Data Envelopment Analysis and Economics

Data Envelopment Analysis (DEA) is a nonparametric method of measuring the efficiency of a decision-making unit (DMU) such as a firm or a public-sector agency, first introduced into the Operations Research (OR) literature by Charnes, Cooper, and Rhodes (CCR) (European Journal of Operational Research [EJOR], 1978). The original CCR model was applicable only to technologies characterized by constant returns to scale globally. In what turned out to be a major breakthrough, Banker, Charnes, and Cooper (BCC) (Management Science, 1984) extended the CCR model to accommodate technologies that exhibit variable returns to scale. In subsequent years, methodological contributions from a large number of researchers accumulated into a significant volume of literature around the CCR–BCC models, and the generic approach of DEA emerged as a valid alternative to regression analysis for efficiency measurement. The rapid pace of dissemination of DEA as an acceptable method of efficiency analysis can be inferred from the fact that Seiford (1994) in his DEA bibliography lists no fewer than 472 published articles and accepted P h. D. dissertations even as early as 1992. In a more recent bibliography, Tavares (2002) includes 3,183 items from 2,152 different authors. Indeed, at the present moment, an Internet search for DEA produces no fewer than 12,700 entries! Parallel development of computer software for solving the DEA linear programming (LP) problems made it considerably easier to use DEA in practical applications.