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We thank Rachel van Elkan, Roberto Guimarães-Filho, Romain Duval, Piti Disyatat, Sonali Das, Jihad Dagher, Machiko Narita, IMF Interdepartmental Monetary Policy Group seminar participants, IMF Asia and Pacific departmental discussion forum participants, and the Bank of Thailand’s PIER Research Exchange workshop participants for helpful comments. All remaining errors and opinions expressed in this paper are sole responsibility of the authors.
Banks have been an important source of finance in most Asian economies. The total size of banks’ private credit in emerging Asia is roughly 70-80 percent of GDP. Although stock markets have been growing quite rapidly, access to stock markets as an alternative source of funding is still limited to very large firms. Corporate bond markets in most emerging Asian countries have been relatively underdeveloped, though they are now evolving into an alternative and viable nonbank source of funding.
The bank lending channel is one of the two possible mechanisms of the credit channel (Bernanke and Gertler, 1995). It emphasizes on the possible amplification effects of monetary policy actions on the supply of loans offered by the banking system. The other mechanism, dubbed the balance sheet channel, focuses the impact of monetary policy on the borrower’s balance sheet. More recently, the risk-taking channel has been identified as another bank-based transmission mechanism (Borio and Zhu, 2007).
Jain-Chandra and Unsal (2012) estimate the pass-through from domestic policy rates to lending rates from 2000-09 and find that, while monetary policy transmission remains effective in Asia, it is weaker in periods of large and volatile capital inflows, amidst easy external financial conditions.
Global financial conditions index is the Chicago Fed’s adjusted U.S. National Financial Conditions Index (NFCI). Positive (negative) values indicate financial conditions that are tighter (looser) than average. See further description in the Data section.
We originally include China (76 banks after data cleaning) in our sample and regressions. However, Chinese banks tend to behave quite differently from typical banks in Asia and bias the overall results due to the large number of banks. Thus, we exclude China from our sample and leave it for future research.
We used the following criteria to clean the data and remove outliers. First, we eliminate observations with missing values for loans and total assets. Second, we drop observations with loan growth that exceed plus or minus 200 percent. Third, we delete observations with negative values for equities. Forth, we remove banks that are very small—defined as banks that on average have an asset share or loan share less than 0.05 percent of total domestic bank assets and credit. Fifth, we remove banks that have too few observations, i.e., no data for at least two consecutive years. Finally, we delete observations that have loan-to-deposit ratio over 500 percent. About 40 banks and 350 observations were removed after the above data cleaning process.
Non-commercial bank financial institutions include savings banks, cooperative banks, real estate and mortgage banks, investment banks, other nonbank credit institutions, specialized government credit institutions, and micro-financing institutions.
As a robustness check, we have used both measures of ownership dummy variables in our regressions, and results remain robust. Therefore, we only report the results and stylized facts based on the time-invariant ownership dummy variables.
Consumer price index (CPI) may not be the more relevant price index for deflating credit data, especially for the part of loans that are denominated in foreign currencies. Due to unavailability of a deflator that is more appropriate while consistent across countries, we follow the existing literature and use CPI as a proxy for credit price index. As a robustness check, we also run the main regressions using all nominal variables. Overall results are unchanged.
The representativeness of foreign banks in Singapore as shown in Figure 4 is vastly under-stated. This is because most of the 120 foreign banks in Singapore are operated as foreign branches, not subsidiaries, for which balance sheet information is not provided in Bankscope.
EM Asia’s bank concentration remains high. However, based on the Herfindahl-Hirschman (HH) index we construct, EM Asia’s bank concentration declined from 45 to 40 in the period of 2000-13. ASEAN-5 remains more concentrated with the HH index at 55 (although it too has declined by 5 percentage points over the same period). The index is calculated as:
This VAR approach may not be able to distinguish credit supply from credit demand, thus what we measure here is the broad credit channel rather than the bank lending channel which refers to the policy-induced change in credit supply alone.
As for a robustness check, VARs in first differences are also performed. The difference between the cumulative responses of output to interest rate shocks when credit is exogenous relative to when credit is endogenous also turns out to be very small.
In other words, in the ‘exogenous credit’ setting, credit is not allowed to react to the endogenous variables in the VAR system, while the endogenous variables respond to the exogenous credit path.
To the extent that an increase in interest rates also deteriorates firms’ balance sheets, making it more difficult for firms to borrow from banks, a decline in bank credit, and subsequently in real activity, following an interest rate shock may also be due to this “balance sheet” channel of monetary policy transmission.
Interest rate shocks correspond to an increase in interest rates by an average of 1.13 percent for Asia, 1.06 percent for ASEAN-5, 1.96 percent for Latin America, and 4.17 percent for other emerging markets in the sample.
