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Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia Muhammad Rizky Prima Sakti * & Tami Astie Ulhiza Researcher at ISEFID (Islamic Economics Forum for Indonesian Development) Research assistant at IRTI - IDB (Islamic Research & Training Institute – Islamic Development Bank) Lomba Karya Ilmiah Stabilitas Sistem Keuangan (LKI-SSK) 2016 Bank Indonesia

Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

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Page 1: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Muhammad Rizky Prima Sakti * & Tami Astie Ulhiza• Researcher at ISEFID (Islamic Economics Forum for Indonesian Development)• Research assistant at IRTI - IDB (Islamic Research & Training Institute – Islamic Development Bank)

Lomba Karya Ilmiah Stabilitas Sistem Keuangan (LKI-SSK) 2016 Bank Indonesia

Page 2: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Background

Financial crisis, greater economic cost, i.e finanical

crisis 1997 has a cost of 51% of GDP

Systemic risks in economy & financial

instability.

Global financial crisis 2009

interconnectedness, contagion effect.

The relationship between financial

sector & macroeconomic

FINANCIAL SYSTEM STABILITY

The procyclicality of banking system

Page 3: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Main components of financial system stability

Stable macroeconomic environment

Sound framework of macroprudential

supervision

Well-managed finanical institutions

Safe & Robust payment system

Sound framework of prudential supervision

FINANCIAL SYSTEM STABILITY

Page 4: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Financial system stabilityWhy its important for Islamic Banks (iB)?

Financial stability becomes important for iB due to: (1). iB are closely interact with

conventional ones in dual-banking system

(2). iB have limited hedging instruments to protect their risk-exposure due to a small

size compared to conventional ones.

Shariah values of Islamic banksiB is derived from shariah principles

towards achieving the maqasid al-shariah (the objective of shariah)

Promoting risk-sharing and equity based transactions

Essential features of Islamic banking & financeiB provides various instruments in line with

Islamic principles: prohibition of riba (usury), gharar (excessive uncertainty) &

maysir (speculation)

iB / finance must be linked with real economic activities, or be accompanied by underlying productive economic activities

Page 5: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Procyclicality of banking system

Page 6: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Research Objectives

To examine bank lending behaviour in a dual banking system in Indonesia

To ascertaining whether Islamic banks have a role in stabilizing the credit.

To test the procyclicality of Islamic and conventional banks in Indonesia using a dynamic panel regression

1

2

3

Page 7: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

a All journals are categorized under the subject of business, economics, finance, and accounting. b Using keyword ‘procyclicality and financial stability’

Database or publisher Total no. of journals a No. of procyclicality articles b

Thomson Reuters (ISI) 439 25

Scopus 1,166 35

Emerald Insight 481 39

Springer 36 95

Taylor & Francis 264 191

Wiley-Blackwell 429 248

Science Direct 3,876 443

Publication of procyclicality & financial stability research

Page 8: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Significance of ResearchBank lending

behaviourBank-level

data iB vs CB

• Ensuring whether the iB have a role in stabilizing the credit

• Place an attention on heterogeneous responses of banks during economic crisis

• The impact of iB system on lending procyclicality

• Prior studies rely on bank-level panel data from many countries. In this case, we employ bank-level panel data of only a single country, i.e. Indonesia

• We focus on bank lending procyclicalty in dual banking system.

• We believe that its will more meaningful to look at how iB adjust their financing decision vis a vis to CB counterpart

Page 9: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Literature ReviewIslamic banking and financial stability

Studies Pros & Cons Findings

Chapra (2009) ProsPLS contract will ensure the greater discipline of iB, and such discipline carries greater stability and efficiency

Buiter (2014) ProsThe inherent stability of iB due to the ban of interest in deposit-lending activities, condemnation of leverage, and excessive speculation

Galati & Moesner (2013) Pros Moral values enshrined in sharia make iB more stable

than conventional ones,

Husman (2015) Pros iB is relatively stable

Chong & Liu (2009) Cons No difference between iB & CB since the PLS constitute only a small portion of iB assets

Abdul Rahman et al (2014) Cons Question the ability of iB to uplift the PLS activities

Hasan & Dridi (2011) Cons The profitability of iB is more negatively affected when the crisis hit the real sector

Page 10: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Literature ReviewProcyclicality of banking system

Studies Samples Findings

Ascarya et al (2016) iB & CB Indonesia

iB is more procyclical than conventional ones. Yet, this procyclicality can be regarded as good procyclicality since it does not create credit bubbles

