.94204
5
-12.05311
-8.291136
-10.54083
6
-11.99532
-7.509885
-10.19221
7
-12.06948
-6.860587
-9.975549
8
-12.09985
-6.167504
-9.715099
9
-12.47020
-5.814390
-9.794619
10
-12.77742
-5.398153
-9.811016
11
-13.31642*
-5.213700
-10.05920
Notes: Asterisks (*) and bold print indicate the optimum lag level.
Cointegration Test Results
The criterion for testing this cointegration is based on the trace-statistic which is compared with the
real level value of 5% and 1%, if the trace-statistic value is greater than the real level value then the number
of cointegration ranks can be accepted.
Table 4. Cointegration test results of mudharabah contracts
Hypothesized
Trace
5 Percent
1 Percent
No. of CE(s)
Statistic
Critical Value
Critical Value
None
104.4162
68.52
76.07
At most 1**
63.10202
47.21
54.46
At most 2*
35.03696
29.68
35.65
At most 3*
15.57080
15.41
20.04
At most 4*
5.981860
3.76
6.65
Notes: The asterisk * (**) 5% (1%) and bold indicates there is cointegration in the model.
Table 4. Cointegration test results of musyarakah contract
Hypothesized
Trace
5 Percent
1 Percent
No. of CE(s)
Statistic
Critical Value
Critical Value
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None
109.1684
68.52
76.07
At most 1**
60.61267
47.21
54.46
At most 2*
34.50891
29.68
35.65
At most 3*
18.59474
15.41
20.04
At most 4*
6.564224
3.76
6.65
Notes: The asterisk * (**) 5% (1%) and bold indicates there is cointegration in the model.
Table 4. Cointegration test results of murabaha contracts
Hypothesized
Trace
5 Percent
1 Percent
No. of CE(s)
Statistic
Critical Value
Critical Value
None
109.1684
68.52
76.07
At most 1**
60.61267
47.21
54.46
At most 2*
34.50891
29.68
35.65
At most 3*
18.59474
15.41
20.04
At most 4*
6.564224
3.76
6.65
Notes: The asterisk * (**) 5% (1%) and bold indicates there is cointegration in the model.
Haji VAR Model Stability Test
Table 5. Stability test results of var model of mudharabah, musyarakah and murabahah contracts
Model
Modulus
Lag
MUD
0.220558 - 0.894077
6
MUS
0.150955 - 0.917884
6
MUR
0.261221 - 0.911416
6
VECM Test Results and Analysis
The VECM estimation results can show the short and long term information of the research
variables which can be seen in table 6.
Table 6. VECM test results for mudharabah contracts
SHORT TERM
VARIABLES
COEFICIENT
T-STATISTICS
CointEq1
-0.011922
[-1.92971]
D(MUD(-1))
-0.042691
[-0.38962]
D(INF(-1))
0.224403
[ 1.52194]
D(LNIPI(-1))
0.376352
[ 0.40157]
D(LNNT(-1))
-5.055894
[-1.92508]
D(LNM2(-1))
3.801595
[ 0.82697]
LONG TERM
INF(-1)
2.030519
[ 1.53663]
LNIPI(-1)
-11.04964
[-0.39279]
LNNT(-1)
-324.1889
[-5.43932]
LNM2(-1)
92.22304
[ 5.40058]
Notes: Bold indicates significant results at 5% real level (>1.96).
The VECM test results of the model (MUD) show a long-run equilibrium with a negative ECT
value (-). In the long run, LNNT and LNM2 significantly affect MUD. LNNT has a negative effect of -
5.43932, meaning that a 1% increase in LNNT decreases MUD by -5.43932, indicating that MUD's non-
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performing financing tends to decrease. This finding is in accordance with the research of Beck et al. (2015)
which confirms the significant effect of LNNT on non-performing financing.
LNM2 positively affects MUD by 5.40058, meaning that every 1% increase in LNM2 will increase
MUD by 5.40058. and this influence can be concluded that MUD's problematic financing will increase in
the future. These results are in accordance with research conducted by Iriani and Yuliadi (2015) which states
that the variable money supply (LNM2) has a significant effect on non-performing loans.
