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	Malmquist Firm Productivity Index	Data Envelopment Analysis of Firm Strategic Efficiency
Field	Strategic Management	Strategic Management
Family	MCDM	MCDM
Year of origin	1994	1978
Originator	Rolf Fare, Shawna Grosskopf, Mary Norris & Zhongyang Zhang; Douglas Caves, Laurits Christensen & Erwin Diewert	Abraham Charnes, William W. Cooper & Edwardo Rhodes; Rajiv Banker, Charnes & Cooper
Type	Distance-function index of total factor productivity change for firms	Nonparametric linear-programming efficiency frontier for firm benchmarking
Seminal source	Fare, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66-83.	Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
Aliases	Malmquist TFP Index for Firms, Firm Productivity Change Decomposition, Distance-Function Productivity Index, Malmquist Total Factor Productivity Index	DEA Firm Efficiency Benchmarking, Strategic Efficiency Frontier Analysis, Firm-Level Data Envelopment Analysis, DEA Best-Practice Benchmarking
Related	3	3
Summary	The Malmquist firm productivity index measures how a firm's total factor productivity changes between two periods and decomposes that change into two strategically meaningful parts: catching up to best practice (efficiency change) and the best-practice frontier itself shifting (technical change). The index is grounded in Caves, Christensen and Diewert's 1982 theory of productivity index numbers built from distance functions, and was made operational for empirical work by Fare, Grosskopf, Norris and Zhang in 1994, who showed how to compute it from data using linear-programming distance functions and to split it into efficiency-change and frontier-shift components. For firms, it answers whether productivity gains came from better management closing the gap to the leaders or from the whole industry's technological possibilities expanding.	Data Envelopment Analysis (DEA) of firm strategic efficiency benchmarks each firm or strategic business unit against a best-practice frontier built directly from the data, with no need to assume prices, weights, or a functional form. Introduced by Charnes, Cooper and Rhodes in 1978 under constant returns to scale (the CCR model) and extended by Banker, Charnes and Cooper in 1984 to variable returns (the BCC model), DEA uses linear programming to envelop the observed firms with a piecewise-linear frontier and scores each one by its radial distance from it. In strategic management it answers a sharply practical question: given the resources a firm consumes, how much more output could it produce if it operated like the best comparable firms, and which efficient peers should it emulate.