The Lending Channel in Emerging Economies
Are Foreign Banks Different?
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

This paper exploits a panel dataset comprising 1,565 banks in 20 emerging countries during 1989- 2001 and compares the response of the volume of loans and the rates on loans and deposits to various measures of monetary conditions across domestic and foreign banks. It also looks for systematic differences in the behavior of domestic and foreign banks during periods of financial distress and tranquil times. Using differences in bank ownership as a proxy for financial constraints, the paper finds weak evidence that foreign banks have a lower sensitivity of credit to monetary conditions relative to their domestic competitors, with the differences driven by banks with lower asset liquidity and/or capitalization. The lending and deposit rates of foreign banks tend to be smoother during periods of financial distress. However, the differences across domestic and foreign banks do not appear to be strong. These results provide weak support to the existence of supply-side effects in credit markets and suggest that foreign bank entry in emerging countries may have contributed somewhat to stability in credit markets.

Abstract

This paper exploits a panel dataset comprising 1,565 banks in 20 emerging countries during 1989- 2001 and compares the response of the volume of loans and the rates on loans and deposits to various measures of monetary conditions across domestic and foreign banks. It also looks for systematic differences in the behavior of domestic and foreign banks during periods of financial distress and tranquil times. Using differences in bank ownership as a proxy for financial constraints, the paper finds weak evidence that foreign banks have a lower sensitivity of credit to monetary conditions relative to their domestic competitors, with the differences driven by banks with lower asset liquidity and/or capitalization. The lending and deposit rates of foreign banks tend to be smoother during periods of financial distress. However, the differences across domestic and foreign banks do not appear to be strong. These results provide weak support to the existence of supply-side effects in credit markets and suggest that foreign bank entry in emerging countries may have contributed somewhat to stability in credit markets.

I. Introduction

Foreign bank entry into emerging market economies has become an important component of financial globalization since the mid-1990s. Facilitated by financial liberalization and the need to recapitalize banking systems in the aftermath of financial crises, the volume of cross-border mergers and acquisitions (M&As) targeting banks in emerging markets surged from about US$6 billion during 1990-1996, to almost US$50 billion—roughly one-third of the global amount— during 1997-2000 (BIS, 2004). The increase in the foreign bank presence in emerging markets has been uneven, entailing significant changes in the structure of bank ownership in many recipient countries such as Mexico, where the share of banking system assets controlled by foreign institutions increased from 2 percent in 1990 to 82 percent in 2004.

The speed and depth of foreign bank entry has potentially important implications for financial and macroeconomic stability in recipient countries, and arguments have been made in both directions. On the one hand, it has been argued that foreign banks could play a stabilizing role on the supply of credit and deposits through upstream financing from their mother companies and reputation effects, particularly during periods of financial distress. On the other, foreign banks might be quick to pull out from emerging markets and could transmit external shocks into host countries. Empirical evidence on the implications of foreign bank entry for financial and macroeconomic stability in emerging markets, however, is limited to a paper by Dages, Goldberg, and Kinney (2000) analyzing the behavior of domestic and foreign banks in Mexico and Argentina during the Tequila crisis, and a paper by Detragiache and Gupta (2004), using data for Malaysia during the Asian crisis. Overall, these two papers find mild support for the first view. On the other hand, evidence from the 2002 crisis in Argentina seems to be more mixed, with some foreign banks opting to exit in the context of a broader international asset relocation, and others reducing their lending activities in line with the behavior of domestic banks.

At a more general level, the view that banks may play a nontrivial role in the transmission of shocks into credit markets, via supply-side effects, has received considerable attention in the literature of monetary policy transmission. Early work includes Bernanke and Blinder (1988), Kashyap, Stein, and Wilcox (1993), Bernanke and Gertler (1995), and Kashyap and Stein (1995). The basic idea is that financial constraints on banks impair their ability to offset negative shocks to deposits with alternative financing sources, generating supply-side effects in credit markets, and amplifying economic fluctuations. While the evidence seems to be broadly consistent with this proposal, identifying suitable proxies for unobserved financial constraints on banks has been a key challenge.

This paper builds on the idea that differences in bank ownership can serve as a proxy for unobserved financial constraints on banks and, combined with other observable bank characteristics (such as asset liquidity and capitalization), can help to identify changes in credit supply. To implement this, it uses a panel dataset of 1,565 banks in 20 Asian and Latin American countries during 1989-2001 and tests for systematic differences in the sensitivity of loans, deposits, and bank-specific lending and deposit rates to various measures of monetary conditions, across domestic and foreign banks. It also looks for systematic differences in the behavior of domestic and foreign banks during tranquil times and periods of financial distress, exploiting various definitions of banking and currency crises available in the literature. The regions studied here are relevant to the issues at hand, as they endured several financial crises during the 1990s. In addition, Latin America accounted for 48 percent of all cross-border M&As targeting banks in emerging markets between 1991-2005, followed by Asia, with an additional 36 percent.

The results indicate that domestic and foreign banks behave roughly similarly along the dimensions considered, providing only weak support to the existence of supply-side effects in credit markets. In particular, loan and deposit growth are highly sensitive to economic activity, in a manner that does not differ significantly across domestic and foreign banks. At the same time, periods of tighter monetary conditions are associated with lower loan and deposit growth, with foreign banks displaying a somewhat lower sensitivity. This finding is driven by banks with relatively less liquid assets and/or lower capitalization, suggesting that it is not entirely attributable to potential differences in the characteristics of the borrowers and depositors of foreign banks. The results also show slight differences in the cross-sectional behavior of interest rates. Lending and deposit rates of foreign banks tend to react less during periods of financial distress. Taken together, these results indicate that foreign bank participation in emerging economies has not lead to increased instability in credit markets, and may have even had a beneficial effect.

The main contributions of the paper are as follows. First, it adds to the literature on the effects of foreign bank entry on financial stability, exploiting a comprehensive bank-level panel dataset that covers the main Latin American and emerging Asian countries during the 1990s. The paper tracks the evolution of bank ownership by crossing the sample of banks with a complete list of mergers and acquisitions during the sample period. Second, it adds to the literature on the lending channel outside the United States, particularly in emerging markets1 by exploiting differences in bank ownership to identify supply-side effects in credit markets. As a by-product, the paper provides a novel dataset on reserve requirements for the countries in the sample, using information from central bank reports.

