Indonesia: Selected Issues
Author:
International Monetary Fund. Asia and Pacific Dept
Search for other papers by International Monetary Fund. Asia and Pacific Dept in
Current site
Google Scholar Close
Publication Date:
02 Mar 2021
eISBN:
9781513570860
Language:
English
Keywords:
ISCR; CR; government bond bond yield; IMF staff estimate; loan growth; headline CPI inflation; government bond bond market; liquidity provision; deposit growth; nonpublic bank; programs scheme; lending behavior; Liquidity; Liquidity management; Loans; Bank credit; State-owned banks; Global
Download PDF (768.7 KB)

Selected Issues

Abstract

Selected Issues

Covid-19 and the Decline in Bank Lending in Indonesia: what can we Learn from Previous Stress Episodes?1

A historical look at loan growth dynamics in Indonesia highlights that higher lending from public banks during stress episodes has often provided some cushion against relatively lower lending from nonpublic banks. A similar pattern is taking place during the COVID-19 pandemic, but not to the same degree, possibly reflecting unique features of the pandemic, including its unprecedented balance sheet effects on the nonfinancial sector. An empirical analysis of bank-level lending behavior in Indonesia suggests that deposit growth and liquidity conditions remain important determinants of loan growth also in stress episodes. These results suggest that increased central bank liquidity provision, as initiated during the pandemic, helps support lending activity and the ensuing economic recovery, especially in the case of solvent banks with initial moderate levels of liquidity.

A. Bank Lending Dynamics

1. Bank lending has decelerated rapidly in recent months, as the economic fallout of the COVID-19 pandemic has unfolded in Indonesia. From 8 percent (y/y) at end-2019, loan growth quickly declined in 2020 and fell below 2 percent (y/y) in July 2020, a record low in recent history. The slowdown in bank lending appears more pronounced for private and foreign banks, whose lending contracted in June and July 2020, while public banks, including SOEs, and regional banks, have maintained positive, albeit declining, loan growth. While the pandemic is unique in many ways, including through its unprecedented balance sheet effects on the nonfinancial sector, a historical look at loan growth in Indonesia highlights some comparable dynamics during recent stress episodes. The gap in loan growth between public and nonpublic banks widened during the taper tantrum, the 2015–16 stock market turbulence in China, and the 2018 emerging market (EM) sell-off, with higher lending from public banks providing some cushion against relatively lower lending from nonpublic banks (see also Bosshart and Cerutti 2020 for similar evidence for other emerging market countries during the Global Financial Crisis).

Loan Growth

(In percent, year-on-year, 3-month moving average)

Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A003

Sources: Otoritas Jasa Keuangan; and IMF staff estimates.

Loan Growth

(In percent, year-on-year, 3-month moving average)

Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A003

Sources: Otoritas Jasa Keuangan; and IMF staff estimates.

2. A stronger deposit base for public banks may support their lending activity. From around 6 percent (y/y) at end-2019, deposit growth rose to about 9 percent by July 2020, driven by deposit growth in SOE banks (13 percent y/y), which, in addition to potential safe haven perception by depositors, benefited from fiscal resources to support credit activity as part of the pandemic recovery program. Public banks’ deposit growth has always exceeded that of nonpublic banks at the end of recent stress episodes. For instance, in end-2018, public banks recorded a deposit growth of 24 percent (y/y), compared to 8 percent for private and joint banks.

Deposit Growth

(In percent, year-on-year, 3-month moving average)

Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A003

Sources: Otoritas Jasa Keuangan; and IMF staff estimates.

3. Bank lending dynamics have not closely followed the changes in monetary policy rates in recent years. Indonesia has experienced sizeable shifts in monetary policy rates during stress episodes in recent years. As monetary policy appeared to have responded to changes in both domestic and external conditions, bank lending dynamics and changes in the policy rate have been asynchronous at times, especially since 2018. For instance, it took several months for credit growth to reverse following the increases in the monetary policy interest rate in 2018. More recently, pre-COVID-19 cuts in the BI policy interest rate in 2019 had yet to transmit to loan growth, which was on a sustained declining trend. Other factors, including the retrenchment in credit demand during the COVID-19 crisis, have also been affecting bank credit growth.