The confidence intervals for the two scenarios overlap in most cases, implying that they may not be statistically different from each other.
In the case of Thailand, Disyatat and Vongsinsirikul (2010) suggest that the weakening of the bank lending channel may have been due to structural problems relating to non-performing loans during the post-Asian crisis period and, more recently, a smaller sensitivity of both bank loans to monetary policy and of output to bank loans as firms have increasingly turned to non-bank financing.
See section II for review of past research that uses bank-level data in studying the bank lending channel. In particular, we follow mainly the strategy described in Wu, et al. (2011) in constructing the variables and designing the specification.
The “balance sheet channel”—the potential impact of changes in monetary policy on borrowers’ balance sheets and income statements which may affect their credit worthiness—cannot be directly controlled for since we do not have information on firms’ balance sheet, although balance sheet strength is likely correlated with output growth which is included as a proxy for the demand effects in the model.
Many recent studies of the bank lending channel using bank-level data tend to estimate the loan growth equation using the dynamic panel data approach (eg. Arellano-Bond generalized method of moments) with a lagged dependent variable as a regressor based on the assumption of persistent loan growth. However, we do not find loan growth to be persistent or to have a dynamic process in our sample. This could be because our sample period covers atypical times in that it encompasses the global financial crisis. Moreover, we find that, although loan growth tends to persist from month to month, or quarter to quarter, it does not have strong persistence after a year. Thus, we deem the fixed-effects method to be appropriate.
Ideally, observations should be collected based on monetary policy cycles (instead of being aggregated over a year). But this is constrained by the availability of the bank balance sheet data which are reported on an annual basis. However, this problem is somewhat mitigated since most of the policy changes within a year are usually in the same direction.
When we explore the asymmetric effects of monetary policy across different ownership types, we find that foreign-owned banks are those driving this perverse result in the loosening cycle, while state-owned and private domestic banks react in the expected direction (increasing loan supply) in response to monetary policy loosening but only marginally.
Das (2015) also finds evidence of an asymmetric response of banks to monetary policy tightening and loosening in India.
In fact, in the following subsection, we find that foreign banks respond more strongly to global financial conditions, rather than monetary policy in host countries, compared to domestic banks.
This is in line with the literature that claims foreign banks to be a major channel of shock transmission. For example, using a sample of U.S. banks, Cetorelli and Goldberg (2012) find evidence that global banks usually manage liquidity on a global scale, actively using internal capital markets in reallocating funds in response to local shocks, thus contributing to international transmission of liquidity shocks and dampening the effect of domestic monetary policy. Jeon et. al. (2014) finds that foreign bank branches reduce their lending amidst expansionary monetary policy in Korea due to the existence of internal capital markets operated by multinational banks and the impact is larger than that of foreign subsidiaries.
One could argue that quasi-fiscal lending according to the non-commercial mandates, on the contrary, should result in a weak response of state-owned banks to monetary policy. This is true for the non-commercial bank sample where we find that stateowned banks (aka, “specialized financial institutions”) response less to monetary policy.
Traditional banking subsample include banks that rely on deposits greater than 70 percent of total funding and have gross loans accounting for greater than 60 percent of total assets. Non-deposit funding banks are banks that rely on wholesale and interbank borrowing, and other non-deposit funds, greater than 30 percent of total funding.
When we divide the sample into crisis (2008 and 2009) and non-crisis period, we find that the effect of global financial conditions (apart from global macro economic conditions) on domestic bank loans is more pronounced during the global financial crisis.
Foreign bank penetration is proxied by the share of foreign bank assets in total domestic banking sector assets. We also check for robustness using instead the share of foreign bank loans in total domestic banking sector loans. The main results are unchanged. As a caveat, however, our foreign presence measure may understate the true level of foreign bank participation, since we do not include foreign bank branches due to data unavailability. As noted in IMF (2011), in several Asian economies (apart from Asian financial centers), foreign bank branches have an equally or larger presence compared to foreign subsidiaries. For instance, in the Philippines, total assets of foreign branches are 50 percent larger than total assets of foreign subsidiaries. In Korea, assets of foreign branches are only marginally smaller than those of foreign subsidiaries.
Asia’s growing cross-border bank lending and increased (foreign-currency) corporate bond financing in recent years may further weaken the bank lending channel of domestic monetary policy.
Bank competition is measured by the Herfindahl-Hirschman (HH) bank concentration index, calculated as:
Ideally, the bank-specific interest rates used here should be marginal lending rates and deposit rates. However, due to lack data, we proxy them by average lending and deposit rates, calculated as interest income (expense) divided by earning assets (total deposits).