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Dissections of input and output efficiency: A generalized stochastic frontier model

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  • Kumbhakar, Subal C.
  • Tsionas, Mike G.

Abstract

This paper considers a model that accommodates both output and input-specific inefficiency components (input slacks). We use a translog function to represent the underlying production technology in which the input slacks are generalized to have both deterministic (functions of exogenous variables) and stochastic components. Consequently, the composed error term becomes a nonlinear function of several error components, viz., a one-sided input slack vector (the dimension of which depends on the number of inputs), a one-sided output technical inefficiency and a two-sided random noise. Identification of two sets of one-sided errors is possible in a translog model because the vector of one-sided input slacks appears in additive form as well as interactively with the (log) inputs. Distributional assumptions on technical inefficiency and slacks also help in identification. Bayesian inference techniques are introduced, organized around Markov Chain Monte Carlo, especially the Gibbs sampler with data augmentation, to estimate these inefficiency components. For an empirical application we use a large unbalanced panel of the U.K. manufacturing firms. Slacks associated with labor and capital are found to be 2.35% and 10.74%, on average. Output (revenue) loss from technical inefficiency is, on average, 2.43%, while revenue loss from input slacks is, on average, 9.2%.

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  • Kumbhakar, Subal C. & Tsionas, Mike G., 2021. "Dissections of input and output efficiency: A generalized stochastic frontier model," International Journal of Production Economics, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:proeco:v:232:y:2021:i:c:s0925527320302930
    DOI: 10.1016/j.ijpe.2020.107940
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    Cited by:

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    4. Liang, Jing & Yang, Shilei & Xia, Yu, 2023. "The role of financial slack on the relationship between demand uncertainty and operational efficiency," International Journal of Production Economics, Elsevier, vol. 262(C).

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    More about this item

    Keywords

    Input and output inefficiency; X-efficiency; Markov chain Monte Carlo; Endogeneity; Nonlinear error components;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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