IDEAS home Printed from https://ideas.repec.org/p/ags/eaa114/61353.html
   My bibliography  Save this paper

A dynamic dual model under state-contingent production uncertainty

Author

Listed:
  • Serra, Teresa
  • Stefanou, Spiro E.
  • Oude Lansink, Alfons G.J.M.

Abstract

In this paper we assess how production costs and capital accumulation patterns in agriculture have evolved over time, by paying special attention to the influence of risk. A dynamic state-contingent cost minimization approach is applied to assess production decisions in US agriculture over the last century. Results suggest the relevance of allowing for the stochastic nature of the production function which permits to capture both the differences in the costs of producing under different states of nature, the differences in the evolution of these costs over time, as well as the differential impacts of different states of nature on investment decisions.

Suggested Citation

  • Serra, Teresa & Stefanou, Spiro E. & Oude Lansink, Alfons G.J.M., 2010. "A dynamic dual model under state-contingent production uncertainty," 114th Seminar, April 15-16, 2010, Berlin, Germany 61353, European Association of Agricultural Economists.
  • Handle: RePEc:ags:eaa114:61353
    DOI: 10.22004/ag.econ.61353
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/61353/files/serra_1.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.61353?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. C. J. O'Donnell & W. E. Griffiths, 2006. "Estimating State-Contingent Production Frontiers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(1), pages 249-266.
    2. Yir-Hueih Luh & Spiro E. Stefanou, 1996. "Estimating Dynamic Dual Models under Nonstatic Expectations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(4), pages 991-1003.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jesse B. Tack & Rulon D. Pope & Jeffrey T. LaFrance & Ricardo H. Cavazos, 2015. "Modelling an aggregate agricultural panel with application to US farm input demands," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 42(3), pages 371-396.
    2. Céline Nauges & Christopher J. O'Donnell & John Quiggin, 2011. "Uncertainty and technical efficiency in Finnish agriculture: a state-contingent approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 38(4), pages 449-467, October.
    3. Zein Kallas & Teresa Serra & Jos頠 M. Gil, 2012. "Effects of policy instruments on farm investments and production decisions in the Spanish COP sector," Applied Economics, Taylor & Francis Journals, vol. 44(30), pages 3877-3886, October.
    4. Moro, Daniele & Sckokai, Paolo, 2013. "The impact of decoupled payments on farm choices: Conceptual and methodological challenges," Food Policy, Elsevier, vol. 41(C), pages 28-38.
    5. Sansi Yang & C Richard Shumway, 2018. "Asset fixity under state-contingent production uncertainty," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(5), pages 831-856.
    6. 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.
    7. Carpentier, Alain & Gohin, Alexandre & Sckokai, Paolo & Thomas, Alban, 2015. "Economic modelling of agricultural production: past advances and new challenges," Revue d'Etudes en Agriculture et Environnement, Editions NecPlus, vol. 96(01), pages 131-165, March.
    8. Sansi Yang & C. Richard Shumway, 2014. "Dynamic Adjustment in U.S. Agriculture under Climate Uncertainty," 2014 Papers pya413, Job Market Papers.
    9. D. Verreth & G. Emvalomatis & F. Bunte & A. Oude Lansink, 2015. "Dynamic and Static Behaviour with Respect to Energy Use and Investment of Dutch Greenhouse Firms," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 61(4), pages 595-614, August.
    10. Bouali Guesmi & Teresa Serra & Amr Radwan & José María Gil, 2018. "Efficiency of Egyptian organic agriculture: A local maximum likelihood approach," Agribusiness, John Wiley & Sons, Ltd., vol. 34(2), pages 441-455, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sansi Yang & C Richard Shumway, 2018. "Asset fixity under state-contingent production uncertainty," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(5), pages 831-856.
    2. Jean-Paul Chavas, 2012. "On learning and the economics of firm efficiency: a state-contingent approach," Journal of Productivity Analysis, Springer, vol. 38(1), pages 53-62, August.
