Abstract
ABSTRACTThis paper investigates the efficiency of 188 economics departments around the world using data from RePEc. We go beyond the heavily used data envelopment analysis and utilize partial frontier analysis – specifically order-α and order-m – which addresses some of the drawbacks of the standard efficiency frontier analysis and allows for so-called super-efficient departments. We examine the particularities of these approaches and find that the super-efficient departments are not only the ‘usual suspects’. Furthermore, standard output rankings are not well correlated with our estimated efficiency rankings, which themselves are rather similar.
Highlights
IntroductionThere exists plenty of literature that examines the efficiency of (higher) education institutions
There exists plenty of literature that examines the efficiency of education institutions
The lion’s share of efficiency literature uses either data envelopment analysis (DEA)1 or stochastic frontier analysis (SFA), both methods have severe caveats that have been pointed out many times
Summary
There exists plenty of literature that examines the efficiency of (higher) education institutions. The lion’s share of efficiency literature uses either data envelopment analysis (DEA) or stochastic frontier analysis (SFA), both methods have severe caveats that have been pointed out many times. The nonparametric DEA suffers from being highly vulnerable to potential outliers and measurement error, because every unit is related to the most efficient units. This problem is illustrated, which depicts a cross-plot of the main factor of RePEc rankings as output variable on the vertical axis and full-time equivalents as input variable on the horizontal axis.. There is one obvious outlier (Harvard University) dominating the bulk of economics departments, making them inefficient in a DEA. The problem is less severe, but remains if the free disposal hull (FDH) approach, which is less restrictive in defining the efficiency frontier, is used
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