THE IMPACT OF MACROECONOMIC AND BANKING
PERFORMANCE ON LENDING TO THE INFRASTRUCTURE
SECTOR
Introduction:
The infrastructure sector is an important sector in supporting economic development.
The infrastructure sector plays a role in driving the real sector and can provide a multiplier
effect that directly affects the lives of many people (Ganelli and Tervala 2016). World Bank
(1994) divides infrastructure into three main components, one of which is economic
infrastructure. This infrastructure includes public utilities (electricity, telecommunications,
water, sanitation, gas), and public works (roads, railways, ports, airfields, and so on).
Meanwhile, according to Grigg (1998) infrastructure includes transportation, irrigation,
buildings and other public facilities needed to meet basic human needs.
Research conducted by Prasetyo and Firdaus (2009), states that the availability of
infrastructure has a very important role in supporting regional economic development and
income. The World Development Report (World Bank 1994) also found that infrastructure
plays an important role in increasing economic growth. In addition, higher economic growth
is usually found in areas with sufficient infrastructure availability.
When compared to other countries in the world, Indonesia's infrastructure condition is
still far behind. Based on Table 1, it can be seen that the quality of Indonesia's infrastructure
is still lagging behind other countries in the world, such as Singapore, Japan, China and
India. Based on data from the World Bank (2016), the highest ranking for Asia is Singapore,
second out of 144 countries in the world. Meanwhile, Indonesia's overall infrastructure
quality is ranked 80th. Indonesia is above Vietnam (85th), but below Russia (74), Thailand
(72), India (51), China (43), and Malaysia (19). Ranking 80 out of 144 countries is an
indicator that Indonesia needs to improve competitiveness through infrastructure
development.
The importance of infrastructure for a country is to avoid high cost economy, so that
the burden of logistics costs borne by producers can be reduced. In addition, infrastructure
also acts as a driver of economic growth and national development. Infrastructure also
encourages foreign and domestic investment interest. Infrastructure also contributes to the
smooth distribution of goods and services between regions.
Indonesia's lack of infrastructure quality is inseparable from funding issues. Based on
data from the Ministry of Finance in 2016, Indonesia's infrastructure investment spending is
still low and inadequate to finance infrastructure development that covers Indonesia's vast
territory. Spending on infrastructure from the state budget in 2013 was only around 2.3
percent of the Gross Domestic Product (GDP) or Rp 203 trillion. When combined with other
sources (APBD, state-owned enterprises and the private sector) total spending on
infrastructure reached IDR 438 trillion or 4.7 percent of GDP. Thus, infrastructure
development in Indonesia still relies on APBN and APBD funds, while the role of the private
sector has not been significant.
The infrastructure sector needs support in obtaining credit financing. If it only relies
on financing from the state budget, then infrastructure development will be slow. Data from
PT Sarana Multi Infrastruktur (SMI) in 2016 states that the state budget is only able to
finance 41 percent of the total existing infrastructure financing budget. It takes 22 percent of
funding from SOEs and 37 percent of funding from the private sector. The banking sector,
which is one of the driving forces for the distribution of capital from the private sector, plays
an important role in this regard, especially in the form of lending from third parties to the
infrastructure sector.
Financing Indonesia's infrastructure requires contributions and support from the
government and all Indonesian people, along with institutional aspects. One institution that
has an important role in funding is the banking sector. Banks play an important role in
providing capital assistance to the real sectors in Indonesia through the distribution of funds
in the form of credit. Banks have an advantage in channeling credit compared to other
financial institutions. Non-bank financial institutions, such as cooperatives, generally do not
have as much capital as banks, so the volume of credit disbursed is not greater than banks.
Another advantage of banks is their ability to anticipate the risk of default by customers
through borrower history data and credit usage guidance (Wicaksono, 2007).
The banking industry plays an important role in the economy as an intermediary
institution that channels public funds into productive asset investments that will boost real
sector productivity, capital accumulation, and aggregate output growth (Bencivenga and
Smith 1991; Hung and Cothern 2002). The main function of banks in Indonesia as
intermediary institutions is to collect and distribute funds to the public. Funds collected from
the public can be in the form of savings, deposits and current accounts, which will then be
channeled in the form of credit. Distribution of funds in The form of credit is emphasized to
drive the real sector. These banking activities aim to support the implementation of equitable
development, economic growth and national stability, so that public welfare is achieved
(Bank Indonesia 2015).
Indonesian Banking Statistics classifies 18 economic sectors receiving credit, among
which there are three sectors included in the infrastructure sector, namely the electricity,
water and gas sector, the construction sector and the transportation, communication and
storage sector. Based on data from Indonesian Banking Statistics, the average percentage of
lending by commercial banks in the infrastructure sector is only 10 percent. The low lending
in the infrastructure sector can be influenced by many things, both from the banking side and
Indonesia's macroeconomic conditions. According to Salachas et al (2016) the amount of
credit disbursed by banks is significantly positively influenced by central bank interest rates.
Meanwhile, Agung et al (2001) state that lending is influenced by banking performance as
seen from banking assets, Non Performing Loan (NPL) ratios, banking capital, and the
availability of loanable funds.
Research conducted by Reed (1989) shows that the factors that influence the amount
of credit disbursed by banks can come from internal and external banking conditions, and can
come from macroeconomic factors in a country. In general, the factors that influence the
amount of credit disbursed by banks are the bank's capital position, the risk of the type of
credit, the stability of funds from third parties, economic conditions, prevailing fiscal and
monetary policies, and credit needs in a region. According to Kasmir (2008) banking
performance variables can be viewed from the ratio of liquidity, solvency, profitability, NPL
sector to be observed and DPK. While Indonesia's macroeconomic variables can be seen
from the variables of inflation, SBI interest rates, investment lending rates and working
capital lending rates (Budiarti 2012).
The performance approach used in this research is because performance is a reflection
of the bank's ability to manage and allocate its resources. Meanwhile, good macroeconomic
conditions reflect the stability of economic conditions. The better performance should affect
the greater lending as well. Therefore, this study focuses on the relationship of
macroeconomic and banking performance variables to infrastructure sector lending in
Indonesia. The difference between this study and previous studies is that apart from the time
period, this study compares lending in three infrastructure sectors, namely electricity, water
and gas, construction and transportation, communication and warehousing.
Problem Formulation
Infrastructure is an important sector to drive economic growth. Adequate
infrastructure can reduce production costs, attract foreign investment and reduce the level of
income inequality between regions. The infrastructure sector provides many jobs and
contributes to development and national income.
The infrastructure sector needs support in obtaining credit from the banking sector as
a source of funding from the private sector. Although the amount of credit from 2010 to 2015
tended to increase, the proportion of infrastructure sector lending to the total credit disbursed
by commercial banks to the economic sector was still low, averaging 10 percent from 2010 to
2015 (SPI 2016).
Based on Figure 1, it can be seen that in the period 2010 to 2015 there was an increase
in the amount of commercial bank loans distributed to the economic sector. This shows that
banking conditions are getting better and public confidence in keeping their money in the
bank is also increasing. Although the amount of credit from 2010 to 2015 tended to increase,
the proportion of infrastructure sector lending to total credit disbursed by commercial banks
to the economic sector was still low, which had an average of 10 percent throughout 2010 to
2015 (SPI 2016).
