Determinants of Islamic Rural Bank Financing in Indonesia

Arif Widodo 1 , Istianah Asas 2

1 Centre for Development of Islamic Economics and Philanthropy, Universitas Muhammadiyah Yogyakarta

2 Faculty of Economics, Universitas Madura

Permalink/DOI: http://dx.doi.org/10.15294/jejak.v10i2.11293

Received: October 2016; Accepted: February 2017; Published: September 2017

Abstract

This research is designed to empirically investigate the determinants of Islamic rural banking financing in Indonesia after 2008 global financial crisis covering period 2009.1-2014.12. The methods applied in this research are Error Correction Model (ECM) and VAR/VECM. The results of ECM model demonstrate that the variable third party funds (DPK) and non-performing financing can significantly affect Islamic rural banking financing both in the short run and long run, while Return on Asset (ROA) and Profit-and-loss sharing does not have a significant influence. Islamic rural bank financing, however, was influenced by inflation and exchange rate as the proxy of macroeconomic variables in the short and long run. Furthermore, Impulse Response Function (IRF) and variance decomposition results show that Profit-and-loss sharing (PLS) has the largest positive impact to financing (39.08%), followed by third party fund (19.6%) and inflation (8.9%). While, the variables that contribute to reduce financing are non-performing financing (9.02%), followed by ROA (7.76%) and exchange rate (2.48%).

Key words : Islamic rural bank, financing, Profit-and-loss sharing, Error Correction Model, VAR/VECM.

How to Cite: Widodo, A., & Asas, I. (2017). Determinants of Islamic Rural Bank Financing in Indonesia. JEJAK: Jurnal Ekonomi Dan Kebijakan, 10(2), 273-288. doi:http://dx.doi.org/10.15294/jejak.v10i2.11293

 Corresponding author :

ISSN 1979-715X

Address: Jl. Ringroad Selatan, Tamantirto, Bantul, Yogyakarta, Indonesia

E-mail: rifdoisme@gmail.com

274 Arif Widodo , Determinants of Islamic Rural Bank Financing

INTRODUCTION

2009 to 3,156 in 2012 (the Indonesian Banking Statistics, Dec. 2012).

The role of financial institutions is Because of the biggest funds used in

crucial in accelerating economic growth, banking operations are public fund. Banks use

without exception

microfinance

the equivalent rate to attract customers putting institutions. The existence of microfinance

their funds into the bank so that the third-party institution operates as financial intermediary

that has a main function to collect funds from fund can increase, if the bank receives a huge those who have excessive funds to those who

fund so that the market share will increase. The have not, it is expected the country's economy

increasing of market share can increase financing Islamic rural bank performances. The

could run well. Besides, the economic growth increasing of third-party fund is also in line with

of a country is primarily influenced by the the fluctuation of equivalent rate value of 12.30 in

productivity of

2009 and 11.61 in 2010, it means that there is entrepreneurships (SMEs) sectors. Some of

increasing by 11.72. Value equivalent rate in 2009 them, cannot be accessed by conventional

had the highest value compared to the value in banks. The development of the SMEs sector

can greatly be assisted by the microfinance 2010 and 2011; it caused bank needs the trust of institutions that have a function to distribute

people to saving their money in banks so that funds into productive sector.

they give high value equivalent rate to attract customers in order to save money in their bank

In this case, the microfinance (Indonesian Banking statistics, Dec. 2012).

institution is Islamic rural banks (BPRS), since Non Performing Financing (NPF) value

there have been many Islamic rural banks of the post-crisis in 2009 has been decrease by

operating in rural environment. Moreover,

7.30 in 2009, 6.50 in 2010 and 6.11 in 2011. It shows Islamic rural banks have been famous in rural

communities, so that both parties have as a the non-performing financing in a better business partner. The growth of Islamic rural

condition after economic crisis in 2008. Low of bank can be enhanced with the support of the

non-performing financing can increase the banking variables and macro-economic

confidence of the Islamic rural banks to distribute its financing, so that that market

factors. Table 1 depicts the performance of discipline into distribution of funding will

bank financial performance. In this case the support the growth of financing itself and

most important factor is the growth of third improve the profitability of the Islamic rural

party funds (DPK), since it saw a rapid growth

bank (www.bi.go.id).

during four years, increasing from 0.725 in

Table 1. The growth of Islamic rural bank behavior and Macroeconomic Variable (%)

Year Indicator

Depositor Fund (DPK)

