Syntax
Literate: Jurnal Ilmiah Indonesia p�ISSN: 2541-0849
e-ISSN: 2548-1398
Vol. 7, No.
11, November 2022
THE EFFECT OF FUNDING POLICY AND MACROECONOMICS ON ECONOMIC
VALUE-ADDED CREATION IN INFRASTRUCTURE STATE-OWNED ENTERPRISES
Ratu Chumairoh
Fakultas Ekonomi dan
Bisnis, Universitas
Indonesia
E-mail: [email protected]
Abstract
Determining
the optimal capital structure for a company is crucial as it impacts its value.
Additionally, macroeconomic conditions such as Inflation and interest rates can
affect a company's value. This study aims to establish a correlation between
capital structure, measured by the Debt to Equity (DER) ratio, macroeconomic
factors, reflected in Inflation and interest rates, and the creation of
economic value added (EVA) and company profitability, measured by the ratio of
return on invested capital (ROIC). A panel data regression methodology was used
to conduct this research, and a sample of infrastructure state-owned
enterprises that carried out National Strategic Projects (PSN) between 2011 and
2021 was taken. The results showed that DER had a negative and significant
impact on EVA and ROIC, while Inflation did not affect ROIC and EVA.
Additionally, the interest rate had a positive and significant impact on ROIC
but not on EVA.
Keywords:
capital structure, DER, EVA, macroeconomic, National Strategic Projects.
Introduction
The fundamental objective of a corporation is to generate
profits by maximizing its shareholders' value (Andreadakis, 2012).
To achieve this objective, firms
strive to decrease their total capital costs and increase their stock market
price (Kimathi
et al., 2015; Mbonu & Amahalu, 2021). One approach to minimizing capital
costs is to secure the firm's capital using an appropriate mix of debt and
equity (Jreisat
et al., 2021; Khan et al., 2022). Typically, businesses finance
their activities and investment needs through either debt or equity. The
composition of a company's capital, in terms of debt and equity, is referred to
as Capital Structure and is usually measured using ratios such as
debt-to-equity or debt-to-total asset. Determining whether to use debt or
equity to finance operations involves many variables, and striking an optimal
balance between the two can be challenging (Team,
2022).
The
topic of Capital Structure has attracted significant attention from scholars,
especially since Modigliani (1958) presented their arguments. They
claimed that the amount of debt a firm issues does not affect its market value
under certain circumstances, such as unconstrained arbitrage, no bankruptcy,
and no corporate taxes. However, the assumptions presented by Modigliani and
Miller are seldom met in reality. Later studies have shown that Capital
Structure is relevant to a company's market value, and excessive debt levels
may lead to a decrease in value.
In
addition to the Modigliani and Miller theory, there are other prevailing
theories on firms' optimal Capital Structure, such as the trade-off theory, the
pecking order theory, and the agency theory. These theories suggest that
Capital Structure decisions involve balancing tax benefits and the potential
for financial risk, borrowing instead of issuing stock when there are
insufficient internal funds, and using debt to increase a company's value if
its operating cash flows exceed its investment needs.
Despite
the widely accepted Modigliani and Miller theory that states that a company's
capital structure does not affect its value, recent studies have shown that
Capital Structure composition is crucial for a firm's profitability and value.
In the infrastructure industry in Indonesia, particularly in the toll road
industry, state-owned enterprises usually fund toll road construction with 70%
debt and 30% equity (Chan
& Pribadi, 2022), which raises questions about the
impact of Capital Structure on the profitability of infrastructure SOE.
This
study aims to investigate the Capital Structure-profitability relationship in
the infrastructure industry in Indonesia. The study employed panel data
methodology and analyzed a sample of 9 SOEs in Indonesia that managed National
Strategic Projects between 2011 and 2021. This study contributes to the
existing literature by providing empirical evidence on the effect of Capital
Structure on firm performance, particularly in the infrastructure sector in
Indonesia. The results will benefit business managers, policymakers,
regulators, and academics.
Literature Review
Every
company faces the problem of deciding on the appropriate source, funding
scheme, or capital structure to support its business activities (K.