From the test results above, the VECM equation obtained is that macroeconomic indicators strongly
influence and determine non-performing financing in Islamic banking as follows:
∆MUD = α0 - α1 ∆INF t-1 + α2 ∆IPI t-1 + α3 ∆NT t-1 + α4 ∆M2 t-1 - λ(MUS - INF - IPI - NT -
M2) + ε
If the coefficient values are entered into the equation, the VECM equation is obtained as follows:
∆MUD = α0 - 0.173336 ∆INF - 0.560814 ∆IPI + 1.291168 ∆NT + 1.202279 ∆M2 - λ(MUS +
2.030519 - 11.04964 - 324.1889 + 92.22304) + ε
Table 6. VECM test results for musyarakah contracts
SHORT TERM
VARIABLES
COEFICIENT
T-STATISTICS
CointEq1
-0.039969
[-1.92662]
D(MUS(-1))
-0.120981
[-1.15827]
D(INF(-1))
-0.173336
[-2.03957]
D(LNIPI(-1))
-0.560814
[-1.04010]
D(LNNT(-1))
1.291168
[ 0.85432]
D(LNM2(-1))
1.202279
[ 0.42802]
LONG TERM
INF(-1)
-0.248969
[-0.99266]
LNIPI(-1)
2.166237
[ 0.40147]
LNNT(-1)
49.79237
[ 4.44749]
LNM2(-1)
-11.22285
[-3.52579]
Notes: Bold indicates significant results at 5% real level (>1.96).
The VECM test results of the model (MUS) show a long-run equilibrium with a negative ECT value
(-). In the long run, LNNT and LNM2 significantly affect MUS. LNNT has a positive effect of 4.44749,
meaning that a 1% increase in LNNT increases MUS by 4.44749, indicating that MUS problematic
financing tends to rise. This finding is consistent with the research of Jayaraman et al. (2019) and Budiman
et al. (2018) which confirmed the significant effect of LNNT on non-performing financing.
LNM2 negatively affects MUS by -3.52579, meaning that every 1% increase in LNM2 will reduce
MUS by -3.52579. and this effect can be concluded that MUS problematic financing will decrease in the
future.
From the test results above, the VECM equation obtained is that macroeconomic indicators strongly
influence and determine non-performing financing in Islamic banking as follows:
∆MUS = α0 + α1 ∆INF t-1 + α2 ∆IPI t-1 + α3 ∆NT t-1 + α4 ∆M2 t-1 - λ (MUD - INF - IPI - NT -
M2) + ε
If the coefficient values are entered into the equation, the VECM equation is obtained as follows:
∆MUS = α0 + 0.224403 ∆INF + 0.376352 ∆IPI - 5.055894 ∆NT + 3.801595 ∆M2 - λ(MUD -
0.248969 + 2.166237 + 49.79237 - 11.22285) + ε
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Table 6. Vecm test results for murabaha contracts
SHORT TERM
VARIABLES
COEFICIENT
T-STATISTICS
CointEq1
-0.096940
[-2.22373]
D(MUR(-1))
-0.126202
[-1.07531]
D(INF(-1))
-0.012290
[-0.19302]
D(LNIPI(-1))
-0.245499
[-0.61150]
D(LNNT(-1))
0.848637
[ 0.73621]
D(LNM2(-1))
2.622106
[ 1.25675]
LONG TERM
INF(-1)
-0.122429
[-1.59095]
LNIPI(-1)
0.647774
[ 0.38701]
LNNT(-1)
21.92952
[ 6.42248]
LNM2(-1)
-0.440928
[-0.45168]
Notes: Bold indicates significant results at 5% real level (>1.96).
The results of the VECM model (MUR) test on the influence of macroeconomic indicators on the
problematic financing of Islamic banking with a musyarakah contract can be said to show a long-term
balance which can be seen from the ECT (Eror Corection Model) value which is negative (-). In the long-
term analysis, the LNNT variable significantly affects MUR.
LNNT positively affects MUR by 6.42248, meaning that every 1% increase will increase MUR by
6.42248. and this influence can be concluded that MUR's non-performing financing will increase in the
future. This result is in accordance with research conducted by (Jayaraman, et al 2019), Budiman et al.
(2018) which states that the exchange rate variable (LNNT) has a significant effect on non-performing loans.