The rest of the paper is organized as follows. Section II places the paper in the context of the literature. Section III discusses the methodology and the hypotheses tested, as well as potential endogeneity problems and sources of bias. Section IV describes the data. Section V compares the response of selected financial variables (including loan growth, deposit growth, and bank-level lending and deposit rates) to various measures of monetary conditions, across domestic and foreign banks. Section VI focuses more closely on the response of loan growth to monetary conditions across domestic and foreign banks, after splitting the sample by capitalization and liquidity levels. Section VII explores for systematic differences in the behavior of domestic and foreign banks during episodes of financial distress and tranquil periods. Section VIII concludes.

II. Related Literature

Most studies comparing the behavior of domestic and foreign banks in emerging economies focus on the efficiency effects of foreign bank entry.2 An incipient strand of the literature, to which this paper belongs, looks at the effects of foreign bank entry on financial stability and the response of credit markets to domestic and external shocks. Dages, Goldberg, and Kinney (2000) compared the behavior of bank lending across domestic and foreign banks in Mexico and Argentina during the Tequila crisis and concluded that foreign banks exhibited stronger and less volatile loans growth than domestic banks, but differences in asset quality, rather than ownership, appeared to be decisive in explaining the behavior of bank credit. Using data for Malaysia, Detragiache and Gupta (2004), found evidence that foreign banks with sufficient international diversification played a stabilizing role during the Asian crisis, while the behavior of foreign banks with operations concentrated in Asia was roughly similar to the behavior of domestic banks.

At a more general level, this paper is also related to the literature on the lending channel of monetary transmission, which focuses on the potential role of banks propagating shocks into credit markets via supply-side effects. The basic hypothesis is that capital market imperfections may prevent (at least some) banks from freely substituting away a negative shock to deposits with other sources of funding. In consequence, financially constrained banks may optimally choose to cut lending in response to a shock to deposits, thereby affecting the availability of funds to bank-dependent firms. The chief obstacle in testing the lending channel is disentangling whether the response of credit to monetary shocks originates from loan demand—as implied by interest rate channels—or from changes in loan supply.

To get around the identification problem, recent empirical studies have increasingly resorted to bank-level data, testing for cross-sectional differences in the response of bank lending to monetary shocks across banks with different degrees of financial constraints. Since financial constraints are not directly observable, they have been usually proxied by bank characteristics such as liquidity, size, and capitalization (for example, Jayaratne and Morgan, 2000; Kishan and Opiela, 2000; and Kashyap and Stein, 2000). Financial constraints have been also proxied by bank ownership. Houston, James, and Marcus (1997) explored the role of internal markets in banking in the United States and found that the loan growth of bank subsidiaries is sensitive to the financial position of their holding companies. A similar approach was implemented by Ashcraft (2000), who exploited a panel database of U.S. banks and used bank affiliation with multi-bank holding companies to proxy for financial constraints. In the international context, Peek and Rosengren (1997) looked at data on Japanese banks operating in the United States and found that binding risk-based capital requirements associated with the Japanese stock market decline of the late 1980s, translated into a decline in lending by their U.S. branches.

This paper follows a similar approach, exploiting the presence of internal capital markets as a potential source of cross-sectional variation between domestic and foreign banks. In particular, the paper develops a series of tests based on panel regressions, comparing the behavior of domestic and foreign banks under different monetary conditions. A baseline exercise compares the response of selected balance sheet components to monetary conditions across domestic and foreign banks, after controlling for changes in loan demand, proxied by GDP growth, and observable bank characteristics such as size, liquidity and capitalization. A second, more restrictive set of tests, further explores systematic differences in the response of loan growth to monetary conditions across domestic and foreign banks, in the subsets of banks with lower liquidity and capitalization. Lastly, a third test uses various definitions of currency, banking, and debt crises; and compares the behavior of domestic and foreign banks throughout crises and tranquil periods.

The validity of the tests presented in this paper hinges on the validity of two assumptions. First, all else equal (i.e., capitalization, asset liquidity, and other bank characteristics), foreign banks have to be less financially constrained than domestic, either because they can resort to funding from their parent institutions, or because they enjoy a more stable deposit base. Second, the loan demand facing domestic banks cannot be systematically different than the loan demand of foreign banks.

A few comments are convenient to place this paper in context. While the literature on the lending channel focuses on the role of banks in the transmission of monetary policy to the credit market, this paper takes a broader approach. It studies the effects of changes in monetary conditions on the credit market, regardless of whether changes are induced by monetary policy or not. This difference in emphasis is necessary since the paper focuses on emerging markets, where monetary conditions are typically affected by an open capital account. Consequently, monetary conditions here not only include money market rates, as usual in the lending channel literature, but also international interest rates and the change of the foreign exchange rate, exploiting the uncovered interest parity condition. The justification for the latter is straightforward, since currency depreciation increases the opportunity cost of holding bank deposits denominated in local currency, affecting their stability. Monetary conditions in this paper also include reserve requirements, which are safely ignored in the lending channel literature as they are not used as a monetary policy tool in the United States, but become relevant to the case at hand.3

III. Methodology

A series of tests were implemented to explore the response of selected balance sheet and income statement components to changes in monetary conditions, across domestic and foreign banks, after controlling for some observable bank characteristics. More specifically, the tests comprised six separate specifications sharing the general form:

yi,c,t=αi+s=1rβsxc,ts+ρzi,c,t1+s=1qδsmc,ts+uit(1)

where i=1,…,N refers to individual banks (panels), c=1,…,C to countries, and t=1,…,Ti to the time dimension (the sample is unbalanced, so Ti varies across banks). The constants, αi, are the bank- level fixed effects. Each specification used a different (bank-level) dependent variable, yict. A first set of regressions employed quantity-related dependent variables: loan growth, deposit growth, and the ratio of net loans to deposits. A second set of regressions employed price- related dependent variables: lending rates, deposit rates, and lending minus deposit spreads. Loan and deposit growth were computed by first differencing the logarithm of the corresponding series, measured in constant (1995) local currency units. Bank-specific lending and deposit rates were estimated by combining information from income statements and balance sheets. Specifically, lending rates were obtained by dividing interest revenues over average loan volume, and deposit rates were obtained by dividing interest expenses over average deposit volume. The spreads between lending and deposit rates were computed as the difference between these two. Admittedly, these variables are noisy indicators of the target series, as interest revenues include interests received from investments, while interest expenses are affected by interests paid on liabilities other than deposits. These, however, seem to be the best available indicators of bank-specific interest rates.