Shahveisi et al., 2022). Determining the capital structure
is crucial because the cost of capital will serve as the basis for investment
decision-making. Capital structure refers to the proportion of a company's
funding, which can come from outside the company (debt financing) or through
self-financing by issuing shares (equity financing), or a combination of both (Şamiloğlu-Riegermann,
2014).
Modigliani-Miller
(MM) theory (Modigliani
& Miller, 1958) states that the capital structure
will not affect the company's value. However, this theory assumes that there is
a perfect market with symmetrical information among economic actors. Contrary
to this theory, a study held in Vietnam found that capital structure will affect
a company's performance (Phan
et al., 2017). In addition to the MM theory,
there are other theories regarding capital structure in imperfect markets, such
as the trade-off theory, the pecking order theory, and the agency theory (Ahmed
& Bhuyan, 2020).
The
trade-off theory, developed by Kraus and Litzenberger
in 1973 and Myers in 1984, assumes that to maximize the company's value, the
company's management will trade off the cost of debt with the benefits of debt.
The benefits of debt are primarily obtained from tax reductions resulting from
a decrease in the company's profits due to interest payments. Meanwhile, debt
costs can be reduced by direct and indirect bankruptcy costs through an
increase in financial risk (Ricca
et al., 2021).
In
addition to the above studies, there is another theory in the field of
corporate finance known as the pecking order theory (Chen
et al., 2013). This theory proposes a specific
order in which companies prefer to obtain financing, with internal financing
being the first choice, followed by debt financing. Equity financing is
considered a last resort and is only pursued when other sources of financing
have been exhausted.
Lastly,
the agency theory, developed by Jensen and Meckling
in 1976, focuses on the relationship between different company stakeholders,
particularly shareholders, and management. This theory suggests that the
interests of these parties may not always be aligned, and conflicts of interest
may arise. The agency theory proposes that the optimal capital structure can
impact a company's value by minimizing these conflicts of interest. If the
capital structure is structured to align the interests of various stakeholders,
it can increase the company's overall value. It encourages all stakeholders to
work together to maximize the company's value (Singh
& Bagga, 2019).
The
influence of Capital Structure on profitability in various industries has been
a research topic for some scholars. Additionally, numerous studies have been
conducted on the relationship between capital structure and firm performance in
developing and developed countries. Some studies found a non-linear
relationship between capital structure and firm performance, which can have positive
and negative impacts. In developing countries, the capital structure tends to
have a negative effect on firm performance, while in developed countries, it
tends to have a positive impact (Detthamrong
et al., 2017).
A
study on 30 selected companies from the FTSE-100 Index on the London Stock
Exchange also examined the impact of capital structure on firm profitability.
The results showed that the debt-to-equity ratio (DER) has a significant
positive impact on return on equity (ROE) but has a significant negative impact
on return on assets (ROA) and return on invested capital (ROIC). A further
study recommended that to achieve the targeted efficiency level in business,
companies should use an optimal capital structure level and effective resource
utilization and allocation (Nasimi,
2016). A study using panel data analysis
of 208 Canadian non-financial firms listed on the Toronto Stock Exchange
between 1999 and 2016 found that factors such as age, liquidity, asset
tangibility, size, growth opportunities, and profitability are significant
determinants of capital structure (Gebru
et al., 2021).
In
a different study focused on the Indian service sector, the researchers used
panel data to investigate the impact of capital structure on firm performance.
The results indicated that short-term debt to total assets and long-term debt
to total assets had a negative and significant relationship with firm
performance, as measured by ROA, ROCE, and EPS (Farhan
et al., 2020). Similar research was also
conducted by C�mara (2022), who examined the effect of the
TLTA and TETA ratios on the ROA and ROE of telecommunications companies in the
United States. The study found that TLTA and TETA significantly impact ROA,
while they do not affect ROE.