Figure 3 shows the changes in the amount of loans disbursed by commercial banks in
Indonesia to the infrastructure sector from 2011 to 2015. The three infrastructure sectors
experienced different trends throughout the period, although all three sectors experienced
upward trends. The most stable upward trend was in the construction sector, while the
electricity, water and gas sector fluctuated slightly. The transportation, warehousing and
communication sector received the largest amount of credit among other infrastructure
sectors, and the trend tended to be constant.
Based on the data above, it can be seen that the distribution of infrastructure loans in
the last five years has not experienced a large increase and between infrastructure sectors
have different trends. In the electricity, water and gas sector, the distribution only reached
three percent of the total commercial bank loans disbursed. Meanwhile, the transportation,
communication and warehousing sector reached six percent. The low credit received by the
electricity, water and gas sector is quite alarming because this sector is the source of energy
for production. The significant difference in the amount of credit between infrastructure
sectors is interesting to study because this could lead to inequality in development between
infrastructure sectors.
There are several things that can cause a decrease and increase in the amount of
lending in the three infrastructure sectors above, which can be caused by macroeconomic
conditions and banking performance. Research related to the impact of macroeconomic and
banking performance on lending in the three infrastructure sectors has not been widely done,
so it is very interesting to analyze further.
Infrastructure Sector
Infrastructure is a physical system that provides transportation, irrigation, drainage,
buildings and other public facilities needed to meet basic human needs, both social and
economic (Grigg 1998). Based on this definition, there are three infrastructure sectors that
receive lending from commercial banks, namely the electricity, water and gas sector, the
construction sector and the transportation, communication and storage sector.
Construction is an activity whose end result is a building or construction that is integrated
with the land where it is located. Construction activities include planning, preparation,
manufacture, demolition, and repair or remodeling of buildings (BPS 2015). The manufacturing
industry sector and the non-oil and gas mining sector are sectors that provide raw materials for
construction in the form of industrial products (cement, iron, steel) and excavation materials such
as sand, lime, and so on. Meanwhile, other business sectors such as agriculture, trade, oil and gas
mining and the service sector are users of construction sector products.
The transportation, communication and storage sector has a role as a service provider
for population mobility and the economy. The transportation subsector covers public
transportation activities for goods and passengers by land, sea, river, lake and air. This
includes passenger services that include the provision of services or facilities that support and
facilitate transportation activities, such as terminal services, ports, loading and unloading, toll
roads, warehousing and other supporting services. The communication subsector acts as a
provider of telecommunication services that can connect islands throughout Indonesia. The
communication subsector includes various activities related to the world of information such
as radio and TV broadcasting, publishing books, journals, famphlets and newspapers. In
addition, the communication sector also provides telecommunication services such as
programming, computer consulting both hardware and software, web hosting services, and
the provision of voice transmission networks and transmitters (BPS 2015).
Macroeconomic Conditions
Macroeconomic conditions describe the economy as a whole, including income
growth, price changes, and unemployment rates. By looking at the state of a country's
macroeconomic conditions, regulators both the government and the monetary authorities can
take a policy that aims to improve economic performance (Mankiw 2007). Macroeconomic
conditions have a strong influence on the performance of the banking sector and bank lending
to the real sector. The existence of economic fluctuations characterized by increases and
decreases in economic activity is relatively highly variable.
The following are some of the macroeconomic factors that affect the amount of credit
extended:
1. Inflation Rate
Inflation is a value where the general price level of goods and services increases and
there is a decrease in the value of money. Inflation is generally measured by changes in the
prices of a group of goods and services that are consumed by the majority of the population.
High inflation is usually associated with economic conditions experiencing demand for
products that exceed the supply capacity of these products. This is also referred to as an
overheated economy. Such conditions will reduce the purchasing power of money and reduce
the level of real income earned by investors from their investments (Budiarti 2014).
The level of the inflation rate greatly affects economic conditions, especially banking
activities. The high inflation rate has caused Bank Indonesia (BI) to issue regulations to
increase lending rates for banks in Indonesia. This is done so that inflation can be controlled.
As a result, banks are forced to raise lending rates so as not to experience negative spread,
which is a condition where the deposit interest rate is higher than the lending rate. This will
make it difficult for banks to carry out their activities.
2. BI Rate
Based on the definition provided by Bank Indonesia, the BI Rate is a monetary policy
interest rate set by Bank Indonesia and announced to the public. The BI Rate is announced by
the Board of Governors of Bank Indonesia through a board of governors meeting held every
month and is implemented in monetary operations conducted through liquidity management
in the money market to achieve the operational objectives of monetary policy.
Bank Indonesia will generally raise the BI Rate if future inflation is expected to
exceed the predetermined target, conversely Bank Indonesia will lower the BI Rate if future
inflation is expected to be below the predetermined target.
3. Industrial Production Index (IPI)
According to the Central Bureau of Statistics, the Industrial Production Index (IPI) is
an index number that describes the development of industrial sector production early because
it is designed on a monthly periodic basis. A good index number illustrates the conducive
state of a country's economy, which means it is a good signal for investors to invest in the
real sector (Fahmi and Hadi 2010). In this study, IPI is used to replace real GDP in
representing Indone economic growth.
Classical Theory of the Quantity of Money
The quantity theory of money was developed by Classical Economists, the quantity
theory of money is the theory of how the sum of the values of aggregate income is
determined. The theory describes money held as a certain amount of aggregate income. An
important part of the theory states that the interest rate h a s no influence on the demand for
money (Mishkin 2007).
Money Velocity and Transaction Equation
This theory was first proposed by Irving Fisher in the book The Purchasing Power of
Money in 1911 (Mishkin 2006). Fisher examined the relationship between the quantity of
money M (Money Supply) and the total expenditure on goods and services produced in the
economy (P x Y). P is the price level and Y is total output (income). P x Y is also referred to
as the economy's aggregate income or GDP. The conceptual link between the quantity of
money and income is called the velocity of money.
Keynes' demand for money theory
JM Keynes refuted the classical theory of money demand that believes in constant
velocity and developed a theory of money demand that emphasizes the importance of the
interest rate. Keynes explained it in the book The General Theory of Employment, Interest,
and Money in 1936 about Keynes' theory of money demand known as Liquidity Preference
Theory (liquidity preference theory).
Keynes stated that the motive for people to hold money is the transaction motive
(transaction), precautionary, and speculation (speculative). Keynes distinguishes between
the nominal quantity and the real quantity of money demanded by the transaction,
precautionary and speculation motives. Money is valued for its utility. If prices increase
twice, money will be able to buy a portion of the quantity of goods. Keynes argued that
people hold money because the real money balance (M/P) is related to the level of income
(Y) and the interest rate (i).
