DEA I

SB36.1 DEA with Reverse Inputs: Measuring the Relative Efficiencies of Major League Baseball Teams

• Herbert F. Lewis; SUNY, Harriman Sch. of Mgmt & Policy, Stony Brook, NY 11794-3775; hlewis@notes.cc.sunysb.edu
• Thomas R. Sexton; SUNY, Harriman Sch. of Mgmt & Policy, Stony Brook, NY 11794-3775; tsexton@notes.cc.sunysb.edu

We develop a model in which measurement scales for certain inputs are such that larger values indicate lower input consumption. Properties and interpretations of this model are discussed. The model is applied to measure the relative efficiencies of major league baseball teams.

SB36.2 Intertemporal Productivity Analysis & Model Invariance in DEA

• Anthony D. Ross; Texas A&M University, Mays Coll. of Business, College Station, TX 77840; aross@acs.tamu.edu

We present an approach for identifying best practice decision making units in a multiproduct supply network operation. Using incomplete operational data presents many challenges when used by researchers and analysts. An invariant DEA model and some procedures for handling such empirical realities are specified. The model examines the productive efficiency of DMUs over multiple planning horizons.

SB36.3 On Returns to Scale under Weight Restrictions in DEA

• Kaoru Tone; National Graduate Institute for Policy Studies, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo, 162-8677 , Japan; tone@grips.ac.jp

We extend the concept of returns to scale in DEA to the weight restriction environments. The definition and computational procedure will be presented. It is demonstrated that the most productive scale size will usually narrowed by this addition of weight restrictions. An empirical study will follow.

SB36.4 Overcoming the Inherent Dependency of Data Envelopment Analysis Efficiency Scores: A Bootstrap Approach

• Mei Xue; University of Pennsylvania, Dept. of OPIM, The Wharton Sch., Philadelphia, PA 19104-6366; xuem@wharton.upenn.edu
• Patrick T. Harker; University of Pennsylvania, Dept. of OPIM, The Wharton Sch., Philadelphia, PA 19104-6366; harker@wharton.upenn.edu,, http://opim.wharton.upenn.edu/~harker

The efficiency scores generated by DEA models are clearly dependent on each other in the statistical sense. Hence, the conventional procedure of regression analysis generally followed in the DEA literature is invalid. We provide a bootstrap method to overcome this dependency problem.