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I thank Jeffrey Franks, Alberto Ramos, Thomas Reichmann, Evan Tanner, Bob Traa, and participants of a seminar in the Fund’s Research Department for comments on an earlier draft of this paper. This paper has benefitted greatly from discussions with Marco Terrones and the use of a similar modeling strategy we have jointly developed in a forthcoming paper. I should also thank Alejandra Anastasi, Beatriz Biasone, and Fernando Martin of the Central Bank of Argentina for providing me with Argentine banking data, and Patricia Quiros and Jorge Shepherd for secretarial and research assistance, respectively. The usual caveats apply.
For the case of Peru, see Segura (1995). Intermediation spreads in Uruguay can be gauged from bank deposit and lending rates provided in IMF, International Financial Statistics.
Data for Peru is reported for comparison purposes since it is also a dual currency economy which have witnessed major financial sector reforms in recent years. For all countries, 1994 was chosen as the reference year due to a lack of comparable data for 1996, and also because it seems a better choice than 1995 given the large exchange rate realignments amongst OECD countries during 1995 and the effects of the “Tequila” crisis on Argentina.
The implicit or effective interest rate can differ markedly from the ex-ante or the contract interest rate in countries with a high incidence of nonperforming loans and refinancing operations.
Although it can be rightly argued that 1994 was an atypical year regarding bank profitability, the ratio of profits to net worth of the consolidated banking sector in Argentina has not been overly high. It marginally exceeded 10 percent during the 1993 boom and averaged 7 percent in 1997 (Figure 8). The average rate of return on equity for the U.S. big banks, in comparison, has fluctuated in the range of 15 to 20 percent in recent years. See The Economist, April 11th–17th, 1998, pp. 55–6.
It has been shown that in high inflation countries banks tend to develop an extensive network of local branches aimed at reducing the high transactions costs in these economies (Revell, 1981; Hanson and Rocha, 1986). With the sharp drop in inflation following macroeconomic stabilization, part of this network becomes redundant. This leads to higher overhead costs per unit of loan which, in turn, prevents a faster decline in intermediation margins.
Dick (1996) estimates the level of X-inefficiencies in Argentina’s banking industry at 57 percent. In other words, if less efficient banks were to disappear or start operating on the production frontier estimated for efficient banks, the average cost of financial intermediation in Argentina would decline by as much as 57 percent.
For instance, an accurate assessment of the banking system’s net worth in developing countries is often marred by looser accountancy practices, shallow capital markets and the prevalence of ownership concentration. As most domestic banks raise only a small fraction of their capital at stock exchanges they are not subject to extensive market monitoring and pricing of their capital base. In fact, it is not unusual to have banks’ capital measured at historical book value rather than at current market prices, often entailing an overestimation of banks’ actual net worth. The prevalence of ownership concentration also complicates the assessment of the aggregate net worth of the banking system insofar as large shareholders can offset their equity position in a bank with a liability position to the same bank or to banks owned by a related party, entailing a de facto reduction in capital adequacy standards (Rojas-Suarez and Weisbroad, 1995). In light of these difficulties in assessing banks’ true net worth and systemic credit risk, it has been argued that reserve/liquidity requirements in developing and transition economies ought to be significantly higher than in developed countries (Fernandez and Guidotti, 1995).
At the time, system averages hid significant shortfalls in capital requirements on the part of small private institutions and provincial government banks, whose fragility was brought into sharp relief by the deposit outflow of late 1994/early 1995.
Prior to 1993, legal reserve requirements included banks’ technical cash in vault. From March 1993, legal reserve requirements were lowered but banks’ cash in vault was no longer allowed to be counted as part of the requirement. In 1995, in an effort to ease monetary conditions during the banking crisis, the authorities allowed banks to use up to 50 percent of their cash in vault to meet reserve requirements. This measure was abolished in February 1996, following the reflow of deposit into the banking system and end of the financial crisis. In order to keep the presentation consistent, the series plotted in Figure 11, adds banks’ cash in vault to the legal reserve requirements throughout 1991–1997.
