Determinants of Indonesia's External Debt 31 Years
HERU WAHYUDI*
Economic Development, Faculty of Economics and Business,
University of Lampung,
Jln. H. Komarudin, Rajabasa Raya, Rajabasa, Bandar Lampung,
INDONESIA
DRIYA WIRAWAN
Management, Faculty of Economics and Business,
University of Lampung,
Jl. Nusa Indah No. 53, Kel. Rawalaut, Kec. Enggal, Bandar Lampung,
INDONESIA
I WAYAN SUPARTA
Economic Development, Faculty of Economics and Business
University of Lampung,
Perum Bataranila, GG. Sakura, Dusun 4, Desa Hajimena, Kec. Natar, Lampung Selatan,
INDONESIA
WIDIA ANGGI PALUPI
Economic Development, Faculty of Economics and Business,
University of Lampung,
Wisma Cantik Manis, Rajabasa, Rajabasa, Bandar Lampung,
INDONESIA
Abstract: - External debt can be used as a source of development in developing countries, including Indonesia.
External debt can be affected by various factors. This study aims to determine the short- and long-term
relationship between the exchange rate, foreign exchange reserves, and state revenues for Indonesia's external
debt from 1990 to 2021, sourced from the World Bank. The method used in this study is the Error Correction
Model (ECM). The results of this study show that the exchange rate in both the short and long term has a
significant negative effect on foreign debt. In the short and long term, foreign exchange reserves positively
impact external debt. Meanwhile, state revenues in a short time do not affect external debt and have a
significant positive effect in the long run. This research can be used as one of the guidelines for determining
policies related to foreign debt.
Key-Words: - ECM, exchange rate, external debt, foreign exchange reserves, state revenues
Received: May 4, 2023. Revised: July 26, 2023. Accepted: August 2, 2023. Published: August 11, 2023.
1 Introduction
Economic growth is the most powerful instrument
for reducing poverty and improving the quality of
life in developing countries, [1]. Every government
certainly wants to realize economic improvement,
including developing countries. Indonesia is one of
the developing archipelagic countries on the Asian
continent. According to, [2], external debt is a
developing count countries significant development
source.
According to, [3], Indonesia is one of the
developing countries that uses foreign loans in
economic activities. In the short term, external debt
can cover the budget deficit due to numerous
financing but a limited income. The national al debt
is like a double-edged sword, which has both
positive and negative impacts on economic growth.
Thus, it needs an app. Therefore policies and
analysis to mock debt to improve the welfare of the
people. This study will analyze the factors that
influenced external debt in Indonesia from 1990 to
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2021. The following is the development of
Indonesia's external debt from 1990 to 2021.
Based on Figure 1, it can be seen that
Indonesia's external debt trend continues to increase.
In 2011 Indonesia's external debt was enormous,
reaching 50.15%. This condition is caused by an
increase in the budget deficit, which in 2010
amounted to -98,010 and in 2011 to -124,656; A
large amount of Indonesia's external debt in 2011
was caused because 2011 the state budget received
many fuel subsidies, so 2011 the budget deficit was
even more significant, [4], in addition to being
influenced by the budget deficit, research from [5],
stated that the exchange rate affected foreign debt.
Here is a graph of Indonesia's exchange rate and
external debt.
Fig. 1: Development of Indonesia's External Debt 1990-2021 (% of GDP)
Source: World Bank (2023)
Fig. 2: Exchange Rate and External Debt of Indonesia 1990-2021
Source: World Bank (2023)
Fig. 3: Foreign exchange reserves and Indonesia's External Debt 1990-2021
Source: World Bank (2023)
0
10
20
30
40
50
60
1990
1991
1992
1993
1994
1995
1996
1997
1998
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2001
2002
2003
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2021
0,00
10,00
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30,00
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16000
1990
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2007
2008
2009
2010
2011
2012
2013
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2015
2016
2017
2018
2019
2020
2021
External debt (ULN)
Exchange rate (NT)
NT ULN
0,00
10,00
20,00
30,00
40,00
50,00
60,00
0
2E+10
4E+10
6E+10
8E+10
1E+11
1,2E+11
1,4E+11
1,6E+11
1990
1991
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2006
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2011
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2021
External debt (ULN)
Foreign exchange reserves (CD)
CD ULN
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Fig. 4: State Revenue and External Debt of Indonesia 1990-2021
Source: World Bank (2023)
Based on Figure 2, it can be seen that
Indonesia's exchange rate and external debt tend to
fluctuate. Research conducted by, [6], found that the
exchange rate positively affects foreign debt.
