The role of bank capital on the bank lending channel of monetary policy transmission: An application of marginal analysis approach

Abstract

While there is a large body of research on the bank lending channel of monetary policy transmission and the distributional dependence of this transmission on bank characteristics, the asymmetric effect of bank capital on monetary policy—bank loan supply nexus has been ignored. To fill this void, the new post-estimation approach of marginal analysis based on the two-step system-GMM methodology is conducted for the dynamic panel data of Vietnamese commercial banks covering the period of 2007–2020. The results confirm the inertia related to loan growth and the presence of a monetary policy bank lending mechanism, which is robust across a series of monetary policy instruments and the approach of variable exclusion from the baseline model. In addition to previous empirical evidence on less sensitivity of well-capitalized banks to tightened monetary policy, this study shows the specific range value of bank capital that monetary policy stance has no impact on bank loan supply in a time of monetary restrictions. Furthermore, better capitalized banks could take more advantage of the expansionary monetary policy by lending more. The relevant policy recommendations for policy-makers are also provided to the best practice of monetary policy implementations considering these asymmetric effects.

Keywords:

• Bank lending mechanism
• monetary policy transmission
• marginal analysis
• bank capital

JEL classifications:

• E52
• G21

PUBLIC INTEREST STATEMENT

Current paper addresses the different response of bank loan supply to the shocks of monetary policy depending on various levels of bank capital, which remains scarce in previous studies. Among financial indicators, the capital level of banks plays a critical role in maintaining lending activities and a buffer for bank’s stability. This study gives rise to paying much attention to the differentiated impact of bank capital on the well-established bank lending channel of monetary policy pass-through. Specifically, well-capitalized banks evidently benefit by lending more in periods of monetary policy loosening; however, these banks also show different responses of lending behaviors to monetary restrictions according to their different capital levels. In addition, we provide a detailed description for the marginal analysis in an attempt to encourage further research employing this approach in the topic of monetary policy transmission in bank-level data.

Our research highlights

• A comprehensive sample of 30 commercial banks from Vietnam covering the period of 2007-2020.

• The asymmetric effect of bank capital on the bank lending mechanism of monetary policy pass-through is explored.

• The new approach of marginal effect analysis in combination with the representative plots is employed.

• The persistence of bank supply and bank lending channels of monetary policy transmission exists.

• There is the presence of bank capital levels that monetary policy has no influence on bank loan supply in a time of monetary restrictions.

• During the monetary loosening, well-capitalized banks could benefit more by lending extension than poorly capitalized counterparts.

Acknowledgements

This paper captures the main part of the thesis conducted by Thanh Phuc Nguyen, Ph.D. Candidate of University of Economics Ho Chi Minh city, under the supervision of Ngoc Tho Tran. We also appreciate the helpful and constructive comments from Toan Luu Duc Huynh. We are also grateful for the valuable comments and suggestions from five anonymous reviewers that greatly improve the quality of this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. According to (2021), the banking system plays a leading position in the financing activities of modern economies. It is more pronounced in the context of ASEAN countries where the security and bond market are under-developed (Hamid & Yunus, 2020).

2. There are several transmission channels through which monetary policy could transmit such as risk-taking channel (Kishan & Opiela, 2012), interest rate channel (B. S. Bernanke, 1990), exchange rate channel (Golinelli & Rovelli, 2005), asset price channel (Mishra & Montiel, 2013), and credit channel (B. S. B. S. Bernanke & Gertler, 1995) but this paper is limited to analyzing monetary policy transmission through the bank lending channel. Accordingly, the bank lending mechanism emphasizes the role of credit supply to transmit monetary policy into the real economy (Hamid & Yunus, 2020).

3. Reform programmes include the ceilings for deposit and loan rate, restrictions for opening branching, constrained investment portfolios, and foreign penetration due to regulatory loosening.

4. See Appendix A for interest rate-based implementation by the SBV.

5. For instance, monetary tightening could limit the corporate investment by an increased interest rate, leading to a decrease in terms of demand side for bank loan supply. These policy shifts in loan demand could be considered as a macroeconomic shock. In addition, the heterogeneity in bank loan changes could be viewed as the shifts in the loan supply side. This could not be captured by the usage of time series data (Hosono, 2006).

6. The separation between loan supply side and loan demand side allows testing loan supply movements affected by bank-specific characteristics and this approach could not apply to the loan demand movements (Hamid & Yunus, 2020).

7. This approach is mainly focused on the evaluation of the difference of groups and periods for traditional financial research (i.e., market timing of mutual funds (Treynor & Mazuy, 1966)) but remains scarce in the analysis of the bank lending channel.

8. For example, Bashir et al. (2020) has used the median value of each variable accounting for size, capitalization, and liquidity to divide the full sample into subsamples according to these bank-specific characteristics. However, Gambacorta (2005) defined under-capitalized and well-capitalized banks as under 10th percentile and 90th percentile and greater, respectively.

9. These studies are summarized in Appendix B for more reference.

10. Studies conducted in Euro area also support that the under-capitalized banks are much more susceptible than well-capitalized counterparts (Altunbaş et al., 2002; Gambacorta, 2005; Gambacorta & Mistrulli, 2004; Jiménez et al., 2012).

11. Khan et al. (2016) indicate the significant role of market concentration on monetary policy transmission via the bank lending mechanism for ASEAN countries. In this regard, the increase in market concentration could weaken the effectiveness of monetary policy. Therefore, the variables accounting for market concentration are included to test whether any meaningful linkage could be found between market structure and the monetary policy bank lending channel.

12. This approach has been widely conducted in previous studies (Ehrmann et al., 2001; Gambacorta, 2005; Gambacorta & Marques-Ibanez, 2011; Jimborean, 2009).