The vector x contains country-level variables, aimed to control for changes in loan demand. Here the specification includes GDP growth, also measured in 1995 local currency. The vector z contains bank-level characteristics to control for financial constraints. Following a standard practice in the monetary transmission literature, three indicators were used: (i) a measure of bank size; (ii) an indicator of asset liquidity; and (iii) an indicator of bank capitalization. Regarding bank size, the presumption is that bigger banks face lower external finance premiums and are thus better equipped to substitute away a negative shock to deposits with other sources of financing. To eliminate possible trends in bank size, the estimation used a relative measure, computed as the difference between the log of assets of a bank in a given year (in 1995 local currency) and the average computed over all banks in the same country and year:

Sizei,c,t=ln(Assetsi,c,t)icln(Assetsi,c,t)Nc,t,for c=1,,C(2)

Where Nct stands for the number of banks in country c in year t. Therefore, the resulting measure is a normalized variable with zero mean for each country and year. The second variable, asset liquidity, was computed as the proportion of liquid assets to total assets.4 The inclusion of this variable follows the presumption that banks with more liquid assets are better positioned to meet loan demand in the face of unexpected shocks to deposits. The third variable, capitalization, was defined as equity capital over total assets. The presumption is that better-capitalized banks tend to pay lower risk premiums on non-insured debt and, therefore, face lower financing restrictions. These two variables were normalized with respect to the sample averages of each country. For example, the transformation applied to liquidity was:

Liquidityi,c,t=Liquidityi,c,ttiLiquidityi,c,tNc(3)

Where Nc is the number of observations in country c over the whole period. Capitalization was treated similarly. Potential endogeneity problems and sources of bias associated with these variables are discussed below.

Going back to the specification, the vector m contains two measures of monetary conditions. First, the evolution of liquidity in the banking system was captured by the interest rates on short- term lending between financial institutions, money market rates. Second, the evolution of required reserves was tracked with reserve requirements, an indicator variable constructed on the basis of central bank reports (see Appendix I to III for details). This indicator was allowed to vary on a scale from 1 to 5, with a larger number indicating higher reserve requirements.5 A comparison between these two variables on a country-by-country basis suggests that they convey complementary information on monetary conditions (Figures 1 and 2).

Figure 1.
Figure 1.

Money Market Rates and Reserve Requirements, Asian Countries, 1990–2000

Citation: IMF Working Papers 2007, 048; 10.5089/9781451866124.001.A001

Sources: Central Bank reports; International Financial Statistics, and authors’ calculations.Note: For countries with incomplete or unavailable information on money market rates, an alternative indicator was used. The call money rate (series 60) was used for India, the one-month average interbank offer rate for Hong Kong, and the interbank rate for Taiwan.
Figure 2.
Figure 2.

Money Market Rates and Reserve Requirements, Latin America, 1990–2000

Citation: IMF Working Papers 2007, 048; 10.5089/9781451866124.001.A001

Sources: Central Bank reports; International Financial Statistics, and authors’ calculations.Note: For countries with incomplete or unavailable information on money market rates, an alternative indicator was used. Deposit rates (series 60L) were used for Bolivia, Chile, Colombia, Panama, Paraguay, and Venezuela.

As a robustness check, an alternative set of monetary conditions was used, exploiting the uncovered interest parity. In particular, money market rates were replaced by two variables: the yearly percent change of the average market exchange rate, depreciation, and the three- month U.S. treasury bill (T-bill) rate. The inclusion of these two variables follows from the fact that all countries studied here are small open economies, and the stability of bank deposits may be influenced by developments in the foreign exchange market.

In all the regressions, the target parameters are the coefficients of the monetary conditions (i.e., the δ’s). Differences across domestic and foreign banks were tested by interacting each explanatory variable with a dummy FOREIGN, which equals one for foreign banks and zero for domestic. An additional, more restrictive test was also implemented by further splitting the sample by bank characteristics. In particular, dummy variables were created to separate banks with lagged capitalization above the 75th percentile with respect to the sample of banks operating in the same country. Similarly, another set of dummy variables was created to separate banks with lagged liquidity above the 75th percentile with respect to the rest of banks in the same country. As a by-product, the coefficients associated with GDP growth (the β’s) also allow us to explore for systematic differences in the cyclical behavior of the selected endogenous variables, across domestic and foreign banks.

Separate regressions were estimated for Asia and Latin America on the notion that differences in macroeconomic performance and banking practices between these two regions render the population parameters different. It is well recognized, for example, that foreign bank entry into emerging markets has led to the emergence of “regional evolvers,” that is, banks that use their relative advantages in a region (i.e., historic and cultural links with host countries) to focus their international expansion, as in the case of Spanish banks in Latin America and Japanese banks in East Asia.

A. Expected Results

Consider the set of regressions dealing with quantity-related endogenous variables (i.e., loans and deposits). The first specification provides a test for the sensitivity of loan growth to changes in monetary conditions. Under the lending channel hypothesis, financially constrained banks are expected to be more sensitive to monetary conditions, implying that the coefficients associated with domestic banks are higher in absolute value (i.e., more negative) than those for foreign banks. The second specification further explores for differences in the sensitivity of DEPOSIT GROWTH to monetary conditions across domestic and foreign banks. If banks have the capacity to adjust their deposit rates to partially offset a negative shock to deposits, the lending channel hypothesis would imply a lower sensitivity of deposits to monetary conditions for more financially constrained banks—as they are less capable to substitute them with other sources of funds. The third specification is a combination of the previous two. It checks for the sensitivity of LOAN TO DEPOSIT ratios to changes in monetary conditions. The lending channel hypothesis implies that the associated coefficient has to be non-significant for more financially constrained banks, and positive for less financially constrained banks, since the later would tend to finance a lower proportion of loans with customer deposits in response to tighter monetary conditions.

