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Development Research Group, The World Bank, and Research Department, International Monetary Fund. This paper was prepared for the 1998 World Bank Annual Conference on Development Economics and will be published in the conference proceedings. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, the International Monetary Fund, their Executive Directors, or the countries they represent. We wish to thank Jerry Caprio, George Clarke, Stijn Claessens, Phil Keefer, Ross Levine, Miguel Savastano, and Peter Wickham for helpful comments, and Anqing Shi and Thorsten Beck for excellent research assistance.
Empirical research on the relationship between interest rates and savings in countries that liberalize financial markets has generally failed to find clear evidence of a significant and sizable positive correlation. This failure is generally attributed to the strong positive wealth effect of interest rate increases (see Fry (1997) for a survey). However, empirical studies tend to support the proposition that moderately positive real interest rates have a positive effect on growth (see, among others, Roubini and Sala-i-Martin (1992) and Bandiera and others (1997)).
Financial markets allow agents to diversify and hedge risk, thereby making high-risk, high-return investments attractive to investors; financial markets also allow the pooling of liquidity risk, as in Diamond and Dybvig (1983); stock markets disseminate information over corporate values (although—if information revelation is too extensive this may actually make incentives for information collection too low, as argued by Stiglitz (1985)), and allow the market for corporate control to emerge. Financial intermediaries, such as banks, make savings available to entrepreneurs who may lack own resources to finance investment and technology acquisition; they also screen and monitor loan applicants, thereby improving the allocation of resources. By exploiting economies of scale, intermediaries can also make saving mobilization more efficient (Levine, 1997).
The Chilean experience, which shares many features with the current East Asian crises, is analyzed in Diaz-Alejandro (1985). Other case studies of banking crises are presented in Sundararajan and Baliño (1991), Drees and Pazarbašioglu (1995), and Sheng (1995).
In some countries, the authorities may explicitly forbid commercial banks from entering certain segments of the credit market that are deemed excessively risky, such as credit to security dealers. Such restrictions are sometimes relaxed as part of the liberalization process.
This problem is exacerbated if financial liberalization takes place before macroeconomic stabilization, as emphasized by McKinnon (1993).
Due to lack of data, for some countries the observations included in the panel do not cover the entire 1980-95 period.
If the outliers are introduced in the panel, the results do not change much, except that the estimated coefficient for inflation and the real interest rate become smaller. Peru also had a hyperinflation during the sample period, but the hyperinflation years are excluded from the panel because of missing data.
For more details on the relationship between the theory of banking crises and the choice of control variables, see Demirgüç-Kunt and Detragiache (1998).
To minimize potential endogeneity problems, to measure the real interest rate we use the rate on short-term government paper or a central bank rate, such as the discount rate, and not a bank interest rate. In six countries, however, neither measure was available, and we used the bank deposit rate.
The model χ2 tests the joint significance of the regressors by comparing the likelihood of the model with that of a model with the intercept only. The AIC criterion is computed as minus the log-likelihood of the model plus the number of parameters being estimated, and it is therefore smaller for better models. This criterion is useful in comparing models with different degrees of freedom. The percentage of crises that are correctly classified and the total percentage of observations that are correctly classified are reported to assess the prediction accuracy of the model. A crisis is deemed to be accurately predicted when the estimated probability exceeds the frequency of crisis observations in the sample (around 5 percent). This criterion tends to downplay the performance of the model, because in a number of episodes the estimated probability of a crisis increases significantly a few years before the episode begins and those observations are considered as incorrectly classified by the criterion (see Demirgüç-Kunt and Detragiache (1998) for some examples).
The indexes measuring “law and order”, the quality of the bureaucracy, and corruption range between 0 and 6, while the index of bureaucratic delay and that of contract enforcement range from 0 to 4.
It is worth noticing that the proxies do not measure the quality of the laws and regulations in a particular country, but rather factors that affect the extent to which laws and regulations are enforced.
Keeley (1990) presents empirical evidence that supports this view. First, he shows that in the 1970s U.S. thrift institutions began to lose charter value owing to the relaxation of various regulatory entry restrictions and because of technological changes. Second, he shows that banks with larger charter value were less risky, as measured by the risk-premium on uninsured bank CDs.
Of course, for given franchise value, large capitalization and large liquidity should create less incentives to take on risk.
The control variables, also similar to those used by King and Levine (1993), are the logarithm of GDP per-capita and of the secondary school enrollment ratio at the beginning of the subperiod, the share of government consumption expenditure in GDP, the inflation rate, the ratio of the sum of imports and exports to GDP, the real interest rate, and a period dummy variable.
The financial liberalization dummy variable takes the value of one if interest rate liberalization began in any of the years of the subperiod or if markets were liberalized in the preceding subperiod; the banking crisis dummy variable takes the value of one if a crisis was on-going in any of the years of the subperiod. The results are robust to redefining the dummy variables by treating a subperiod as a one only if the change in policy (crisis) occurs in the first three years of the subperiod. If the change in policy (crisis) takes place in the last or second-to-last period, then the dummy for the following period is set to one.
When we estimate a growth regression including the banking crisis dummy and the financial liberalization dummy, however, the coefficients are not significant, suggesting that the dummies have a negligible direct impact on growth.
Roubini and Sala-i-Martin (1992) find the negative growth effects of financial repression to bestronger in financially repressed countries than in financially restrained countries.
The panel includes countries that liberalized well before the beginning of the sample period. It may be argued that whether those countries were financially repressed or restrained at the time of liberalization should not affect their economic performance in 1980-94. As a robustness test, we repeated the tests described below dropping those countries from the panel. The basic results remain unchanged.