The demand for money is negatively affected by the interest rate, when the interest
rate rises, f(i,Y) will fall, and the velocity of money turnover will rise. If the interest rate
rises, then people will be encouraged to hold less real money equilibrium at a certain level of
income, then the velocity of money turnover becomes higher. The implication of the liquidity
preference theory is that interest rates are always fluctuating which causes the velocity of
money circulation to fluctuate as well. The Keynesian model of speculative money demand
provides a reason for the velocity of money velocity not to be fixed. If people expect interest
rates in the future to be higher than today, they will expect bond prices to fall and will
anticipate capital losses. The expected income from owning bonds will fall, and cash will be
more attractive than bonds. As a result, the demand for money will increase, the real value of
money will fall and the velocity of money circulation will fall.
According to Keynes in the Liquidity Preference analysis, two factors that cause the
movement of money demand are income and price level. An increase in income causes the
demand for money to increase, otherwise if income decreases, it will reduce the demand for
money. Income has a positive effect on the demand for money. An increase in inflation will
cause the demand for money to increase and vice versa. Inflation has a positive effect on the
demand for money.
Money supply is fully controlled by the Central Bank, so the money supply is
autonomous. In order to look at the liquidity preference framework, we can use the interest
rate change analysis. We can see some application changes that can be useful in seeing the
effect of monetary policy on the interest rate. When income increases, the demand for money
increases and causes the interest rate to increase.
Equilibrium in the Money Market: Supply and Demand for Funds Loans
There is an equation between supply and demand for goods And services, and the supply and
demand for borrowed funds (credit). In this case, the "good" is borrowed funds and the price"
is the interest rate. The interest rate is the cost of borrowing and the return for lending funds
to the financial market, so the role of interest rates is more easily understood in the economy
by examining the money market.
Y- C- G = I
Y- C- G is the output that remains after consumer and government demand has been
met, which is called national savings (S). National savings represent the supply of borrowed
funds, and investment represents the demand for these funds. In this form, national income
shows that savings are equal to investment.
S = (Y - T - C) + (T -C) =I
Y - C (Y - T) - G = I (r)
The left-hand side of the equation shows that the supply of borrowed funds depends
on income and fiscal policy. The right-hand side shows that the demand for borrowed funds
depends on the interest rate. The interest rate is adjusted to balance the supply and demand
for borrowed funds.
The interest rate adjusts until the number of firms that want to invest equals the
number of households that want to save. If the interest rate is too low, investors want more
economic output than households want to save. In other words, the amount of borrowed
funds demanded exceeds the amount offered. When this happens, the interest rate increases.
Conversely, if the interest rate is too high, households want to save more than firms want to
invest, because the amount of loan funds offered is greater than the amount desired, the
interest rate falls. The equilibrium interest rate is at the intersection of the two curves at point
E in figure 4.
Monetary Policy Mechanism
The monetary policy mechanism describes Bank Indonesia's actions through changes
in monetary instruments and operational targets in influencing various economic and
financial variables. The mechanism occurs through interactions between the Central Bank,
the banking and financial sector, and the real sector. Changes in the BI Rate affect Inflation
through various channels, including the interest rate channel, credit channel, exchange rate
channel, asset price channel, and expectation channel (Bank Indonesia 2015).
Figure 5 illustrates the path of the monetary policy transmission mechanism. In the
interest rate channel, changes in the BI Rate affect deposit rates and bank lending rates. If the
economy is in recession, Bank Indonesia can use expansionary monetary policy through
interest rate cuts to encourage economic activity. A reduction in lending r a t e s will also
lower the cost of capital for companies to invest. This will all increase consumption and
investment activities, thus increasing economic activity.
If inflation rises, Bank Indonesia responds by raising the BI Rate to prevent economic
activity that is too fast so as to reduce inflation (Bank Indonesia 2015). The mechanism of
monetary policy transmission through the credit channel is that Bank Indonesia lowers the BI
Rate which will cause the interbank rate to fall and make the deposit rate fall. This will cause
the loan rate to fall so that bank loans increase and have implications for increasing
economic growth.
Non-Performing Loan (NPL)
Non-Performing Loan (NPL) shows the collectability of a bank in collecting back the
loans issued by the bank until it is paid off. NPL is the percentage of non-performing loans
(with substandard, doubtful, and loss criteria) to total loans issued by the bank. NPL has a
negative relationship with lending.
Profitability Ratio
Profitability ratios measure the level of business efficiency and profitability achieved
by the bank concerned, besides that it can be used to measure the health level of the bank.
Profitability is the basis of the relationship between operational efficiency and the quality of
services produced by a bank. Return on Asset (ROA) is one of the profitability ratios.
Kuncoro (2002) states that ROA shows the ability of bank management in managing
available assets to get net income
Framework of Thought
The availability of infrastructure is an important requirement to increase the
productivity and growth rate of the Indonesian economy. The provision of infrastructure is
inseparable from financing issues. Currently, the development of infrastructure projects in
Indonesia requires a lot of funding assistance, especially from banks as institutions that
channel funds to the real sector through credit. Based on the economic sectors receiving
credit classified by Indonesian Banking Statistics, there are three sectors included in the
infrastructure sector. The three infrastructure sectors are the electricity, water and gas sector,
the construction sector and the transportation, communication and storage sector.
During the period 2010 to 2015, the ratio of commercial bank lending to the
infrastructure sector was still low when compared to other economic sectors. The low credit
to the infrastructure sector can be influenced by several factors, both from macroeconomic
and banking performance. Macroeconomic performance indicators used in this study include
BI Rate, IPI and inflation, while banking performance includes ROA, DPK, BOPO, and NPL
for each sector. The research was conducted to see the response of lending in each sector to
shocks in macroeconomic and banking performance variables. To provide an overview of the
flow of thought in this study, the following research framework is depicted, as in Figure 6.
Research Hypothesis
The low distribution of infrastructure loans can be influenced by several factors, both
from the macroeconomic and banking sides. Unstable inflation, IPI (economic growth) and
BI Rate will affect the instability of the investment climate, thus reducing investment interest
in the real sector. Meanwhile, banking conditions will also affect the amount of credit
disbursed. The healthier the banking conditions, which are indicated by high profits and low
operating costs, the lending will increase. Based on the description above, the hypothesis of
this study is as follows:
1. Inflation has a negative influence on lending in the electricity, water and gas sector, the
construction sector and the transportation, communication and warehousing sector.
2. BI Rate has a positive influence on lending in the electricity, water and gas sector, the
construction sector and the transportation, communication and warehousing sector.
3. IPI has a positive influence on lending in the electricity, water and gas sector, the
construction sector and the transportation, communication and warehousing sector.
4. DPK has a positive influence on lending in the electricity, water and gas sector, the
construction sector and the transportation, communication and warehousing sector.
5. ROA has a positive influence on lending in the electricity, water and gas sector, the
construction sector and the transportation, communication and warehousing sector.
6. BOPO has a negative influence on lending in the electricity, water and gas sector, the
construction sector and the transportation, communication and warehousing sector.
7. NPL has a negative influence on lending in the electricity, water and gas sector, the
construction sector and the transportation, communication and warehousing sector.
Variables and Operational Definitions
The following are the variables used in the study along with their operational
definitions:
1. Loans for electricity, water and gas (K_LAG) is the amount of loans disbursed by
commercial banks to the electricity, water and gas business sector.