Consider now the regressions with price-related endogenous variables (i.e., deposit rates, lending rates, and lending minus deposit spreads). The lending channel hypothesis implies that financially constrained banks display a larger response of lending and deposit rates to monetary conditions. Moreover, the lending minus deposit spread is expected to increase under tighter monetary conditions for financially constrained banks. This is because, in response to a negative shock to deposits, banks would try to resort to alternative forms of financing, increasing their premium on noninsured debt and, by cost minimization, their equilibrium deposit rates. This increase in the cost of funding would tend to be translated more than proportionally into lending rates due to the tax-like effect of reserve requirements on insured deposits and the cost of maintaining precautionary liquid assets.

B. Sources of Bias and Endogeneity Problems

As with any reduced-form estimations, there are potential endogeneity problems and bias associated with the use of bank characteristics (i.e., size, liquidity, and capitalization). Regarding size, there is possible joint determination since a bank may actually become larger precisely because of large deposit (and loan) growth. Regarding capitalization, a financially constrained bank may choose to be more capitalized, eroding the usefulness of this indicator as a measure of financial constraints. In fact, balance sheet data indicates that capitalization decreases systematically with bank size, suggesting that it may be a poor indicator of financial constraints on banks. A similar problem arises with the use of liquidity ratios. A bank may optimally choose to have a more liquid asset structure to compensate for higher financial constraints. Again, it is unclear whether a less liquid asset structure is a clear-cut indicator of higher financial constraints. To reduce these endogeneity problems, the regressions use lagged values of bank- level characteristics.

A related problem, spurious correlation induced by mean-reversion, may arise from the use of liquidity ratios as defined. To see why, suppose that bank assets are composed only of liquid instruments and loans. In this simplified balance sheet, a bank with higher-than-average liquid assets in period t-1 will tend to display a higher-than-average loan growth in year t. Therefore, interacting monetary conditions with a liquidity indicator will tend to erode the power of the test, biasing the results in favor of the lending channel hypothesis (i.e., banks with more liquid balance sheets having a lower sensitivity of loan growth to monetary disturbances). This problem can be avoided by choosing a different scaling variable. For example, liquid assets could be scaled by total deposits, which in fact seems to be the relevant measure if deposits are the main source of shocks to banks’ liabilities. For comparative purposes, this paper computes liquidity in the usual way (scaling liquid assets by total assets), but an additional exercise was implemented using deposits as the scaling variable with similar qualitative results.

IV. Data

Macro data come from the International Financial Statistics. The series include money market rates (series 60b); the yearly percent change of the average market exchange rate; depreciation (series rf); the three-month U.S. T-bill rate (series 11160c); and GDP Growth(series 99b), expressed in constant (1995) local currency units using consumer price indexes (series 64).6

Bank-level data (i.e., financial statements) come from the Bankscope database. Series are yearly, covering a sample of 1,565 banks in 20 countries during 1989-2001. The sample of countries includes all major Latin American and Southeast Asian countries.7 Comparing the behavior of domestic and foreign banks in this sample offers a rich experiment, since it covers pre- and post- entry years, as well as several banking and balance of payment crises. In total, the sample has 8,574 observations, distributed across time and countries as shown in Table 1. The decrease in the number of banks in Asia after 1997 reflects the consolidation process following the Asian crisis.

Table 1.

Sample Coverage by Regions and Bank Ownership

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Sources: BankScope and authors’ calculations.This table shows the temporal distribution of the bank-level data. The sample covers 20 emerging economies in Asia and Latin America.

Using the Bankscope database has two major advantages. First, the coverage is fairly comprehensive, with sampled banks accounting for about 90 percent of total assets in each country, according to the source. Second, the accounting information at the bank level is presented in standardized form, after making adjustments for differences in accounting and reporting standards across countries. On the other hand, the data has some limitations. First, there is a sample-selection bias in favor of large banks which weakens somewhat its usefulness, as small banks may tend to be more financially constrained than large banks. Second, the data do not provide a breakdown of loan portfolios by sectors or by borrower types, precluding the use of controls for bank-specific changes in loan demand. Third, the data do not provide information on the currency composition of loans and deposits, which could be a potentially useful source of cross-sectional variation in the open economy context.

While in many cases Bankscope reports both consolidated and unconsolidated financial statements, this paper uses unconsolidated figures to the extent possible, to reduce variations arising from changes in subsidiaries’ ownership and to work with comparable accounting data. From the original source, unconsolidated figures were available in all but 73 cases. For the purposes of the exercises below, balance sheet figures were converted into constant 1995 local currency using consumer price indexes (series 64 of the IMF: International Financial Statistics). Series in constant 1995 U.S. dollars were also computed using the average market exchange rate for each country (series rf of the IMF: International Financial Statistics).

Outliers were identified through the application of several filters, including limits on the yearly change in total assets, on the yearly growth rate of loans and deposits, and on the ratio of net loans to deposits. A few cases with other data deficiencies were also removed.8

The identification of foreign banks in each country was achieved through several complementary steps aimed to minimize misclassifications. A bank was classified as “foreign” in a given year if it had at least 51 percent of its capital in the hands of residents of industrial OECD countries (i.e., excluding Mexico and Korea). The ownership structure at the end of 2001, for each bank in the sample, was obtained from Bankscope and from central banks. To reconstruct backwards the chronological evolution of ownership throughout the period, the list of banks was intersected with a comprehensive list of mergers and acquisitions targeting financial institutions in the sampled countries (a detailed description is given in Appendix IV). Due to data limitations, no distinction was made between subsidiaries and branches of foreign banks—an otherwise relevant separation, since subsidiaries’ access to capital from their parent institutions may not be automatic, as in the case of branches.

Descriptive evidence on the structure of balance sheets across regions and bank sizes is presented in Table 2. No clear patterns arise in the balance sheets of banks operating in Latin America. On the other hand, banks operating in Asia display some regularities similar to those reported in Kayshap and Stein, 1994. In particular, larger banks tend to have a higher proportion of loans to assets, and they rely more on nondeposit financing and less on equity. These patterns have been interpreted as consistent with the presence of imperfect substitution between deposits and other sources of financing, especially for smaller banks. If small banks cannot completely offset shocks to deposits with other financing sources, they will optimally hold a buffer stock of liquid assets to reduce the costs of early loan liquidation. In equilibrium, they will also tend to rely less on nondeposit financing and more on internal capital.

Table 2.