H1:
Capital structure affects ROIC
H2:
Capital structure affects EVA
H3:
Inflation has a negative impact on ROIC
H4:
Inflation does not affect EVA
H5:
Interest rates have a positive impact on ROIC
H6:
Interest rates have a positive impact on EVA
Research Method
Data
The
data was collected using purposive sampling based on specific considerations.
The population in this study is State-Owned Enterprises (SOEs) that carry out
national strategic projects. The sample criteria used in this study are SOEs
that carry out national strategic projects in the road, port, railway, and
airport sectors listed in Presidential Regulation Number 109 of 2020 on the
Third Amendment to Presidential Regulation Number 3 of 2016 concerning the
Acceleration of National Strategic Project Implementation and SOEs that have
not undergone any corporate merger during the study sample year (2011 to 2021).
The data collection was carried out by collecting secondary data from the
Company's Annual Reports and Bloomberg. From the criteria that have been set, 9
(SOEs) meet these criteria as follows:
Table 1
Criteria
|
No |
Company |
|
1 |
PT Adhi Karya (Persero) Tbk |
|
2 |
PT
Pembangunan Perumahan (Persero)
Tbk |
|
3 |
PT Wijaya Karya (Persero) Tbk |
|
4 |
PT Jasa Marga (Persero) Tbk |
|
5 |
PT Waskita Karya (Persero) Tbk |
|
6 |
PT Hutama Karya |
|
7. |
PT Kereta Api (Persero) |
|
8. |
PT Angkasa Pura I (Persero) |
|
9. |
PT Angkasa Pura II (Persero) |
Table 2
Variable
|
Variable |
Formula |
|
|
Independent |
Debt to
Equity Ratio |
DER =
Total Debt / Total Equity |
|
|
Macroeconomic |
Inflation:
Yearly inflation rate released by BPS Interest Rate: BI Rate |
|
Dependent |
Economic
Value Added (EVA) |
EVA =
invested capital x (ROIC �WACC) |
|
|
Return
on Invested Capital (ROIC) |
ROIC = NOPAT / Total Invested Capital |
Data Analysis
The
data analysis in this research uses the panel data regression method, using the
software Eviews 12. Specifically, the model can be
presented as follows:
Yit
= a + b1X1it + b2X2it + b3X3it+uit
Where:
Yit = ROIC or Economic Value Added for
the company i in the year t
a = Constanta
b = Regression coefficient
X1it = Debt to Equity Ratio for a
company i in the year t
X2it = Inflation for a company i in the year t
X3it = Interest Rate for a company i in the year t
uit = Standard Error
Result
and Discussion
The aim is to determine whether there is an influence between the Debt to
Equity Ratio, Inflation, and BI Rate on ROIC and EVA. Hypothesis testing and
data analysis were performed through descriptive analysis and statistical
analysis of panel data regression models.
Descriptive
Analysis
Based on the model used, statistical descriptions of data
for independent variables and bound variables are obtained as shown in Table 3
below:
Table 3
|
` |
DER |
INFLASI |
BIRATE |
ROIC |
EVA |
|
�Mean |
107.849192 |
4.019091 |
5.613636 |
7.084343 |
-2.655859 |
|
�Median |
94.210000 |
3.350000 |
5.750000 |
7.210000 |
-2.410000 |
|
�Maximum |
571.030000 |
8.380000 |
7.750000 |
23.770000 |
13.090000 |
|
�Minimum |
3.320000 |
1.680000 |
3.500000 |
-5.070000 |
-16.000000 |
|
�Std. Dev. |
85.482179 |
2.188941 |
1.450841 |
5.021224 |
4.935918 |
|
�Skewness |
2.378212 |
1.212367 |
0.119899 |
0.044501 |
-0.167832 |
|
�Kurtosis |
12.159315 |
3.122556 |
1.724836 |
3.942610 |
3.998029 |
|
|
|
|
|
|
|
|
�Jarque-Bera |
439.381096 |
24.314232 |
6.944627 |
3.697797 |
4.573519 |
|
�Probability |
0.000000 |
0.000005 |
0.031045 |
0.157410 |
0.101595 |
|
|
|
|
|
|
|
|
�Sum |
10677.070000 |
397.890000 |
555.750000 |
701.350000 |
-262.930000 |
|
�Sum Sq. Dev. |
716105.888935 |
469.563218 |
206.284091 |
2470.843232 |
2387.601602 |
|
|
|
|
|
|
|
|
�Observations |
99 |
99 |
99 |
99 |
99 |
From the table, it can be seen that from 2011 to 2021,
the mean DER of 9 SOEs was 107.84%, with a median of 94.21%. The maximum DER is
571.03% which is the DER of PT Waskita Karya (Persero) Tbk. in 2020, and the minimum DER is 3.32% which is the DER
of PT Angkasa Pura II in 2014.
