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Integrating spatial dependence into Stochastic Frontier Analysis

Author

Listed:
  • Francisco José Areal
  • Kelvin Balcombe
  • Richard Tiffin

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.
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Suggested Citation

  • Francisco José Areal & Kelvin Balcombe & Richard Tiffin, 2012. "Integrating spatial dependence into Stochastic Frontier Analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 56(4), pages 521-541, October.
    • Handle: RePEc:bla:ajarec:v:56:y:2012:i:4:p:521-541
    DOI: j.1467-8489.2012.00597.x
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    File URL: http://hdl.handle.net/10.1111/j.1467-8489.2012.00597.x
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    References listed on IDEAS

    as
    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.
    2. Fernandez, Carmen & Koop, Gary & Steel, Mark, 2000. "A Bayesian analysis of multiple-output production frontiers," Journal of Econometrics, Elsevier, vol. 98(1), pages 47-79, September.
    3. Alexandra Schmidt & Ajax Moreira & Steven Helfand & Thais Fonseca, 2009. "Spatial stochastic frontier models: accounting for unobserved local determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 31(2), pages 101-112, April.
    4. Brummer, B. & Glauben, T. & Lu, W., 2006. "Policy reform and productivity change in Chinese agriculture: A distance function approach," Journal of Development Economics, Elsevier, vol. 81(1), pages 61-79, October.
    5. Won Kim, Chong & Phipps, Tim T. & Anselin, Luc, 2003. "Measuring the benefits of air quality improvement: a spatial hedonic approach," Journal of Environmental Economics and Management, Elsevier, vol. 45(1), pages 24-39, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Orea, Luis & Álvarez, Inmaculada C., 2017. "A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms," Efficiency Series Papers 2017/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    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. 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.
    6. repec:bla:agecon:v:49:y:2018:i:2:p:213-223 is not listed on IDEAS
    7. repec:bla:agecon:v:49:y:2018:i:3:p:301-312 is not listed on IDEAS
    8. repec:ags:phajad:265768 is not listed on IDEAS
    9. 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.
    10. repec:sag:seajad:v:14:y:2017:i:2:p:103-125 is not listed on IDEAS
    11. repec:kap:jproda:v:49:y:2018:i:2:d:10.1007_s11123-018-0529-7 is not listed on IDEAS

    More about this item

    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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