Balance Sheet Structure by Regions and Quintiles of Bank Size

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Sources: BankScope; and authors’ calculations.Note: This table shows the average structure of bank balance sheets by regions and quintiles of bank size Other earning assets include due from Central Banks, deposits with banks, bonds, securities, and equity investments. Total deposits include demand deposits, saving deposits, certificates of deposits, and banks deposits. Equity includes equity reserves and share capital.

This presumption can be further checked by splitting the sample across domestic and foreign banks. Foreign banks could be more aggressive in lending if they have access to internal financial resources from their mother institutions. Also, they could have systematic differences in the liability structure of their balance sheets with respect to domestic banks. Table 3 presents summary statistics on loan growth, deposit growth, and several indicators of the structure of balance sheets for domestic and foreign banks and by regions. On average, foreign banks in Latin America have higher rates of deposit and loan growth than domestic banks, but the opposite holds true for Asia. In general, there are not strong differences in the structure of balance sheets structure across domestic and foreign banks, so the data do not seem to fit into the hypothesized pattern.

Table 3.

Summary Statistics by Regions and Bank Ownership

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Sources: BankScope and authors’ calculations.Note: This table presents summary statistics of selected variables for domestic and foreign banks, and by regions.

V. Baseline Results

The results of baseline regressions for the Asian and Latin American subsamples are presented in Tables 4 and 5. Given the nature of the data, which combines a cross-section and a time-series dimension, the equations were estimated with generalized least squares (GLS) to accommodate possible autocorrelation within panels and heteroscedasticity across panels. The estimation allowed for panel-specific AR(1) processes. Cross-sectional correlations between panels were not considered since the number of panels is much larger than the time-series dimension.

Table 4.

Latin-America, GLS Estimates of Selected Variables on Monetary Conditions

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Sources: BankScope and authors’ calculations.Note: This table presents the results of GLS panel regressions with bank-level fixed effects, and allowing for panel-specific AR(1) errors. The sample comes from the Bankscope database and covers banks operating in selected Latin American countries from 1989-2001. Robust standard errors are reported between square brackets. Statistical significance at one, five, and ten percent level, are indicated by ***, **, *, respectively. Six models are considered, each one presented in a separate column. Each model uses a different dependent variable, specified in the first row of the table. All models share the same set of explanatory variables, including country-level controls (GDP growth), bank-level controls (bank size, bank liquidity, and bank capitalization), and two indicators of monetary conditions (an index that tracks the evolution of reserve requirements, and the money market rate). The sample is split across domestic and foreign banks with the use of a dummy (“Foreign”) which equals one for foreign banks and zero otherwise.
Table 5.

Asia, GLS Estimates of Selected Variables on Monetary Conditions

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Sources: BankScope and authors’ calculations.Note: This table presents the results of GLS panel regressions with bank-level fixed effects, and allowing for panel-specific AR(1) errors. The sample comes from the Bankscope database and covers banks operating in selected Latin American countries from 1989-2001. Robust standard errors are reported between square brackets. Statistical significance at one, five, and ten percent level, are indicated by ***, **, *, respectively. Six models are considered, each one presented in a separate column. Each model uses a different dependent variable, specified in the first row of the table. All models share the same set of explanatory variables, including country-level controls (GDP growth), bank-level controls (bank size, bank liquidity, and bank capitalization), and two indicators of monetary conditions (an index that tracks the evolution of reserve requirements, and the money market rate). The sample is split across domestic and foreign banks with the use of a dummy (“Foreign”) which equals one for foreign banks and zero otherwise.

For each subsample, six regressions were computed, using identical specifications except for the dependent variables. Those presented in the first three columns are quantity-related (loan growth, deposit growth, and loan TO deposit ratios), and those in columns four to six are price-related (lending rates, deposit rates, and lending minus deposit spreads). To facilitate reading, the explanatory variables are divided in two groups. The upper panel presents the coefficients of GDP growth and the bank-level controls, while the lower panel presents the monetary conditions. To compare the responses across domestic and foreign banks, all the explanatory variables were interacted with a dummy variable, FOREIGN, which equals one for foreign banks and zero otherwise. Robust standard errors are reported in square brackets.

In the first two columns, the results show that loan and deposit growth tend to be highly procyclical (especially the former), with no statistically significant differences across domestic and foreign banks. A similar result was obtained in Dages, Goldberg, and Kinney (2000) for Mexico and Argentina. In addition, banks with higher asset liquidity or capitalization at the end of the previous year tend to display stronger loan growth, with some indication that the response is larger among the subset of foreign banks. Going to the lower panel, loan growth decelerates in response to tighter monetary conditions, with some support for the view that loan growth of foreign banks tends to be less sensitive to changes in money market rates. Interestingly, the results in the third column indicate that loans and deposits move one-for-one at the one-year frequency, independently of the economic cycle, monetary conditions, and bank characteristics, including ownership.

Going to columns four to six, the upper panel shows that deposit rates tend to be countercyclical, with some evidence suggesting that this is less intense in the case of foreign banks in the Asian subsample. Banks with higher liquidity tend to pay lower deposit rates and also charge lower interest spreads, a result that appears to be mainly attributable to changes in lending rates. However, no significant differences arise between domestic and foreign banks. In the lower panel, periods of tight monetary conditions are associated with higher lending and deposit rates, with inconclusive results in terms of spreads (for example, spreads go up for the Latin American subsample, and decrease for the Asian subsample). In the Asian subsample, foreign banks tend to display a lower sensitivity of lending and deposit rates to changes in monetary conditions.

Overall, the results provide only weak support to the lending channel hypothesis. In particular, loan growth of foreign banks is less sensitive to money market rates in both Asia and Latin America, and some evidence suggests that deposits on foreign banks are also less sensitive to monetary conditions (in the Latin America subsample). On the other hand, the results show no statistically significant differences in the response of loan growth to changes in reserve requirements across domestic and foreign banks. All these results were qualitatively robust to the removal of 58 banks changing ownership during the period.