Furthermore, Inflation, with a mean of 4.01%, a median of
3.35%, and a maximum of 8.38%, is Inflation in 2013, and a minimum of 1.68% is Inflation
in 2020. In line with Inflation, the BI Rate also experienced a maximum rate in
2014 of 7.75% and a minimum rate of 3.5% in 2021, with an average of 5.61% in
2011-2021 and a median of 5.75%.
In the dependent variable, ROIC, the mean is 7.08%, and
the median is 7.21%. The maximum ROIC is 23.77% which is the ROIC of PT Adhi Karya (Persero)
Tbk in 2011. Meanwhile, the minimum ROIC is the ROIC
from PT Angkasa Pura I (Persero)
in 2021 of -5.07%.
The average EVA variable was -2.65%, and the median was
-2.41%. Similar to ROIC, the maximum EVA of 13.09% is also the EVA of PT Adhi Karya (Persero)
Tbk in 2011, and the minimum EVA of PT Angkasa Pura I (Persero) in 2021
of -16.0%.
Regression Model
Analysis
Panel
data regression model analysis is carried out through several stages, including
the selection of panel data regression model estimates, classical assumption
tests, hypothesis tests, and interpretation of test results. The selection of
panel data regression model estimates is carried out with statistical
considerations through testing, with the aim of obtaining precise and efficient
guesses about the relationship between dependent and independent variables. A
classical assumption test is performed to check whether the panel data
regression model has satisfied the classical assumptions. The hypothesis test
was performed to test the significance of the influence of the independent
variable on the dependent variable. Finally, the test results are interpreted
to provide conclusions regarding the relationship between dependent and
independent variables and the strength and direction of the relationship. By
conducting panel data regression model analysis, researchers can understand the
relationship between variables in research and answer problem formulations more
comprehensively.
1. Model
Testing
Panel data were used in this study because it combines
the use of time series data and cross-section data, which can
provide a larger amount of data and increase the degree of freedom. The time
series data in this study covers an 11-year period from 2011 to 2021, while
the cross-section data involves 9 SOEs involved in the implementation of
PSN. The selection of panel data regression model estimates was carried out
using three approaches, namely the Common Effect Model (CEM), Fixed Effect
Model (FEM), and Random Effect Model (REM). The CEM approach assumes that all
units (SOEs) in the study have the same effect, while the FEM approach assumes
a unique fixed effect on each unit (SOEs). Meanwhile, the REM approach assumes
that the effect is random and unknown beforehand. By considering these three
approaches, researchers can choose the most suitable and accurate model to
perform panel data analysis in the study. Model testing was carried out twice,
first testing the effect of the independent variable on the dependent variable
ROIC and second testing the effect of the independent variable on the dependent
variable EVA.
a.
Model
Testing for ROIC Variables
Table 4
Results of Panel Data Regression
with CEM � ROIC
Results of Panel Data Regression
with FEM � ROIC
Table
6
Results of Panel Data Regression with REM � ROIC
After testing the model, the next step is to determine
the estimated model to be used by conducting a Chow, Hausman, and Lagrange multiplier
lag test.
1)
F
test (Chow Test)
This
test compares which model is best between CE and FE. If Prob > α, then
the selected model is CE; otherwise, if Prob < α, then the selected
model is FE.
F - ROIC Test Results
Based
on the results of the F test, the probability value is 0.0001, or < 0.05, which
means that the FE model is more appropriate to use than the CE model.