However, [7], study researchers found that the
exchange rate significantly negatively impacts
foreign debt. In addition to the exchange rate
phenomena, foreign exchange reserves, based on
research from, [8], can affect foreign debt. The
following is a chart between the value of foreign
exchange reserves and Indonesia's external debt
from 1990-2021.
Based on Figure 3, it can be seen that foreign
exchange reserves and external debt have a positive
trend. This means external debt increases when
foreign exchange reserves increase, and vice versa.
This phenomenon is in line with the research by,
[9], but needs to be with the research carried out by,
[10]. The differences in research results and
phenomena seen in Figure 3 make researchers want
to analyze further the relationship between foreign
exchange reserves and Indonesia's external debt.
Research from, [11], reveals that state revenues also
affect foreign debt. According to, [12], foreign
loans themselves are carried out because
government revenues derived from taxes and other
revenues are insufficient to finance government
expenditures, both for public expenditures and
apparatus expenditures. The data used in the study
are state revenues sourced from cash receipts from
taxes, social contributions, and other incomes such
as fines, fees, rent, and income from property or
sales. Grants are also considered income but are
excluded from the data used. The following is a
chart between Indonesia's state revenue and external
debt from 1990-2021.
Based on Figure 4, it can be seen that
Indonesia's acceptance tends to be volatile. Research
conducted by, [13], found that state revenues
positively affected Indonesia's external debt.
Meanwhile, research by, [14], found that state
revenues negatively affect Indonesia's external debt.
This study will analyze how state revenues affect
external debt in Indonesia. Based on the background
that has been explained, this study aims to
determine the short-term and long-term relationship
between exchange rates, foreign exchange reserves,
and state revenues to foreign debt. The novelty of
this study uses a combination of free variables and
years of analysis that are different from previous
studies.
2 Methodology
This research is a quantitative descriptive study
using secondary data obtained from the World
Bank. Independent variables used in this study
include exchange rates, foreign exchange reserves,
and state revenues. Meanwhile, the bound variable
is Indonesia's external debt from 1990 to 2021.
The data analysis method in this study uses the
Error Correction Model (ECM) method as an
econometric tool and a descriptive method, which
aims to identify the presence or absence of long-
term and short-term relationships that occur due to
cointegration between research variables. The use of
this method is in line with the study’s objectives.
Before ECM estimation and descriptive
analysis, several stages must be carried out, such as
the static data test and cointegration degree test.
After the data is declared stationary and
cointegrated, classical assumptions are tested and
then based on the discussion results. Here is the
equation of the model used in this study.
Short-
term
D(ULt)
=
β0 - β1D(NTt) + β2D(LNCDt)
+ β3D(PENt) - ECTt-1 (1)
0,00
10,00
20,00
30,00
40,00
50,00
60,00
0
5
10
15
20
25
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
External debt (%)
Revenue (%)
PEN ULN
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Long-
term
ULt
=
β0 – β1NTt + β2LNCDt +
β3PENt + ᵋt (2)
Where D indicates the short term, β0 is a
constant, β1,2,3 is the coefficient, UL is Indonesia's
external debt (%), NT is the exchange rate (%),
LNCD is the logarithm of foreign exchange reserves
(USD), PEN is state revenue (%), ECT is Error
Correction Term, ɛ is an error term, and t is time
series (1990-2021).
