Data Envelopment Analysis: A Nonparametric Approach to Efficiency Measurement

Data envelopment analysis (DEA) is a nonparametric methodology employed to assess the relative efficiency of decision-making units (DMUs). Pioneered by Charnes et al. (1978), DEA differentiates itself from parametric approaches by circumventing the need for a priori specification of a functional form for the production function or the assignment of explicit weights to evaluation criteria. Instead, DEA leverages input-output data from the DMUs under consideration to construct an empirical efficient frontier. By comparing the performance of each DMU against this frontier, DEA derives efficiency scores that reflect the extent to which a given DMU is utilizing its inputs to generate outputs relative to its peers. This approach renders DEA a versatile tool applicable to a wide array of scenarios involving performance benchmarking and efficiency analysis across diverse sectors.