2. Construction credit (K_KONS) is the amount of credit extended by commercial
banks to the construction sector.
3. Credit for transportation, communication and warehousing (K_TKP) is the amount
of credit extended by commercial banks to the transportation, communication and
warehousing business sector.
4. Non-performing loan in the electricity, water and gas sector (NPL_LAG) is the
amount of non-performing loans in the electricity, water and gas sector to loans in
the electricity, water and gas sector by commercial banks.
5. Non-performing loan in the construction sector (NPL_KONS) is the amount of non-
performing loans in the construction sector against construction sector loans by
commercial banks.
6. Non-performing loans in the transportation, communication and warehousing sector
(NPL_TKP) is the amount of non-performing loans in the transportation,
communication and warehousing sector to loans in the transportation,
communication and warehousing sector by commercial banks.
7. Third Party Funds (DPK) is the amount of third party funds raised by commercial
banks.
8. The inflation rate (Inflation) describes a generalized and continuous rise in prices.
9. Profitability Return on Asset (ROA) measures the ability of bank management to
earn profits generated from the bank's average total assets.
10. Efficiency Operating Expenses to Operating Expenses (BOPO) measures the ability
of bank management to control operating costs. So that the smaller the BOPO ratio,
the smaller the operating costs incurred.
11. The BI Rate (BIRATE) is an interest rate set by Bank Indonesia and is an instrument
of monetary policy.
12. The Industrial Production Index (IPI) is a proxy for national output. In order to
obtain monthly data, national output is proxied by the IPI which is a measure of the
output of medium and large industries on a monthly basis and expressed as an index.
13.
Data Analysis and Processing Methods
The method of analysis used in this research is the Vector Error Correction Model
because there is a cointegration relationship in variables that are not stationary at the level,
but stationary at first difference.
Data processing is carried out in stages, before arriving at the VAR and VECM
analysis, several pre-estimation tests need to be carried out, namely, data non-stationarity test
or unit root test, determination of the optimum lag length, and VAR stability test.
Furthermore, Granger causality test, cointegration test, VECM, impulse response function
(IRF), and forecast error decomposition of variance (FEDV) techniques will be conducted.
The following are the testing stages carried out in this study:
1. Data Non Stationarity Test
One of the important requirements for applying the time series model is the
fulfillment of normal or stable data assumptions from the variables forming the regression
equation. The use of data in this study has the potential to cause non-stationary data due to
the presence of a unit root at the level level, so in this study it is necessary to conduct a
stationary test. This stationarity test is conducted using the Augmented Dickey Fuller test at
the level and first difference level.
2. Optimal Lag Test
Determining the optimum lag aims to show how long the reaction of a variable to
other variables and eliminate autocorrelation problems in a VAR system (Firdaus, 2011).
Testing the lag length is determined based on the smallest Akaike Information Criterion
(AIC), Schwarz Criterion (SC) and Hanan-Quinn (HQ) criteria. In this study, the VAR
model is estimated with different lag levels and then compared to the AIC value. The
smallest AIC value is used as a reference for the optimal lag value.
3. VAR Model Stability Test
The lag length obtained in the optimum lag test will then be tested for stability. The
VAR stability test is conducted to obtain valid results on IRF and FEVD. The VAR model
can be said to be stable if its root modulus is less than one.
4. Vector Autoregression (VAR)
The Vector Autoregression (VAR) model was introduced by Christopher Sims in
1980. Stock and Watson (2001) in Firdaus (2011) explained that if previously univariate
autoregression was a single equation with a single variable linear model, where the present
value of each variable is explained by its own lag value, as well as its current and past values.
The VAR model does not depend much on theory in modeling. The things that need
to be determined in the VAR model are the interacting or mutually influencing variables that
need to be included in the model. Secondly, the number of lagged variables included in the
model is expected to capture the relationship between variables in the system.
The VAR model has advantages over analysis with other models. The advantages of
this model are first, the VAR model is a simple model and does not need to distinguish
between endogenous and exogenous variables. All variables in the VAR model can be
considered endogenous variables. Second, the way to estimate the VAR model is very easy,
namely by using OLS on each equation separately. Third, forecasting using the VAR model
is in some ways better than forecasting using models with more complex simultaneous
equations. Fourth, all variables in the VAR model must be stationary. If the variable data is
not stationary then it must be transformed first to make it stationary. Fifth, the interpretation
of the estimated parameters in the VAR model is not easy.
According to Gujarati in Firdaus (2011), the VAR model also has several weaknesses,
including the first, the VAR model is more theoretical because it does not utilize information
from previous theories. Second, because it focuses more on forecasting, the VAR model is
considered inappropriate for policy implications. Third, the toughest challenge of VAR is
choosing the right lag length. Fourth, all variables used in the VAR model must be stationary.
The fifth weakness of VAR is that the coefficients in the VAR estimation are difficult to
interpret.
5. Cointegration Test
Cointegration test is conducted to determine whether the variables that are stationary
at the first difference level are cointegrated or not. The cointegration test implies that in the
system of equations there is an error correction model that describes the existence of short-
term dynamization consistently with the long-term relationship. The cointegration test in this
study uses the Johansen approach by comparing the trace statistic with a critical value of 5
percent. If the trace statistic value is greater than the critical value then there is cointegration
in the system of equations.
6. Vector Error Correction Model (VECM)
Data that is not stationary at the level has the possibility to be cointegrated. VECM is
a restricted VAR model used for nonstationary variables that have the potential to be
cointegrated. Additional restrictions on the VECM must be imposed due to the presence of
shape and data that are not stationary at level, but cointegrated. The VECM then utilizes the
cointegration restriction information into its specification. If it is proven that there is a
cointegration relationship in the model, then the analysis is carried out using the short run and
long run with IRF (Impulse Response Function) and VD (Variance Decomposition) analysis.
7. Impulse Response Function (IRF) Simulation
Impulse Response Function (IRF) is a method used to determine the response of an
endogenous variable to a particular shock. This is because the shock of the i-th variable does
not only affect the i-th variable, but is transmitted to all other endogenous variables through
the dynamic structure or lag structure in VAR, or in other words, IRF measures the effect of a
shock at a time on the innovation of endogenous variables at that time and in the future.
IRF aims to isolate a shock to be more specific, which means that a variable can be
affected by a specific shock. If a variable cannot be affected by a shock, then the specific
shock cannot be known but the general shock (Firdaus 2011).
8. Forecasting Error Variance Decomposition (FEDV)
A method that can be used to see how changes in a variable shown by changes in
error variance are affected by other variables is FEVD. This method characterizes a dynamic
structure in the VAR model, in which the strengths and weaknesses of each variable can be
seen to affect other variables over a long period of time.
FEVD breaks down the variance of forecasting errors into c o m p o n e n t s that can
be attributed to each endogenous variable in the model. By calculating the percentage of
squared k-stage ahead error predictions of a variable that can be innovated in other variables,
it can be seen how much difference between the error variance before and after a shock
originating from itself or from other variables. So through FEVD, it can be known exactly the
factors that affect fluctuations.