From the test results above, the VECM equation obtained is that macroeconomic indicators strongly
influence and determine non-performing financing in Islamic banking as follows:
∆MUR = α0 + α1 ∆INF t-1 + α2 ∆IPI t-1 + α3 ∆NT t-1 + α4 ∆M2 t-1 - λ (MUR - INF - IPI - NT -
M2) + ε
If the coefficient values are entered into the equation, the VECM equation is obtained as follows:
∆MUR = α0 - 0.012290 ∆INF - 0.245499∆IPI + 0.848637∆NT + 2.622106 ∆M2 - λ(MUR -
0.122429 + 0.647774 + 21.92952 - 0.440928) + ε
From the results of the VECM test of Mudharabah, Musyarakah and Murabahah Acts in table 4.1.6,
it can be explained that the variables that have a significant influence in the long-term economic and
financial time can be interpreted as follows:
1. Economically
a. Inflation: High or uncontrolled inflation can reduce the purchasing power of money and lead to
increased prices of goods and services. In the context of financing, high inflation can affect
borrowers' ability to repay their loans, especially if their income has not kept up with the rate of
inflation.
b. Exchange Rates: Fluctuations in currency exchange rates can affect the competitiveness of exports
and imports, as well as the price of raw materials needed in business. If exchange rates have an
influence on non-performing loans, this suggests that exchange rate instability may affect business
results and the ability of borrowers to meet their obligations.
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c. Money Supply: The money supply reflects the liquidity of the economy. Rapid or uncontrolled
growth in the money supply can lead to inflation risk or reduce the value of money. If money
supply has an influence on non-performing financing, it means that this factor also has an
important role in business health and financing.
2. Financially
a. Long-term Effect: The significant long-run effect of Inflation, exchange rate and money supply
on mudharabah, musyarakah and murabahah non-performing financing may indicate that long-
lasting fluctuations may lead to potential losses.
b. Risk Management: The financial implications of these results emphasize the importance of risk
management in mudharabah, musyarakah and murabahah businesses The parties involved need
to consider how to manage inflation, exchange rate and money supply risks in their mudharabah,
musyarakah and murabahah cooperation agreements to minimize the risk of loss.
3. Practical Implications
a. Contract Structure: The results of this study may lead to changes in the structure of mudharabah
and musyarakah contracts. Parties may need to include clauses or mechanisms that consider the
impact of exchange rates in the sharing of profits and losses.
b. Risk Management: It is important to consider how the parties will manage exchange rate risks that
may occur during the cooperation period. A more detailed and proactive risk management
approach may be required.
4. Business Continuity
This finding may affect perceptions of the long-term sustainability of the business within a
mudharabah framework. The parties involved should consider the impact of exchange rate
fluctuations on the ability of the business to generate sufficient profits.
The results of this study can provide a deeper insight into how fluctuations in Inflation, exchange
rates and money supply can impact business performance and results within the framework of mudharabah
and musyarakah. This confirms the importance of a deeper understanding of exchange rate risk and risk
management in Islamic banking operations that adopt the mudharabah principle.
Impulse Response Function (IRF) Test Results
In the VECM method there is one of the main forms of analysis, namely the Impulse Response
Function (IRF) which aims to see the current and future response traces of a variable to a shock from a
particular variable (Ascarya, 2009, p. 19).
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Figure 1. Impulse response function (irf) test results of
mudharabah, musyarakah and murabahah contracts
Forecast Error Variance Decomposition (FEVD) Test Results
After analyzing through Impulse Response Function (IRF), we will then see or predict the
contribution of each variable to shocks or changes in certain variables through Forecast Error Variance
Decomposition (FEVD) Analysis (Ascarya, 2009, p. 21).
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Table 2. Results of forecast error variance decomposition (fevd) test for mudharabah, musyarakah and
murabahah contracts
Discussion
Analysis Impulse Response Function (IRF) Test Results
MUD response to INF
Figure 3. IRF Test Result Graph of MUD Response to INF
INF shocks are responded positively by MUD by 0.069752 to 0.074157 and stabilized in period 17.
This shows that inflation affects MUD positively because inflation reflects the amount of money in
circulation, encouraging banks to raise interest rates to control inflation. This result is consistent with the
research of Damanhur et al. (2018), Jayaraman et al. (2019), Kjosevski and Petkovski (2017), Nkusu (2011),
and Skarica (2014) which state that inflation has a significant effect on non-performing financing.
