Malmquist Firm Productivity Index
Also known as: Malmquist TFP Index for Firms, Firm Productivity Change Decomposition, Distance-Function Productivity Index, Malmquist Total Factor Productivity Index
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.
Key highlights
- Decomposes total factor productivity change into managerial catch-up (efficiency change) and frontier shift (technical change), a distinction with direct strategic meaning.
- Requires only input and output quantities — no prices, cost shares, or assumed functional form.
- Built on the rigorous distance-function index theory of Caves, Christensen and Diewert, with a base-period-neutral geometric-mean construction.
- Computed straightforwardly from DEA linear programs and chained over time to trace each firm's productivity trajectory.
Intuition
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How it works
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When to use it
Use the Malmquist firm productivity index when you have panel data on the same firms' inputs and outputs over time and want to measure and, crucially, decompose total factor productivity change without relying on prices or a specified production function. It is ideal for tracking whether firms are catching up to industry leaders versus benefiting from a shifting frontier, for comparing productivity dynamics across firms or sub-industries, and for studying the productivity effects of deregulation, technology adoption, or strategic change. It is less appropriate with only a single period (it inherently needs two), with very noisy data (because the underlying DEA distances are deterministic and outlier-sensitive), or when you require statistical inference, in which case bootstrapping the index or using a stochastic-frontier-based Malmquist index is preferable.
Strengths & limitations
- Decomposes total factor productivity change into managerial catch-up (efficiency change) and frontier shift (technical change), a distinction with direct strategic meaning.
- Requires only input and output quantities — no prices, cost shares, or assumed functional form.
- Built on the rigorous distance-function index theory of Caves, Christensen and Diewert, with a base-period-neutral geometric-mean construction.
- Computed straightforwardly from DEA linear programs and chained over time to trace each firm's productivity trajectory.
- Because it rests on deterministic DEA distance functions, it has no error term and is sensitive to outliers and measurement noise.
- It needs balanced panel data over time; gaps or entry and exit of firms complicate the cross-period distance computations.
- Mixed-period linear programs can occasionally be infeasible under variable returns to scale, requiring care or alternative formulations.
- Standard versions yield point estimates without confidence intervals unless the index is bootstrapped.
Common pitfalls
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Applications
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Frequently asked
What is the difference between efficiency change and technical change in the Malmquist index?
Efficiency change (the catch-up term) measures whether a firm moved closer to or farther from the best-practice frontier of its own period — essentially whether management improved relative to peers. Technical change (the frontier-shift term) measures whether the frontier itself moved outward, reflecting new technology or knowledge that expands what is achievable from given inputs. Fare, Grosskopf, Norris and Zhang showed their product equals total factor productivity change, so a firm can grow productivity by catching up, by riding an advancing frontier, or both, and the decomposition tells you which.
Why use the geometric mean of two indexes?
A Malmquist index can be defined relative to the earlier period's technology or the later period's technology, and the two need not agree. Rather than arbitrarily pick one base period, Fare and colleagues take the geometric mean of the two, following the index-number tradition of Caves, Christensen and Diewert. This makes the measure symmetric and base-period-neutral, so the productivity comparison does not depend on which period you happen to treat as the reference.
How is the Malmquist index actually computed from data?
In the Fare, Grosskopf, Norris and Zhang approach, the distance functions are estimated nonparametrically with DEA, so computing the index for one firm over one period pair requires solving four linear programs: two ordinary within-period DEA distances and two mixed cross-period distances that evaluate one period's data against another period's frontier. No prices or functional form are needed — only input and output quantities — and the LPs are solved for every firm and each adjacent pair of periods.
Sources
- 1.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.
- 2.Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50(6), 1393-1414.
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ScholarGate. (2026, June 23). Malmquist Firm Productivity Index. ScholarGate. https://scholargate.app/strategic-management/malmquist-firm-productivity