3.156 Retrun on Asset

3.58 3.49 2.67 2.64 Non Performing Financing

7.03 6.50 7.05 6.15 Profit and Loss Sharing

12.30 11.61 11.72 6.27 Inflation

2.78 6.96 3.79 4.30 Exchange rate

9.15 9.11 9.11 9.17 Source: Indonesian Banking Statistics, 2014

JEJAK Journal of Economics and Policy Vol 10 (2) (2017): 273-288 275

Islamic rural bank (BPRS) as financial rationale for the operation of BPRS is in addition institutions based on real sector, on the

based on the demands of imple mentation shari’a external side of BPRS has direct relevance and

which is the desire of Muslims in Indonesia, as higher

well as an active step in a restructuring economy conditions occur. As the macro variables that

of Indonesia who poured in a variety of packages are a reflection of the Indonesian economy,

of financial prudence, monetary, banking in the symptoms caused by macroeconomic

general. In particular is to fill it up to the wisdom conditions feared to have negative impacts on

that frees banks in setting interest rate, later the performance of BPRS. When the good

known as the bank without interest (Sumitro, macroeconomic

2004). The legal basis of Islamic banking is to performance of BPRS financing will also be

performance,

the

avoid usury, and the second practice Islamic good and vice versa.

principle in banking, especially rural banks is the According

purpose of the benefit. As inscribed in the holy Development Report (LPPS) in 2009, the

to Islamic Banking

book Quran on the prohibition of usury among growth of Islamic bank assets has been slowed

Surah Ar-Rum (30): 39, Surah Al-Baqarah (2): in 2009, despite the all of Islamic bank asset

275, Surah An-Nisa (4): 146.

tends positive. Slowdown of asset growth BPRS establishment is inseparable from affected by the condition of real sector such as

the history of conventional BPR. The legal status the persistence of the national economic

of RB was first recognized in Pakto October 27, down turn, customer’s purchasing power has

1998, as part of the Policy Package for Financial, not fully recovered the high economic costs

Monetary and Banking. Historically, ISLAMIC impacts on their business expansion

RURAL BANKis the manifestation of many restriction and reduction of consumption.

financial institutions, such as Bank Village, However, the slowdown in the Islamic

Village Office, Market Bank, Bank Employees banking industry is relatively minor

Lumbung Select Nagari (LPN), Village Credit compared with the slowdown in the national

Institutions (LPD) and other institutions can be financial industry in general.

equated with it. Since the issuance of Law No. 7 Based on the explanation above, this

of 1992 on the subject of banking, financial paper aims to examine the growth of Islamic

institutions are clarified through permission rural bank financing in 2009 because it has

from the finance minister (Sudarsono, 2008: 90). begun to recover the economy in Indonesia

Implementation of ISLAMIC RURAL after the 2008 crisis and it is also used as a

BANKconducting business based on sharia reference standard economic growth in

principles was in turn governed by Decree of the Indonesia that is represented by the growth of

Director of Bank Indonesia No. 32/36 / KEP / DIR Islamic financing in Indonesia, as well as

/ 1999 on 12 May 1999 of the Rural Bank based on analyze the factors in the bank that may affect

Sharia principles. In this case, technically, sharia the fluctuation of Islamic bank financing in

ISLAMIC RURAL BANKcan be interpreted as a Indonesia.

financial institution as a conventional BPR, the Islamic Rural bank (BPRS) as one of the

operation using the principles of sharia (ibid.). financial institutions of Islamic banking,

With the development of Islamic Rural which is the pattern of operations follows the

Bank in Indonesia, studies related to the Rural principles of sharia or Islamic Muamalat. The

Bank Sharia, both in terms of financing and its

276 Arif Widodo , Determinants of Islamic Rural Bank Financing

efficiency also needs to be discussed. used in this research is multiple regression Therefore, most researchers still focus the

analysis. The results showed that the banking discussion on conventional bank financing

financial ratios affecting SME lending. While a products. Several studies conducted in

stable macro variables also affect the provision of Indonesia, for example Haryati (2009) which

SME loans.

investigated the growth of bank credit in As performed by Utari et.al (2012) examine Indonesia in 2004-2008. Variables used in this

the optimal real credit growth in Indonesia. research is the growth of excess liquidity,

Variables used in this research are deposits, NPL, deposit growth, the growth of loans / deposits

real GDP, inflation and lending rates in received, equity growth, Indonesia bank rate,

Indonesia. The method used in this study is a inflation rate and the foreign exchange rate.

Markov Switching (MS) MS Univariate and The method used is multiple linear

Vector Error Correction Model. The results regression analysis. The results showed that

showed that there is a co-integration each variable in the variable national banks

relationship between real credit to the real GDP, deposit growth and loan growth received

inflation and lending rates. In the long term, significant positive effect on credit growth,

demand for loans was positively affected by while the foreign banks-a mixture of all the

economic activity and negatively by the credit growth variables funding sources had

interest rates and inflation. While in the short significant influence on the growth of credit.

term loan growth affected by NPL ratio and third Macroeconomic variables on national banks

party funds (DPK).

all have significant influence on the growth of In addition to research conducted in credit; BI interest rate and exchange rate have

Indonesia, the research on financing or bank

a significant negative effect, inflation has a credit also performed in India as done by significant positive effect.