    3. Barnes, Andrew Peter & Revoredo-Giha, Cesar & Sauer, Johannes, 2011. "A metafrontier approach to measuring technical efficiencies across the UK dairy sector," 122nd Seminar, February 17-18, 2011, Ancona, Italy 99369, European Association of Agricultural Economists.
    4. CARPENTIER, Alain & GOHIN, Alexandre & SCKOKAI, Paolo & THOMAS, Alban, 2015. "Economic modelling of agricultural production: past advances and new challenges," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement (RAEStud), Institut National de la Recherche Agronomique (INRA), vol. 96(01), March.
    5. Sriram Shankar & John Quiggin, 2013. "Production under uncertainty: a simulation study," Journal of Productivity Analysis, Springer, vol. 39(3), pages 207-215, June.
    6. Robert, Marion & Thomas, Alban & Bergez, Jacques Eric, 2016. "Processes of adpatation in farm decision-making models. A review," TSE Working Papers 16-731, Toulouse School of Economics (TSE).
    7. O'Donnell, Christopher J. & Shankar, Sriram, 2009. "Estimating State-Allocable Production Technologies When There are Two States of Nature and State Allocations of Inputs are Unobserved," 2009 Conference (53rd), February 11-13, 2009, Cairns, Australia 50898, Australian Agricultural and Resource Economics Society.
    8. Billé, AG & Salvioni, C. & Benedetti, R., 2015. "Spatial Heterogeneity In Production Functions Models," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212662, European Association of Agricultural Economists.
    9. Just, Richard E. & Just, David R., 2011. "Global identification of risk preferences with revealed preference data," Journal of Econometrics, Elsevier, vol. 162(1), pages 6-17, May.
    10. Feng, Chao & Zhang, Hua & Huang, Jian-Bai, 2017. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 859-872.
    11. Arnade, Carlos Anthony & Gopinath, Munisamy, 1998. "Capital Adjustment In U.S. Agriculture And Food Processing: A Cross-Sectoral Model," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 23(01), pages 1-14, July.
    12. Lin, Ni & Davis, George C. & Shumway, C. Richard, 1998. "Aggregation Without Separability: Tests Of U.S. And Mexican Agricultural Production Data," 1998 Annual meeting, August 2-5, Salt Lake City, UT 20927, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    13. Adamson, David & Mallawaarachchi, Thilak & Quiggin, John, 2004. "Modelling basin level allocation of water in the Murray Darling Basin in a world of uncertainty," Risk and Sustainable Management Group Working Papers 149844, University of Queensland, School of Economics.
    14. Ragnar Tveteras & Ola Flaten & Gudbrand Lien, 2011. "Production risk in multi-output industries: estimates from Norwegian dairy farms," Applied Economics, Taylor & Francis Journals, vol. 43(28), pages 4403-4414.
    15. Adamson, David & Mallawaarachchi, Thilak & Quiggin, John C., 2007. "Water use and salinity in the Murray–Darling Basin: A state-contingent model," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(3), pages 1-19.
    16. Yin, Pengzhen & Sun, Jiasen & Chu, Junfei & Liang, Liang, 2016. "Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspectiveAuthor-Name: Wu, Jie," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1047-1062.
    17. Adam Loch & David Adamson, 2015. "Drought and the rebound effect: a Murray–Darling Basin example," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 1429-1449, December.
    18. Preciado Arreola, José Luis & Johnson, Andrew L. & Chen, Xun C. & Morita, Hiroshi, 2020. "Estimating stochastic production frontiers: A one-stage multivariate semiparametric Bayesian concave regression method," European Journal of Operational Research, Elsevier, vol. 287(2), pages 699-711.
    19. Huettel, Silke & Narayana, Rashmi & Odening, Martin, 2011. "Measuring dynamic efficiency under uncertainty," Structural Change in Agriculture/Strukturwandel im Agrarsektor (SiAg) Working Papers 129062, Humboldt University Berlin, Department of Agricultural Economics.
    20. Silva, Elvira & Lansink, Alfons Oude & Stefanou, Spiro E., 2015. "The adjustment-cost model of the firm: Duality and productive efficiency," International Journal of Production Economics, Elsevier, vol. 168(C), pages 245-256.

    More about this item

    Keywords

    Agricultural and Food Policy; Farm Management; Land Economics/Use;
    All these keywords.

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:eaa114:61353. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/eaaeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.