13. The best selection of interest rate-based monetary policy instruments are inconclusive (Altunbas et al., 2010; Chen et al., 2017; Ehrmann et al., 2003; Khan et al., 2016; De Moraes et al., 2016; Olivero et al., 2011; Sáiz et al., 2018; Yang & Shao, 2016).

14. For instance, the lending rate could affect the funding cost of borrowers while the refinance and rediscount rate are implemented by the SBV as the role of the last resort, applying for the direct loans and the discountable valuable papers, respectively. In addition, the reserve rate and basic interest rate tools are not employed in this paper due to the long-time stable characteristics.

15. To the best of understanding, scarce studies have included the squared interaction term between monetary policy proxies and the variables of interest to address the asymmetric impacts of the monetary policy via the bank lending mechanism (Cantero-Saiz et al., 2014; Sáiz et al., 2018; Sanfilippo-Azofra et al., 2018). For example, Sanfilippo-Azofra et al. (2018) analyze the marginal effect of monetary policy changes on bank loan supply depending on the financial development levels, suggesting the potency of monetary policy transmission via the bank lending mechanism varying according to different figures of financial development.

16. The normalized bank-specific characteristics such as CAP, SIZE, LIQ, and LLP take the average zero in its values (Zhan et al., 2021).

17. In this study, the mean annual increase and decrease in ∆it could be employed to account for the contractionary and expansionary monetary policy, respectively (Sanfilippo-Azofra et al., 2018). Since the variable MC is not in the normalized form, the median value of MC is used to compute marginal values (Sáiz et al., 2018). In this regard, the increased mean values of lending rate, interbank interest rate, refinance rate, and rediscount rate are 1.83%, 2.55%, 3.63%, and 3.94%, respectively; meanwhile, the decreased mean figures of these interest rates are −2.33%, −1.92%, −1.54%, and −1.60%, respectively.

18. In the case of this research, 30 commercial banks and 14 years (from 2007 to 2020) were retrieved to build the panel data.

19. This method takes advantage of first-step residual estimation using as instruments to make regression coefficients more efficient than one-step system GMM (Arellano & Bover, 1995; Blundell & Bond, 1998).

20. The experiment approach is applied to a two-step system GMM to choose the optimal number of instruments, which avoids the over-fitting issue when the model has additional instruments (Matousek & Solomon, 2018).

21. Based on the bank information published by the SBV for the end of 2020, there are 31 commercial joint stock banks operating in Vietnam. Because of special controls in 2015, Dong A Commercial Joint Stock Bank (DAB) was removed from the studied sample to remain consistent in normal operation among these banks. All studied banks are reported in Appendix E. In addition, the standardized pattern of financial reports from Vietnamese commercial banks has been implemented since 2007. Furthermore, prior to 2007, the published and audited financial reports mainly belonged to a few large banks.

22. Several studies also employed the dataset on the annual basis (Gambacorta, 2005; Nguyen et al., 2020). These authors suggest that using the annual observations are enough to address the heterogeneity in the adjustment of bank lending supply to monetary policy.

23. This adoption could facilitate the testing of second-order serial correlations to remain the robust estimates carried out by system-GMM (Arellano & Bond, 1991).

24. For example, Hanoi Building Commercial Joint Stock Bank (HBB), Housing Bank of Mekong Delta (MHB), and Southern Bank (PNB) were removed due to being under mergers and acquisition in August—2012, May—2015, and October—2015, respectively.

25. This winsorizing approach is followed by the work of Dang and Nguyen (2020).

26. The threshold value of 0.8 is similarly used by Vo (2018).

27. For the research on bank lending channel, Cantero-Saiz et al. (2014) has used the baseline model including well-established bank-specific characteristics (SIZE, LIQ, and CAP) and then adding the variable accounting for loan loss provision (LLP) into this model, followed by inclusion of the variable accounting for market concentration (MC). This approach is named as the “nested stepwise regression” (Zhan et al., 2021).

28. This means the extent to which a bank remains in the same distribution of loan supply, referring to the explanation of Le and Ngo (2020) for bank profitability.

29. The sign of squared interaction terms between monetary policy and bank capital could reveal the asymmetric signal of bank capital’s impact on monetary policy transmission; hence, plot charts for both regimes of monetary policy are supplementary constructed to have the proper understandings of the response of bank lending to changes in monetary transmission, which could vary with the different levels of bank capital (Sáiz et al., 2018). We use the syntax of Stata 15.1 software as described in Appendix D.

30. Vo and Nguyen (2014) also address the lag in policy’s effect by inclusion of a lagged interest rate in the model of monetary policy transmission of the bank lending channel.

31. This approach is similar to the confidence interval calculation in the work of Aiken et al. (1991).

32. ∆it takes the specific mean increased/decreased value, thereby treated as a constant.

Additional information

Funding

This research is funded by the University of Economics Ho Chi Minh City (UEH) and Van Lang University (VLU).

Notes on contributors

Thanh Phuc Nguyen

Phuc Nguyen Thanh is a Ph.D. Candidate in the School of Finance from University of Economics Ho Chi Minh City, Vietnam (UEH). His research interests mainly revolve around topics related to banking and finance, risk management, and financial management. He has currently served as an anonymous reviewer for the Journal of Asian Business and Economic Studies (UEH).

Thi Thu Hong Dinh

Hong Dinh Thi Thu is a Ph.D and Senior Lecturer in the School of Finance from University of Economics Ho Chi Minh City, Vietnam (UEH). She is currently the Dean of the School of Banking (UEH). Her current research interests mainly include banking and finance, risk management, and financial markets.