Zhang & Zoli (2016) Asian market Loan-loss provision is an important instrument to address procyclicality

Ibrahim (2016) iB & CB Malaysia

iB (full-fledged in particular) are more counter-cyclical in their financing decision

Farooq & Zaher (2015) `

iB are less prone to liquidiity shocks, it showing the potential stabilizing effect of their financing decision

Page 11: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Data & Methodology

Why use GMM? (1). Autocorrelation problem resulted from the incorporation of a lagged dependent variable into regressors

(2). Effects of heterogeneity among the individuals

Methodology (GMM Estimator)GMM estimator can take care of problems of fixed effects and endogeneity without producing dynamic panel bias

GMM model is flexible in handling unbalanced panels, such as micro panel data used in this research

DataAll data for bank lending procyclicality were retrieved from Bank Scope. The macroeconomic information was retrieved from Bank of Indonesia website

We include 60 banks covering both CB & iB in Indonesia, which consists of 50 CB and 10 iB. Our dataset spans from 2001 until 2015.

Page 12: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Model Estimation & Testing

Autocorrelation Test (AR1/AR2)

Instrumental Variable Test (Sargan Test)

Page 13: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Model Estimation & Testing

= Level of deflated gross loan of bank i in period t

= The lagged of deflated gross loan of bank i in period t

= A scalar

= The explanatory variables of bank i in period t

= A random error term which consists of two components= The unobservable time-invariant individual or bank

specific effects= The remainder disturbance

Page 14: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Model Estimation & Testing (2)

= Natural logarithm of CPI-deflated gross loans of bank i in period t

= The lagged of CPI-deflated gross loans of bank i in period t

= Natural logarithm of real GDP

= A vector of bank-specific variables

Inf = Inflation rate= The first difference of operator

= Bank-specific effects

= A random error term

Page 15: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Descriptive statistics

Lowest 3.6%

Highest 6.35%

GFC, immune

Peak inf 13%

lowest inf 4.3%

In average, annual growth rate of 5.3%While inflation record 7.65% over 2001-2015

Page 16: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Descriptive statistics (2)

Variables All Samples Conventional Banks Islamic Banks

Loans measures

Gross loans 40,300,000 44,900,000 10,400,000

% growth 32.77 31.59 41.06

Net loans 38,800,000 43,200,000 10,100,000

% growth 33.61 32.52 41.30

Bank-specific variables

Real assets (log) 16.90 17.10 15.63

Equity-asset ratio (%) 12.21 11.41 17.42

loans-deposits ratio (%) 91.84 81.57 164.23

CB greater loans

IB better capitalized

CB larger size

Page 17: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

System GMM – Baseline Results

Variables (1) (2) (3) (4)ΔL1it-1 0.6505*** 0.6831*** 0.6481*** 0.6771***

(0.0000) (0.0000) (0.0000) (0.0000)Δyit 0.147*** 0.131* 0.199*** 0.553**

(0.090) (0.0791) (0.0000) (0.278)Δyit x IBi - - -0.331*** -0.629***

(0.0000) (0.0000)LnSIZEit-1 0.3029*** 0.2512*** 0.3149*** 0.2690***

(0.0000) (0.0000) (0.0000) (0.0000)CAPit-1 -0.02723*** 0.0281*** -0.0274*** -0.0285***

(0.0000) (0.0000) (0.0000) (0.0000)FUNDit-1 0.0003** 0.0002** 0.0003** 0.0002*

(0.034) (0.031) (0.039) (0.078)Inft - -0.0114*** - -0.0102***

(0.0000) (0.0000)

P-values

AR(2) 0.1476 0.25 0.1565 0.2521Sargan test 0.2151 0.2461 0.217 0.2258         

Both Sargan & AR tests affirm the model estimated using GMM

Add INF as control variable1 percentage point increase in GDP growth 0.13 to 0.14 increase growth gross loans

The diff on CB loan & iB financing

(-) sign, this coeff > GDP growth iB more counter-cyclical

Page 18: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

System GMM – Different size groups

Variables Model 1 Model 2 Model 3  (small size) (medium size) (large size)ΔLit-1 0.2696* 0.3408*** 0.6857***