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MUD's response to IPI
Figure 4. IRF Test Result Graph of MUD Response to IPI
IPI shocks are responded positively by MUD by 0.024923 in period 2, negatively in period 4 (-
0.028004 to -0.002346), and again positively in period 5 (0.014197 to 0.023848) before stabilizing in period
20. This response reflects a complex relationship, where IPI growth supports income but can increase the
risk of non-performing financing if imbalances occur. This result is different from the research of Budiman
et al. (2018) and Nursechafia and Abduh (2014).
MUD response to NT
Figure 5. IRF Test Result Graph of MUD Response to NT
The NT variable shock is responded negatively by MUD in period 2 by -0.014207, then positively
in period 3 by 0.012564 until it reaches 0.189759 in period 22 and stabilizes in period 23. The positive
impact of currency depreciation is not always evenly distributed across all sectors, depending on economic
dynamics and business structure. This result is in line with Beck et al. (2015) but different from Jayaraman
et al. (2019) and Budiman et al. (2018).
MUD response to M2
Figure 6. IRF Test Result Graph of MUD Response to M2
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M2 variable shocks are responded positively by MUD by 0.032516 to 0.015351 in period 4, then
negatively by -0.002566 to -0.038970 in period 24 and stable in period 25. The increase in money supply
does not have a direct impact on Mudharabah non-performing financing, because every Rp 1 increase
decreases non-performing financing by -0.039115. This negative effect occurs if the increase in money
supply is not matched by healthy economic growth, which can cause inflation and customer difficulties in
repaying credit.
MUS response to INF
Figure 7. IRF Test Result Graph of MUS Response to INF
The INF variable shock is negatively responded by the MUS variable by -0.064174 to -0.081333 in
period 10 and stabilizes in period 11. This shows that when there is an increase in inflation, it does not have
an impact on the high distribution of musyarakah financing which is also followed by high non-performing
financing.
MUS response to IPI
Figure 8. IRF Test Result Graph of MUS Response to IPI
The IPI variable shock is negatively responded by MUS by -0.034384 to -0.011012 in period 14
and stabilizes in period 15, indicating that Musyarakah non-performing financing is not affected by IPI.
Good industrial performance remains important for economic stability, and policies that support the
industrial sector can avoid the negative impact of a decline in I.
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MUS response to NT
Figure 9. IRF Test Result Graph of MUS Response to NT
The NT variable shock was negatively responded by the MUS variable by -0.007958 to -0.012216
in period 24 and stabilized in period 25. The Islamic banking sector has grown and developed significantly
in various countries. Nevertheless, it should be recognized that the financing channeled by Islamic banks in
Indonesia still has a limited scope, especially domestically.
MUS response to M2
Figure 10. IRF Test Result Graph of MUS Response to M2
M2 variable shocks are responded positively by MUS by 0.018460 to 0.015055 in period 21 and
stabilized in period 22. This indicates that money supply has a significant impact on financing disbursement.
Money supply management by the central bank and government through monetary and fiscal policies is
important to maintain economic stability and financing effectiveness.
MUR response to INF
Figure 11. IRF Test Result Graph of MUR Response to INF
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The INF variable shock is responded negatively by MUR by -0.003254 in period 2, then positively
by 0.007275 to 0.013373 in period 3, and stable in period 7. This result shows that inflation affects MUR
positively because an increase in inflation reflects the amount of money in circulation, encouraging banks
to raise interest rates to control inflation. This finding is in line with the research of Damanhur et al. (2018),
Jayaraman et al. (2019), Kjosevski and Petkovski (2017), Nkusu (2011), and Skarica (2014).
MUR response to IPI
Figure 12. IRF Test Result Graph of MUR Response to IPI
MUR responded negatively to IPI shocks by -0.017072 to -0.028597 in period 8 and stabilized in
period 9, indicating that Murabahah's non-performing financing is not affected by IPI. Good industry
performance remains important to support economic stability and sustainable growth.
MUR response to NT
Figure 13. IRF Test Result Graph of MUR Response to NT
The MUR variable responds negatively to the NT variable shock by -0.015348 to -0.079271 in
period 8 and stabilizes in period 9. The Islamic banking sector has grown and developed significantly in
various countries. Nevertheless, it needs to be recognized that the financing channeled by Islamic banks in
Indonesia still has a limited scope, especially domestically.