The unit root station test is the first to perform
before performing an ECM analysis. A critical
concept for time series data is the condition of
stationary or not stationary data. Data is said to be
inactive if it is close to its average and unaffected by
time. With stationary data, the time series model can
be more stable. If the estimation uses non-stationary
data, the data is reconsidered for validity and
stability because regression results from non-
stationary data will cause spurious regression.
Spurious regression means that regression results
from the one-time series variables on one or another
time series variable tend to produce biased
estimation conclusions indicated by characteristics
such as obtaining a high R2. Still, in reality, the
relationship between these variables has no
meaning. The condition is that if the probability
value is smaller than the significance level, the data
period is declared stationary, and vice versa. The
data is not static when the probability value exceeds
the significance level.
When the data is stationary, a cointegration test
is carried out. The primary purpose of this
cointegration test is to determine whether the
residual is stationarily cointegrated. If the variables
are cointegrated, there is a stable relationship in the
long run. Conversely, if there is no cointegration
between variables, the implication is that there is no
relationship in the long run. Cointegration is also an
error because the deviation from the long-run
equilibrium is corrected gradually through a partial
series of short-run adjustments. The condition is that
the data are cointegrated when the probability value
is smaller than the significance level (0.05).
If the data is not stationary at the level but
stationary at the level of differential and the two
variables are cointegrated or, in other words, have a
long-term relationship or equilibrium. In the short
term, there may be an imbalance. That is, what
economic actors want is different from what
happens. There are differences in what economic
actors wish to do and what happens, so adjustments
are needed. The model that incorporates adjustments
to make corrections for imbalances is called the
Error Correction Model (ECM), [15].
3 Result and Discussion
3.1 Result
3.1.1 Unit Root Test Results
Data stationery is one of the requirements for
conducting an ECM test. Unit root tests determine
whether the time series data is stationary. This study
uses the Augmented Dicky Fuller (ADF) test. The
provision of the root test of this unit is that when the
probability value is below the critical value (0.05),
the data is said to be stationary, [16]. Here are the
results of static testing.
Based on Table 1, it is known that all variables
are not stationary at the level. While in the first
difference, all variables are stationary, this can be
seen in probability values that are less than 0.05.
When the data is stationary, it can be continued by
conducting a cointegration test.
3.1.2 Cointegration Test
Furthermore, a cointegration test is carried out when
the data is stationary. A cointegration test is
performed to test whether the variables used have a
long-term relationship. The process to find out the
results of the cointegration test is to create an
equation using the least squares approach (OLS),
then find the residual value and perform the test
using the Augmented Dicky Fuller (ADF) test. After
that, it can be known whether the residual value is
stationary. Here are the results of the cointegration
test.
The cointegration test in Table 2 shows a
probability value of 0.0329, smaller than the critical
value (0.05). Thus it can be concluded that there is a
cointegration between the variables used in this
study.
3.1.3 Error Correction Model Analysis
After it has been confirmed that stationary and
cointegrated data can be carried out short-term and
long-term testing using the ECM method. Here are
the results of the short-term and long-term estimates
in this study.
Based on Table 3, the short- and long-term
equations can be obtained as follows:
D(ULt)
=
-1.450698-0.000692D(NTt)+
27.8155D(LNCDt)+0.17865D(PENt)
- 0.198322ECTt-1
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(3)
ULt
=
-471.6861-0.001465NTt+
20.07647LNCDt + 1.196303PEN (4)
Based on the short-term equation in equation
(3), it can be seen that the exchange rate, at a
coefficient of -0.000692, has a negative and
significant influence on Indonesia's external debt. If
the exchange rate increases by 1 rupiah, the external
debt will decrease by 0.000692%, cateris paribus.
