IDEAS home Printed from https://ideas.repec.org/a/ags/aareaj/280240.html
   My bibliography  Save this article

Efficiency analysis under uncertainty: a simulation study

Author

Listed:
  • Shankar, Sriram

Abstract

We model production technology in a state-contingent framework assuming that the firms maximize ex ante their preference function subject to stochastic technology constraint; in other words, firms are assumed to act rationally. We show that rational producers who face the same stochastic technology can make significantly different production choices. Further, we develop an econometric methodology to estimate the risk-neutral probabilities, efficiency scores and the parameters of stochastic technology when there are two states of nature and only one of which is observed. Finally, we simulate noiseless data based on our state-contingent specification of technology. Our state-contingent estimator recovers technology parameters and other economic quantities of interest without any error. But, when we apply conventional efficiency estimators to the simulated data, we obtain biased estimates of technical efficiency.

Suggested Citation

  • Shankar, Sriram, 2015. "Efficiency analysis under uncertainty: a simulation study," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 59(2), April.
    • Handle: RePEc:ags:aareaj:280240
    DOI: 10.22004/ag.econ.280240
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/280240/files/ajar12055.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.280240?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. John Quiggin & Robert G. Chambers, 2006. "The state-contingent approach to production under uncertainty ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 50(2), pages 153-169, June.
    2. Sriram Shankar & John Quiggin, 2013. "Production under uncertainty: a simulation study," Journal of Productivity Analysis, Springer, vol. 39(3), pages 207-215, June.
    3. Robert G. Chambers & John Quiggin, 1998. "Cost Functions and Duality for Stochastic Technologies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(2), pages 288-295.
    4. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    5. Terrell, Dek, 1996. "Incorporating Monotonicity and Concavity Conditions in Flexible Functional Forms," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 179-194, March-Apr.
    6. Shankar, Sriram, 2013. "Firm behaviour under uncertainty: a simple parametric model," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(1), pages 1-11.
    7. Chambers,Robert G. & Quiggin,John, 2000. "Uncertainty, Production, Choice, and Agency," Cambridge Books, Cambridge University Press, number 9780521622448, October.
    8. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    9. H. Alan Love & Steven T. Buccola, 1999. "Joint Risk Preference-Technology Estimation with a Primal System: Reply," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(1), pages 245-247.
    10. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
    11. Robert G. Chambers & John Quiggin, 2002. "The State-Contingent Properties of Stochastic Production Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(2), pages 513-526.
    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. Alghalith, Moawia, 2016. "A note on the theory of the firm under multiple uncertainties," European Journal of Operational Research, Elsevier, vol. 251(1), pages 341-343.
    2. Zheng, Hongyun & Ma, Wanglin & Wang, Fang & Li, Gucheng, 2021. "Does internet use improve technical efficiency of banana production in China? Evidence from a selectivity-corrected analysis," Food Policy, Elsevier, vol. 102(C).

    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. Shankar, Sriram, 2012. "Production economics in the presence of risk," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 56(4), pages 1-24, December.
    2. Serra, Teresa & Oude Lansink, Alfons, 2014. "Measuring the impacts of production risk on technical efficiency: A state-contingent conditional order-m approach," European Journal of Operational Research, Elsevier, vol. 239(1), pages 237-242.
    3. Coelli, Tim J., 1995. "Recent Developments In Frontier Modelling And Efficiency Measurement," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 39(3), pages 1-27, December.
    4. Cherchye, L. & Post, G.T., 2001. "Methodological Advances in Dea," ERIM Report Series Research in Management ERS-2001-53-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Bravo-Ureta, Boris E. & Pinheiro, Antonio E., 1993. "Efficiency Analysis Of Developing Country Agriculture: A Review Of The Frontier Function Literature," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 22(1), pages 1-14, April.
    6. 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.
    7. 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.
    8. André Leclerc & Mario Fortin, 2003. "Production et rationalisation des intermédiaires financiers: Leçons à tirer de l’expérience des Caisses Populaires Acadiennes," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 74(3), pages 397-432, September.
    9. Macedo, Pedro & Scotto, Manuel, 2014. "Cross-entropy estimation in technical efficiency analysis," Journal of Mathematical Economics, Elsevier, vol. 54(C), pages 124-130.
    10. Madau, Fabio A., 2012. "Technical and scale efficiency in the Italian Citrus Farming: A comparison between Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis(DEA) Models," MPRA Paper 41403, University Library of Munich, Germany.
    11. Tai-Hsin Huang & Nan-Hung Liu & Subal C. Kumbhakar, 2018. "Joint estimation of the Lerner index and cost efficiency using copula methods," Empirical Economics, Springer, vol. 54(2), pages 799-822, March.
    12. Vaninsky, Alexander, 2010. "Prospective national and regional environmental performance: Boundary estimations using a combined data envelopment – stochastic frontier analysis approach," Energy, Elsevier, vol. 35(9), pages 3657-3665.
    13. Ablam Estel APETI & Bao-We-Wal BAMBE & Jean Louis COMBES, 2022. "On the Macroeconomic Effects of Fiscal Reforms : Fiscal Rules and Public Expenditure Efficiency," LEO Working Papers / DR LEO 2985, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    14. Mike Tsionas & Marwan Izzeldin & Arne Henningsen & Evaggelos Paravalos, 2022. "Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach," Empirical Economics, Springer, vol. 62(3), pages 1345-1363, March.
    15. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    16. Cooper, W. W. & Kumbhakar, Subal & Thrall, Robert M. & Yu, Xuelin, 1995. "DEA and stochastic frontier analyses of the 1978 Chinese economic reforms," Socio-Economic Planning Sciences, Elsevier, vol. 29(2), pages 85-112, June.
    17. Luis R. Murillo-Zamorano & Juan Vega-Cervera, "undated". "The Use of Parametric and Non Parametric Frontier Methods to Measure the Productive Efficiency in the Industrial Sector. A Comparative Study," Discussion Papers 00/17, Department of Economics, University of York.
    18. Hailu, Getu & Goddard, Ellen W. & Jeffrey, Scott R., 2005. "Measuring Efficiency in Fruit and Vegetable Marketing Co-operatives with Heterogeneous Technologies in Canada," 2005 Annual meeting, July 24-27, Providence, RI 19507, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    19. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    20. Serra, Teresa & Zilberman, David & Gil, Jose Maria, 2008. "Farms' technical inefficiencies in the presence of government programs," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(1), pages 1-20.

    More about this item

    Keywords

    Demand and Price Analysis;

    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:aareaj:280240. 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/aaresea.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.