The goal of this monograph is to very concisely outline the economic theory foundations and trends of the field of Effciency and Productivity Analysis, also sometimes referred to as Performance Analysis. I start with the profit maximization paradigm of mainstream economics, use it to derive a general profit effciency measure and then present its special cases: revenue maximization and revenue effciency, cost minimization and cost effciency. I then consider various types of technical and allocative effciencies (directional and Shephard’s distance functions and related Debreu–Farrell measures as well as non-directional measures of technical effciency), showing how they fit into or decompose the profit maximization paradigm. I then cast the effciency and productivity concepts in a dynamic perspective that is frequently used to analyze the productivity changes of economic systems (firms, hospitals, banks, countries, etc.) over time. I conclude this monograph with an overview of major results on aggregation in productivity and effciency analysis, where the aggregate productivity and effciency measures are theoretically connected to their individual analogues.
Performance Analysis: Economic Foundations and Trends provides a relatively concise overview of Efficiency and Productivity Analysis—a very important field of research and practice, spanning over and engaging with many disciplines, most prominently Economics (theoretical and applied), Statistics (and therefore Econometrics), Operations Research (OR) and Management Science (MS), as well as Business Analytics and Business Information Systems, Computer Science and Engineering, etc. Methods developed in this field became very popular in practice for analyzing the efficiency of various economic systems: firms or its distinct departments, branches or plants, entire industries or sub-industries, countries, regions or provinces, as well as various groups or unions of countries, such as APEC, EU, OECD, etc.
Performance Analysis: Economic Foundations and Trends aims to complement and update the existing literature, aiming to be fairly broad, yet with some rigor, and yet also be relatively concise, with numerous references where more details can be found. The author provides a foundation that an interested reader may find useful before learning more, whether it be in terms of theory or in terms of empirical work involving any estimator, and whether it is the DEA or its alternatives, such as Stochastic Frontier Analysis (SFA) or a symbiosis of them. This makes this part useful by itself, even if the reader decides not to pursue with DEA, SFA or any other particular approach.