2)
Hausman test
The
Hausman Test was conducted to compare the most appropriate model between FE and
RE. If Prob > α, then the selected model is� RE; otherwise, if Prob < α, then the
selected model is FE.
Hausman Test Results - ROIC
Based
on the results of the Hausman test, the probability value is 1.00, or >
0.05, which means that the RE model is more appropriate to use than the FE
model.
3)
Lagrange
Multiplier Test
LM-Test
is conducted to compare which model is most appropriate between CE and RE. If LMcalculate > Chi Squared Table, then the RE model is
selected, but if LMcalculate < Chi Squared Table,
then the model chosen is CE. From the results of the Lagrangge
multiplier test (Appendix 1), LMcalculate is 20.6373,
which is greater than t = 0. 3518. This means that the RE model is more
appropriate than the CE model.
Based
on the test results of the above model, it was found that the RE model was
selected twice in the Hausman test and the Lagrange test, while the FE model
was only selected in the Chow test. The CE model was not selected at all.
Therefore, it can be concluded that the ER model is better at explaining the
relationship between the independent variable and the dependent variable ROIC
in this study. The RE model can provide more accurate and consistent results in
estimating the effect of independent variables on ROIC on SOE panel data
involved in PSN implementation.
b. Model Testing for EVA Variables
Table 9
Panel Data Regression Results
with CEM - EVA
Table
10
Panel
Data Regression Results with FEM - EVA
Table
11
Panel
Data Regression Results with REM � EVA
Table 22
F test
Based
on the results of the F test, the probability value is 0.0001, or < 0.05,
which means that the FE model is more appropriate to use than the CE model.
2)
Hausman
Test
Table 33
Hausman Test Results
Based on the results of the Hausman test, the probability
value is 1.00, or > 0.05, which means that the RE model is more appropriate
to use than the FE model.
3) Lagrangge
Multiplier Test
From the results of the Lagrangge
multiplier test (Appendix 2), the LM calculation is 23.07083, which is greater
than t = 0. 3518. This means that the RE model is more appropriate than the CE
model.
Based on the test results of the above model, it was
found that the RE model was selected twice, namely in the Hausman test and the
Lagrange test. Meanwhile, the FE model was only selected on the Chow test, and
the CE model was not selected at all. Thus, it can be concluded that the ER
model is better at explaining the influence of the independent variable on the
EVA dependent variable in this study. The RE model provides more accurate and
reliable results in estimating the effect of independent variables on EVA on
the panel data of SOEs involved in the implementation of PSN.
2. Classical Assumption Test
The classical
assumption test is a statistical test performed to determine the relationship
between variables. The classical assumption test in this study is only a
multicollinearity test, which aims to see the correlation between independent
variables. Studies using panel data should not use all classical assumption
tests for the following reasons: linearity tests are hardly performed on every
linear regression model because it is assumed that the model is already linear;
normality tests provide results that are not actually intended for studies that
use samples of more than one company, panel data, and secondary data;
autocorrelation tests provide results that will be more meaningful to the study
which only uses time series data, and heteroscedasticity tests provide results
that are not intended for studies that use panel data because the data is
assumed to already have heteroscedasticity
Based on these
reasons, the classical assumption test used in this study is only a
multicollinearity test. From Table 4.13, The results of the multicollinearity
test show that the correlation value between independent variables does not
exceed 0.90, so it is concluded that there is no multicollinearity between
independent variables.
Table 44
Multicollinearity Test Result
|
|
DER |
INFLASI |
BIRATE |
|
DER |
1.00000 |
-0.37165 |
-0.45776 |
|
INFLASI |
-0.37165 |
�
1.00000 |
0.779724 |
|
BIRATE |
-0.45776 |
0.779724 |
� 1.00000 |
3. Feasibility
test model
Feasibility tests model are used to evaluate whether the
data regression model formed is feasible or not feasible to be used as an
explanation of the influence of the independent variable on the dependent
variable. In this study, the next step is to conduct a model feasibility test
after selecting a panel data regression estimation model using the Random
Effect Model (REM) approach. The model feasibility test consists of a
hypothesis test (partial test or F test, as well as a simultaneous test or t-test)
and an R2 determination test. The purpose of the model feasibility test is to
determine the extent to which the selected regression model can explain
variations in the dependent variable based on the influence of the independent
variable under study.