VI. A Closer Look at Loan Growth

This section focuses more closely on the response of loan growth to monetary conditions given its importance in the monetary transmission mechanism. The regressions parallel those presented before, but add interacting terms between bank ownership and other bank characteristics. In particular, besides partitioning the sample across domestic and foreign banks, the sample was first split by capitalization, separating banks with capitalization above (and below) the 75th percentile with respect other banks operating in the same country.9 Second, the sample was split by asset liquidity, separating banks above (and below) the 75th liquidity percentile with respect to other banks operating in the same country. Subject to the caveats discussed above, banks with stronger capitalization and more liquid assets could be considered to be less financially constrained, and therefore better equipped to isolate loan growth from changes in monetary conditions. Therefore, the differences between domestic and foreign banks reported before are expected to be larger in the subsamples of banks with lower liquidity and/or capitalization.

Summary results of three sets of regressions, using loan growth as dependent variable, are presented in Tables 6 and 7. To facilitate the reading, the only coefficients reported are those associated with the monetary conditions (i.e., MONEY MARKET RATE and RESERVE requirements). The upper panel displays the results of the regressions covering the whole sample (and are therefore identical to those presented before). The regression in the middle panel splits the sample by bank ownership and capitalization, and the regression in the lower panel splits the sample by bank ownership and liquidity. Each panel displays the coefficients of domestic banks alongside the matching coefficients for foreign banks, and the p-values for the null(s) of coefficient equality between square brackets.

Table 6.

Latin America, GLS Regressions of Loan Growth on Monetary Conditions (I)

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Source: BankScope and authors’ calculations.This table presents selected coefficients from six sets of fixed-effects panel regressions for the Latin American subsample. In all cases, the dependent variable is loan growth in constant local currency units. The reported coefficients are those associated with monetary conditions, measured by money market rates and an indicator of reserve requirements (which goes from 1 to 5, where a higher number indicates higher reserves). Controls, not reported here, include GDP growth and a set of bank characteristics (size, asset liquidity and capitalization). The table is divided in three panels. The upper panel displays the results of the regression based on the whole sample. The middle panel reports the results of two regressions, splitting the sample between banks with capitalization above (and below) the 75 percentile relative to other banks operating in the same country. The lower panel follows a similar structure, but the sample is split by liquidity levels. The estimation is based on GLS, allowing for panel-specific AR(1) processes.Standard errors between parenthesis. * significant at 10%; ** significant at 10%; *** significant at 1%. The p -values corresponding to the null of coefficient equality across domestic and foreign banks between square brackets.
Table 7.

Asia, GLS Regressions of Loan Growth on Monetary Conditions (I)

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Source: BankScope and authors’ calculations.Note: This table presents selected coefficients from six sets of fixed-effects panel regressions for the Asian subsample. In all cases, the dependent variable is loan growth in constant local currency units. The reported coefficients are those associated with monetary conditions, measured by money market rates and an indicator of reserve requirements (which goes from 1 to 5, where a higher number indicates higher reserves). Controls, not reported here, include GDP growth and a set of bank characteristics (size, asset liquidity and capitalization). The table is divided in three panels. The upper panel displays the results of the regression based on the whole sample. The middle panel reports the results of two regressions, splitting the sample between banks with capitalization above (and below) the 75 percentile relative to other banks operating in the same country. The lower panel follows a similar structure, but the sample is split by liquidity levels. The estimation is based on GLS, allowing for panel-specific AR(1) processes.Standard errors between parenthesis. * significant at 10%; ** significant at 10%; *** significant at 1%. The p -values corresponding to the null of coefficient equality across domestic and foreign banks between square brackets.

Going to the upper panel, the coefficients associated with the money market rate are statistically significant and have the expected (negative) sign for domestic banks, but are not different from zero in the case of foreign banks. As discussed before, the null of coefficient equality across domestic and foreign banks can be rejected in both the Latin American and the Asian subsamples. The results in the two lower panels indicate that loan growth of banks with lower capitalization and/or liquidity tend to be more sensitive to changes in money market rates in the two subsamples. While this applies to both domestic and foreign banks, the coefficients of the latter are not significantly different from zero in most cases. A stricter comparison indicates that the null of coefficient equality across domestic and foreign banks can be rejected only when the sample is partitioned by liquidity, but not by capitalization, with the evidence providing some support to the lending channel hypothesis. On the other hand, a look at the coefficients associated with reserve requirements indicates that, while they have the expected negative sign, their standard errors are too large and the null of coefficient equality between domestic and foreign banks cannot be rejected in most cases.

As a complementary exercise, parallel regressions were computed using an alternative set of indicators of monetary conditions. The new set also included reserve requirements, but replaced money market rates with the nominal exchange rate depreciation and international interest rates (proxied by the federal funds rate). The results, presented in Tables 8 and 9 are roughly comparable to those reported above, providing some evidence in support of the lending channel hypothesis. In both subsamples, loan growth decelerates with exchange rate depreciation, with foreign banks generally displaying a lower sensitivity. Moreover, the differences appear to be driven by less liquid and/or less capitalized banks.

Table 8.

Latin America, GLS Regressions of Loan Growth on Monetary Conditions (II)

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Source: BankScope and authors’ calculations.Note: This table presents selected coefficients from six sets of fixed-effects panel regressions for the Latin American sub-sample. In all cases, the dependent variable is loan growth in constant local currency units. The reported coefficients are those associated with monetary conditions, measured by the Federal Funds rate, the yearly variation of the exchange rate depreciation (+) and an indicator of reserve requirements (which goes from 1 to 5, where a higher number indicates higher reserves). Controls, not reported here, include GDP growth and a set of bank characteristics (size, asset liquidity and capitalization). The table is divided in three panels. The upper panel displays the results of the regression based on the whole sample. The middle panel reports the results of two regressions, splitting the sample between banks with capitalization above (and below) the 75 percentile relative to other banks operating in the same country. The lower panel follows a similar structure, but the sample is split by liquidity levels. The estimation is based on GLS, allowing for panel-specific AR(1) processes.Standard errors between parenthesis. * significant at 10%; ** significant at 10%; *** significant at 1%. The p- values corresponding to the null of coefficient equality across domestic and foreign banks between square brackets.
Table 9.