The variable foreign exchange reserves, with a
coefficient of 27.81551, have a significant favorable
influence on Indonesia's external debt, meaning that
if foreign exchange reserves increase by 1%, then
the external debt will increase by 0.278155%,
cateris paribus. Furthermore, the variable income
has a coefficient value of 0.178656 and does not
affect Indonesia's external debt in the short term. In
Table 3, it can be seen that the probability of the F-
statistic is 0.000, meaning that the variables of
exchange rates, foreign exchange reserves, and state
revenues together can affect Indonesia's external
debt. In addition, in Table 3, it can be seen that the
short-term R2 value is 0.808102, meaning that the
exchange rate, foreign exchange reserves, and state
revenues can affect external debt by 80.8102%. At
the same time, the remaining 0.191898 is influenced
by other variables that are not included in the model.
Based on Table 3, it can be seen that negative and
significant ECT values indicate that the model is
accurate, or in other words, there is no reason to be
rejected, [17].
Based on the long-term equation in equation (4),
it can be seen that all independent variables affect
Indonesia's external debt. The exchange rate
variable, with a coefficient of -0.001465, means that
if the exchange rate decreases by 1 rupiah,
Indonesia's external debt will increase by
0.001465%, cateris paribus. The variable foreign
exchange reserves with a coefficient of 20.07647
significantly positively influence Indonesia's
external debt, meaning that if foreign exchange
reserves increase by 1%, external debt will increase
by 0.200764%, cateris paribus. Furthermore, the
variable income has a coefficient value of 1.196303,
meaning that in the long run, if income increases by
1%, external debt will increase by 1.196303%,
cateris paribus. Has no influence on Indonesia's
external debt in the short term. Table 3 shows that
the probability of the F-statistic is 0.000, meaning
that in the long run, the variable exchange rate,
foreign exchange reserves, and state revenues
together can affect Indonesia's external debt. In
addition, in Table 3, it can be seen that the long-
term R2 value is 0.888820, meaning that the
exchange rate, foreign exchange reserves, and state
revenues can affect external debt by 88.8820%. At
the same time, the remaining 0.11118 is influenced
by other variables that are not included in the model.
Table 1. Stationary Test Results with ADF Method
Variable
Level/1st
Difference
Prob.
Information
ULN
Level
0.5496
Non-Stationary
1st Difference
0.0000
Stationary
NT
Level
0.9192
Non-Stationary
1st Difference
0.0000
Stationary
LNCD
Level
0.3400
Non-Stationary
1st Difference
0.0000
Stationary
PEN
Level
0.1599
Non-Stationary
1st Difference
0.0000
Stationary
Source: EViews 10
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Table 2. Cointegration Test Results
Variable
Coefficient
Std. Error
t-Statistic
Prob.
RES(-1)
-0.391835
0.174922
-2.240061
0.0329
C
-0.297084
0.591201
-0.502510
0.6191
Source: EViews 10
Table 3. ECM Test Results
Short-Term ECM Estimation Results
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-1.450698
0.437028
-3.319460
0.0027
D(NT)
-0.000692
0.000271
-2.553518
0.0169
D(LNCD)
27.81551
2.806097
9.912525
0.0000
D(PEN)
0.178656
0.190863
0.936041
0.3579
ECT(-1)
-0.198322
0.114836
-1.726998
0.0096
R-squared
0.808102
Mean dependent var
0.722599
Adjusted R-squared
0.778579
S.D. dependent var
3.922203
S.E. of regression
1.845609
Akaike info criterion
4.210185
Sum squared resid
88.56306
Schwarz criterion
4.441474
Log-likelihood
-60.25787
Hannan-Quinn criteria
4.285580
F-statistic
27.37209
Durbin-Watson stat
1.218358
Prob(F-statistic)
0.000000
Long-Term ECM Estimation Results
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-471.6861
51.67956
-9.127130
0.0000
NT
-0.001465
0.000394
-3.717661
0.0009
LNCD
20.07647
2.076552
9.668176
0.0000
PEN
1.196303
0.401152
2.982167
0.0059
R-squared
0.888820
Mean dependent var
27.50490
Adjusted R-squared
0.876907
S.D. dependent var
11.44920
S.E. of regression
4.016900
Akaike info criterion
5.735367
Sum squared resid
451.7936
Schwarz criterion
5.918583
Log-likelihood
-87.76586
Hannan-Quinn criteria
5.796098
F-statistic
74.61435
Durbin-Watson stat
0.820788
Prob(F-statistic)
0.000000
Source: EViews 10
3.1.4 Test Classical Assumptions
Before discussing it too far, the data must be tested
for classical assumptions. This is done so that the
estimate meets the requirements of the Best Linear
Unbiased Estimator (BLUE), namely avoiding the
problems of normality, multicollinearity,
heteroskedasticity, and autocorrelation.
