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Optimally Managing a Stochastic Renewable Resource under General Economic Conditions

Author

Listed:
  • McGough Bruce

    (Oregon State University)

  • Plantinga Andrew J.

    (Oregon State University)

  • Costello Christopher

    (University of California, Santa Barbara)

Abstract
Empirical evidence indicates that environmental fluctuations have important effects on fisheries production. However, existing analytical solutions of stochastic fisheries models have been produced only under highly simplified economic and biological conditions. The main contribution of this paper is to derive under general conditions a policy function for the management of a stochastic fishery. Our model includes general specifications of demand and cost relationships and a stochastic biological growth function with serially-correlated shocks. Applying methods from the theory of dynamic stochastic general equilibrium modeling and multivariate linear expectational difference equations, we derive a linear approximation of the solution to the model. Our main result is a reduced-form expression for an approximation to optimal escapement, which is shown to be a function of the current stock, past environmental shocks, and model parameters. This theoretically-grounded policy function has intuitive appeal, yields insights into comparative statics, and provides a theoretically-grounded, practical starting point for fisheries management.

Suggested Citation

  • McGough Bruce & Plantinga Andrew J. & Costello Christopher, 2009. "Optimally Managing a Stochastic Renewable Resource under General Economic Conditions," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 9(1), pages 1-31, December.
    • Handle: RePEc:bpj:bejeap:v:9:y:2009:i:1:n:56
    DOI: 10.2202/1935-1682.2313
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    References listed on IDEAS

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    Cited by:

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    2. Kelsall, Claudia & Quaas, Martin F. & Quérou, Nicolas, 2023. "Risk aversion in renewable resource harvesting," Journal of Environmental Economics and Management, Elsevier, vol. 121(C).
    3. Vincent Martinet & Julio Peña-Torres & Michel Lara & Hector Ramírez C., 2016. "Risk and Sustainability: Assessing Fishery Management Strategies," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 64(4), pages 683-707, August.
    4. Leizarowitz, Arie & Tsur, Yacov, 2012. "Renewable resource management with stochastic recharge and environmental threats," Journal of Economic Dynamics and Control, Elsevier, vol. 36(5), pages 736-753.
    5. Tahvonen, Olli & Quaas, Martin F. & Voss, Rüdiger, 2018. "Harvesting selectivity and stochastic recruitment in economic models of age-structured fisheries," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 659-676.
    6. Jules Selles, 2018. "Fisheries management: what uncertainties matter?," Working Papers hal-01824238, HAL.
    7. Fishman, Ram & B Krishnamurthy, Chandra Kiran, 2021. "An ecological golden rule," Resource and Energy Economics, Elsevier, vol. 64(C).
    8. Poudel, Diwakar & Sandal, Leif K. & Steinshamn, Stein I. & Kvamsdal, Sturla F., 2012. "Do Species Interactions and Stochasticity Matter to Optimal Management of Multispecies Fisheries?," Discussion Papers 2012/1, Norwegian School of Economics, Department of Business and Management Science.
    9. Hansen, Lars Gårn & Jensen, Frank, 2017. "Regulating fisheries under uncertainty," Resource and Energy Economics, Elsevier, vol. 50(C), pages 164-177.
    10. Da-Rocha, Jose-Maria & García-Cutrin, Javier & Gutierrez, Maria Jose & Touze, Julia, 2016. "A note on CES Preferences in Age-Structured Models," MPRA Paper 75298, University Library of Munich, Germany.

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