4. Hypothesis test
The hypothesis test is used to test the significance of
the regression coefficients obtained in the regression model of the panel data.
In hypothesis decision-making, the probability value is compared to the
predetermined α significance level (usually 0.05). There are two types of
hypothesis tests: partial tests (t-tests) and simultaneous tests (F tests).
a)
Partial
Test
Partial tests are used to test the significance
individually of each independent variable. Suppose the probability value
(p-value) resulting from the t-test is less than the established significance
level (α). In that case, the null hypothesis is rejected, meaning that
there is a significant influence between the partially independent variable and
the dependent variable. Meanwhile, suppose the p-value is greater than α.
In that case, the null hypothesis is accepted, meaning that there is no
significant influence between the partially independent variable and the
dependent variable.
Table 55
T- test � ROIC
Based on the table, it can be seen that the DER variable
has a regression coefficient of -0.02287 and a t-statistic value of -3.784145
with a probability of 0.0003, which is smaller than α=0.05, so it can be
concluded that the DER, variable significantly and negatively affects ROIC. Meanwhile, the Inflation variable has a regression
coefficient of 0.180855 with a t-statistic value of 0.6642 and a probability
value of 0.5082 which is greater than α=0.05, so it can be concluded that
the Inflation variable does not significantly affect ROIC. Furthermore, the BI
Rate variable has a regression coefficient of 0.918367 with a t-statistic value
of 2.09659 and a probability value of 0.0387, which is smaller than
α=0.05, so it can be concluded that the BI Rate variable significantly and
positively affects ROIC.
Table 66
T- test � EVA
From the table, it can be seen that the DER coefficient
has a value of -0.018952 with a standard error value of 0.006254 and
t-statistics of -3.030412. The probability value (Prob.) of 0.0031 is less than
the significance level α = 0.05, so it can be concluded that DER partially
has a negative and significant effect on EVA. The BI Rate coefficient has a
value of 0.55247 with a standard error value of 0.451988 and t-statistics of
1.22231. The probability value (Prob.) of 0.2246 is greater than the
significance level α = 0.05, so it can be concluded that partially the BI
Rate does not have a significant effect on EVA.
b)
�Simultaneous Test
Table 77
F-test � ROIC
Based on the table,
the value of the F-statistic is 20.14442 with a probability value (Prob
(F-statistic)) of 0 or less than α 0.05, meaning that simultaneously the
independent variable has a significant effect on the dependent variable ROIC.
Table 88
F-test � EVA
In the table, the F test is used to simultaneously test
the significance of the effect of the independent variable (DER, INFLATION, BIRATE)
on the dependent variable EVA. The F-statistic value obtained is 13.57707 with
a probability value of 0, which shows that simultaneously the independent
variable has a significant effect on the dependent variable.
c)
�Determination
test
The coefficient of determination test (R2 test) is useful
for evaluating how much influence the independent variable has on the dependent
variable.
Table 99
Determination test
� ROIC
In the table, an R-squared value of 0.388805 means that the
independent variables (DER, Inflation, and BI rate) can explain 38.88%
variation in the dependent variable (ROIC). This value also indicates that the
regression model used is good at explaining the relationship between
independent and dependent variables. And 61.12% were explained or illustrated
by other variables that were not included in this study.
Table 20
Determination test
� EVA
Based on the table of the results of the coefficient of
determination test (R2 test), it can be known that the R-squared value is
0.300087� or 30.00%. From the results of
the coefficient of determination test (R2 test), it can be interpreted that the
independent variables, namely DER, Inflation, and BI rate, can explain or
describe the dependent variable, namely EVA of 30.00%. And 70.00% were
explained or illustrated by other variables that were not included in this
study.
5.