Vighneswara Swamy (2012) who examined the Pratama (2010) examined the factors

bank credit in India during the pre and post affecting bank lending policies (Study on

global crisis in 2008. The variables used were Commercial Banks in Indonesia Period 2005-

aggregate Deposits, Investments, money at call 2009). The variables used were DPK, CAR,

and short notice, borrowings, bank nifty, lending NPL, and the interest rate of Bank Indonesia

rates, Cash-Deposit Ratio, Investment-Deposit Certificates (SBI). The analysis technique

Ratio, credit-deposit ratio. Methods of data used is multiple linear regression. Based on

analysis used in this study is a method that uses this study showed that the DPK positive and

a model JJ Vector Auto Regressive (VAR) and significant impact on bank lending. CAR and

estimates based on the method Maximum NPL significantly negative effect on bank

likelihood (ML). The results showed that the lending. While the interest rate of Bank

studied those variables significantly affect the Indonesia Certificates (SBI) and not

bank credit.

significant positive effect on bank lending. In addition to research in India, Berrospide Kusnandar (2012) examined the factors

and Edge (2010) examined the effect of bank affecting Small Medium Enterprise (SME)

capital (Equity / Assets, Tier 1 Capital Ratio, Total lending by banks in Indonesia. Variables used

Capital ratio) and macroeconomic variables in this research are banking financial ratios

(GDP, inflation, and interest rates) against credit (CAR, NPL, DPK, ROA) and macro variables

in the United States. The method used in this (GDP, inflation, exchange rate). The method

research is the Panel regression.

JEJAK Journal of Economics and Policy Vol 10 (2) (2017): 273-288 277

Internal factors Macroeconomic

NPF Exchange rate

(Rupiah to US Depositor Fund dollar (DPK)

Total financing

islamic rural

Return on Asset

bank

(ROA) Inflation

Equivalent Rate (profit sharing)

Figure 1. Research Framework

The results showed that the bank's consideration of the availability of the data after capital significant positive effect on the

the 2008 global crisis as well as the number of provision of credit. This means a growing

observations as much as 72 (monthly data) is capital was highly likely to increase credit

deemed to have representative. The data used is growth. However, for the macroeconomic

the level of funding channeled, Deposiotor variables it was evident that only GDP had a

fund/Third Party Fund (DPK), Return on Assets significant influence in lending performance.

(ROA), Non Performing Financing (NPF), Profit Islamic Rural Bank, as conventional

share (equivalent rate), exchange rate and banks in general, have the same internal

inflation. Data of Depositor Fund (DPK), factors albeit there are differents in principle.

Financing (FINC) and Exchange rate are Thus, the above studies can be concluded that

transformed into the natural logarithm (ln) to there are some internal variables banks and

obtain valid and consistent results macroeconomic variables that affect the

Method of Error Correction Model (ECM) financing in the bank, in this case the rural

applied in this study is based on Error Correction banks of Sharia, namely: Third Party Funds,

Model Domowitz-Elbadawi proposed by return on assets (ROA), Non-Performing

Domowitz and Elbadawi (1987). This method Financing (NPF), Profit-and-loss sharing

had also been applied by previous researchers, (equivalent rate) of the bank's internal

among others, Utama (2013) and Revania (2014) variables as well as inflation, exchange rate of

who attempted o empirically identify the factors macroeconomic variables.

affecting import. Step in formulating a ECM model according Domowitz and Elbadawi is as

RESEARCH METHODS

follows (Widarjono, 2013):

The data used in this research is Specifying the expected relationship in the secondary data. Secondary data such as time

observed model.

series (time series) as well as the monthly 𝑙𝑛𝐹𝐼𝑁𝐶 𝑡 =𝛼 0 +𝛼 1 𝑙𝑛𝐷𝑃𝐾 𝑡 +𝛼 2 𝑁𝑃𝐹 𝑡 + Islamic Rural Bank independent variable data 𝛼 3 𝑅𝑂𝐴 𝑡 +𝛼 4 𝑃𝐿𝑆 𝑡 +𝛼 5 𝑙𝑛𝐸𝑥𝑟𝑎𝑡𝑒 𝑡 + that are external to the period January 2009 - 𝛼 6 𝐼𝑁𝐹 𝑡 ……………………………………………………(3.1) December 2014. The selection of these data in

278 Arif Widodo , Determinants of Islamic Rural Bank Financing

FINC t = bFINC* t + (1 – b)FINC t-1 + (1 – b) f t (Z t –Z t imbalance adjustment according Domowitz

The formation

process

variable

1 ).......................................................................(3.5) and Elbadawi which is based on a single

where b = b 0 /(b 0 +b 1 )

quadratic cost function can be formulated as ft consists f1 = fDPK, f2 = fNPF, f3 = fROA, f4 = follows:

fPLS, f5= fExrate, f6 = fINF

C t =b 0 [FINC t – FINC* t ] 2 +b 1 {( FINC t – FINC t-

Furthermore, substituting the equation

1 ) –f t (Z t –Z t-1 )} 2 ...........................................(3.2) (3.1) into the equation (3.5) in order to obtain the following equation:

Equation (3.2) is a single quadratic cost function. The first component of this

lnFINC t = b(  0 +  1 lnDPK t +  2 NPF t +  3 ROA t + equation describes the cost imbalance and the

