SLIDE 1
Banking risks, the bank lending channel, and the macro-prudential policy in Indonesia
NBRM 7th Annual Research Conference
The views expressed in this paper are of the author’s only and do not necessarily reflect those of Bank Indonesia. All errors are the author’s only.
SLIDE 2 Motivation
Graph 1. Credit/GDP Major Asia Countries Graph 2. Indonesia credit, GDP growth, BI rate
The blue rectangular boxes represent loosening periods while the red rectangular box indicates tightening periods of macro-prudential policy.
- Credit growth is a key driver for economy growth.
- Low credit growth may hamper the economy growth, but excessive growth
may fuel greater risks for macro and financial stability.
- Effectiveness of monetary and macro-prudential polices to manage credit
growth.
Data sources: BIS statistics and Bank Indonesia Data Sources: BIS statistics and Bank Indonesia
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Objective & Research questions
What factors drive credit growth in Indonesia?
Liquidity? Capital? Credit risk? Monetary interest rate policy (bank lending channel)? Macro-prudential policy? Interaction of banking characteristics with macro-prudential policy? Interaction of monetary and macro-prudential policy?
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Credit growth & monetary policy (bank lending channel)
Bernanke&Blinder (1992), Kashyap&Stein (1994), Morris&Sellon (1995), Gambacorta & Marques-Ibanez (2011).
Credit growth & macro-prudential policy
cross countries evidence: Claessens, et al (2011), Lim, et al (2011), Zhang&Zoli (2014), Cerutti, et al (2017), case study of a country: Igan&Kang (2011), Wong, et al (2011), Jimenez, et al (2012), Aiyer, et al (2014).
Credit growth & banking characteristics (liquidity, credit risk, sources of
financing, capital) Bernanke & Lown (1991), Peek & Rosengreen (1995), Kashyap, et al (2002), Cornett, et al (2010), Gambacorta & Marques-Ibanez (2011), Kapan & Minoiu (2013), Klein (2013), Accornero, et al (2017).
Literature review
SLIDE 5 The Model
Expanding Kashyap & Stein (1994) ∆𝑚𝑝 𝑑𝑠𝑓𝑒𝑗𝑢 𝑗𝑢 ∶ quarterly changes of log nominal credit bank 𝑗 at period 𝑢 Quarterly macro-economic and individual data of 94 Indonesian commercial banks
- ver the period of 2005-2016.
∆𝑚𝑝 𝑑𝑠𝑓𝑒𝑗𝑢 𝑗𝑢 = 𝜄∆𝑚𝑝 𝑑𝑠𝑓𝑒𝑗𝑢 𝑗𝑢−1 + 𝛿∆𝑚𝑝 𝐻𝐸𝑄 𝑢−𝑙 + 𝛽∆𝑚𝑝 𝐷𝑄𝐽 𝑢−𝑙 + 𝛾∆𝐷𝐶𝑢−𝑙 + 𝜀𝑌𝑗𝑢−𝑙 + 𝜒1𝑁𝑄𝐽1𝑢−𝑙 + 𝜒2𝑁𝑄𝐽2𝑢−𝑙 + 𝛾1
∗𝑁𝑄𝐽1𝑢−𝑙∆𝐷𝐶𝑢−𝑙
+ 𝛾2
∗𝑁𝑄𝐽2𝑢−𝑙∆𝐷𝐶𝑢−𝑙 + 𝜀1 ∗𝑁𝑄𝐽1𝑢−𝑙𝑌𝑗𝑢−𝑙 + 𝜀2 ∗𝑁𝑄𝐽2𝑢−𝑙𝑌𝑗𝑢−𝑙 + 𝜁𝑗𝑢
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Variables (1)
MPI1: dummy variables of phase 1 macro-prudential policy (MP)
Code 1 for loosening period Q1 2011-Q2 2012 , code -1 for tightening period Q3 2012-Q2 2015, and 0 otherwise.