Monetary Policy Rates

(In percent)

Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A003

Sources: CEIC Data Co., Ltd.; and IMF staff estimates.

B. The Role of Banks’ Characteristics in Bank Lending Dynamics

4. This section explores how bank characteristics influence their lending behavior in Indonesia. In addition to monetary policy shocks, the empirical literature has identified banks’ size, liquidity, and capitalization as three potential drivers of loan growth in emerging market economies.

5. The analysis draws on monthly bank balance sheet data. The dataset from the CEIC portal (collected by OJK) covers aspects of assets and liabilities of 86 commercial banks, representing more than 80 percent of loans provided by commercial and rural banks in Indonesia. The sample for this study includes foreign, foreign joint venture, private, and public banks and covers the period from January 2012 to July 2020 given limited data availability in the period before.

6. The empirical strategy investigates the determinants of loan growth, through the following equation:

Δloanit = a + γXit-1 + δStressit + φbi + θtt + εit

Where Δioanit captures the monthly loan growth rate of bank i at time t. Xit-1 captures bank level characteristics, such as size and liquidity, with a one period lag to mitigate potential endogeneity from reverse causality. Size is captured by the share of each bank’s asset in total bank assets. Liquidity is measured by the ratio of liquid assets (e.g., cash, securities, and short-term placements) to total assets of a bank. Stress is a dummy taking one during episodes of stress, defined as 12 months following the onset of the shock. In addition to the COVID-19 shock, the stress dummy captures the taper tantrum, the 2015–16 Chinese stock market turbulence, and the 2018 EM sell off. bi and tt represent respectively bank fixed effects and time (monthly) fixed effects. Beyond unobservable fixed factors, controlling for bank fixed effects allows us to account for time-invariant characteristics such as bank ownership (public vs. nonpublic). By controlling for common shocks across all banks during a given month, such as monetary policy shocks, time fixed effects allow us to focus on time varying bank specific characteristics that are deemed important for loan growth. εit is the error term.

7. The results highlight the importance of bank-level liquidity for loan provision (Table 4 and Figure 1). The baseline results show that more liquid banks have higher loan growth. While episodes of stress are associated with lower loan growth on average, banks with higher liquidity appear to provide some cushion by providing relatively more loans. Deposit growth is positively associated with loan growth, confirming the pass-through effect of deposits on loan growth.

Table 1.

Indonesia: Distribution of Liquidity Ratios Across Banks and Time

article image
Table 2.

Funding for Lending Programs Schemes Implemented by Selected Advanced Economy Central Banks

article image
Sources: Bank of England; Bank of Japan; European Central Bank; Reserve Bank of Australia; Sveriges Riksbank; World Bank; and ANZ Research.
Table 3.

Funding for Lending Programs Schemes Implemented by Selected Emerging Market Central Banks

article image
Sources: European Comission website; The National Bank of Hungary; Central Bank of Chile; Bank of Thailand; Bank Negara Malaysia; and Reuters.
Table 4.

Indonesia: Baseline Estimations 1/

article image

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Figure 1.
Figure 1.

Estimated Impacts of Liquidity on Loan Growth 1/

Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A003

Source: Author’s estimates.1/ These figures illustrate coefficients and confidence intervals from three bank-level estimations of the impact of liquidity ratio on loan controlling for deposit growth, banks’ size, and bank fixed effects and time fixed effects. (a) illustrate both the impact for all periods and for stress episodes such as the taper tantrum, the 2015–16 stock market turbulence in China, the 2018 EM sell off, and the 2020 COVID-19 pandemic. (b) illustrate both the impact for all periods and periods without significant economic/financial stress. The error bars refer to the 95 percent confidence intervals around the estimated coefficients. ** p<0.05; *** p<0.01.

8. The results also confirm that lending from public banks is more stable during episodes of stress. Episodes of stress are associated with lower loan growth from nonpublic banks, but these episodes do not seem to be associated with materially lower lending from public banks (Table 5 and Figure 2). This result supports the observation that higher lending from public banks has provided some cushion against relatively lower lending from nonpublic banks in difficult times.