 4 PLS t +  5 lnEXrate t +  6 INF t ) + (1-b) FINC t-1 + (1- second component is the cost of adjustment.

t b) f (Z t –Z t-1 )....................................................(3.6) FINC t represent the number of FINC actual in

period t, Z t is a vector of variables that affect Equation (3.6) is a short-term analysis of FINC which in this case is influenced by

Islamic rural bank financing even though short- independent variables X (DPK, NPF, ROA,

term results were able to make predictions on

the long term. However, the main problem arises that gives weight to each costs and f t is a row

PLS, eXRate and INF), b 0 and b 1 is a row vector

when modeling equations used are not vector that weights the elements of Z t and Z t-1.

stationary, because when a model is not Minimizing the cost function in

stationary it can’t be estimated by using OLS equation (3.2) to variable FINC and equating

(ordinary least squares) and will generate to zero will produce the following equation: spurious regression. As a workaround, use the possibility of change (Δ) in each variable, thus:

b 0 [FINC t – FINC* t ]+b 1 [(FINC t – FINC t-1 ) –f t

(Z t –Z t-1 )] = 0 ∆𝑙𝑛𝐹𝐼𝑁𝐶 𝑡 =𝛽 0 +∑𝛽 1 𝑙𝑛𝐷𝑃𝐾 𝑡 +∑𝛽 2 𝑁𝑃𝐹 𝑡

or can be written into the following

equation: +∑𝛽 3 𝑅𝑂𝐴 𝑡

(b 0 +b 1 )FINC t =b 0 FINC* t +b 1 FINC t-1 +b 1 f t (Z t – 𝑛

Z t-1 ) ..............................................................(3.3) +∑𝛽 5 𝑙𝑛𝐸𝑥𝑟𝑎𝑡𝑒 𝑡 +∑𝛽 6 𝐼𝑁𝐹 𝑡

Vector Z consists of variable X (DPK, +𝛽 7 ∆𝑙𝑛𝐷𝑃𝐾 𝑡−𝑖 +𝛽 8 ∆𝑁𝑃𝐹 𝑡−𝑖 NPF, ROA, PLS, EXRate and INF) so that the

equation (3.3) can be expressed as follows: +𝛽 9 ∆𝑅𝑂𝐴 𝑡−𝑖 +𝛽 10 ∆𝑃𝐿𝑆 𝑡−𝑖

12 ∆𝐼𝑁𝐹 𝑡−𝑖 + 𝐸𝐶𝑇 + 𝜇 𝑡 Z t-1 ) .............................................................(3.4)

(b 0 +b 1 )FINC t =b 0 FINC* t +b 1 FINC t-1 +b 1 f t (Z t – +𝛽

𝑡−1 + ∆𝑁𝑃𝐹 𝑡−1 𝑡−1 + ∆𝑃𝐿𝑆 𝑡−1 + ∆𝑙𝑛𝐸𝑋𝑟𝑎𝑡𝑒 𝑡−1 + following equation:

+ ∆𝑅𝑂𝐴 Equation (3.4) can be expressed in the

JEJAK Journal of Economics and Policy Vol 10 (2) (2017): 273-288 279

Where: analysis conducted by considering some of the FINC : Total financing of Islamic bank

endogenous variables jointly in a single model. obtained from Islamic Banking

Each endogeneous variable is explained by its Statistics (SPS)-BI.

value in the past and past values of all other DPK : Total Depositor Fund or Third-party

endogeneous variables in the model were fund obtained from Islamic Banking

analyzed. However, if there is a long-term Statistics (SPS)-BI.

relationship in a variable then the model can be NPF : Non-Performing Financing (%) of

developed into VECM (Ascarya, 2012). The Islamic rural bank obtained from

general model of VECM can be expressed as Islamic Banking Statistics (SPS)-BI.

equation (3.18)

ROA : Return on Asset obtained from Islamic Banking Statistics (SPS)-BI.

PLS : the rate of profit and loss sharing

(Equivalent rate) obtained from

Islamic Banking Statistics (SPS)-BI.

Where:

EXrate : Exchange rate, Rupiah against US x k is k selected endogenous variables, specific for Dollar obtained from Indonesian

each model;

Financial Statistics-BI. ε k is disturbance or error term with zero means INF

: Inflation obtained from Indonesian and constant variance-covariance. Financial Statistics-BI.

