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Adolfo Barajas is a senior economist with the IMF Institute, Emiliano Basco is the division head of the Economic Research Department of the Central Bank of Argentina, V. Hugo Juan-Ramon is the deputy division chief of the Western Hemisphere Division of the IMF Institute, and Carlos Quarracino is the director of information and short-term analysis of the National Macroeconomic Policy Directorate of the Ministry of the Economy of Argentina. We thank an anonymous referee as well as Eduardo Levy-Yeyati, Miguel Messmacher, Hemant Shah, Rodolfo Luzio, Ernesto Ramírez, and seminar participants at the IMF Institute and the Universidade de São Paulo for their helpful comments, and Takayuki Tsuruga and Wolfgang Harten for outstanding research assistance.
During 1991–97, Argentina was one of the fastest-growing economies in Latin America, with an average growth rate of 6.7 percent.
Argentina is less open than most Latin American countries. During 1990–95, total trade represented 16 percent of GDP, whereas it was 37 percent in Mexico and 46 percent in Chile. Moreover, as pointed out by the Independent Evaluation Office of the IMF (2004), the differences in Argentina’s degree of openness in relation to other hard peg economies are even more pronounced; for a sample of eight hard peg economies throughout the world, total trade averaged 96 percent of GDP.
Analysts as well as the government agreed that the exchange rate was overvalued, although there was no consensus on the degree of overvaluation. As the study by the Independent Evaluation Office (2004) reports, by spring of 2000 overvaluation was estimated at 7 percent by Goldman Sachs, 13 percent by JP Morgan, and 17 percent by Deutsche Bank. Ex post, Perry and Servén (2002) estimated the Argentine peso to be overvalued by 55 percent by 2001.
There is an extensive literature linking bank capital to a more prudent behavior toward risk in general.
However, it is worth noting that the conversion of loans at par may have had a positive—and therefore partially offsetting—effect on net worth by reducing the rate of default by imperfectly hedged bank borrowers.
The acronym stands for Bonds, Auditing, Supervision, Information, and Credit Rating. The BASIC approach was introduced following the Tequila crisis of 1994–95 and relied heavily on providing timely and relevant information on individual banks to both private markets and regulators. In turn, regulators and markets would then use this information to punish banks for excess risk taking: regulators, by imposing higher capital requirements, and markets, by pricing down banks’ subordinated debt.
For example, the capital-to-asset ratio was 10.5 percent and the capital-to-risk-weighted-assets ratio was 21.2 percent. Provisioning had been well above 100 percent of nonperforming loans during 1997–99, but an increase in nonperforming loans owing to the recession brought the provisioning ratio to 77.1 percent in 2000 (Perry and Servén (2002) based on data from the Central Bank of Argentina).
The acronym stands for a ratings scheme for banking systems based on a composite index of capital adequacy, asset quality, management (percentage of foreign ownership), liquidity, operating environment, and transparency.
For example, in Figure 11 in de la Torre, Levy-Yeyati, and Schmukler (2003), voluntary domestic financing of the central government is shown to increase steadily between 1994 and 2001, with banks and pension funds being the major contributors.
It should be noted that this behavior was common to a number of emerging economies since the mid-1990s, as documented by a recent study by Hauner (2006). For a sample of 42 middle-income countries, Hauner shows that the share of the public sector in total bank credit grew from 7 percent in 995 to more than 27 percent in 2003.
Barajas and Steiner (2002) analyze recent credit slowdowns in eight Latin American countries, including Argentina. Using a breakdown of major changes in banks’ balance sheets, the authors rank the relative importance of different factors, such as deposit growth or alternative uses of funds raised. It is interesting to note that, up until 2000, government financing did not appear to be a major factor causing the slowdown in Argentina. However, if the analysis were to be repeated including 2001, this factor would most likely enter the picture significantly.
Because of the severe recession that began in 1999, the credit-GDP ratio understates the magnitude of the credit decline up to 2001.
This includes demand, time, savings, and foreign-currency-denominated deposits held by the private sector.
The acronym refers to the evaluation of the financial condition of individual banks based on the following criteria: capital adequacy, asset quality, management, earnings, and liquidity.
In order to avoid double counting, we excluded from FPL foreign currency loans to the government, because these are already included in our measure of financing to the government, NGOVB.