Analysis
The
test results showed that the DER variable significantly and negatively affected
ROIC and EVA. These results are in line with the trade-off theory developed by
Kraus and Litzenberger in 1973; and Myers, 1984,
where ethical leverage is high enough, increasing debt ratios can decrease a
company's performance because the benefits of debt are outweighed by the costs
of debt, including financial losses and debt agency costs. This research is
also in line with the results of (Le
& Phan, 2017; Nasimi, 2016). In
contrast to capital structure, testing results show that Inflation has no
effect on ROIC and EVA. Although Inflation is often considered a factor that
can affect a company's financial performance, the test results show that Inflation
has no effect on ROIC and EVA in SOEs implementing PSN in Indonesia. This
suggests that other factors may be more influential in determining the
financial performance of these SOEs. One of the factors that can affect the
test results is the business characteristics of the infrastructure sector,
which tend to be more stable and less affected by fluctuations in Inflation.
Infrastructure businesses tend to require large and long-term investments and
be involved in projects with certain contracts and agreements. This makes
inflation fluctuations not greatly affect the financial performance of
infrastructure SOEs because these contracts and agreements allow SOEs to earn a
fixed income for a set period of time.
The
results showed that the BI rate positively and significantly affected ROIC in
SOEs implementing PSN in Indonesia. However, the BI rate has no significant
effect on EVA. This shows that the effect of the BI rate on ROIC and EVA has a
significant difference. The BI rate is an interest rate set by Bank Indonesia
to regulate borrowing costs and stabilize the rupiah exchange rate. The BI rate
affects the company's cost of capital and, in turn, can affect the company's
financial performance. In the context of SOEs implementing PSN, the BI rate can
affect ROIC. The difference in the effect of the BI rate on ROIC and EVA can be
explained by the different concepts used in measuring financial performance.
ROIC measures a company's efficiency in generating profits with its capital. In
this case, an increased BI rate can affect the company's cost of capital and,
in turn, can decrease efficiency in capital use, thus affecting ROIC.
Meanwhile,
EVA measures the added value generated by a company after accounting for the
cost of capital. In this case, the effect of the BI rate on EVA is more complex
because it involves other factors, such as asset management and a more specific
cost of capital. In addition, the difference in the effect of the BI rate on
ROIC and EVA can also be influenced by other factors such as capital structure,
business risk, and different market conditions. Different capital structures
can affect a company's cost of capital and affect the effect of the BI rate on
EVA. For example, if a company has a capital structure that uses more debt,
then an increased BI rate can affect the interest costs that the company must
pay and, in turn, affect the EVA.
In
addition, the effect of the BI rate on EVA can also be influenced by business
risks faced by the company. Higher business risk can make an increased BI rate significantly
affect the company's financial performance. In addition, keep in mind that the
results of the study may only apply to SOEs implementing PSN in Indonesia and
cannot be generalized to other companies. Market conditions and regulations
applicable in other countries can affect the effect of the BI rate on the
company's financial performance. Therefore, SOEs need to conduct a more
in-depth analysis to understand the effect of the BI rate on financial
performance.
Conclusion
In
conclusion, this study found that in the context of Indonesian State-Owned
Enterprises (SOEs) implementing PSN, the Debt-to-Equity Ratio (DER) has a
significant and negative effect on both the Return on Invested Capital (ROIC)
and the Economic Value Added (EVA). The BI rate, set by Bank Indonesia, was
shown to positively and significantly affect ROIC, but it did not significantly
impact EVA. This disparity may be due to the distinct financial performance
metrics used in determining ROIC and EVA. Contrarily, Inflation was found to
have no impact on either ROIC or EVA. This outcome suggests that the unique
characteristics of infrastructure businesses, which are more stable and less
susceptible to inflation fluctuations, play a role in their financial
performance. These findings stress the importance of SOEs conducting in-depth
analyses to comprehend the impacts of factors such as DER and the BI rate on
their financial performance. However, it should be noted that these findings
may be specific to Indonesian SOEs implementing PSN and may not be
generalizable to other contexts owing to different market conditions and
regulations.
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