FEVD in this study will be used to discuss how the role of various macroeconomic
and banking performance variables contained in the scope of the study in explaining
fluctuations in infrastructure credit. In addition, FEVD also aims to explain the percentage
contribution of each macroeconomic and banking variable shock in affecting infrastructure
credit in each sub-sector. The time period used in projecting this FEVD is 48 months (four
years). In this study, the FEVD will be summarized in the form of a column chart.
VECM Model
In this study, all equations in the model show cointegration, so the model used to
estimate the electricity, water and gas sub-sector, the construction sub-sector, and the
transportation, communication and storage sub-sector model is the VECM model. By using
VECM estimation, we can find out the short-term and long-term relationship between
variables. Through the comparison of the t-statistic on the variables contained in the study at
the critical level used, it can be seen what variables are significant to the main endogenous
variables observed, both in the short and long term. The difference between the VAR and
VECM models is only in long-term information. The model used in this study was adopted
from the Ncube and Ndou (2011) model.
Development of Macroeconomic Performance
In general, Indonesia's macroeconomic conditions explained through real GDP
growth, inflation and BI Rate during the period January 2010 to December 2015 as shown in
Figure 8 showed fluctuating movements of all three. In December 2010 and December 2015,
inflation was at 6.5 and 7.5, respectively, which was relatively low as the post-crisis
economic order was being rebuilt during this period.
As for the BI Rate, movements in the BI Rate will affect bank interest rates, including
lending rates. For example, an increase in the BI Rate will generally be responded by banks
by increasing deposit rates, such as current accounts, deposits, and savings. This is because if
it is not increased, customers will move to other banks or in other words, the bank will lose
its market share. This in turn will increase the cost of funds of the bank. In order not to erode
bank profits/margins, the simplest way is for banks to increase their lending rates. However,
there are also banks that do not necessarily increase their lending rates, but instead reduce
them. This condition can be found in banks that have high efficiency and increasing lending
activities.
The movement of economic growth over the five years from 2010 to 2015 tended to
decline. This is due to various factors, including the decline in food prices at the global level,
so that the economic growth in the five years from 2010 to 2015 tends to decline caused
Indonesia's export earnings to decline, as most of Indonesia's export commodities are still
dominated by foodstuffs. In addition, the decline in economic growth was also caused by a
reduction in people's purchasing power due to a decrease in people's real income which was
eroded by inflation. The movement of the BI interest rate, inflation and economic growth
from January 2010 to 2015 can be seen in Figure 7.
In general, macroeconomic variables that are often used as determinants of banking
performance from various studies are national income or economic growth, inflation, and
interest rates. Naceur (2003) uses GDP per capita growth and inflation as macro variables
that affect banking performance. Ali et al (2011), Mirzaei et al (2011) used economic growth
and inflation variables.
Performance Development of Commercial Banks
Banking performance can be seen from various aspects, including ROA, BOPO,
DPK, the number of loans disbursed and NPL rates. ROA is used to look at bank profitability
as a bank-specific determinant of risk management efficiency. Ashcraft's (2006) research
shows that a bank with the best performance indicated by a high return on assets and good
management policies has good capital for future operations. Ashcraft (2006) also explained
that there is a positive relationship between capital and bank profitability. Chimkono et al
(2016) have examined the effect of non-performing loan ratio and other determinants on the
financial performance of commercial banks in the Malawi banking sector. The authors
concluded that non-performing loan ratio, cost efficiency ratio and average lending rate have
a significant influence on the performance of banks in Malawi.
Figure 8 shows the movement of ROA of commercial banks with different patterns
throughout the study period. Based on the figure, it can be seen that the movement of ROA of
commercial banks fluctuates every year. In November-December 2011, there was a
significant increase from 3.0 percent in October 2011 to 3.7 percent in November 2011. This
increase in ROA is closely related to the increase in profit in that period.
The ROA ratio is used to measure a bank's ability to earn overall profit. The greater
the ROA owned, the greater the profit earned, and the better the position of the bank in terms
of asset utilization. The emergence of non-performing loans at a bank has implications for
reducing ROA because non-performing loans can reduce the profit owned by the bank. In
general, the return on Provision for Earning Assets (PPAP) turned out to be quite significant
in supporting the ROA of Indonesian banks, and without this income component, the bank's
ROA would be lower than published.
NPL can be used to measure the bank's ability to cover its risk of default on loan
payments by debtors. Based on Bank Indonesia Circular Letter No. 13/24 / DPNP dated
October 25, 2011 concerning commercial banks, non-performing loans (NPLs) are loans to
non-bank third parties consisting of non-performing loans (substandard), doubtful and bad
debts. The higher the NPL level, the greater the credit risk borne by the bank. The level of
NPL can affect the efficiency level of the bank.
Two frequently used measures of bank performance are return on assets (ROA) and
return on equity or ROE (Rumler and Waschiczek 2010; Mirzaei et al 2011; Ali et al 2011;
Abiodun 2012). Festic and Beco (2008) used non-performing loan (NPL) variable as one of
the bank performance indicators.
Figure 9 shows a graph of the development of NPLs in the three infrastructure sub-
sectors from 2010 to 2015, the three NPLs fluctuate but have an increasing trend. The LAG
sector has the lowest NPLs, with an average of less than Rp 1,000 billion, while the
construction and crime scene NPLs are not much different. The increasing trend of NPLs
indicates that commercial banks have not improved their performance in managing and
minimizing bad debts from the infrastructure sector.
Analysis of the Impact of Macroeconomic and Banking Performance Variables on
Lending to the Infrastructure Sector
Macroeconomic and banking performance variables have an impact on infrastructure
sector lending. The impact of each variable is different for each infrastructure sector. The
VECM method is used to see the significance of each variable on infrastructure sector
lending. In this section, the model developed to explain the impact of macroeconomic and
banking performance variables is discussed. In summary, before processing using the VECM
method, pre-estimation tests are carried out on the variables of each model. These pre-
estimation tests include unit root test to determine data stationarity, optimum lag test, VAR
stability test, and cointegration test to see if there is a long-term relationship between
variables.
The test method used to test data stationarity is the Augmented Dicky Fuller-Test
(ADF-test). In this test, automatic lag selection is used based on the Schwarz Information
Criterion (SIC) criteria. The stationarity test results show that almost all variables are not
stationary at the level except for the electricity, water and gas NPL, construction loans and
BOPO variables. However, the data that are not stationary at the level have been stationary in
the first difference test.
In this study, the determination of the optimum lag is based on the Schwarz
Information Criterion (SIC). The determination of the optimum lag selection is based on the
smallest lag in the lag interval used. The lag optimum test results show that all infrastructure
sub-sectors have an optimum lag at lag one. Then the VAR stability test is conducted, it is
found that the entire model is stable. After that, the last pre-estimation test, namely the
cointegration test, shows that the three models have cointegration, which means that there is a
long-term relationship between variables. The results of the pre-estimation test can be seen in
full in the appendix.
After the significance test is conducted, it is followed by a test that looks at the impact
of macroeconomic and banking performance variables on infrastructure lending. This study
uses IRF and FEVD analysis. IRF analysis shows the response of a variable in the system to
shocks from other variables. While FEVD analysis provides an overview of the proportion of
sequential movements due to shocks to the variable itself compared to shocks to other
variables.