In earlier drafts we used a country risk indicator, CRISK, measured as the JP Morgan Emerging Market Bond Index (EMBI) spread for Argentina. Following comments received, and in order to isolate the currency risk component not directly captured by the other two macroeconomic controls, we opted to use CURISK instead. The results were similar in all regressions except in those for government financing, where CURISK had a more intuitively reasonable effect than CRISK. We now report only the regressions with CURISK.
We also used the headline MERVAL Argentine stock market index in earlier drafts of the paper. However, in order to rule out the possibility that overall banking sector performance—through the stock price—was driving the relationship between MERVAL and individual bank behavior, we constructed our own nonbanking stock market index, MERVALNB, by excluding banking institutions. We found that the degree of correlation between the two indices, although high throughout the sample period, declined appreciably from the third quarter of 1999 onward. Furthermore, both MERVAL and MERVALNB performed well in our regressions, with consistently significant coefficients in virtually all of the equations. We now report only the regressions using the non-banking index.
In all regressions, we eliminated outliers; observations containing unrealistically high liquidity; loan-asset, government financing, nonperforming loan, or dollarization ratios; or growth rates of loans or deposits. We also excluded cases in which the capitalization ratios were negative. These outliers were generally the result either of data reporting errors or of relatively new institutions growing at high rates from a small base.
Defined as 1 −
A clarification is warranted. We showed in the descriptive section that over the entire study period the share of foreign currency deposits increased and overall deposits contracted. However, a distinction should be made between an earlier stage, in which there was a pure composition switch toward foreign currency deposits, and a later stage—the deposit run—during which all deposits declined, but the domestic currency component did so more rapidly.
In fact, when we estimated this equation using an alternative measure for nonperforming loans, the “broad nonperforming loan ratio,” this effect disappeared.
In Table 6—as well as in Tables 7 and 9—we report the OLS coefficients for only the three nonfundamental bank characteristics, although all fundamental and macro variables were also included. The OLS results on these variables did not differ substantially from those obtained in the FE estimations shown in the upper panel.
Similarly, as imperfectly hedged borrowers perceived an increase in the risk of a devaluation, their demand for foreign currency loans may have declined as well.
The dependent variable NGOVB is defined as the broad measure of financing to the government, as in Table 2. It includes government bonds plus lending to the government.
Also it is possible that safer banks had higher costs of compliance with prudential regulations; thus, lending to the government would have helped those banks to level the playing field vis-à-vis the banks with weaker fundamentals and therefore lower costs of compliance.
For this comparison we used the simplest specification, which includes the fundamentals not related to currency exposure (CAPR, LIQ, PROFIT, NPLL, and NGOVB) and the three macroeconomic controls (CURISK, FISCAL, and MERVALNB).
Some of the results of robustness checks are not fully reported in this paper. However, they are available upon request from the authors.
As Table 9 shows, we found the effect of currency risk (CURISK) on deposit growth to be negative. Thus, a positive coefficient on the interaction terms SIZE*CURISK and TIMEDEP*CURISK implies that larger and more time deposit-oriented banks were less likely to suffer deposit withdrawals from an increase in currency risk. Perhaps depositors perceived that these banks were managing their currency risk more effectively; at the same time, depositors may have felt that there was little these banks could do in the face of other deteriorating macroeconomic conditions.
One possible interpretation is that, as fiscal performance faltered, the exposure of banks to the private sector became less relevant to depositors. Of course, this argument would be stronger if one observed simultaneously that depositors became more responsive to banks’ levels of government financing. Unfortunately, this is not the case, as Table 10 shows.
We preferred this ending date over December 2001 because the latter contained the effect of a regulation in November 2001 requiring private pension funds to increase their holdings of government bonds. Because this caused an additional and somewhat indiscriminate deposit run in December, the significance of bank-specific variables was higher when using November 2001 as the ending date.
DEPR was defined as the ratio between monthly interest paid on deposits to the average monthly stock of deposits, then deflated by the percentage change in the CPI.
Note that these results continued to hold when considering time deposits instead of total deposits.
Note that when we used longer lag lengths, this trade-off became weaker.
Although we do recognize the counterintuitive negative coefficient on capitalization in the panel data regressions at relatively short lags.