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This paper presents evidence on the industry effects of bank lending in Germany and identifies the industry effects of bank lending associated with changes in monetary policy and industryspecific bank credit demand. To this end, we estimate individual bank lending functions for 13 manufacturing and non-manufacturing industries and five banking groups using quarterly bank balance sheet and bank lending data for the period 1992:1-2002:4. The evidence from dynamic panel data models shows that industry-specific bank lending growth predominantly responds to changes in industry-specific bank credit demand rather than to changes in monetary policy. In fact, conclusions regarding the bank lending effects of monetary policy are very sensitive to the choice of industry. The empirical results lend strong support to the existence of industry effects of bank lending. Because industries are a prominent source of variation in the bank lending effects of bank credit demand and monetary policy, the paper concludes that the industry composition of bank credit portfolios is an important determinant of bank lending growth and monetary policy effectiveness.

In this paper, we show the importance of accounting for heterogeneity among sample firms in stochastic frontier analysis. For a fairly homogenous sample of German savings and cooperative banks, we analyze how alternative theoretical assumptions regarding the nature of heterogeneity can be modeled and the extent to which the respective empirical specifications affect estimated efficiency levels and rankings. We find that the level of efficiency scores is affected in the case of both cost and profitmodels. On the cost side especially, level and rank correlations show that different specifications identify different banks as being best or worst performers. Our main conclusion is that efficiency studies in general and bank efficiency studies in particular should account for heterogeneity across sample firms. Especially when efficiency measures are employed for policy purposes, a careful choice of models and transparency regarding maximization methods are essential to be able to make inferences about managerial behavior.

The inability of most bank merger studies to control for hidden bailouts may lead to biased results. In this study, we employ a unique data set of approximately 1,000 mergers to analyze the determinants of bank mergers. We use data on the regulatory intervention history to distinguish between distressed and non-distressed mergers. We find that, among merging banks, distressed banks had the worst profiles and acquirers perform somewhat better than targets. However, both distressed and non-distressed mergers have worse CAMEL profiles than our control group. In fact, non-distressed mergers may be motivated by the desire to forestall serious future financial distress and prevent regulatory intervention.