ECT : Error correction or residual lag 1 of Islamic rural bank financing model: equivalent

ε t : Standar Error x k = [ Finc, DPK, NPF, ROA, PLS, EXrate,

In this research, we are also using vector

inflation]

auto regressive (VAR)/vector error correction

model. In general, VAR is used to analyze the Following the model in equation (3.1), the dynamic impact of the surprise factor

equation of VAR model in matrix for Islamic contained in the system variables. VAR

rural bank financing can be written as follows:

l𝑛 𝐸𝑋𝑟𝑎𝑡𝑒 𝑡 𝛼 60 𝛼 61 𝛼 62 𝛼 63 𝛼 64 𝛼 65 ln 𝐸𝑋𝑟𝑎𝑡𝑒 𝑡−1 𝜀 6𝑡

280 Arif Widodo , Determinants of Islamic Rural Bank Financing

𝜀 5𝑡 ∆ l𝑛 𝐸𝑋𝑟𝑎𝑡𝑒 𝑡 𝛼 60 𝛼 61 𝛼 62 𝛼 63 𝛼 64 𝛼 65 ∆ ln 𝐸𝑋𝑟𝑎𝑡𝑒 𝑡−1 𝜀 6𝑡

Furthermore, if the data shows followed by a integration degree test at first stationary in first difference and cointegration

difference. The following table result of unit root between variables, the the VAR model will be

test at level. Table 2 shows that all variables are combined with correction error model,

stationary at first differences. It can be shown of namely Vector Error Correction Model

ADF value (financing, third-party fund, return (VECM). Therefore, VECM model equation of

on asset (ROA), non-performing financing Islamic rural bank financing system are as

(NPF), equivalent rate, inflation, exchange rate) equation 3.20. Where, Δ is the change of

is bigger than critical value of Mac Kinnon. These variable from previous period, 𝜆 is an

data shows probability value of each variable adjustment level from short-term to long-

smaller than 1 percent, 5 percent and 10 percent. term equilibrium.

In the ECM methods, the requirements that must be fulfilled are all variables must be in the

RESULTS AND DISCUSSION

same degree. By testing of integration degree test Testing the unit root is using

above means that all the requirements has been Augmented Dicky-Fuller (ADF), which aims

fulfilled at first difference.

to see whether the time series data is Once the variable has been stationary in stationary or not with comparing statistical

the first differences, then we can conduct Mac Kinnon Critical value at α = 1 percent, 5

cointegration test to know whether there is long- percent and 10 percent. If the current level of

term relationship among all variables. This study data tested is not stationary, it will be

is using Johansen co-integration test.

Table 2. ADF Test Summary

McKinnon Critical Value (0.05) Variable

ADF Value

Level

1 st Difference

Level

1 st Difference

lnFINC -2.107834

-2.903566 Source: Primary data, processed.

JEJAK Journal of Economics and Policy Vol 10 (2) (2017): 273-288 281

If the trace statistic value and max- percent, so it can be concluded that all variables eigen value statistic is greater than the critical

namely third-party fund (DPK), return on asset value 0.05, it means that it has cointegration

(ROA), non-performing financing (NPF), among all variables. The following table test

Inflation, PLS (Profit-and-Loss sharing) and results Johansen cointegration (see Appendix

exchange rate, they have long term effects on 1).

Islamic rural bank financing. Based on the test results of

Error Correction Model test shows that all cointegration Johansen above, it can be seen

variables cointegrated which means have long- from the test strace statstic and max-

term relationship. Here are the results of long- eigenvalues statistic indicate the existence of

term estimates:

cointegration at signific ance level α = 5

Table 3. Error Correction Model Estimation Result

Long-term Variable

5.40*** ∆ln DPK

0.648 ∆ln EXrate

-4.27*** ∆ln DPK (-1)

∆ROA (-1)

∆NPF (-1)

∆INF (-1)

∆PLS (-1)

∆ln EXrate (-1)

Adjusted R 2 0.997

S.E. of regression

D-W stat

BG-LM test

ARCH test

Partial Correlation

Notes: ***, **, * represent significant at 1%, 5%, and 10%, respectively. BG-LM test is used to detect auto-correlation problem; ARCH test is applied to test the homogeneity of variance; and Partial Correlation is to detect multi-correlation. Source: Data Processed.

282 Arif Widodo , Determinants of Islamic Rural Bank Financing

The estimation equation in the long term suggests that in the long run third-party fund and short term can be obtained, showing the

variable has a significant positive effect on the coefficient of determination adjusted (Adjusted

changing of Islamic rural bank financing. R 2 ) is 0.99, it means the ability of third-party

The effect of return on asset (ROA) fund, return on asset (ROA), non-performing

toward Islamic rural bank financing by t- financing (NPF), Inflation, PLS (Equivalent

statistic test shows that the coefficient Rate) and exchange rate to explain the

negatively of 0.67345, t-statistic value changing of Islamic rural bank financing is

negatively 1,273743 and probability value of 99.9084 per cent while the remaining 0.016

0.2079 which is not significant α = 1 percent, 5 percent influenced by other factors outside

percent and 10 percent. It shows that ROA does model. Generally, the result of parameter

not have significant effect in changing on significant test known that the F-statistic of

Islamic rural bank financing. 5876.451 with a probability value of F-statistic

The effect of non-performing financing 0.000 < α (level of significance) 1 percent. This

toward Islamic rural bank financing by t- shows that third-party fund, return on asset

statistic test shows that the coefficient (ROA), non-performing financing (NPF),

positively of 3.773358, means that if the Inflation, profit-and-loss sharing and exchange

variable of non-performing financing increased rate are significantly on Islamic rural bank

by 1 percent, there will be an increase of 3.773 financing.

to variable financing with a statistic value of Based on the estimation results can be

8.652624 and probability value of 0.0000 which seen that the coefficient on the variable Error

is significant at α = 1 percent. This suggests that Correction Term (ECT) is significant at the 1

in the long run, non-performing financing percent level of significance and has a positive

variable has a significant positive effect on the sign and is therefore said that the model ECM

changing of Islamic rural bank financing. can be used to estimate the model in this study.