3.1.4.1 Normality Test
The normality test was carried out by comparing the
Jarque-Fallow probability value and significant
level. Where this study used a substantial level of
0.05. The following is presented normality test for
short-term and long-term equations.
Based on Figure 5 and Figure 6, it is known that
the probability values of Jarque-Bera for the short-
term and long-term, respectively, are 0.903472 and
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0.745847, more significant than the significance
level of 0.05. By keeping both in the short and long
term, the data is free from the problem of normality.
0
1
2
3
4
5
6
7
8
-4 -3 -2 -1 0 1 2 3 4 5
Series: Residuals
Sample 1991 2021
Observations 31
Mean -1.46e-16
Median 0.050323
Maximum 4.334525
Minimum -3.513327
Std. Dev. 1.718168
Skewness 0.179114
Kurtosis 3.169850
Jarque-Bera 0.203020
Probability 0.903472
Fig. 5: Short-Term Normality Test
Source: EViews 10
0
2
4
6
8
10
-10.0 -7.5 -5.0 -2.5 0.0 2.5 5.0 7.5 10.0
Series: Residuals
Sample 1990 2021
Observations 32
Mean -1.72e-14
Median 0.396442
Maximum 8.505447
Minimum -9.883245
Std. Dev. 3.817589
Skewness -0.292911
Kurtosis 3.312072
Jarque-Bera 0.587434
Probability 0.745487
Fig. 6: Long-Term Normality Test
Source: EViews 10
3.1.4.2 Multicollinearity Detection
In the regression model, one of the things that must
be fulfilled is the absence of correlation between
free variables or commonly referred to as
multicollinearity. Multicollinearity testing in this
study used the Variance Inflation Factor (VIF)
method. By looking if the VIF value is more than
10, there is a multicollinearity problem, [18]. Here
are the results of multicollinearity in the short and
long term.
Based on Table 4, it is known that the value of
the VIF is less than 10. Thus it can be concluded
that in the short-term and long-term equations, there
is no problem of multicollinearity.
Table 4. Short-Term and Long-Term
Multicollinearity Detection
Variable
Short-term
Long-term
D(NT)
1.423643
5.343201
D(LNCD)
1.089159
6.777160
D(PEN)
1.145525
2.152563
Source: EViews 10, processed
3.1.4.3 Heteroskedasticity Test
The next test of classical assumptions is the
heteroskedasticity test. This test is carried out to
determine whether the residual's variance is constant
or arbitrary. If the residual variance changes, there is
a heteroskedasticity problem, [15]. This study used
the Breusch-Pagan-Godfrey method to examine
heteroscedasticity problems' presence or absence.
Here are the results of the heteroskedasticity test in
the short and long term.
Based on Table 5, it is known that the short-
term and long-term F probability values are
continuously 0.9794 and 0.1549. This value is
greater than the significance level of 0.05.
According to, [16], if the probability value is greater
than the significance level, there is no
heteroskedasticity problem. Thus it can be
concluded that there is no problem with
heteroskedasticity in the model used.
3.1.4.4 Autocorrelation Test
One of the classic assumption tests that must be met
is the autocorrelation test. This study uses the
Durbin-Watson (DW) method to test the presence or
absence of autocorrelation. According to, [16], it
was revealed that when DW values are located
between -2 to +2, the regression model is free from
autocorrelation problems. Based on Table 3, it is
known that the DW values of the short-term and
long-term models are respectively 1.218358 and
0.820788. It can be concluded that the data used in
this study are free from autocorrelation problems.