Table 5.

Indonesia: Public Versus Non-Public Banks 1/

article image

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Figure 2.
Figure 2.

Estimated Impacts of Stress Episodes on Loan Growth 1/

Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A003

Source: Author’s estimates.1/ These figures illustrate coefficients and confidence intervals from two bank-level estimations of the impact of a stress episode dummy (as defined in figure 5) on loan controlling for deposit growth, banks’ size, liquidity ratio, bank fixed-effects, and time fixed-effects. The bars illustrate the impact for public banks and nonpublic banks. The error bars refer to the 95 percent confidence intervals around the estimated coefficients. ** p<0.05; *** p<0.01.

9. The main results are robust to a variety of tests. These include additional control variables such as the capital adequacy ratio and gross nonperforming loans (Table 6).2 We do not find evidence that larger banks are associated with relatively larger loan growth on average. Liquidity seems to matter equally for loan growth during 2012–2017, a period of steady decline in loan growth, and afterwards (Table 7). Deposit growth appears to play a lesser role in supporting loan growth after 2017, suggesting that the effectiveness of increasing deposits through fiscal support to encourage bank lending might be smaller than in the past.

Table 6.

Indonesia: Robustness Check: Additional Control Variables 1/

article image

Standard errors in parentheses. “* p<0.01, “ p<0.05, * p<0.1

Table 7.

Indonesia: Possible Structural Change in Loan Growth 1/

article image

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

10. The impact of liquidity on loan growth may be non-linear. As the Indonesian banking system is highly liquid overall, the marginal impact of higher liquidity on bank loan growth may differ according to bank specific initial conditions. For instance, higher liquidity may have a proportionally larger impact on the lending activity of banks with moderate liquidity. Spline fixed-effect regressions allow identifying thresholds of liquidity ratio coinciding with a significant change in the relationship with loan growth.3 The spline estimations indicate that the marginal positive effect of liquidity ratio on loan would be significantly higher when the bank’s liquidity ratio is below 34 percent of total assets (Table 8).

Table 8.

Indonesia: Possible Non-Linearity: Spline Estimations

article image

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Liquidity ratio 1” and 2 “ILiquidity ratio 2” differentiate the coefficients of liquidity ratio depending on whether the latter is either below (Liquidity ratio 1) or strictly above (Liquidity ratio 2) a threshold (the knot position).

11. A look at liquidity distribution across banks further highlights a decline in liquidity for some banks with already low liquidity. For instance, the liquidity ratio of the 10th percentile, the banks with the lowest liquidity ratios, further declined in 2020 to 15 percent (Table 1).

12. The results suggest that strengthening liquidity of solvent banks could support lending activity and the ensuing economic recovery. By combining supply and demand shocks with large balance sheet effects on the nonfinancial sector, the pandemic differs from previous relatively milder stress episodes in this analysis. Supporting liquidity, in particular for solvent banks with strong fundamentals and relatively low liquidity that have experienced a further drop in liquidity since the onset of the pandemic, could support lending activity and the ensuing economic recovery. Monitoring and providing funding to banks would facilitate reductions in banks’ interest rates as concerns about loss of funding or deposit base could be contained. The additional measures that the government has taken in the form of SME loan guarantees (e.g., insurance covered loan with premium paid by the government), SME temporary interest payment subsidies, as well as state fund deposits with low interest (under the condition of further lending to MSME) would complement the liquidity availability.

13. If credit growth does not recover in early 2021—when weak demand from borrowers as well as lending risk aversion from lenders subsides—other complementarity forms of support could be considered.4 For instance, many countries have implemented funding for lending programs in episodes of crisis (Tables 2 and 3). Under these programs, the central bank provides relatively cheap funding to eligible banks with explicit requirements to lend, for example to SMEs. Important design elements of funding for lending schemes have included: (i) incentives such as favorable interest rate or higher funding caps to use the funds for new lending; and (ii) borrowing caps to limit bank borrowing and facilitate the exit strategy. While Malaysia and Thailand implemented broad funding to lending programs by the Central Bank to support SMEs, Philippines and Indonesia took a slightly different route by providing direct assistance to the government, through monetary budget financing, which launched SME packages using some of the central banks’ funds (Cerutti and Helbling, 2020). Considering BI’s legal limitation to provide direct lending to banks, expanding the provision of fiscal resources to all eligible banks (beyond SOE banks) with appropriate safeguards and incentives to lend could further support credit creation.