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MUR response to M2
Figure 14. IRF Test Result Graph of MUR Response to M2
M2 shocks are responded positively by MUR by 0.030025 to 0.044284 in period 7 and stabilized in
period 8, indicating that money supply has a significant impact on financing. Liquidity management through
monetary and fiscal policy is important to maintain economic stability and effectiveness of financing
distribution.
Analysis of Forecast Error Variance Decomposition (FEVD) Test Results
Macroeconomic Variable Shocks to MUDs
Figure 15. Graph of FEVD Test Results of Macroeconomic Variable Shocks to MUDs
Figure 15 above provides an overview of the influence or contribution of each variable that affects
the problematic financing with the Mudharabah (MUD) contract. The level of the MUD variable is
influenced by the INF variable from the 3rd period of 2.556562 to the 48th period of 2.155874. This shows
that the INF variable makes a significant contribution to MUD because the figure in that period is above
1%.
The LNIPI variable in period 2 of 0.126589 to period 48 of 0.199424 shows an insignificant effect
because in that period the LNIPI variable shows a number below 1%.
The LNNT variable in period 2 of 0.041130 to period 4 of 0.768039 shows an insignificant influence
because in that period the inflation variable shows a number below 1%, and in the next period, namely
period 5 of 2.055740 to period 48 of 11.923230, in that period shows that the LNNT variable makes a
significant contribution to the MUD variable, because the figure in that period is above 1%.
The LNM2 variable in period 2 of 0.215461 to period 48 of 0.496148 shows an insignificant effect
because in that period the LNM2 variable shows a number below 1%.
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Macroeconomic Variables Shocks to MUS
Figure 16. Graph of FEVD Test Results of Macroeconomic Variable Shocks to MUS
It can be seen in Figure 16 above that it provides an overview of the influence or contribution of
each variable that affects the problematic financing with the Musyarakah contract (MUS). The level of the
MUS variable is influenced by the INF variable in the 2nd period by 2.822344 to the 48th period by
13.897670, this shows that the INF variable makes a significant contribution to MUS because the figure in
that period is above 1%.
The LNIPI variable in period 2 of 0.810203 to period 48 of 0.297926 shows an insignificant effect
because in that period the LNIPI variable shows a number below 1%. The LNNT variable in period 2 of
0.043402 to period 48 of 0.489066 shows an insignificant effect because in that period the LNNT variable
shows a number below 1%. The LNM2 variable in period 2 of 0.233525 to period 48 of 0.510203 shows an
insignificant effect because in that period the LNNT variable shows a number below 1%.
Macroeconomic Variable Shocks to MUR
Figure 17. Graph of FEVD Test Results of Macroeconomic Variable Shocks to MUR
It can be seen in Figure 17 above that it provides an overview of the influence or contribution of
each variable that affects the problematic financing with the Musyarakah contract (MUR). The level of the
MUR variable is influenced by the INF variable in the 2nd period by 0.014832 to the 48th period by
0.841381, this shows that the INF variable does not significantly contribute to MUR because the figure in
that period is below 1%.
The LNIPI variable in period 2 of 0.408154 shows an insignificant effect because in that period the
LNIPI variable shows a number below 1%, and in the next period, namely period 3 of 1.138428 to period
48 of 3.504973, in that period it shows that the LNIPI variable makes a significant contribution to the MUR
variable, because the figure in that period is above 1%.
The LNNT variable in period 2 of 0.329891 to period 3 of 0.432920 shows an insignificant influence
because in that period the LNNT variable shows a number below 1%, and in the next period, namely period
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4 of 2.078018 to period 48 of 26.651280, in that period shows that the LNNT variable makes a significant
contribution to the MUR variable, because the figure in that period is above 1%.
The MUR variable is influenced by the INM2 variable in period 2 of 1.262485 to period 48 of
8.391600, this shows that the LNM2 variable makes a significant contribution to MUR because the figure
in that period is above 1%.
This study also found that different types of Islamic financing have different levels of non-
performing financing risk. Musyarakah financing has a higher level of non-performing financing risk than
mudharabah and murabahah financing. This is because musyarakah financing is a partnership agreement
between the bank and the customer, where the bank and the customer share the risks and profits of the
project. If the project fails, the bank and the customer will suffer losses together.
This study provides evidence that macroeconomic conditions and the type of Islamic financing have
a significant influence on Islamic banking non-performing financing. Islamic banks need to consider these
factors when making decisions on the type of financing to offer and how to manage the risk of non-
performing financing.