The effect of inflation toward Islamic Or in other words the specification of the

rural bank financing by t-statistic test shows model used in this study are valid.

that the coefficient positively of 0.246909, Based on estimation result, it can be

means that if the variable of inflation increased created mathematic equation as follows:

by 1 percent, there will be an increase of 0.246 lnFINC = 2,164423 + 1,038631 lnDPK +

percent to variable financing with a statistic 3,971966 npf -0,190979 lnKurs + 0,997165

value of 2.046354 and probability value of lnDPK(-1) + 3,773358 npf(-1) + 0,246909 inf(-1) -

0.0000 which is significant at α = 5 percent. 0,236852 lnKurs(-1) - 0,992607 ect

This suggests that in the long run, inflation Interpretation of individual regression

variable has a significant positive effect on the coefficients (t-test) long term are as follows:

changing of Islamic rural bank financing. Third-party fund (DPK) variable effect on

The effect of PLS (Equivalent rate) the growth of Islamic rural bank with statistical

toward Islamic rural bank financing by t- t-test, it shows that coefficient value positively

statistic test shows that the coefficient of 0.997165 means that if the variable in

negatively of 0.116496, t-statistic value 0.36104 deposits increased by 1 percent, there will be an

and probability value of 0.7194 which is not increase of 0.997 to variable financing with a

significant α = 1 percent, 5 percent and 10 value of 107.3304 and probability value of

percent. It shows that equivalent rate does not 0.0000 which is significant at α = 1 percent. This

have significant effect in changing on Islamic

JEJAK Journal of Economics and Policy Vol 10 (2) (2017): 273-288 283

rural bank financing. The effect of exchange optimal lag length should be applied. rate toward Islamic rural bank financing by t-

Therefore, optimal lag length should be statistic test shows that the coefficient

obtained using test of optimal lag. The negatively of -0.236852, means that if the

selection of optimal lag length in this study will variable of exchange rate increased by 1 percent

be based on the shortest lag of Schwarz or depreciation, there will be a decrease of 0.236

information criterion (SC), Final prediction percent to variable financing with a statistic

error (FPE) and Hannan-Quinn information value of 7.695763 which is significant at α = 1

criterion (HQ). Table 2. in appendix shows the percent. This suggests that in the long run,

results of the optimal lag selection for Islamic exchange rate variable has a significant effect

rural bank financing. Based on SC, FPE and HQ on the changing of Islamic rural bank

the optimal lag for financing is 1 (one). financing.

All variables in financing model are Vector Error Correction Model (VECM) is

stationary at first difference I(1), so that the several procedures of data testing should be

long-term relationships among variables can followed as a standard procedures for using

only be obtained if they have satisfied the VAR/VECM method, such as unit root test,

criteria of integration process. Cointegration stability test, optimum lag test, and

test based on trace statistics will be applied to cointegration test. After all requirements have

determine the number of equation systems that been met, results can be generated. The

can explain long-term relationship. Johansen complete results of all VECM procedures can be

cointegration test results for Islamic rural bank obtained from the authors.

financing model with Trace Statistic show that Unit root test results show that all of

there exist 3 (three) cointegrating equations at variables are not stationary in level, except

the 5% critical value (see Appendix 3). three variables reach stationer in level in

The results of Impulse Response McKinnon Critical Value 0.10 that is inflation,

Function show responses variable Financing growth and PLS, but all variables are stationary

(FINC) against shocks from the bank's internal in first difference (see table 4 in previous

variable (bank behavior), which in this case is section.)

the Islamic rural bank, as well as of The VAR system at its optimal lag should

Macroeconomics variables. In Figure 2 shows

be stable. Unstable VAR system will make the that there are three variables that were result of Impulse Response Function (IRF) and

positively responded by Financing (FINC), Forecast Error Variance Decomposition

namely the profit-and-loss sharing 'PLS' (FEVD) are not valid. The stability test based on

(equivalent rate), Third-party fund (lnDPK) modulus or unit circle will be applied to

and Inflation (INF). This means that these three determine if the VAR system at its optimal lag

variables have a positive impact on Islamic is stable within its unit-circle or modulus less

rural banks financing in Indonesia. In addition, than one. Stability test result for original VAR

there are three variables that reduce Financing system (optimal lag = 1) shows that Islamic

(FINC), namely Non-Performing Financing rural bank financing model is stable with

(NPF). Return on Assets (ROA) and the maximum lag up to six as show at table 6.