The first analysis conducted is to see the impact of inflation on infrastructure lending.
Judging from the IRF analysis in Figure 10, it can be concluded that inflation has a negative
impact on lending in all three infrastructure sub-sectors. In the LAG sub-sector, the largest
decline occurred in period two, amounting to 0.017 percent and this impact began to stabilize
in period ten. While in the construction sub-sector, the biggest decline occurred in period 18,
which amounted to 0.014 and began to experience stability until period 60. In the TKP sub-
sector, the biggest decline occurred in period 10 by 0.062 percent and then experienced
stability until period 60.
Inflation represents macroeconomic conditions. Unstable macroeconomic conditions
will affect the amount of credit that will be disbursed by banks. Banks will tend to reduce the
credit ratio if there is macroeconomic instability to avoid credit risk. The mechanism of the
decline in infrastructure sector credit caused by inflation is through the amount of savings or
third party funds. Inflation makes market prices more expensive, so people will spend more
money by taking from savings. The use of more money will reduce the amount of money in
savings and have implications for the decline in the amount of investment from third party
funds which is the largest capital of banking funds.
The results of this study are in accordance with research conducted by Naceur and
Kandil (2009) explaining the negative relationship between inflation and credit, the results of
the study found that higher inflation rates increase uncertainty and reduce credit demand.
However, other studies (Alexiou and Sofoklis (2009); Athanasoglou et al (2008); Claeys and
Vennet (2008); García et al (2009); Kasman et al (2010) confirm a positive relationship
between inflation and credit.
Pasiouras (2007) conducted a study and stated that inflation itself can have a positive
or negative effect on bank performance. Inflation caused by business cycle developments will
cause the economy to boom. Inflation that occurs because of this usually has a greater effect
on the revenue side than the cost side, and results in improved bank performance. The effect
of inflation itself depends on whether the inflation has been anticipated or not by the bank.
Aviliani et al (2015) found that if inflation is fully anticipated, then the interest rate applied
by the bank will increase to cover the risk of inflation. So that the increase in income is faster
than the increase in costs, so that it has a positive impact on bank performance, especially the
level of profitability. But if bank management does not anticipate changes in inflation, then
interest rates experience slow adjustment, so that the increase in costs is faster than the
increase in income, and finally inflation has a negative impact on profitability and has
implications for reducing credit. The next analysis is to analyze the contribution of inflation
to lending to the infrastructure sector, using FEVD analysis. The summary of the FEVD table
can be seen in Table 3. Table 3 sh o w s the contribution of inflation to each loan in the three
infrastructure sectors. The largest contribution of inflation is in the construction sub-sector,
where in the construction sector in the 48th period or the next four years, inflation contributes
8.293 percent. FEVD estimation results can be seen in full in the appendix.
IRF analysis is then used to see the impact of BI Rate on lending in the infrastructure
sector. The analysis of BI Rate on lending to the infrastructure sector from the IRF results in
Figure 11 shows that BI Rate gives positive shocks to lending in all three infrastructure
sectors in the first to 60th periods.
In the LAG sub-sector, the largest increase due to BI Rate shocks occurred in the
early period, which amounted to 0.023 percent and began to stabilize in the eighth period,
which increased by 0.017 percent. In the construction sub-sector, the highest increase was in
the sixth period, which amounted to 0.018 percent and began to experience stability in the
fifth period, which experienced an increase of 0.017 percent. The largest increase in crime
scene credit occurred in the sixth period, which amounted to 0.032 percent and began to
stabilize from this period to the end of the period.
Bank Indonesia as an institution that maintains the stability of the country's economy,
takes several monetary policies such as increasing or decreasing the value of the BI Rate. The
positive response given by the BI Rate to infrastructure sector credit is due to the increase in
the BI Rate which has an impact on increasing deposit interest rates which in turn results in
high lending rates. The increase in lending rates will shift the supply of credit and increase
the amount of credit offered by banks. Research by Kishan et al (2000) found that changes in
the federal funds rate are positive and statistically significant to lending for banks that have
assets of less than $ 300. This is also supported by research conducted by Friedman et al
(1993) showing that the total credit of large banks to changes in the Federal Rate is positive.
Research by Garcia-Posada and Marchetti (2015) assessed the impact of monetary policy
implemented by the European Central Bank (ECB) on the credit supply of Spanish non-
financial companies during the financial crisis was positive. Their findings showed that the
ECB's monetary strategy had a positive effect on the supply of corporate credit.
The next analysis is the FEVD analysis. The summary of the FEVD table can be seen
in Table 4. Table 4 shows the contribution of BI Rate to each credit in the three infrastructure
sub-sectors. The largest BI Rate contribution is in the LAG sub-sector, where in the LAG
sub-sector in the 48th period or the next four years, the BI Rate contributes 4,029 percent. The
FEVD estimation results can be seen in full in the appendix.
IRF analysis is then used to see the impact of IPI on lending in the infrastructure
sector. The analysis of IPI on lending to the infrastructure sector from the IRF results in
Figure 12 shows that IPI gives positive shocks to lending in all three infrastructure sectors in
the first to 60th periods. In the LAG sub-sector, the largest increase due to IPI shocks
occurred in the initial period, which amounted to 0.03 percent and began to stabilize in the
seventh period, which increased by 0.02 percent. In the construction sub-sector, the highest
increase occurred in the initial period, which amounted to
0.02 percent and began to experience stability in the fifth period by 0.01 percent. Meanwhile,
in the crime scene sector, the exchange rate responded stable from the initial period, which
amounted to 0.03 percent.
The positive relationship between IPI and lending occurs because the greater the
value of IPI, indicating better economic growth, then lending will increase. Conversely, the
lower the IPI value, indicating weak economic growth and lending will decrease. This is in
accordance with the initial hypothesis which states that the IPI variable has a positive
relationship to the amount of credit channeled to the sector infrastructure. This situation is
because when the economy is in good condition, it will support the implementation of
banking activities, including lending activities for t h e infrastructure sector, so that when IPI
increases, the distribution of funds for credit will increase, including for infrastructure sector
financing. The results of this study are also in accordance with research conducted by
Kusumawati (2013) which states that IPI has a positive effect on financing provided by banks
in Indonesia.
The next analysis is the FEVD analysis. The summary of the FEVD table can be seen
in Table 5. Table 5 shows the contribution of IPI to each credit in the three infrastructure sub-
sectors. The largest IPI contribution is in the TKP sector, where in the TKP sector in the 48th
period or the next four years, IPI contributes 1,129 percent. The FEVD estimation results can
be seen in full in the appendix.
Changes in the amount of third party funds (DPK) can affect the distribution of the
amount of credit to the infrastructure sector. DPK reflects funds collected from the public,
which is the largest source of funds that banks rely on (Dendawijaya 2000). This is because
DPK can reach 80-90 percent of all funds managed by the bank. High growth in deposits will
increase the lending capacity of the bank thus increasing the bank's ability to extend credit.
