This article demonstrates a methodology using panel data analysis to estimate confidence intervals for the data envelopment analysis efficiency of individual decision making units (DMUs), and the statistical significance of trends in individual DMU efficiency. The procedure accounts for...

This article addresses Data Envelopment Analysis (DEA) efficiency analysis in organizations with multiple production processes. It shows how to measure the impact on an organization's overall efficiency of (a) inefficient and superefficient subunits and (b) the efficiency with which input...

In Data Envelopment Analysis (DEA) applications involving multiple inputs and outputs, inputs are aggregated into the total amounts of each type of input. For example, if input types 'labour' and 'capital' are used to produce multiple outputs, the total amount of labour used to produce all...

In Data Envelopment Analysis (DEA), the two-stage method is a popular procedure for accounting for exogenous influences on efficiency. With the conventional two-stage method, a DEA is first conducted using only traditional (endogenous) inputs and outputs. Then, the first-stage DEA scores are...

The study shows that technical efficiency scores in Data Envelopment Analysis are biased by linear aggregation of the quantities of the same type of input that are used by all outputs (intra-input aggregation). Fare et al. 's (2004) models are expanded to account for this additional bias, and...

Building on Data Envelopment Analysis technical efficiency models developed by Fare et al. (2004) and Barnum and Gleason (2005), a procedure is illustrated for estimating a firm's efficiency in allocating inputs among the production technologies producing its outputs.