An 'Ideal' Decomposition of Industry Dynamics: An Application to the Nationwide and State Level U.S. Banking Industry

This paper considers the aggregate performance of the banking industry, applying a modified and extended dynamic decomposition of bank return on equity. The aggregate performance of any industry depends on the underlying microeconomic dynamics within that industry . adjustments within banks, reallocations between banks, entry of new banks, and exit of existing banks. Bailey, Hulten, and Campbell (1992) and Haltiwanger (1997) develop dynamic decompositions of industry performance. We extend those analyses to derive an ideal decomposition that includes their decomposition as one component. We also extend the decomposition, consider geography, and implement decomposition on a state-by-state basis, linking that geographic decomposition back to the national level. We then consider how deregulation of geographic restrictions on bank activity affects the components of the state-level dynamic decomposition, controlling for competition and the state of the economy within each state and employing fixed- and random-effects estimation for a panel database across the fifty states and the District of Columbia from 1976 to 2000.

An 'Ideal' Deconposition of Industry Dynamics: An Application to the Nationwide and State Level U.S. Banking Industry

This paper considers the aggregate performance of the banking industry, applying a modified and extended dynamic decomposition of bank return on equity. The aggregate performance of any industry depends on the underlying microeconomic dynamics within that industry --- adjustments within banks, reallocations between banks, entry of new banks, and exit of existing banks. Bailey, Hulten, and Campbell (1992) and Haltiwanger (1997) develop dynamic decompositions of industry performance. We extend those analyses to derive an ideal dynamic decomposition that includes their dynamic decomposition as one component. We also extend the decomposition, consider geography, and implement decomposition on a state-by-state basis, linking that geographic decomposition back to the national level. We then consider how deregulation of geographic restrictions on bank activity affects the components of the state-level dynamic decomposition, controlling for competition and the state of the economy within each state and employing fixed- and random-effects estimation for a panel database across the fifty states and the District of Columbia from 1976 to 2000.

Has Deregulation Affected Births, Deaths, and Marriages in the U.S. Commercial Banking Industry?

Regulatory change not seen since the Great Depression swept the U.S. banking industry beginning in the early 1980s and culminating with the Interstate Banking and Branching Efficiency Act of 1994. Banking analysts anticipated dramatic consolidation with large numbers of mergers and acquisitions. Less well documented, but equally important, was the continuing entry of new banks, tempering the decline in the overall number of banking institutions. This paper examines whether deregulation affected bank new-charter (birth), failure (death), and merger (marriage) rates during the 1980s and 1990s after controlling for bank performance and state economic activity. We find evidence that intrastate deregulation stimulated births and marriages, but not deaths. Moreover, we find little evidence that interstate deregulation affected births, deaths, or marriages, except that the marriage rate rose after the implementation of the Interstate Banking and Branching Efficiency Act. Finally, pair-wise temporal causality tests among births, deaths, and marriages show that mergers temporally lead new charters and that failures lead mergers (a demonstration effect).

Has Deregulation Affected Births, Deaths, and Marriages in the U.S. Commercial Banking Industry?

Regulatory change not seen since the Great Depression swept the U.S. banking industry beginning in the early 1980s and culminating with the Interstate Banking and Branching Efficiency Act of 1994. Banking analysts anticipated dramatic consolidation with large numbers of mergers and acquisitions. Less well documented, but equally important, was the continuing entry of new banks, tempering the decline in the overall number of banking institutions. This paper examines whether deregulation affected bank new-charter, failure, and merger rates during the 1980s and 1990s after controlling for bank performance and state economic activity. We find evidence that intrastate deregulation stimulated new charters and mergers, but not failures. Moreover, we find little evidence that interstate deregulation affected new charters, failures, or mergers.

Deregulation and Structural Change in the U.S. Commercial Banking Industry

Regulatory change not seen since the Great Depression swept the U.S. banking industry beginning in the early 1980s and culminated with the Interstate Banking and Branching Efficiency Act of 1994. Banking analysts anticipated dramatic consolidation with large numbers of mergers and acquisitions. Some expressed concern about the long-term health of the smaller community banks. This paper describes and discusses the actual evolution of the U.S. banking industry over the past two decades, using the 1976 to 1998 Report of Condition and Income (Call Report) and merger data recently posted on the web site of the Federal Reserve Bank of Chicago. Among several results, more permissive interstate banking and branching regulation significantly associates with higher merger rates, with lower net entry rates, and with higher concentration within states. Interestingly, more permissive intrastate banking and branching regulation only associates with higher concentration.

Technical Efficiency and Stock Market Reaction to Horizontal Mergers

This study examines the relationship between stock market reaction to horizontal merger announcements and technical efficiency levels of the participating firms. The analysis is based on data pertaining to eighty mergers between firms in the U.S. manufacturing industry during the 1990s. We employ Data Envelopment Analysis (DEA) to measure technical efficiency, which capture the firms. competence to produce the maximum output given certain productive resources. Abnormal returns related to the merger announcements provide the investor.s re-evaluation on the future performance of the participating firms. In order to avoid the problem of nonnormality, heteroskedasticity in the regression analysis, bootstrap method is employed for estimations and inferences. We found that there is a significant relationship between technical efficiency and market response. The market apparently welcomes the merger as an arrangement to improve resource utilizations.

Bank Performance: Market Power or Efficient Structure?

Regulatory change not seen since the Great Depression swept the U.S. banking industry beginning in the early 1980s, culminating with the Interstate Banking and Branching Efficiency Act of 1994. Significant consolidations have occurred in the banking industry. This paper considers the market-power versus the efficient-structure theories of the positive correlation between banking concentration and performance on a state-by-state basis. Temporal causality tests imply that bank concentration leads bank profitability, supporting the market-power, rather than the efficient-structure, theory of that positive correlation. Our finding suggests that bank regulators, by focusing on local banking markets, missed the initial stages of an important structural change at the state level.

A Bootstrap-Regression Procedure to Capture Unit Specific Effects in Data Envelopment Analysis

The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes that all firms have the same probability of getting an efficiency score from any specified interval within the [0,1] range. We propose a bootstrap procedure that empirically generates the conditional distribution of efficiency for each individual firm given systematic factors that influence its efficiency. Instead of resampling directly from the pooled DEA scores, we first regress these scores on a set of explanatory variables not included at the DEA stage and bootstrap the residuals from this regression. These pseudo-efficiency scores incorporate the systematic effects of unit-specific factors along with the contribution of the randomly drawn residual. Data from the U.S. airline industry are utilized in an empirical application.