The response of infrastructure sector credit due to deposit shocks is positive. This can
be seen in Figure 13. In the first period, DPK shocks are responded by positive LAG loans by
0.004 percent. In the eighth period, the impact of DPK shocks on LAG loans has begun to
stabilize, which is responded by 0.003 percent. Meanwhile, in the construction sector, in the
first period, the DPK shock was responded negatively by the construction sub-sector credit,
but this impact was not long, then in the fourth period until the end of the period, the
response given was positive. The response of construction loans to DPK shocks in period 15
until the end of the period is 0.004 percent. Meanwhile, the response given by crime scene
loans is not much different from the response given by LAG loans, which is positive from the
beginning of the period to the end of the period. The DPK shock in the first period was
responded positively by 0.008 percent by TKP loans and was the lowest response. In the third
period, it responded by 0.013 and was the largest response, while in the 12th period until the
end of the period, it responded by 0.012 percent.
The positive response of infrastructure sector credit due to third party fund shocks
indicates that when there is an increase in the amount of third party funds, it will increase the
source of funding for banks, so that banks will be able to increase their funding. This may
increase the role of banks in lending, including credit to the electricity, water and gas sector,
the construction sector and the transportation, communication and warehousing sector. The
magnitude of the response given is very low, which indicates that the changes in credit that
occur due to an increase in deposits will not be large. This study is in line with the research of
Berrospide and Edge (2010) who examined the effect of third party funds as bank capital on
bank lending behavior as measured by credit growth, and found that capital has a positive
influence on loans or credit. Bridges et al. (2014) also investigated the impact of changes in
bank capital on lending behavior, the results showed that changes in bank capital from third
party funds positively affect lending. Dohan Kim and Wook Sohn (2017) examined the
Effect of Bank Capital on Lending. The findings show that the effect of increased bank
capital from increased third-party collections on loan growth is significantly positive only
after large banks maintain sufficient liquid assets.
Jung. H, Kim. D. (2015) found that when there is a severe liquidity shock, banks
generally reduce the amount of credit extended, but banks with a large funding ratio tend to
increase credit extended to companies. Research by Kashyap et al. (2002) shows that during
periods of liquidity shocks, third party funding decreases and thus, the amount of funding for
loans also decreases. This will reduce the bank's ability to increase the amount of credit, so
that the credit offered also decreases.
Banks are the largest financial institutions worldwide. However, in their operations,
commercial banks face risks, one of the most significant of which is the risk of non-
performing loans. Given that lending is one of the main sources of income in commercial
banks. Therefore, managing the risks associated with such loans affects the profitability of
the bank and has an impact on the sustainability of the amount of credit disbursed by banks to
sectors that have non-performing loans.
NPLs are important because they affect the financial intermediation role of
commercial banks, which is the main source of income for banks, and in turn, will affect the
financial stability of an economy (Klein 2013). The impact of a large number of NPLs in
banking is bank failure and also economic slowdown. The causes of non-performing loans
are usually due to the absence of effective monitoring and supervision on the part of banks,
lack of effective lending channels, weaknesses in legal infrastructure, and lack of effective
debt recovery strategies (Adhikary 2007). Research by Al-Abedallat and Al- Shubiri (2013)
shows that bad debts are one of the main risks that seriously affect bank stability.
NPLs take a lot of time and effort for banks to manage. NPLs are an indirect cost that
banks have to bear due to poor asset quality. NPLs not only block interest income, but they
are investments that fail to return, thus affecting future profit streams. NPLs imply income
blocking that hinders banks from getting cash. Hence, the bank is forced to borrow more and
this results in additional costs for the bank. In addition, NPLs also pose a reputational risk to
the bank. If a bank faces NPL problems, it will affect its credit rating and will limit financing
and syndication opportunities with other banks. Thus, a large number of NPLs can affect
profitability and may threaten the viability of commercial banks. If profitability reflects the
quality of the firm's asset management, then this may indicate that the bank will generate
fewer non-performing loans (Bhattarai, 2016).
The results of the IRF analysis show that shocks to NPLs in each sub-sector respond
differently in each sub-sector. In the LAG and TKP sub-sectors, the response given by each
NPL shock is negative. In the first period, the decline in LAG loans due to NPL shocks was
negative LAG is 0.04 percent. Then in the 15th period until the last period the decrease in
LAG credit caused by t h e LAG NPL shock was 0.03 percent. Meanwhile, in the crime scene
sub-sector, the first period of crime scene NPL shocks will reduce crime scene credit by
0.025 percent, then fluctuate until it stabilizes in the 15th period by 0.019 percent.
The decline in LAG and TKP loans due to the respective NPL shocks shows that in
the long run, an increase in LAG NPLs and TKP NPLs will erode bank profits and result in
narrowing bank margins. High NPLs cause banks to form larger write-off reserves. This can
also reduce the bank's interest in lending to the sector and divert credit to other sectors with
lower NPL rates.
There have been many studies on NPLs, including Li's (2014) study which looked at
the relationship between credit risk management and commercial bank profitability in Europe
and investigated whether the relationship was stable or fluctuating. The study found that
credit risk management has a negative effect on commercial bank profitability. NPL has a
significant influence on ROE and ROA. While Saunders and Wilson (2001) showed that a
top performing bank with good return on assets and management policies (high ROA) has
good capital for future operations and for lending.
Tomak (2013) studied the determinants of bank lending behavior in a sample of
Turkish banks, and found a significant relationship between NPLs and bank lending behavior
in state-owned banks and NPLs showed a negative impact on total loan growth. Research by
Karim, Chan and Hassan (2010) in Malaysia and Singapore, clearly shows that high non-
performing loans reduce cost efficiency. According to Sentausa (2009) the number of NPLs
is one of the causes of bank difficulties in providing loans. This study found that the number
of NPLs has a negative and significant effect on bank loans.
Borio et.al (2002), in a study based on a sample of Spanish banks, highlighted that
during recessions, non-performing loans (NPLs) increase as a result of financial stress of
firms and households. When the economy is expanding, firms request more loans and can
repay them more easily, but when the economy stalls, firms show greater pressure and
difficulty to repay debts. In periods of expansion, banks tend to lend to companies with low
credit quality. This causes problems in the future. Thus, NPLs increase during economic
expansion and cause banks to reduce lending.
Cucinelli's (2015) findings show the negative impact of credit risk on bank lending
behavior, according to Keeton (1999) faster credit growth leads to higher credit losses. This
is because during good business cycles, banks tend to lend to customers even with weak
credit histories and even when collateral is low.
Research by Michaelides P.G, et al (2015) shows that NPLs positively affect lending
in the short term, while in the long term, NPLs positively affect lending NPL length is
negatively affected. This result can be attributed to the fact that NPLs follow an increase in
debt. A rise in debt leads to a fall in economic activity, through a number of channels.
Sovereign inability to service public debt which also leads to banking problems in the face of
solvency issues and economic uncertainty, market confidence which affects expectations in
the market and, recessionary measures such as wage and investment cuts, and then NPLs
increase significantly above pre-crisis levels.
The response given by construction loans due to shocks to construction NPLs in the
long run is positive. In the first period, construction NPL shocks are responded by
construction loans by 0.039 percent, but these shocks have stabilized in response in the tenth
period, which is 0.032 percent.
