Testing for Heterogeneity in Data Envelopment Analysis

Abstract

This paper introduces a comprehensive framework for detecting and conceptualizing heterogeneity in data envelopment analysis (DEA), aligning with the microeconomic production theory. Despite DEA’s significant advantage in evaluating DMUs based on their efficiency without assuming a specific functional form of technology, it critically relies on the comparability of these units. We address the persistent issue in DEA modeling that stems from the assumption of homogeneity among DMUs, which is often untested. We propose a novel methodological approach that serves as a testing framework for heterogeneity, predicated on minimal assumptions about data randomness. This framework provides a means to examine the biases introduced by technological disparities among DMUs and offers an approach for practitioners to ensure the validity of DEA modeling across diverse technological settings. This approach not only uncovers biases from technological differences but also serves as a preliminary step to enhance methods like clustering, aiding practitioners in verifying DEA’s applicability across varied technologies.