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Integrating spatial dependence into stochastic frontier analysis

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  • Areal, Francisco J
  • Balcombe, Kelvin
  • Tiffin, R

Abstract

An approach to incorporate spatial dependence into Stochastic Frontier analysis is developed and applied to a sample of 215 dairy farms in England and Wales. A number of alternative specifications for the spatial weight matrix are used to analyse the effect of these on the estimation of spatial dependence. Estimation is conducted using a Bayesian approach and results indicate that spatial dependence is present when explaining technical inefficiency.

Suggested Citation

  • Areal, Francisco J & Balcombe, Kelvin & Tiffin, R, 2010. "Integrating spatial dependence into stochastic frontier analysis," MPRA Paper 24961, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:24961
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    References listed on IDEAS

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    1. O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June.
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    Citations

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    Cited by:

    1. Pede, Valerien O. & McKinley, Justin & Singbo, Alphonse & Kajisa, Kei, 2015. "Spatial Dependency of Technical Efficiency in Rice Farming: The Case of Bohol, Philippines," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205456, Agricultural and Applied Economics Association.
    2. Mamiit, Rusyan Jill & Yanagida, John & Villanueva, Donald, 2020. "Farm locations and dwelling clusters: Do they make production and technical efficiency spatially contagious?," Food Policy, Elsevier, vol. 92(C).
    3. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    4. Jacopo Canello & Francesco Vidoli, 2020. "Investigating space‐time patterns of regional industrial resilience through a micro‐level approach: An application to the Italian wine industry," Journal of Regional Science, Wiley Blackwell, vol. 60(4), pages 653-676, September.
    5. Orea, Luis & Álvarez, Inmaculada C., 2019. "A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms," Journal of Econometrics, Elsevier, vol. 213(2), pages 556-577.
    6. Fei Jin & Lung-fei Lee, 2020. "Asymptotic properties of a spatial autoregressive stochastic frontier model," Journal in Spatial Econometrics, Springer, vol. 1(1), pages 1-40, December.
    7. Laureti, Tiziana & Benedetti, Ilaria & Branca, Giacomo, 2021. "Water use efficiency and public goods conservation: A spatial stochastic frontier model applied to irrigation in Southern Italy," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    8. Ioannis Skevas & Alfons Oude Lansink, 2020. "Dynamic Inefficiency and Spatial Spillovers in Dutch Dairy Farming," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 742-759, September.
    9. Fusco, Elisa & Allegrini, Veronica, 2020. "The role of spatial interdependence in local government cost efficiency: An application to waste Italian sector," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    10. Cavalieri, M.; Di Caro, P.; Guccio, C.; Lisi, D.;, 2017. "Does neighbour’s grass matter? Exploring spatial dependent heterogeneity in technical efficiency of Italian hospitals," Health, Econometrics and Data Group (HEDG) Working Papers 17/13, HEDG, c/o Department of Economics, University of York.
    11. Theodoros Skevas & Jasper Grashuis, 2020. "Technical efficiency and spatial spillovers: Evidence from grain marketing cooperatives in the US Midwest," Agribusiness, John Wiley & Sons, Ltd., vol. 36(1), pages 111-126, January.
    12. Villanueva, Donald B. & Pede, Valerien O. & Rodriguez, U-Primo E. & Sumalde, Zenaida M. & Garcia, Yolanda T., 2017. "Assessment of Neighborhood and Spillover Effects on Technical Efficiency of Irrigated Rice Farmers," Asian Journal of Agriculture and Development, Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA), vol. 14(2), December.
    13. Frederic Ang & Pieter Jan Kerstens, 2016. "To Mix or Specialise? A Coordination Productivity Indicator for English and Welsh farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(3), pages 779-798, September.
    14. Klein, Nadja & Herwartz, Helmut & Kneib, Thomas, 2020. "Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales," Journal of Econometrics, Elsevier, vol. 214(2), pages 513-539.
    15. Orea, Luis & Álvarez, Inmaculada C., 2019. "Spatial Production Economics," Efficiency Series Papers 2019/06, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    16. Thomas Graaff, 2020. "On the estimation of spatial stochastic frontier models: an alternative skew-normal approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 267-285, April.
    17. 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.
    18. Bergantino, Angela Stefania & Intini, Mario & Volta, Nicola, 2020. "Spatial competition and efficiency: an investigation in the airport sector," The Warwick Economics Research Paper Series (TWERPS) 1287, University of Warwick, Department of Economics.
    19. A. G. Billé & C. Salvioni & R. Benedetti, 2018. "Modelling spatial regimes in farms technologies," Journal of Productivity Analysis, Springer, vol. 49(2), pages 173-185, June.
    20. 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.
    21. MCarmen Martínez†Victoria & Mariluz Maté Sánchez†Val & Narciso Arcas†Lario, 2018. "Spatial determinants of productivity growth on agri†food Spanish firms: a comparison between cooperatives and investor†owned firms," Agricultural Economics, International Association of Agricultural Economists, vol. 49(2), pages 213-223, March.
    22. Valerien O. Pede & Francisco J. Areal & Alphonse Singbo & Justin McKinley & Kei Kajisa, 2018. "Spatial dependency and technical efficiency: an application of a Bayesian stochastic frontier model to irrigated and rainfed rice farmers in Bohol, Philippines," Agricultural Economics, International Association of Agricultural Economists, vol. 49(3), pages 301-312, May.
    23. Cavalieri, Marina & Di Caro, Paolo & Guccio, Calogero & Lisi, Domenico, 2020. "Does neighbours' grass matter? Testing spatial dependent heterogeneity in technical efficiency of Italian hospitals," Social Science & Medicine, Elsevier, vol. 265(C).
    24. Yiorgos Gadanakis & Francisco José Areal, 2020. "Accounting for rainfall and the length of growing season in technical efficiency analysis," Operational Research, Springer, vol. 20(4), pages 2583-2608, December.
    25. Vidoli, Francesco & Cardillo, Concetta & Fusco, Elisa & Canello, Jacopo, 2016. "Spatial nonstationarity in the stochastic frontier model: An application to the Italian wine industry," Regional Science and Urban Economics, Elsevier, vol. 61(C), pages 153-164.

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    More about this item

    Keywords

    Spatial dependence; technical efficiency; Bayesian; spatial weight matrix;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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