exchange rate (lnExrate). This means that the One problem of the VAR system is

three variables have negative impacts in term of autocorrelation. To overcome this problem,

reducing Islamic rural banks financing. The

284 Arif Widodo , Determinants of Islamic Rural Bank Financing

impact of shocks variables that were positively more comprehensive. The analysis will begin by responded by Financing (FINC), began to

discussing the bank's internal factors and stabilize after a period of 10-15, while the

continued with a discussion on the impact of variable shocks that were responded negatively

macroeconomic variables. Bank’s internal by Financing stable after 15-20. Profit-and-loss

factors (bank’s behavior)

sharing (PLS) provides the greatest positive In the ECM model, indicates that the impact in improving the financing, followed by

variable Third-party fund (lnDPK) have a

a variable Third-party funds and then inflation. positive effect on the financing of Islamic rural Meanwhile,

bank (BPRS). This can be seen from the Financing (NPF) provides the greatest impact

variable

Non-Performing

coefficient in the short term in period t at in reducing financing in BPRS followed by

1.0386, the previous period t-1 at 0.99716 and Return on Assets and Exchange rate.

the value of long-term coefficient of 0.99654, all Furthermore, the Forecast Error Variance

coefficient is positive and significant effect of Decomposition (FEVD) indicates that the

the financing on the critical value of 5% (see variable PLS make a positive contribution to

table 4). This is similar to the results shown by the financing of 39.08 percent, followed by

the model VECM via Impulse Response lnDPK with a contribution of 19.6 percent and

Function where financing can respond inflation (INF) amounted to 8.9 percent. In

positively to shocks variable lnDPK and contrast, the largest negative contribution to

stabilized after the 13 th period. the financing of BPRS caused by NPF (9.02%),

Third party funds are the source of the ROA (7.76%) and exchange rate (2.48%). These

greatest in the process of distribution of funds results can of course be used as an evaluation

from the public to society (intermediary). The that BPRS financing is not only influenced by

positive influence of DPK to BPRS financing in the internal but also external factors of the

long-term and short-term shows that BPRS in bank, in this case the macroeconomic

Indonesia perform its function both as a conditions.

financial intermediary. DPK has an important role in financial institutions, it is because the

In this study deliberately used two (2) amount of financing depends on the amount of

methods of research to explore and enrich the funds available (Muhammad, 2005: 52).

analysis, so the results will be obtained become

Figure 2. Impulse Response Function (left) and Forecast Error Variance Decomposition (right) for Islamic Rural Bank Financing

JEJAK Journal of Economics and Policy Vol 10 (2) (2017): 273-288 285

When financing submitted by the coefficient of NPF is the largest coefficient in public is getting higher, it would be in line

comparison with other variables that is equal to with the increase in positive surplus in the

3.971966, and amounted to 3.77335 for the period form of income that will be earned by the

t-1, while for long-term coefficient of 3.519818; BPRS itself. Therefore, BPRS need to

every coefficient is positive and significantly implement strategies for effective marketing

affect the financing. This certainly could happen to attract people who have excess funds to be

for several reasons, including the quality factor able to keep it in BPRS. Thus, the results in

of financing, that the high value of NPF cause deposits that affect the growth of BPRS

nominal financing continues to grow. Bad financing in line with research conducted by

financing unresolved but the BPRS should still Pratama (2010) and Haryati (2009).

add financing, ultimately what happens is an Variable Return on Assets (ROA), which

increase in the remaining principal debt indicates the profitability of the BPRS has a

outstanding or unpaid.

negative coefficient on the short-term at The yield on the ECM model has period t with a value of -1.1666, period t-1 with

differences with the output from the VECM the coefficient value of -0.67345 or in the long

model. Through the IRF chart can be seen that term with the coefficient of -0.856787, every

the variable NPF has the largest negative coefficient is worth the negative and not

contribution to the financing, meaning that the significantly affect the financing. While in

more the number of NPF then the loan amount VECM model, ROA has a negative influence

will be further reduced. This negative influence and permanent, meaning that ROA reduce

is beginning to stabilize after a 15 th period (see the impact of financing. BPRS level of profits

figure 2) by reducing the contribution of does not necessarily increase the financing

financing by 9.02 percent. Variable NPF in but it has a tendency to lower the financing.

Islamic banking is an indicator of credit risk are This can happen because in 2010 the

very concerned, because if the credit risk growth of Islamic banks are increasingly

increases, banks will be more cautious in flourishing due to a conventional commercial

providing financing. Thus, it is unlikely that a bank opened Islamic bank makes a new

bank will reduce financing and focus repair, competitor for BPRS so that the public

recovery financing problems. interest would be filing financing in BPRS

The last variable of the banks behavior is declined slightly. As a result, the IRF chart we

profit-and-loss sharing (PLS). In ECM model, can observe that the ROA BPRS moved down,

variable PLS coefficients is positive in period t, profits obtained more allocated to the

the previous period (t-1) as well as in the long development of the company's assets and a

term, although nothing is statistical significantly little portion of the revenue is used to increase

affect the financing with a positive coefficient the capital in expanding its business

means financing provided BPRS has been with expansion.

