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Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model

Guo-liang Yang, Hirofumi Fukuyama and Yao-yao Song

Journal of Informetrics, 2018, vol. 12, issue 1, 10-30

Abstract: This paper investigates the inefficiency and productivity of 64 Chinese research universities and their evolution over the recent period of 2010–2013, where the production process of each research university is described as a general two-stage network process. We first develop a general two-stage network directional distance framework with carryover variables to gauge the universities’ inefficiencies. Second, to study the evolution of the universities, we develop a Luenberger productivity indicator to measure the productivity changes over time, as well as decompositions. The empirical results show that the Luenberger productivity indicator increased significantly over the examined period. The productivity gains were primarily driven by improvements in efficiency. In other words, the efficiency increased on average over the period of 2010–2013. However, technical changes for many universities were below zero, which led to technology deterioration on average. Finally, based on the estimates, we propose several policy suggestions for improving efficiency and productivity.

Keywords: Data envelopment analysis (DEA); Chinese universities; Two-stage network DEA model (search for similar items in EconPapers)
Date: 2018
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