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Efficiency spillovers in Bayesian stochastic frontier models: application to electricity distribution in New Zealand

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  • António Carvalho

Abstract

This paper proposes a spatial Bayesian random effects stochastic frontier model that allows for unobserved heterogeneity and spillovers between firms’ efficiencies with an exogenous spatial weights matrix. Proposals for efficiency measurement in the spatial context add to the debate in the literature. The approach shows good small-sample performance, which is very relevant for applied researchers, and explores guided walk metropolis as a simple and computationally efficient alternative to classic rejection techniques. The approach is applied to a sample of 28 New Zealand electricity distribution firms between 1996 and 2010, finding spatial dependence with a second-order contiguity matrix.

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  • António Carvalho, 2018. "Efficiency spillovers in Bayesian stochastic frontier models: application to electricity distribution in New Zealand," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(2), pages 171-190, April.
    • Handle: RePEc:taf:specan:v:13:y:2018:i:2:p:171-190
    DOI: 10.1080/17421772.2018.1444280
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    Cited by:

    1. Skevas, Ioannis, 2020. "Inference in the spatial autoregressive efficiency model with an application to Dutch dairy farms," European Journal of Operational Research, Elsevier, vol. 283(1), pages 356-364.
    2. Fei Jin & Lung-fei Lee, 2020. "Asymptotic properties of a spatial autoregressive stochastic frontier model," Journal of Spatial Econometrics, Springer, vol. 1(1), pages 1-40, December.
    3. Vidoli, Francesco & Pignataro, Giacomo & Benedetti, Roberto, 2022. "Identification of spatial regimes of the production function of Italian hospitals through spatially constrained cluster-wise regression," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    4. Federico Belotti & Giuseppe Ilardi & Andrea Piano Mortari, 2019. "Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency," CEIS Research Paper 459, Tor Vergata University, CEIS, revised 30 May 2019.
    5. Kassoum Ayouba, 2023. "Spatial dependence in production frontier models," Journal of Productivity Analysis, Springer, vol. 60(1), pages 21-36, August.
    6. Adjin, K. Christophe & Henning, Christian H. C. A., 2020. "Climate variability and farm inefficiency: A spatial stochastic frontier analysis of Senegalese agriculture," Working Papers of Agricultural Policy WP2020-09, University of Kiel, Department of Agricultural Economics, Chair of Agricultural Policy.

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