Measuring the Severity of a Banking Crisis and Finding Its Associated Factors: How Are the Factors Different for Simple and Severe Banking Crises?

Anichul Hoque Khan,Hasnat Dewan

doi:10.4236/tel.2014.49109

Abstract

This study measures the severity of a banking crisis by using its duration and the cost. Using this new methodology, we find that the factors associated with a severe banking crisis are not quite the same as those associated with a simple banking crisis. An ordered logit model and a large panel data set were used for this study. One of our major findings is that there exists a four-year time lag between an economic boom, or financial system liberalization, and the occurrence of a severe banking crisis in a country. This indicates that banking problems start much earlier than the time when they are revealed as banking crises. This study also finds that the lower the remains of a past banking crisis, the higher the probability of a severe banking crisis. It could be due to less-attentiveness of banking sector policy-makers with elapsed time. A high rate of inflation, existence of an explicit deposit insurance scheme, and a weak institutional environment are found to be common factors positively associated with both simple and severe banking crisis.

Highlights

At first, this paper proposes a new methodology to measure the severity of banking crisis, and finds theA
Unlike previous studies that used bi-variatelogit or probit model, we use an ordered logit model and find that the probability of a severe banking crisis is positively associated with an economic boom and a liberalized financial system when both variables are lagged by 4 years
The results show that the GDP growth rate and the terms of trade, when lagged by 4 years, are positively correlated with the probability of a severe banking crisis and are statistically significant at 6.6% and 3.4% level respectively

Summary

Introduction

This paper proposes a new methodology to measure the severity of banking crisis, and finds the. These explanatory variables are GDP growth rate, terms of trade, depreciation rate, rate of inflation, ratio of M2 to foreign exchange reserves, growth rate of real domestic credit in the private sector, using of an explicit DIS, per capita GDP, and the remains of the past crisis. Unlike previous studies that used bi-variatelogit or probit model, we use an ordered logit model and find that the probability of a severe banking crisis is positively associated with an economic boom and a liberalized financial system when both variables are lagged by 4 years It implies that banking problems start much earlier than the time when they are revealed.

Dependent Variable—Severe Banking Crisis

Independent Variables

Estimation Model

Estimation Results

Robustness of the Empirical Findings

Conclusions