This paper examines the determinants of individual bank failures and acquisitions in the UnitedStates during 1984-1993. We use bank-specific information suggested by examiner CAMELrating categories to estimate competing-risks hazard models with time-varying covariates. We focus especially on the role of management quality, as reflected in alternative measures of xefficiency and find the inefficiency increases the risk of failure, while reducing the probability of a bank's being acquired. Finally, we show that the closer to insolvency a bank is, as reflected by a low equity-to-assets ratio, the more likely its acquisition. KEYWORDS:Bank failures, bank acquisitions, managerical efficiency, hazard model estimation We have benefited from comments on an earlier version by Alton Gilbert, Shawna Grosskopf, Subal Kumbhakar, Michel Mouchart, Larry Wall and an anonymous referee. Any remaining errors are our responsibility.

This paper examines the determinants of individual bank failures and acquisitions in the United States during 1984-1993. We use bank-specific information suggested by examiner CAMELrating categories to estimate competing-risks hazard models with time-varying covariates. We focus especially on the role of management quality, as reflected in alternative measures of xefficiency and find the inefficiency increases the risk of failure, while reducing the probability of a bank's being acquired. Finally, we show that the closer to insolvency a bank is, as reflected by a low equity-to-assets ratio, the more likely its acquisition.

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Numerous studies have found that US commercial banks are quite inefficient, and we find that, on average, banks became more technically inefficient between 1984 and 1993. Our analysis of productivity change, however, shows that technological improvements adopted by a few banks pushed out the efficient frontier, and that, on average, commercial banks experienced productivity gains. For banks with assets less than $300 million, however, technological improvement was insufficient to offset increased inefficiency, and thus productivity declined over the period. Our findings suggest that increasing inefficiency is reflective of an industry undergoing rapid technical change and adjustment of average firm size, but not necessarily a long-term decline.

This article surveys recent research on the usefulness of the term spread (i.e., the difference between the yields on long-term and short-term Treasury securities) for predicting changes in economic activity. Most studies use linear regression techniques to forecast changes in output or dichotomous choice models to forecast recessions. Others use time-varying parameter models, such as Markov-switching models and smooth transition models, to account for structural changes or other nonlinearities. Many studies find that the term spread predicts output growth and recessions up to one year in advance, but several also find its usefulness varies across countries and over time. In particular, many studies find that the ability of the term spread to forecast output growth has diminished in recent years, although it remains a reliable predictor of recessions. (JEL C53, E37, E43)

Numerous studies have found that US commercial banks are quite inefficient, and we find that, on average, banks became more technically inefficient between 1984 and 1993. Our analysis of productivity change, however, shows that technological improvements adopted by a few banks pushed out the efficient frontier, and that, on average, commercial banks experienced productivity gains. For banks with assets less than $300 million, however, technological improvement was insufficient to offset increased inefficiency, and thus productivity declined over the period. Our findings suggest that increasing inefficiency is reflective of an industry undergoing rapid technical change and adjustment of average firm size, but not necessarily a long-term decline.

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Numerous studies have found that banks exhaust scale economies at low levels ofoutput, but most are based on the estimation of parametric cost thnctions which misrepresent bank cost. Here we avoid specification error by using nonparametric kernel regression techniques. We modify measures of scale and product mix economies introduced by Berger et al. (1987) to accommodate the nonparametric estimation approach, and estimate robust confidence intervals to assess the statistical significance of returns to scale. We find that banks experience increasing returns to scale up to approximately $500 million ofassets, and essentially constant returns thereafter. We also find that minimum efficient scale has increased since 1985.