MPI 2: dummy variables of phase II macro-prudential policy (MP)
Code 1 for loosening period Q3 2015 - Q4 2016 and 0 otherwise
SLIDE 7 Variables (2)
Independent variables Variables’ description Expected sign LA/D The ratio of liquid assets (cash, placement at the central bank, and high-grade securities) to deposit (%). + NPL The ratio of the non-performing loans to total loan (%).
Deviation
realized Capital Adequacy Ratio (CAR) to minimum capital requirement ratio (%). +/- Asset_shr The ratio of a bank asset to total industry asset (%). It is adjusted to total industry asset in each period to account for possible time trend. +/- Δlog(GDP) The quarterly change of log of gross domestic product. + ΔCB The quarterly change of monetary interest rate policy BIrate.
The quarterly change of log of Consumer Price Index +/-
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Dynamic panel model: Two-step Arellano-Bover/Blundell-Bond
Generalized Method of Moments (GMM).
A consistent estimator, subject to: 𝜄 of ∆𝑚𝑝 𝑑𝑠𝑓𝑒𝑗𝑢 𝑗𝑢−1of GMM
lies between those of FELS (downward bias) and OLS (upward bias) (Bond (2002), Roodman (2006)).
Otherwise, utilize FELS with Nickel bias 1
𝑈−1
For large 𝑈, dynamic panel bias is insignificant, Number of instruments tend to explode with 𝑈 (Roodman, 2006). Endogeneity : utilizing lagged of explanatory variables
Methodology
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Main Result 1: credit growth of all banks
Note: *, **, *** indicate statistical significance at the level of 10%, 5%, and 1%, respectively Note: *, **, *** indicate statistical significance at the level of 10%, 5%, and 1%, respectively
SLIDE 10 Main Result 2: credit growth of large vs. small banks
Note: *, **, *** indicate statistical significance at the level of 10%, 5%, and 1%, respectively
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Main Result 3: post GFC 2008/09 evaluation
Note: *, **, *** indicate statistical significance at the level of 10%, 5%, and 1%, respectively
SLIDE 12 Note: *, **, *** indicate statistical significance at the level of 10%, 5%, and 1%, respectively
Note: dependent variable is 𝒎𝒑𝒉𝒋𝒖 𝑶𝑸𝑴 = 𝒎𝒑𝒉(
𝑶𝑸𝑴𝒋𝒖 𝟐−𝑶𝑸𝑴𝒋𝒖). *, **, *** indicate statistical significance at the level of 10%, 5%, and 1%, respectively
Supporting explanation: credit risk
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Graph 3. Liquidity, credit risk, & credit growth The first and second blue rectangular box represents phase I and II of loosening MP, respectively, while the red rectangular box indicates phase I of tightening MP policy.
Indonesia case: A rise of risk-averse behaviour due to higher credit
risk & liquidity pressure in the midst of economic slowdown leads to substantial lower credit growth despite the loosening policies.
Data sources: Bank Indonesia and Financial Service Authority (OJK)
Graph 4. Retail interest rates and monetary (BI) rate Graph 4. Credit growth, GDP, & BIrate Graph 3. Credit risk, liquidity, & capital buffer
SLIDE 14 Conclusion
Post the GFC 2008/09, the role of monetary interest rate policy on the bank
lending diminishes and affect credit growth indirectly through its stronger impact on credit risk.
The effectiveness of loosening macro-prudential policies to improve credit
growth is subject to bank’s risk condition.
The ineffectiveness of both loosening monetary and macro-prudential
policies to improve credit growth since 2015 is due to intensifying of banking risk-averse behavior.
- Banks choose to have higher liquidity and stronger capital buffer to
anticipate higher credit risk in the midst of economic slowdown, leading to substantial lower credit growth.
Policy challenges in Indonesia are to encourage banks to improve their risks
management and address a sensible balance of the trade-off between banks’ risks and their role in intermediating funds.
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Appendix 1: Descriptive statistics