The construction sector is the only sector that belongs to the infrastructure sector that
is included in the priority sector. The development of the construction sector in the last five
years is being improved, and is included in the priority sector of credit recipients (OJK 2016).
The positive response given also illustrates that commercial banks are overconfident about
lending in the sub-sector. The existence of bad debts in the construction sub-sector does not
affect banks in lending to both construction sub-sectors. This study is also in line with Linda's
(2007) research which found that NPL shocks are responded positively by investment credit
in foreign exchange banks in the long run, which indicates that NPL is no longer a dominant
factor affecting investment credit in foreign exchange banks because it is estimated that there
are other factors that are more influential.
Based on the variant decomposition results that can be seen in table 7, it can be seen
the contribution of each NPL variable in each infrastructure sector. The largest NPL
contribution was in the construction sector lending, which at the end of the period contributed
8.293 percent. While in the LAG and TKP sectors in the final period, NPLs only contributed
0.095 percent and 3.353 percent respectively. This means that LAG NPLs and TKP NPLs do
not contribute much to lending to t h e sector.
The results of the IRF analysis show that the response given by credit in the three
infrastructure sectors due to changes or shocks from ROA is positive. This shows that when
the level of profit achieved by commercial banks increases, it will increase lending in the
three infrastructure sectors in the long term, and vice versa. This can happen because credit is
the largest banking asset and source of banking income. If bank profits decrease, the bank
will experience a decrease in its income and in the future will reduce lending. (Tyastika
2013).
In the LAG sector, ROA shocks are responded negatively at the beginning of the
period until the fifth period. After the fifth period until the last period, it responds positively.
The response at the beginning of the period was a decrease of 0.012 percent, while the
response in month seven was an increase of 0.016 percent, then in periods 10 to 20 it
continued to increase and stabilized in period 25 to the end, and responded by 0.03 percent.
The response given by construction credit from the beginning of the period to the end
fluctuates, which is negative in the fourth and fifth periods, but always positive in other
periods. In the construction sub-sector, the beginning of the period has not responded, but in
the second period it has responded, namely a shock of one standard deviation caused by ROA
makes construction credit increase by 0.03 percent.
0.001 percent. Meanwhile, in the fourth period, it causes a decrease of 0.004 percent. The
ROA shock is responded stably by construction loans in the ninth period, by 0.001 percent.
ROA shocks are responded positively by crime scene loans from the beginning of the
period until the end of the period. The response of crime scene loans to shocks of this
variable reaches its peak in the 25th period and is the point of stability period, the amount of
response given is 0.012 percent. The effect of ROA on investment loans offered is in
accordance with research conducted by Mahrinasari (2006) and Meydianawathi (2006) that
ROA is the level of profit achieved by a bank with all the funds in the bank, if ROA increases
then the funds that can be channeled into credit also increase. According to Suseno and Piter
(2003), ROA affects the bank's decision to extend credit to debtors. ROA itself is an indicator
to see the amount of profit earned by the bank.
The results of this study are also supported by research conducted by Mahrinasari
(2006) and Meydianawathi (2006) that ROA is the level of profit achieved by a bank with all
the funds in the bank, if ROA increases then the funds that can be channeled into credit also
increase. According to Suseno and Piter (2003), ROA affects the bank's decision to extend
credit to debtors. ROA itself is an indicator to see the amount of profit earned by the bank.
ROA significantly affects infrastructure lending in the long run positively. ROA,
which reflects the level of profit achieved by commercial banks, if it increases will encourage
infrastructure lending in the long term, and vice versa. This shows that profit has an
important role in relation to the distribution of commercial bank infrastructure loans. Profits
earned by commercial banks in addition to affecting infrastructure lending, are also used to
fulfill stakeholoders' rights. When there is a decrease in profit, the rights of stakeholders will
be considered by commercial banks before channeling it to the real sector.
The next analysis is the FEVD analysis. The largest contribution that affects the
decrease or increase in the amount of infrastructure credit is the infrastructure credit itself.
From Table 10, it can be seen that the largest contribution of ROA is in the TKP sub-sector
credit, which at the end of the period contributed 2,420 percent, then ROA has a considerable
effect on construction credit, which amounted to 2,018 percent at the end of the period. While
in the LAG sub-sector, ROA is not very influential in the contribution of construction loans.
BOPO includes the ratio of profitability (earnings). The success of the bank is based
on a quantitative assessment of the bank's profitability can be measured using the ratio of
operating expenses to operating income (Kuncoro 2002). According to Dendawijaya (2005),
the operating cost ratio is used to measure the level of efficiency and ability of banks in
carrying out their operations. The ratio of Operating Expenses to Operating Income (BOPO)
often called the efficiency ratio is used to measure the ability of bank management to control
operating costs against operating income. The smaller this ratio means the more efficient t he
operating costs incurred by the bank concerned (Almilia and Herdiningtyas 2005).
Operating efficiency proxied by total operating costs compared to total operating
income (BOPO) shows the level has a negative and significant influence on financial
performance proxied by ROA. The results of this study indicate that the greater the amount of
operating costs (BOPO), the lower the ROA. This condition occurs due to any increase in
bank operating costs that are not accompanied by a greater increase in operating income. The
negative relationship between the construction and crime scene sectors to BOPO occurs
because the lower BOPO value indicates a higher level of banking efficiency. this shows that
if banks are more efficient, the greater the credit channeled to the crime scene and
construction sectors.
The positive response given by the LAG sector to BOPO indicates that the lower level
of banking efficiency actually increases lending to this sector. This is consistent with the
response given by ROA to LAG loans. A higher BOPO indicates poor banking efficiency and
results in a decrease in profits. LAG sector loans respond negatively to ROA and respond
positively to BOPO. The largest response of the increase in LAG loans due to BOPO shocks
occurred in period six which amounted to 0.019 percent, in this period also LAG credit
shocks due to BOPO have stabilized until the end of the period.
Operating efficiency proxied by total operating costs compared to total operating
income (BOPO) has a negative and significant influence on financial performance proxied by
ROA. The results of this study indicate that the greater the amount of operating costs
(BOPO), the lower the ROA. This condition occurs due to any increase in bank operating
costs that are not accompanied by a greater increase in operating income will result in
reduced profit before tax (Mehta 2014).
The next analysis is the FEVD analysis. The largest contribution that affects the
decrease or increase in the amount of infrastructure credit is the infrastructure credit itself.
From table 9 it can be seen that the largest contribution of BOPO is in the construction sub-
sector credit, which at the end of the period contributed 3,450 percent, then BOPO has a small
effect on LAG and construction loans, which amounted to 1,239 percent and 1,355 percent
respectively.
CONCLUSIONS:
Based on the results of the analysis and discussion in the previous section, it can be
concluded as follows:
1. Macroeconomic performance has an impact on lending to the infrastructure sector. Inflation
is responded negatively while BI Rate and IPI are responded positively by the three
infrastructure sectors.
2. Banking performance is responded differently by the three infrastructure sectors. In general,
better banking performance will increase lending in the infrastructure sector.
3. The inflation variable is the variable that contributes the most in influencing lending to the
infrastructure sector.