(0.074) (0.0000) (0.0000)Δyit 0.1076*** 0.660*** 0.186***

(0.002) (0.000) (0.0000)Δyit x IBi -0.531 -0.674 -0.217*

(0.461) (0.296) (0.076)LnSIZEit-1 0.7660*** 0.609*** 0.2905***

(0.000) (0.0000) (0.000)CAPit-1 -0.0154*** -0.0023*** -0.0278***

(0.000) (0.001) (0.000)FUNDit-1 0.012** 0.0002** 0.005**

(0.119) (0.314) (0.002)Inft -0.0005* -0.0043*** -0.0092***

(0.0874) (0.000) (0.000)

P-valuesAR(2) 0.4349 0.4126 0.7496Sargan test 0.4528 0.4374 0.756       

Both Sargan & AR tests affirm the model estimated using GMM

Large size ( >75th percentile), medium size (25th – 75th percentile), & small size (< 25th percentile)

1 percentage point increase in GDP gr 0.11 to 0.66 increase in gross loans

Large iB can be even counter-cyclical have ability to stabilize the credit

Page 19: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Robustness check (1) - System GMM (Net Loans)

Variables (1) (2) (3) (4)ΔL1it-1 0.4203*** 0.4479*** 0.4575*** 0.4823***

(0.0000) (0.0000) (0.0000) (0.0000)Δyit 0.431*** 0.281*** 0.393*** 0.465***

(0.0000) (0.0000) (0.0000) (0.0000)Δyit x IBi - - -0.426*** -0.526***

(0.0000) (0.0000)LnSIZEit-1 0.5143*** 0.4672*** 0.4820*** 0.4382***

(0.0000) (0.0000) (0.0000) (0.0000)CAPit-1 -0.0194*** -0.0206*** -0207*** -0.0215***

(0.0000) (0.0000) (0.0000) (0.0000)FUNDit-1 0.0005** 0.0005** 0.0005** 0.0005***

(0.000) (0.000) (0.000) (0.000)Inft - -0.0079*** - -0.008***

(0.000) (0.000)

P-values

AR(2) 0.1456 0.2069 0.1665 0.1632Sargan test 0.2246 0.2077 0.2116 0.2145         

Both Sargan & AR tests affirm the model estimated using GMM

Add INF as control variable1 percentage point increase in GDP growth 0.28 to 0.43 increase growth gross loans

The diff on CB loan & iB financing

(-) sign, this coeff > GDP growth iB more counter-cyclical

Page 20: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Robustness check (2) - System GMM (Net Loans, different size groups)

Variables Model 1 Model 2 Model 3  (small size) (medium size) (large size)ΔLit-1 0.2686*** 0.3408*** 0.4761***

(0.0074) (0.0000) (0.0000)Δyit 0.1076*** 0.661*** 0.169*

(0.002) (0.000) (0.091)Δyit x IBi -0.153 -0.694 -0.2102*

(0.461) (0.296) (0.0607)LnSIZEit-1 0.7660*** 0.6093*** 0.3936***

(0.0000) (0.0000) (0.0000)CAPit-1 -0.0154*** 0.0022** -0.0113***

(0.000) (0.001) (0.000)FUNDit-1 0.0012 -0.0002 0.0002**

(0.119) (0.314) (0.003)Inft 0.0005 -0.004*** -0.0112***

(0.874) (0.000) (0.000)

P-valuesAR(2) 0.4349 0.4126 0.231Sargan test 0.4629 0.8153 0.278 

Large size ( >75th percentile), medium size (25th – 75th percentile), & small size (< 25th percentile)

Both Sargan & AR tests affirm the model estimated using GMM

1 percentage point increase in GDP gr 0.11 to 0.66 increase in gross loans

Large iB can be even counter-cyclical have ability to stabilize the credit

Page 21: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Conclusion & Policy Recommendations

1

• In all samples, bank procyclicality applied for both conventional & Islamic banks. However, when we categorize into CB & iB, we find no support that Islamic bank is more procyclical in their financing. In fact, iB in general and large size iB in particular can even be counter-cyclical in their financing activities

2• The study unveils the tip of iceberg of the role

played by Islamic banks in smoothing their credit during the time of economic downturns. In all cases, Islamic banks are tend to be counter-cyclical than conventional ones.

3• As for the regulators, procyclicality as one the

major causes of systemic risk should be well understood. Islamic banks in Indonesia tend to be counter-cyclical, while conventional ones is more procyclical in their lending behavior.

4• As a consequence, it is required to established

a sound framework and effective instruments to address the procyclical issues between the two banking system. macroprudential policies and framework for Islamic and conventional banks should be unique and effective to prevent systemic risk and financial imbalances.

Page 22: Bank Lending Procyclicality of Islamic and Conventional Banks in Indonesia

Thank You

Q & A