Academic journal article Journal of Money, Credit & Banking

A Nonparametric Approach for Assessing Productivity Dynamics of Large U.S. Banks

Academic journal article Journal of Money, Credit & Banking

A Nonparametric Approach for Assessing Productivity Dynamics of Large U.S. Banks

Article excerpt

THE BANKING SECTOR is constantly and rapidly evolving; the last two decades, in particular, represent a substantial metamorphosis for banking sectors in countries around the world. For example, the Norwegian banking sector was deregulated in the 1980s, while U.K. banks faced increased competition due to the entry of other nonbank lending institutions. Meanwhile, in the United States, the deregulation movement, which began in the 1970s, expanded to many industries including banks in the 1980s. Under the loosening of regulatory constraints, which continues into the 1990s, U.S. banks have found greater versatility in their operations. In addition, the industry now has available many new financial instruments and technological advances.(1) Finally, the restructuring and consolidation wave of the 1980s, which engulfed the industry,(2) has continued into the present with the recent trend toward megamergers generating empirical evidence that fuels the debate on nationwide branching. As a result of these factors, a substantial literature has developed around the issues of banking efficiency and productivity.(3) This literature has been facilitated by the presence of comprehensive and reliable data sets that are a consequence of the regulatory environment. In addition, the analysis of relative performance among banks is aided by a large degree of product homogeneity. In order to make valid comparisons between efficient and inefficient operations, firms must have the same fundamental characteristics in terms of environment and operations.

Because it encompasses the initial deregulatory push as well as other dramatic fundamental changes, the decade of the 1980s is an especially interesting episode in U.S. banking sector history. It was expected that increased competitive forces, brought about by the changing banking environment, would act as a stimulant to those firms operating inside the production frontier. Banks not allocating their resources efficiently would perish unless they could become more like their efficient competitors by producing more output with existing inputs. Alam and Sickles (2000) find support for this hypothesis in the case of the U.S. airline industry; they present evidence that the Airline Deregulation Act of 1978 led to more efficient resource utilization by firms in that industry over the next decade. This result of mounting competition is separate from the notion of technological innovation although the consequences may be similar. In addition to improving efficiency performance relative to the production frontier in response to greater competition, firms may innovate more as well and, hence, push out the frontier.

The present study evaluates U.S. banking productivity using the Malmquist index approach. This index is a valuable tool since it allows for the decomposition of productivity into the two components discussed above: innovation and imitation. The first component, also called technological change, captures any expansion of the production possibilities frontier. The second component captures the convergence of firms toward the existing technology; this phenomenon is also called efficiency change or "catching up."(4) The Malmquist is calculated within the framework of data envelopment analysis (DEA), which is a linear programming methodology that constructs a nonparametric, piecewise-linear, "best-practice" frontier from observable input and output data. Other authors have used the DEA technique to study the efficiency of the banking sector beginning with Sherman and Gold (1985); more recent studies include Aly et al. (1990), Elyasiani and Mehdian (1990, 1992, 1995), Ferrier and Lovell (1990), and Athanassopoulos (1998), inter alia.(5) Most of these studies, however, have only one or two time periods of data available and hence consider mainly efficiency levels since they can not examine productivity changes in detail.

The focus here is on identifying the degree of productivity progress (or regress) in the 1980s and the degree to which this productivity can be attributed to innovation versus imitation. …

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