The Directional Distance Function and Measurement of Super-Efficiency: An Application to Airlines Data

Lovell and Rouse (LR) have recently proposed a modification of the standard DEA model that overcomes the infeasibility problem often encountered in computing super-efficiency. In the LR procedure one appropriately scales up the observed input vector (scale down the output vector) of the relevant super-efficient firm thereby usually creating its inefficient surrogate. An alternative procedure proposed in this paper uses the directional distance function introduced by Chambers, Chung, and Färe and the resulting Nerlove-Luenberger (NL) measure of super-efficiency. The fact that the directional distance function combines features of both an input-oriented and an output-oriented model, generally leads to a more complete ranking of the observations than either of the oriented models. An added advantage of this approach is that the NL super-efficiency measure is unique and does not depend on any arbitrary choice of a scaling parameter. A data set on international airlines from Coelli, Perelman, and Griffel-Tatje (2002) is utilized in an illustrative empirical application.

Building a Globally Distributed Historical Sheet Map Set

This paper describes the procedures used to create a distributed collection of topographic maps of the Austro-Hungarian Empire, the Spezialkarte der Öesterriechisch-ungarnischen Monarchie, Masse. 1:75,000 der natur. This set of maps was published in Vienna over a period of years from 1877 to 1914. The part of the set used in this project includes 776 sheets; all sheets from all editions number over 3,665. The paper contains detailed information on how the maps were converted to digital images, how metadata were prepared, and how Web-browser access was created using ArcIMS Metadata Server. The project, funded by a 2004 National Leadership Grant from the Institute for Museums and Library Science (IMLS), was a joint project of the Homer Babbidge Library Map and Geographic Information Center at the University of Connecticut, the New York Public Library, and the American Geographical Society’s Map Library at the University of Wisconsin Milwaukee.