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Derivation of netput shadow prices under different levels of pest pressure

Author

Listed:
  • Theodoros Skevas

    (University of Missouri)

  • Teresa Serra

    (University of Illinois)

Abstract

In this paper, we propose a framework to derive agricultural netput shadow prices that assign values to netputs in terms of their contribution to technical and environmental efficiency. Our modeling approach is based on the dual representation of an event-specific data envelopment model and is applied to a panel dataset of Dutch arable farms. Results show that netput shadow prices vary significantly across pest pressure events, suggesting the relevance to consider the event-specific nature of the production technology when computing them. By revealing the relative importance of pesticides in terms of their contribution to environmental efficiency, this study provides a potential framework for constructing penalties aiming to internalize some portion of the social cost of pesticide use.

Suggested Citation

  • Theodoros Skevas & Teresa Serra, 2017. "Derivation of netput shadow prices under different levels of pest pressure," Journal of Productivity Analysis, Springer, vol. 48(1), pages 25-34, August.
    • Handle: RePEc:kap:jproda:v:48:y:2017:i:1:d:10.1007_s11123-017-0507-5
    DOI: 10.1007/s11123-017-0507-5
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    References listed on IDEAS

    as
    1. Singbo, Alphonse G. & Lansink, Alfons Oude & Emvalomatis, Grigorios, 2015. "Estimating shadow prices and efficiency analysis of productive inputs and pesticide use of vegetable production," European Journal of Operational Research, Elsevier, vol. 245(1), pages 265-272.
    2. Theodoros Skevas & Teresa Serra, 2016. "The role of pest pressure in technical and environmental inefficiency analysis of Dutch arable farms: an event-specific data envelopment approach," Journal of Productivity Analysis, Springer, vol. 46(2), pages 139-153, December.
    3. Robert G. Chambers & Atakelty Hailu & John Quiggin, 2011. "Event‐specific data envelopment models and efficiency analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(1), pages 90-106, January.
    4. Alfons Lansink & Elvira Silva, 2003. "CO 2 and Energy Efficiency of Different Heating Technologies in the Dutch Glasshouse Industry," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 24(4), pages 395-407, April.
    5. Timo Kuosmanen, 2005. "Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1077-1082.
    6. Murty, Sushama & Russell, R. Robert, 2010. "On modeling pollution-generating technologies," Economic Research Papers 271176, University of Warwick - Department of Economics.
    7. Alfons Oude Lansink & Alain Carpentier, 2001. "Damage Control Productivity: An Input Damage Abatement Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 52(3), pages 11-22, September.
    8. Fare, Rolf & Grosskopf, Shawna & Noh, Dong-Woon & Weber, William, 2005. "Characteristics of a polluting technology: theory and practice," Journal of Econometrics, Elsevier, vol. 126(2), pages 469-492, June.
    9. Theodoros Skevas & Spiro E. Stefanou & Alfons Oude Lansink, 2013. "Do Farmers Internalise Environmental Spillovers of Pesticides in Production?," Journal of Agricultural Economics, Wiley Blackwell, vol. 64(3), pages 624-640, September.
    10. Fare, Rolf & Grosskopf, Shawna & Weber, William L., 2006. "Shadow prices and pollution costs in U.S. agriculture," Ecological Economics, Elsevier, vol. 56(1), pages 89-103, January.
    11. Skevas, Theodoros & Stefanou, Spiro E. & Oude Lansink, Alfons, 2014. "Pesticide use, environmental spillovers and efficiency: A DEA risk-adjusted efficiency approach applied to Dutch arable farming," European Journal of Operational Research, Elsevier, vol. 237(2), pages 658-664.
    12. Anni Huhtala & Per-Olov Marklund, 2008. "Stringency of environmental targets in animal agriculture: shedding light on policy with shadow prices," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 35(2), pages 193-217, June.
    13. Alfons Lansink & Elvira Silva, 2004. "Non-Parametric Production Analysis of Pesticides Use in the Netherlands," Journal of Productivity Analysis, Springer, vol. 21(1), pages 49-65, January.
    14. Chambers,Robert G. & Quiggin,John, 2000. "Uncertainty, Production, Choice, and Agency," Cambridge Books, Cambridge University Press, number 9780521622448, September.
    15. Skevas, Theodoros & Lansink, Alfons Oude & Stefanou, Spiro E., 2012. "Measuring technical efficiency in the presence of pesticide spillovers and production uncertainty: The case of Dutch arable farms," European Journal of Operational Research, Elsevier, vol. 223(2), pages 550-559.
    16. Robert Chambers & Rolf Färe, 2008. "A “calculus” for data envelopment analysis," Journal of Productivity Analysis, Springer, vol. 30(3), pages 169-175, December.
    17. Serra, Teresa & Poli, Elena, 2015. "Shadow prices of social capital in rural India, a nonparametric approach," European Journal of Operational Research, Elsevier, vol. 240(3), pages 892-903.
    18. Alfons Oude Lansink & Arno Van Der Vlist, 2008. "Non‐Parametric Modelling of CO2 Emission Quota," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(3), pages 487-497, September.
    19. Lansink, Alfons Oude & Peerlings, Jack, 1996. "Modelling the New EU Cereals and Oilseeds Regime in the Netherlands," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 23(2), pages 161-178.
    20. Shaik, Saleem & Helmers, Glenn A. & Langemeier, Michael R., 2002. "Direct And Indirect Shadow Price And Cost Estimates Of Nitrogen Pollution Abatement," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(2), pages 1-13, December.
    21. R. G. Chambers & Y. Chung & R. Färe, 1998. "Profit, Directional Distance Functions, and Nerlovian Efficiency," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 351-364, August.
    22. Murty, Sushama & Robert Russell, R. & Levkoff, Steven B., 2012. "On modeling pollution-generating technologies," Journal of Environmental Economics and Management, Elsevier, vol. 64(1), pages 117-135.
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    Cited by:

    1. Luo Muchen & Rosita Hamdan & Rossazana Ab-Rahim, 2022. "Data-Driven Evaluation and Optimization of Agricultural Environmental Efficiency with Carbon Emission Constraints," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    2. Kevin Schneider & Ioannis Skevas & Alfons Oude Lansink, 2021. "Spatial Spillovers on Input‐specific Inefficiency of Dutch Arable Farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(1), pages 224-243, February.
    3. Amer Ait Sidhoum, 2023. "Assessing the contribution of farmers’ working conditions to productive efficiency in the presence of uncertainty, a nonparametric approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8601-8622, August.

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    More about this item

    Keywords

    Data envelopment analysis; Shadow prices; Event-specific model; Dual approach; Production risk; Pest pressure;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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