Input Aggregation in Models of Data Envelopment Analysis: A Statistical Test with an Application to Indian Manufacturing

A problem frequently encountered in Data Envelopment Analysis (DEA) is that the total number of inputs and outputs included tend to be too many relative to the sample size. One way to counter this problem is to combine several inputs (or outputs) into (meaningful) aggregate variables reducing thereby the dimension of the input (or output) vector. A direct effect of input aggregation is to reduce the number of constraints. This, in its turn, alters the optimal value of the objective function. In this paper, we show how a statistical test proposed by Banker (1993) may be applied to test the validity of a specific way of aggregating several inputs. An empirical application using data from Indian manufacturing for the year 2002-03 is included as an example of the proposed test.

AM 6545: A Novel Peripheral CB1 Antagonist

Obesity and other related metabolic disorders are a common problem in the United States. Consequently, several drug therapies have been developed in an attempt to address this problem. Many older appetite suppressants, such as amphetamines, were dangerous and potentially addictive. For the last few years, the endocannabinoid system was investigated as a potential target for appetite suppression. Unfortunately, early cannabinoid CB1 antagonists came with an unacceptable side effect profile of their own, which is largely due to central actions of these drugs. In an attempt to reduce the side effect profile, researchers are investigating peripherally acting cannabinoid antagonists, which do not penetrate the blood brain barrier. This study investigated AM 6545, a novel peripheral cannabinoid antagonist, for its effects on food reinforced instrumental behavior. In the end, the results indicated that AM6545 produced a dose-related suppression of lever pressing for food reinforcement.

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.