The following are some recommendations that can be made by Islamic banking to reduce the risk
of non-performing financing:
1. Conduct a careful analysis of macroeconomic conditions before offering financing.
2. Choose the type of financing that is suitable for the risk that can be borne.
3. Conduct strict selection and assessment of prospective customers.
4. Manage the risk of non-performing financing well
Hypothesis
Hypothesis of Impulse Response Function (IRF) Test Results
1. Hypothesis I: Inflation variable based on the results of the above research on Mudharabah and
Murabahah financing contracts has a positive or significant influence on non-performing loans, while
the opposite response is shown by the Musyarakah financing contract. With this it can be concluded
that the hypothesis is proven for the Mudharabah and Murabahah financing contracts, while the
Musyarakah contract is not proven.
2. Hypothesis II: The industrial production index (IPI) variable based on the results of the above research
on the Mudharabah financing contract has a positive or significant influence on non-performing loans,
while the opposite response is shown by the Musyarakah and Murabahah financing contracts. With
this it can be concluded that the hypothesis is proven for the Mudharabah financing contract while the
Musyarakah and Murabahah contracts are not proven.
3. Hypothesis III: The Exchange Rate Variable based on the results of the above research on the
Mudharabah financing contract has a positive or significant influence on non-performing loans, while
the opposite response is shown by the Musyarakah and Murabahah financing contracts. With this it
can be concluded that the hypothesis is proven for the Mudharabah financing contract while the
Musyarakah and Murabahah contracts are not proven.
4. Hypothesis IV: The variable money supply based on the results of the above research on the
Musyarakah and Murabahah financing contracts has a positive or significant influence on non-
performing loans, while the opposite response is shown by the Mudharabah financing contract. With
this it can be concluded that the hypothesis is proven for the Musyarakah and Murabahah financing
contracts, while the Mudharabah contract is not proven.
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Hypothesis of Forecast Error Variance Decomposition (FEVD) Test Results
1. Hypothesis I: Inflation variable based on the results of the above research on Mudharabah and
Musyarakah financing contracts has a positive or significant influence on providing shocks to non-
performing loans, while the opposite response is shown by the Murabahah financing contract. With
this it can be concluded that the hypothesis is proven for the Mudharabah and Musyarakah financing
contracts, while the Murabahah contract is not proven.
2. Hypothesis II: The industrial production index (IPI) variable based on the results of the above research
on the Murabahah financing contract has a positive or significant influence on shocks to non-
performing loans, while the opposite response is shown by the Mudharabah and Musyarakah financing
contracts. With this it can be concluded that the hypothesis is proven for the Murabahah financing
contract while the Mudharabah and Musyarakah contracts are not proven.
3. Hypothesis III: The Exchange Rate Variable based on the results of the above research on the
Mudharabah and Murabahah financing contracts has a positive or significant effect on providing
shocks to non-performing loans, while the opposite response is shown by the Musyarakah financing
contract. With this it can be concluded that the hypothesis is proven for the Mudharabah and Murabahah
financing contracts, while the Musyarakah contract is not proven.
4. Hypothesis IV: Based on the results of the above research, the Murabahah financing contract has a
positive or significant influence on non-performing loans, while the opposite response is shown by the
Mudharabah and Musyarakah financing contracts. With this it can be concluded that the hypothesis is
proven for the Murabahah financing contract while the Mudharabah and Musyarakah contracts are not
proven.
Conclusion
This study shows that macroeconomic indicators such as inflation, industrial production index (IPI),
exchange rate, and money supply have a significant influence on non-performing financing on Mudharabah,
Musyarakah, and Murabahah contracts. Inflation and exchange rate are proven to contribute significantly
to non-performing financing in Mudharabah, while in Musyarakah, inflation is the dominant influencing
factor, followed by money supply which has a significant impact on the distribution of financing. For
Murabahah, the exchange rate has the largest influence, followed by money supply. Overall, inflation and
exchange rates are the dominant variables affecting non-performing loans in the three contracts. To
overcome the negative impact of unfavorable economic conditions, strategic steps are needed such as
improving risk management, conducting regular monitoring, adjusting the financing structure, and
providing education and flexible financing alternatives to customers. In addition, open communication and
careful management of assets and collateral are also key. With this strategic approach, Islamic banking can
mitigate the impact of unfavorable economic conditions, maintain business stability, and increase customer
confidence.
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