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Investment and the dynamic cost of income uncertainty: The case of diminishing expectations in agriculture

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  • Heikkinen, T.
  • Pietola, K.

Abstract

This paper studies optimal investment and the dynamic cost of income uncertainty, applying a stochastic programming approach. The motivation is given by a case study in Finnish agriculture. The investment decision of a representative farm is modelled as a Markov decision process, extended to account for risk. A numerical framework for studying the dynamic uncertainty cost is presented, modifying the classical expected value of perfect information to a dynamic setting. The uncertainty cost depends on the volatility of income: e.g. with stationary income, the dynamic uncertainty cost corresponds to a dynamic option value of postponing investment. The model can be applied to agricultural policy planning. In the case study, the investment decision is sensitive to risk.

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  • Heikkinen, T. & Pietola, K., 2009. "Investment and the dynamic cost of income uncertainty: The case of diminishing expectations in agriculture," European Journal of Operational Research, Elsevier, vol. 192(2), pages 634-646, January.
    • Handle: RePEc:eee:ejores:v:192:y:2009:i:2:p:634-646
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    2. Mittenzwei, Klaus & Bullock, David S. & Salhofer, Klaus & Kola, Jukka, 2011. "Towards a Theory of Policy Making," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114639, European Association of Agricultural Economists.
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    5. Heikkinen, T. & Pietola, K., 2009. "Investment and the dynamic cost of income uncertainty: The case of diminishing expectations in agriculture," European Journal of Operational Research, Elsevier, vol. 192(2), pages 634-646, January.
    6. Singh, Amanjot, 2022. "Oil Price uncertainty and labor investment efficiency," Energy Economics, Elsevier, vol. 116(C).
    7. Sushil Gupta & Hossein Rikhtehgar Berenji & Manish Shukla & Nagesh N. Murthy, 2023. "Opportunities in farming research from an operations management perspective," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1577-1596, June.
    8. Trigeorgis, Lenos & Tsekrekos, Andrianos E., 2018. "Real Options in Operations Research: A Review," European Journal of Operational Research, Elsevier, vol. 270(1), pages 1-24.
    9. Viaggi, Davide & Raggi, Meri & Gomez y Paloma, Sergio, 2010. "An integer programming dynamic farm-household model to evaluate the impact of agricultural policy reforms on farm investment behaviour," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1130-1139, December.
    10. Reidsma, Pytrik & Janssen, Sander & Jansen, Jacques & van Ittersum, Martin K., 2018. "On the development and use of farm models for policy impact assessment in the European Union – A review," Agricultural Systems, Elsevier, vol. 159(C), pages 111-125.
    11. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2016. "Handling uncertainty in agricultural supply chain management: A state of the art," European Journal of Operational Research, Elsevier, vol. 254(2), pages 348-359.

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