bank lending procyclicality of islamic and conventional banks in indonesia
TRANSCRIPT
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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
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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
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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
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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
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Procyclicality of banking system
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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
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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
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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
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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
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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
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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.
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Model Estimation & Testing
Autocorrelation Test (AR1/AR2)
Instrumental Variable Test (Sargan Test)
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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Thank You
Q & A