Enterprise State Community College

A. INTRODUCTION In 2010 a raft of articles offered contrasting views on analytic tools for assessing unilateral effects from differentiated products mergers. They provided some useful insights but did not acknowledge that this is just one of many scenarios encountered in merger assessment, and they paid insufficient attention to the problems faced by a competition agency. We revisit this debate to clarify the issues and place them in context. We first review the basics of unilateral merger effects, the model-based analytic tools used for assessing unilateral effects, and the contrasting views expressed in the recent debate. With this foundation, we consider the choice among analytic tools at three stages of a merger assessment—initial screening, ultimate decision, and courtroom presentation. We explain how the proper analytical tool for a particular merger depends on the information available. We also explain how the proper analytic tool for a particular merger depends on the competitive process in which the merging firms engage and how the merger is apt to affect it. Our experience has been that shortrun price competition among differentiated products most often is not a satisfactory characterization of the process, and we offer two scenarios that require tools very different than those applicable to differentiated consumer products. In addition, we address the Upward Pricing Pressure Index (UPPI), which was a focal point in the recent debate. We find some merit in the idea of a very simple tool for differentiated products mergers and suggest a slightly simpler calculation. We also conclude that either as a screen or as part of a full assessment of a merger, other tools are preferable to the UPPI.

This research measures the preferences students express for the communicator styles of their instructors as conceptualized by Norton (1978). The most preferred styles were friendly and attentive followed by relaxed, impression leaving, animated, dramatic, open, precise, dominant and contentious. The patterns of those preferences were then examined to determine if they were related to patterns in achievement scores, but the results indicated that the association was a weak one at best. Instead, it was found that other variables such as IQ, demographics and a student's learning style preference were stronger predictors of achievement.