Asia, GLS Regressions of Loan Growth on Monetary Conditions (II)

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Source: BankScope and authors’ calculations.Note: This table presents selected coefficients from six sets of fixed-effects panel regressions for the Asian sub- sample. In all cases, the dependent variable is loan growth in constant local currency units. The reported coefficients are those associated with monetary conditions, measured by the Federal Funds rate, the yearly variation of the exchange rate depreciation (+) and an indicator of reserve requirements (which goes from 1 to 5, where a higher number indicates higher reserves). Controls, not reported here, include GDP growth and a set of bank characteristics (size, asset liquidity and capitalization). The table is divided in three panels. The upper panel displays the results of the regression based on the whole sample. The middle panel reports the results of two regressions, splitting the sample between banks with capitalization above (and below) the 75 percentile relative to other banks operating in the same country. The lower panel follows a similar structure, but the sample is split by liquidity levels. The estimation is based on GLS, allowing for panel-specific AR(1) processes.Standard errors between parenthesis. * significant at 10%; ** significant at 10%; *** significant at 1%. The p- values corresponding to the null of coefficient equality across domestic and foreign banks between square brackets.

The coefficients associated with reserve requirements and the federal funds rate are less conclusive. For the Latin American subsample both coefficients have the expected (negative) sign, but the standard errors are too high to be conclusive, and there are no significant differences across domestic or foreign banks. For the Asian subsample, foreign banks display a larger sensitivity to reserve requirements than domestic, which runs contrary to expectations, while the coefficients of the federal funds rate are either not significant or have the wrong sign. Similar results were obtained using the money market rates of Japan and Australia as alternative measures of international interest rates, possibly reflecting the fact that Asian countries were mostly nonreliant on foreign capital inflows.

Summing up, the results indicate that loan growth of well capitalized and/or more liquid banks is less sensitive to changes in monetary conditions. While in most cases the differences between domestic and foreign banks are not statistically significant, a few exceptions tend to support the lending channel hypothesis.

The results obtained so far implicitly assume that the behavior of domestic and foreign banks is regular during tranquil times and during periods of financial distress. Differences in the behavior of domestic and foreign banks (and their depositors), however, could be magnified during periods of financial distress. The next section provides a closer look into this.

VII. Foreign Banks During Crisis Periods

A related comparison between domestic and foreign banks can be performed by separating tranquil periods and episodes of financial distress. Arguably, the latter entail larger financial constraints on banks, as well as changes in depositor behavior that may induce relocations of deposits toward larger or sounder banks. Therefore, potential asymmetries in financial constraints across domestic and foreign banks would tend to increase during crisis periods, especially if foreign banks are perceived as safer than domestic. The sample of countries included in this study offers a rich information set to address this issue, since half of them undergo some type of financial crisis during the 1990s.

To implement this exercise, three types of (related) crises are considered: currency, banking, and debt. The definitions of each type of crises, and the series, are borrowed from previous studies. A first exercise exploits the currency and banking crises defined in Kamisnsky and Reinhart (1999),10 and the debt crises provided in Detragiache and Spilimbergo (2001).11 As in the original series, each crisis variable is a dummy that takes the value of one at the crisis year and zero elsewhere.

A first pass at the evidence is provided with the help of a set of crisis windows spanning three years and centered around banking, currency, or debt crisis. The close relationship between these three types of crises—both within and between countries—tends to produce clustering, and therefore the size of the window exceeds the three-year period in many countries. For example, the Mexican currency crisis of 1994 was preceded by a banking crisis in 1992, and therefore the associated crisis window spans over five years (1991–95). Similarly, the Venezuelan currency crisis of 1994–95 was preceded by a banking crisis that started in 1993, and thus the crisis window also spans over five years (1992–96). In other cases, such as Malaysia and Philippines during the 1997 Asian crisis, the currency and banking crises occurred simultaneously, and the crisis window covers three years (1996–98).

Figure 3 presents the behavior of loan growth across domestic and foreign banks for each country, both during crises and tranquil periods.12 The graphs illustrate two results. First, not surprisingly, loan growth decreases sharply at the beginning of the crisis window and tends to recover toward the end. Second, the behavior of loan growth across domestic and foreign banks is remarkable similar, even during periods of financial distress.

Figure 3.
Figure 3.

Loan Growth of Domestic and Foreign Banks and Financial Crises (Kaminsky-Reinhart)

Citation: IMF Working Papers 2007, 048; 10.5089/9781451866124.001.A001

Sources: BankScope, Detriagache and Spilimbergo (2001); Kaminsky and Reinhart (1999); and authors’ calculations.Note: This Figure presents the evolution of loan growth in constant local currency units for domestic and foreign banks. For each country, loan growth is computed as the median across sampled banks. A crisis window, covering a three-year period around either a currency, banking or debt crisis, (based on banking and currency crises by Kaminsky-Reinhart, and debt crises Detriagache-Spilimbergo) is also plotted.
Figure 4.
Figure 4.

Loan Growth of Domestic and Foreign Banks and Financial Crises (Caprio-Klingebiel and Frankel-Rose)

Citation: IMF Working Papers 2007, 048; 10.5089/9781451866124.001.A001

Sources: BankScope, Caprio and Klingebiel (1996); Frankel and Rose (1996); and authors’ calculations.This Figure presents the evolution of loan growth in constant local currency units for domestic and foreign banks. For each country, loan growth i computed as the median across sampled banks. A crisis window, covering a three-year period around either a currency, banking or debt crisis, (based on Caprio-Klingebiel and Frankel-Rose) is also plotted.

A more systematic test comparing the behavior of domestic and foreign banks across crises and tranquil periods was performed by running panel regressions with bank-level fixed effects, and splitting the sample of banks between domestic and foreign with the use of a dummy variable. The results, presented in Tables 10 and 11, are qualitatively similar for the Asian and Latin American subsamples. The first two columns indicate that both loan and deposit growth decrease during crisis periods, with mild or not significant differences between domestic and foreign banks, with the exception of deposit growth in Asia, which shows a larger contraction for the subset of foreign banks. The third column, which uses the ratio of loans to deposits as the dependent variable, indicates that the proportion of loans financed though deposits remains roughly constant during crisis periods. In other words, changes in loans are matched one-for-one by changes in deposits both during crises and tranquil periods, and this tends to apply equally to domestic and foreign banks.

Table 10.

Latin America, Regressions Using a Crises Window

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Sources: BankScope, Detragiache and Spilimbergo (2001), Kaminsky and Reinhart (1999), and authors’ calculations.Note: This table compares the response of selected bank-level variables to GDP growth and crisis/non-crisis periods for domestic and foreign banks in the Latin American sub-sample. The regressions are computed with bank-level fixed effects and robust standard errors, shown in square brackets.* significant at 10%; ** significant at 5%; *** significant at 1%
Table 11.