Table 4. Short-Term and Long-Term
Multicollinearity Detection
Variable
Short-term
Long-term
D(NT)
1.423643
5.343201
D(LNCD)
1.089159
6.777160
D(PEN)
1.145525
2.152563
Source: EViews 10, processed
Table 5. Heteroskedasticity Test
Short-term
Long-term
Prob. F(3,28)
0.9794
0.1549
Prob. Chi-Square(3)
0.9738
0.1461
Prob. Chi-Square(3)
0.9842
0.1902
Source: EViews 10, processed
3.2 Discussion
3.2.1 Effect of Exchange Rate on Foreign Debt
Based on the ECM results, the exchange rate has a
negative and significant effect on external debt both
in the short and long term. This means external debt
will decrease when the exchange rate rises, and vice
versa. When the exchange rate falls, external debt
will increase. The results of this study are in line
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with the research conducted by, [19], in research
entitled “Utang Luar Negeri Negara-Negara
Anggota ASEAN (Studi pada Indonesia dan
Philippines periode 1970-2014”. The increase in
external debt occurred due to the weakening of
Indonesia's exchange rate against other currencies;
the depreciation of the rupiah resulted in increased
production costs and the need for additional capital,
so one of the alternatives was for the government to
raise external debt in addition to overcoming the
surge in production costs, [19]. Thus, external debt
will increase when there is a decrease in the rupiah
exchange rate against the dollar.
This study’s results align with the research
conducted by, [7], entitled “Determining the
Macroeconomic Factors of External Debt
Accumulation in Nigeria: An ARDL Bound Test
Approach.” Indonesia experiences a greater risk of
External debt exchange rates because external debt
is in the form of foreign exchange. When there is an
appreciation or depreciation of the rupiah against
foreign currencies, it will impact foreign debt. When
the rupiah appreciates, it will cause Indonesia's
external debt to decline.
However, there is research that needs to be in
line with the results of this study, namely research
conducted by, [6], which found that the exchange
rate positively affects foreign debt. In addition, the
research from, [5], entitled “Pengaruh Nilai Tukar,
Suku Bunga dan Inflasi Terhadap Utang Luar
Negeri Indonesia Tahun 2001-2020” found that the
exchange rate had a positive and significant effect
on foreign debt. According to, [5], when there is
turmoil in the depreciation of the rupiah exchange
rate where the country makes foreign loans, it will
cause an increase in foreign debt because the loan
value is calculated in foreign currencies.
3.2.2 Effect of Foreign Exchange Reserves on
External Debt
In the short and long term, foreign exchange
reserves positively affect Indonesia's external debt
for the 1990-2021 period. This study’s results align
with the research conducted by, [9]. The
government rolled out an external debt policy to
accumulate foreign exchange reserves because
foreign exchange reserves are one of the essential
indicators to show the strength or weakness of an
economy, [9]. External debt policy is carried out to
cultivate foreign exchange reserves, where foreign
exchange reserves are one of the essential monetary
indicators to show the strength or weakness of an
economy. Foreign exchange reserves are a
guarantee of achieving the financial and
macroeconomic stability of a country. However, in
the long term, the repayment of external debt will
erode the foreign exchange reserves themselves;
coupled with the considerable interest, it can be
interpreted that if a country's foreign exchange
reserves increase, the amount of external debt also
increases.
Foreign exchange reserves can come from
export activities, tourism, loans, grants, and labor
working abroad. Thus, to increase the value of
foreign exchange reserves, the government should
increase export activities and reduce imports. This is
because imports erode available foreign exchange
reserves. Furthermore, the government is working
together to increase Indonesian tourism to attract
foreign tourists to visit Indonesia. Other activities
indeed accompany this to increase foreign exchange
reserves.
This study’s results differ from the research
conducted by, [10], which found that external debt
will decrease when foreign exchange reserves
increase. According to, [10], the increase in foreign
exchange reserves, the component of a country's
foreign income or assets, will also increase; this
increase will undoubtedly raise a country's ability to
conduct international transactions; Therefore,
increasing foreign exchange reserves will increase
the ownership of foreign assets as well as a
country's ability to complete international
transactions so that dependence on debt can
decrease.