the system for the results having an impact Meanwhile, variable Non-Performing

positive for financing. Through VECM approach, Financing (NPF) for ECM model shows a

get a graph which shows that PLS has a positive positive coefficient in both the short term and

impact for financing. This is reinforced by in the long term. In the short term period t

evidence that PLS has the largest positive

286 Arif Widodo , Determinants of Islamic Rural Bank Financing

contribution to the financing (39.08% see in worth, both in the short and long term. This is figure 2). This means that the system for the

evident from the coefficient of –0.190979 t results being able to touch and move the real

period, the previous period (t-1) at –0.23685 and sector economic activities. According Sakti

in the long term by –0.26948; all significant at (2007), PLS implications for the real sector is

the 5% critical value. Meanwhile, VECM model crucial, because if the PLS is applied properly

also shows the same thing that the exchange rate will foster a favorable investment climate and

variable has a negative impact or contribution to fair so that the concentration of wealth can be

the financing of 2.48%; this value is the smallest prevented, the distribution of income and

value when compared with other variables that wealth growth in real sector were stable. The

have negative impact. Variable exchange rate growth of the real sector will increase labor

stabilized after 12 th period (see figure 2). productivity and opening up more job

Exchange rate variable, in this case the value of opportunities, eventually driving the pace of

the rupiah against the US dollar, there is a term economic growth based on the real sector.

appreciation and depreciation. When the value Thus, BPRS financing with PLS system should

of the rupiah against the US dollar increase,

be encouraged and enhanced since that is means is happening appreciation; or Rupiah what will sustain economic growth and

exchange rate become stronger (ex. 1 US $ = Rp promote

12.000 to 1 US $ = Rp 11.000), and vice versa, if Macroeconomics variables

the welfare

society.

Rupiah falls in value against the US dollar, means Not only banks behavior factors that

is happening depreciation, for example, 1 US $ affect the performance of bank financing,

used to equal Rp 10,000 when it turns to 1 US $ is macroeconomic conditions also become an

equal Rp 12,000. In this study, the increase in the integral part of the financing. In this study

exchange rate in question is depreciation, so that macroeconomic variables are represented by

when the exchange rate falls in value (Rupiah

a variable exchange rate and inflation. become weaker) impact on decreasing Islamic Variable exchange rate as described in the

rural bank financing.

ECM model, has a coefficient of negative

PLS Accelerate Investment

Eco. Growth

Wealth &

Productivity &

Income Opportunity

Distribution

Real Sector Grow

Source: Sakti (2007)

Figure 3. Profit-and-Loss Sharing (PLS) Implications

JEJAK Journal of Economics and Policy Vol 10 (2) (2017): 273-288 287

Indonesia as the State importers some long-term and short-term shows that the essential commodities, such as beef, fruits and

ISLAMIC RURAL BANKsharia in Indonesia others, if the Rupiah depreciates (rupiah

perform its function both as a financial become weaker) then the price will rise

intermediary. This means that it is a must for causing sluggish economy and society tends

Islamic rural bank to always focus on improving to depress consumption so that the financing

the quality and quantity of third party fund. In would be decreased. When Rupiah in a bad

addition, Profit-and-loss sharing, as suggested by condition employers will be more careful in

many proponents of Islamic economics, is the taking financing because of the credit-risk will

main factor that differentiates Islamic banking also be high. From the side of BPRS, the bad

practices from conventional counterpart. It is condition of the rupiah will tend to seek

apparent from this study that PLS gives a financing with better quality and of course

significant influence to financing, based on this is not easy at all. Finally, the financing

VECM. Regarding credit risk, BPRS must reduce also declined.

the non-performing financing as it can bring Macroeconomic

about instability of Islamic rural bank contributes to affect financing is inflation. In

condition

that

performance as a whole. Given the ECM model, inflation has a coefficient of

macroeconomic condition, it is also found that 0.303697 worth in period t, amounted to

inflation could not reduce the financing growth, 0.246909 in the previous period (t-1) and

indicating the stability of Islamic rural bank. In amounted to 0.327668 in the long term. All

contrast, the volatility of exchange rate may coefficients are positive but that affect

probably limit financing. Islamic rural bank, significantly only in period t-1, so the inflation

however, should be aware of knowing exchange that occurred earlier period provides a

rate volatility. Second, referring to the variance significant effect for increased financing.

decomposition result, Profit-and-loss sharing Similar results were also obtained from VECM

(PLS) has the largest positive impact to financing model, where variable inflation contributes

(39.08%), followed by lnDPK (19.6%) and positive impact on financing with a value

inflation (8.9%). While, the largest contribution contribution of 8.9% and stabilized in 15 th to the financing of BPRS caused by NPF (9.02%),

period. followed by ROA (7.76%) and exchange rate The impact of inflation towards

financing (bank credit) was accordance with Haryati (2009), where the inflation gave

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Determinants of Islamic Rural Bank Financing in Indonesia