Presented by 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

1. 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

2. 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

3. 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

4. Financial system stability
Why 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 banks
iB 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 & finance
iB 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

5. Procyclicality of banking system

6. 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

7.  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

8. Significance of Research
Bank lending
behaviour
Bank-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

9. Literature Review
Islamic banking and financial stability
Studies Pros & Cons Findings
Chapra (2009) Pros
PLS contract will ensure the greater discipline of iB,
and such discipline carries greater stability and
efficiency
Buiter (2014) Pros
The 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

10. Literature Review
Procyclicality 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

11. 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
Data
All 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.

12. Model Estimation & Testing
 Autocorrelation Test (AR1/AR2)
 Instrumental Variable Test (Sargan Test)

13. Model Estimation & Testing
𝑌𝑖𝑡 = 𝛼𝑌𝑖𝑡−1 + 𝛽𝑋′𝑖𝑡 +𝜀𝑖𝑡
𝜀𝑖𝑡 = µ𝑖𝑡 + 𝜈𝑖𝑡
𝑌𝑖𝑡 = Level of deflated gross loan of bank i in period t
𝑌𝑖𝑡−1 = The lagged of deflated gross loan of bank i in period t
𝛼 = A scalar
𝑋′𝑖𝑡 = The explanatory variables of bank i in period t
𝜀𝑖𝑡 = A random error term which consists of two components
µ𝑖𝑡 = The unobservable time-invariant individual or bank specific
effects
𝜈𝑖𝑡 = The remainder disturbance

14. Model Estimation & Testing (2)
𝛥𝐿𝑖𝑡 = 𝛾𝛥𝐿𝑖𝑡−1 + 𝛽𝛥𝑦𝑡 + 𝜃𝑋𝑖𝑡−1 + 𝜋𝑖𝑛𝑓𝑡 + 𝛼𝑖 + 𝜀𝑖𝑡
𝐿𝑖𝑡 = Natural logarithm of CPI-deflated gross loans of bank i in period t
𝐿𝑖𝑡−1 = The lagged of CPI-deflated gross loans of bank i in period t
𝑦 = Natural logarithm of real GDP
𝑋 = A vector of bank-specific variables
Inf = Inflation rate
𝛥 = The first difference of operator
𝛼𝑖 = Bank-specific effects
𝜀𝑖𝑡 = A random error term

15. 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

16. 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

17. 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 0.2521
Sargan test 0.2151 0.2461 0.217 0.2258
Both Sargan & AR tests affirm the model estimated using
GMM
Add INF as control variable
1 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

18. 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-values
AR(2) 0.4349 0.4126 0.7496
Sargan 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

19. 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 0.1632
Sargan test 0.2246 0.2077 0.2116 0.2145
Both Sargan & AR tests affirm the model estimated using
GMM
Add INF as control variable
1 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

20. 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-values
AR(2) 0.4349 0.4126 0.231
Sargan 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

21. 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.

22. Thank You
Q & A