3.2.3 Effect of State Revenue on Foreign Debt
The study results found that state revenue has no
effect on Indonesia's external debt in the short term,
but it has a positive and significant impact in the
long term. This study’s results align with research
conducted by, [13], which found that state revenues
positively affect foreign debt. One of the state
revenues is from taxes. The existence of the Covid-
19 pandemic in 2020-2021 requires the Indonesian
government to implement a comprehensive fiscal
policy. This results in state spending always being
higher than state revenue. With higher tax revenues,
the government dares to increase debt because it is
optimistic about its ability to repay debt from
increasing tax revenues, [13].
Indonesia is a developing country that requires a
lot of infrastructure development in various regions.
The government needs a large budget to realize
adequate infrastructure for the Indonesian people.
Significant state revenues and great needs in
Indonesia will encourage the government's
optimism in paying debts, so a positive correlation
exists between state revenues and Indonesia's
external debt.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.158
Heru Wahyudi, Driya Wirawan,
I Wayan Suparta, Widia Anggi Palupi
E-ISSN: 2224-2899
1809
Volume 20, 2023
Meanwhile, state revenues do not affect foreign
debt in the short term. This is because the fiscal
policy requires greater inaction than monetary
policy. Mochtar (2004), [20], related to the policy
implications for Bank Indonesia to be effective in
maintaining price stability is the existence of fiscal
backing in the form of the role of budgetary policy
while keeping the government spending program
proportional to the condition of existing government
debt. And monetary policy has a much shorter deep
inaction than fiscal policy because central banks can
decide and implement policy changes in less than a
day. Still, monetary policy has many outside
inactions, [21]. Thus, state revenue, which is one of
the fiscal policies in the short term, has yet to be
able to affect Indonesia's external debt.
4 Conclusion
This study analyzes the short-term and long-term
relationship between exchange rates, foreign
exchange reserves, and state revenues against
Indonesia's external debt from 1990-2021. The
results of this study show that all independent
variables affect Indonesia's short-term and long-
term external debt. In the short and long term,
exchange rate variables negatively affect Indonesia's
external debt. Meanwhile, foreign exchange
reserves in the short and long term have a
significant positive effect on Indonesia's external
debt. Finally, the variable of state revenue in the
short term has no impact on Indonesia's external
debt. Conversely, over a long time, it positively
impacts Indonesia's external debt for the 1990-2021
period.
The results of this study can be used as one of
the policy recommendations for related agencies.
An appreciating exchange rate will reduce
Indonesia's external debt. Efforts that can be made
include increasing exports, reducing imports, and
promoting tourism in the international arena. In
addition to strengthening the rupiah value, such
actions can increase foreign exchange reserves and
state revenue. Although foreign exchange reserves
and state revenues positively affect national debt,
the identification of government optimism in
servicing debt needs to be prioritized in areas that
can boost economic growth in the short and long
term so that the welfare of the Indonesian people
can be achieved.
Using the ECM method is beneficial for the
government related to regulation in the short and
long term so that the output produced is optimal.
Further research can use a combination of
macroeconomic variables and add research data. In
addition, the study can analyze how debt is during
crisis conditions and when it is in normal
conditions.
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I Wayan Suparta, Widia Anggi Palupi
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
-Heru Wahyudi made a research framework. Driya
Wirawan made proposed policy recommendations.
-I Wayan Suparta collected literature reviews.
-Widia Anggi Palupi wrote the research and
collected and processed research data.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
The research in this manuscript is supported by
Lembaga Penelitian dan Pengabdian kepada
Masyarakat (LPPM) Universitas Lampung.
Conflict of Interest
The authors have no conflict of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.158
Heru Wahyudi, Driya Wirawan,
I Wayan Suparta, Widia Anggi Palupi
E-ISSN: 2224-2899
1811
Volume 20, 2023