References

  • Bosshart, Joshua and Eugenio Cerutti, 2020, “Why Did Public Banks Lend More During the Global Financial Crisis?,” IMF Working Paper No. 20/84 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

  • Cerutti, Eugenio and Thomas Helbling, 2020, “Unconventional Monetary Policies in Emerging Asia during the COVID-19 Crisis: Why now? Will they work?,” forthcoming APD Departmental Paper (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

  • Fungacova, Zuzana, Nuutilainena Riikka, and Weill Laurent, 2016, “Reserve Requirements and the Bank Lending Channel in China,” Journal of Macroeconomics, Vol. 20, pp. 37–50.

    • Search Google Scholar
    • Export Citation

  • Kashyap, Anil and Jeremy Stein, 2000, “What Do One Million Observations on Banks Have to Say About the Transmission of Monetary Policy,” American Economic Review, Vol. 80, pp. 1183–200.

    • Search Google Scholar
    • Export Citation

  • Khan, Habib Hussain, Ahmad Rubi Binti, Gee Chan Sok, 2016, “Bank Competition and Monetary Policy Transmission Through the Bank Lending Channel: Evidence from ASEAN,” International Review of Economics and Finance, Vol. 44, pp. 19–39.

    • Search Google Scholar
    • Export Citation

  • Koeva Brooks, Petya, 2007, “The Bank Lending Channel of Monetary Transmission: Does It Work in Turkey?,” IMF Working Paper No. 07/272 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

  • Van den Heuvel, Skander, 2002, “Does Bank Capital Matter for Monetary Transmission?,” Economic Policy Review, Vol. 8, pp. 161–72.

  • Yang, Jun and Shao Hanhua, 2016, “Impact of Bank Competition on the Bank Lending Channel of Monetary Transmission: Evidence from China,” International Review of Economics and Finance, Vol. 43, pp. 468–481.

    • Search Google Scholar
    • Export Citation
1

Prepared by Tidiane Kinda and Agnes Isnawangsih (APD).

2

Our monthly dataset does not include banks’ total equity, which is usually used to define bank capitalization, measured by the ratio of equity to total assets. Using quarterly and more limited data on capital adequacy ratio to test the robustness of our main results leads to a sizeable drop in the sample size. Similar drop in the sample size occurs when controlling for gross nonperforming loans, which is also available on a quarterly basis and with significant data gaps.

3

Spline regressions estimate linear slopes for different ranges of liquidity ratios with the endpoint of each range identified as a “knot.” By default, knots are placed at equally spaced centiles of the distribution of the liquidity ratio. The model starts from the spline specification with the highest possible number of knots and converges towards the best fitting model by eliminating statistically insignificant knots (at the 5 percent level).

4

Improved global perspectives in controlling the pandemic, for instance through vaccination programs, could lower uncertainty and lending risk aversion from lenders, and support credit demand from borrowers.

Same Series

Other IMF Content

Other Publishers

  • Collapse
  • Expand
Cover IMF Staff Country Reports
Indonesia: Selected Issues
Author:
International Monetary Fund. Asia and Pacific Dept
Volume/Issue:
Volume 2021: Issue 047
Publisher:
International Monetary Fund
ISBN:
9781513570860
ISSN:
1934-7685
Pages:
117
DOI:
https://doi.org/10.5089/9781513570860.002

Loan Growth

(In percent, year-on-year, 3-month moving average)

Loan Growth

(In percent, year-on-year, 3-month moving average)

Deposit Growth

(In percent, year-on-year, 3-month moving average)

Monetary Policy Rates

(In percent)
Figure 1.

Estimated Impacts of Liquidity on Loan Growth 1/
Figure 2.

Estimated Impacts of Stress Episodes on Loan Growth 1/