Testing for heterogeneity in data envelopment analysis
本文提出一个检验框架，用于检测数据包络分析中决策单元间的技术异质性，帮助研究者判断DEA模型在不同技术环境下的适用性。
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.