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The analysis of irreversibility, uncertainty and dynamic technical inefficiency on the investment decision in Spanish olive sector

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  • Lambarraa, Fatima
  • Stefanou, Spiro E.
  • Gil, Jose Maria

Abstract

The purpose of this paper is the evaluation of the investment decision under uncertainty and irreversibility allowing for long run inefficiency. The analysis has been applied to a 158 Spanish olive farms using FADN data set. A real option approach has been used to analyse the decision to invest under uncertainty and irreversibility, and a dynamic stochastic frontier model has been developed to estimate the long run technical efficiency and it persistence. The results show that the technical inefficiency persistence parameter is fairly low to unity, which means that small technical inefficiency is transmitted to the next time period. The olive groves investment is irreversible and characterized by uncertainty on price and discount rate. An increase of discount rate means that the farmers take the decision to postpone investment. An increase on price along with a decrease of discount rate leads to the decision to invest with no option value of waiting to invest. The results also suggest that the decision of investment depend on technical inefficiency ant it persistence, the inefficient farmers take time and wait to invest, while at small persistence parameter the decision is to invest.

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Bibliographic Info

Paper provided by International Association of Agricultural Economists in its series 2009 Conference, August 16-22, 2009, Beijing, China with number 51397.

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Date of creation: 2009
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Handle: RePEc:ags:iaae09:51397

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Related research

Keywords: Olive; dynamic efficiency; real option; Production Economics; Q12;

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  1. Seung Ahn & Robin Sickles, 2000. "Estimation of long-run inefficiency levels: a dynamic frontier approach," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 461-492.
  2. Wilson, Paul & Hadley, David & Asby, Carol, 2001. "The influence of management characteristics on the technical efficiency of wheat farmers in eastern England," Agricultural Economics, Blackwell, vol. 24(3), pages 329-338, March.
  3. Jeffrey I. Bernstein & M. Ishaq Nadiri, 1982. "Financing and Investment in Plant and Equipment and Research and Development," NBER Working Papers 1017, National Bureau of Economic Research, Inc.
  4. Myers, Stewart C., 1977. "Determinants of corporate borrowing," Journal of Financial Economics, Elsevier, vol. 5(2), pages 147-175, November.
  5. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
  6. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
  7. Ho-chuan Huang, 2004. "Estimation of Technical Inefficiencies with Heterogeneous Technologies," Journal of Productivity Analysis, Springer, vol. 21(3), pages 277-296, May.
  8. Price, T. Jeffrey & Wetzstein, Michael E., 1999. "Irreversible Investment Decisions In Perennial Crops With Yield And Price Uncertainty," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 24(01), July.
  9. Hester, Susan M. & Cacho, Oscar, 2003. "Modelling apple orchard systems," Agricultural Systems, Elsevier, vol. 77(2), pages 137-154, August.
  10. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676.
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