Asia, Regressions Using a Crises Window

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Sources: BankScope, BankScope, Kaminsky and Reinhart (1999), Detragiache and Spilimbergo (2001), and authors’ calculations.This table compares the response of selected bank-level variables to GDP growth and crisis/non-crisis periods for domestic and foreign banks in the Asian sub-sample. The regressions are computed with bank-level fixed effects and robust standard errors, shown in square brackets.* significant at 10%; ** significant at 5%; *** significant at 1%

Interestingly, differences across domestic and foreign banks during crises appear to be related to the behavior of interest rates. The regressions presented in the fifth and sixth columns indicate that bank-specific deposit and lending rates increase during crises, with a smoother patterns for foreign banks. The behavior of bank spreads during crisis periods, however, is less conclusive, and the results in all cases show no differences between domestic and foreign banks.

A potential drawback of these results is that they are obtained from a crisis window that may be too large, as differences in the behavior of domestic and foreign banks may tend to disappear as the size of the crisis window increases. To take this into account, the same regressions were computed again using a slightly richer set of crisis variables. Specifically, three yearly dummy variables were created to isolate potential differences in bank behavior around crisis episodes. The first variable, Crisis T-1, equals one for the year preceding the crisis and zero elsewhere, the second, Crisis T, equals one in the year of the crisis and zero elsewhere, and the third, Crisis T+1, equals one for the year immediately after the crisis and zero elsewhere. The behavior of domestic and foreign banks around, and during crisis periods, was then compared.

The results displayed in the first two columns of Tables 12 and 13 indicate both loan growth and deposit growth tend to be slightly above average in the year preceding the onset of the crises, and sharply collapse immediately after, with mild evidence indicating a less pronounced decline of credit in the case of foreign banks operating in Latin America, but the opposite in Asia. Looking at the third column, the ratio of loans to deposits tends to decrease during and after crisis episodes, but the differences with tranquil periods tend to be insignificant. In other words, the data strongly indicate that loans and deposits of both domestic and foreign banks move one-to- one during tranquil and crisis periods.

Table 12.

Latin America, Regressions Specifying Pre- and Post-Crises Years

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Sources: BankScope, Detragiache and Spilimbergo (2001), Kaminsky and Reinhart (1999), and authors’ calculations.Note: This table compares the response of selected bank-level variables to GDP growth and crises/non-crises period across domestic and foreign banks. The estimates are based on the Latin American subsample, using bank-level fixed effects and robust standard errors, shown in brackets.* significant at 10%; ** significant at 5%; *** significant at 1%
Table 13.

Asia, Regressions Specifying Pre- and Post-Crises Years

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Sources: BankScope, Detragiache and Spilimbergo (2001), Kaminsky and Reinhart (1999), and authors’ calculations.This table compares the response of selected bank-level variables to GDP growth and crises/non-crises period across domestic and foreign banks. The estimates are based on the Asian subsample, using bank-level fixed effects and robust standard errors, shown in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%

Going to the last three columns, lending rates increase above average one year before the crises, and remain high thereafter (within the crisis window considered). Deposit rates, on the other hand, appear to react more sluggishly, since they do not increase significantly during the year preceding the crises.

To check the sensitivity of the results, the regressions were computed again using two alternative definitions of banking crisis: Frankel and Rose (1996), and Caprio and Kinglebiel (1996). Summary results of these regressions, provided in Tables 14 and 15, support the previous conclusions, in the sense that no systematic differences in loan and deposit growth arise between domestic and foreign banks, regardless of the operational definition of crisis employed. On the other hand, the behavior of deposit and lending rates across domestic and foreign banks tends to differ during crisis periods, with foreign banks displaying, in general, a somewhat lower sensitivity to market conditions.

Table 14.

Latin America, Alternative Crises Definitions

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Sources: BankScope, Caprio and Kinglebiel (1996), Frankel and Rose (1996), Kaminsky and Reinhart (1999), and authors’ calculations.This table reports selected coefficients from a set of 18 panel regressions that compare the behavior of bank loans, deposits, and interest rates, across domestic and foreign banks, around periods of financial crises. The results are based on the Latin American sub-sample. Each column covers three separate regressions that use the same dependent variable (first row), and the same set of (unreported) bank-level controls. The reported coefficients correspond to a set of crises dummy variables. Those labeled with “Crises T” equal one during banking crises and zero elsewhere. Correspondingly, “Crises T-1” equal one a year before financial crises and zero elsewhere, and “Crises T+1” equal one a year after banking crises, and zero elsewhere. Three alternative definitions of banking crises were used: Caprio-Kinglebiel (C-K), Frankel-Rose (F-R), and Kaminsky-Reinhart (K-R). In order to compare the behavior of domestic and foreign banks, each explanatory variable was interacted with a “foreign bank” dummy. All regressions are computed with bank-level fixed effects and robust standard errors, reported between square brackets. * significant at 10%; ** significant at 5%; *** significant at 1%
Table 15.

Asia, Alternative Crises Definitions

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Sources: BankScope, Caprio and Kinglebiel (1996), Frankel and Rose (1996), Kaminsky and Reinhart (1999), and authors’ calculations.This table reports selected coefficients from a set of 18 panel regressions that compare the behavior of bank loans, deposits, and interest rates, across domestic and foreign banks, around periods of financial crises. The results are based on the Asian sub-sample. Each column covers three separate regressions that use the same dependent variable (first row), and the same set of (unreported) bank-level controls. The reported coefficients correspond to a set of crises dummy variables. Those labeled with “Crises T” equal one during banking crises and zero elsewhere. Correspondingly, “Crises T-1” equal one a year before financial crises and zero elsewhere, and “Crises T+1” equal one a year after banking crises, and zero elsewhere. Three alternative definitions of banking crises were used: Caprio-Kinglebiel (C-K), Frankel-Rose (F-R), and Kaminsky-Reinhart (K-R). In order to compare the behavior of domestic and foreign banks, each explanatory variable was interacted with a “foreign bank” dummy. All regressions are computed with bank-level fixed effects and robust standard errors, reported between square brackets. * significant at 10%; ** significant at 5%; *** significant at 1%