Such a positive relationship between concentration, intermediation margins and monopoly profits has been traditionally emphasized by the so-called “structure performance hypothesis” (SPH), a main source of theoretical support for anti-trust legislation in banking.
where n is the number of banks and X usually stands for a deposit or credit variable. It is straightforward to see that the index declines as new banks enter the market, and increases as the number of institutions decreases (or their share in total deposits or credit increases). The index collapses to unity in the pure monopoly case.
In a country with free capital mobility and a long history of capital flight such as Argentina, depositors will arbitrage freely between interest rate at home and abroad. This implies that banks which fail to raise interest rates on deposits in line with changes in exchange rate and default risk premia will face an immediate deposit outflow.
This is analogous to what happens to banks’ profitability when there is an exogenous change interest rates brought about by an unexpected change in monetary policy. For a discussion of the adjustment of banks’ lending and deposit interest rates to changes in monetary policy in the U.S. and the UK, see Hancock (1985) and Heffernan (1997), respectively. Empirical evidence on the hypothesis that intermediation spreads are higher where the volatility of base interest rates is greater, is provided in Ho and Saunders (1981).
In this paper, a dual currency economy is defined as one in which banks carry freely their intermediation operations in domestic currency (pesos)as well as in foreign currency (U.S. dollars).
As previously noted, in a country with a fixed exchange rate regime under free capital mobility, such as Argentina, interest rates on foreign currency denominated domestic deposits gravitate around the respective international rate. The interest rate on deposits denominated in domestic currency will be then determined by the U.S. dollar rate plus the devaluation risk and the marginal cost differential between capturing one unit of domestic denominated deposits relative to foreign currency deposits. A formal derivation of this arbitrage condition is provided in the appendix.
These parameters are expected to be mainly determined by macroeconomic variables exogenous to the model, such as the rate of economic growth and changes in real interest rate (brought about, e.g., by devaluation expectations, changes in country risk premia, and domestic inflation or deflation).
In general the capital adequacy ratios are an inverse function of the quality of the loan portfolio of the bank. It is assumed that the higher the quality of the loan portfolio is, the lower the capital adequacy ratios are.
These relations are obtained by substituting equation (8) in the expressions for the derivative of the Lagrangian in relation to Li and Li*.
In the perfect competition case, i.e. when Ψ = -1, this term drops out.
While the model’s set up laid out in Section III is more suitable to panel data estimation, individual bank data proved difficult to obtain from official sources. Thus, the empirical evidence presented in this paper is limited to aggregate data.
Since we are working with aggregate data, the share of bank i loan in total loans (the variable Si in the model) equals one and so drops out of the model.
The instrument used here to measure such yield differential is the debt consolidation bond (“BOCON”), which is relatively liquid and for which a long series on secondary market prices is available.
The rationale for lagging the Herfindahl coefficient is that concentration usually affects spreads with a lag. Also, in the estimation we have experimented with entering the problem loan variable lagged of one month both to avoid potential simultaneity biases and to take account of the fact that banks, as discussed above, are assumed to take their provisioning and risk-adjusted decisions on setting period t spreads, based on actual default outcomes in period t-1. The respective estimates were virtually unchanged regardless whether we lagged or not the problem loan variable. Likewise, the use of current or lagged values for the tax variable was not critical: in both cases the tax/loan ratio yielded t-ratios well below the standard levels of statistical significance.
Estimation was limited to the post mid–1993 period due to a statistical break in the available series on interest rates. See Central Bank of Argentina, Boletin Estadistico, several issues.
This was calculated by first differencing the variable ε(rd-re), which yields Δε(rd-re) + ε Δ(rd-re) + Δε Δ(rd-re). If neither the rate of remuneration on reserve requirements nor the interest rate on deposits change, the last two terms of the latter expression will vanish. So, the estimated impact of a 1 percentage point increase in 8 on spreads can be obtained by multiplying 0.01 by the estimated coefficient in the regressions times (rd-re), the latter being expressed in percentage terms.
In the particular case in which the reserve requirements are equal across currencies, i.e. ε = ε*, the asset tax rate drops out.