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Catch More to Catch Less: Estimating Timing Choice as Dynamic Bycatch Avoidance Behavior

Author

Listed:
  • Keita Abe

    (Centre for Applied Research at NHH)

  • Christopher M. Anderson

    (University of Washington)

  • Matthew N. Reimer

    (University of California)

Abstract

We model harvesters’ temporal participation behavior in a fishery with individual quotas for both a target and bycatch species. Harvesters make participation decisions given time-varying characteristics of the fishery (e.g., catch rates, price, and bycatch rates) and outside opportunities (e.g., other fisheries). A harvester’s problem is seasonally dynamic under the individual quota scheme because quota acts as an intertemporal budget constraint. We construct a theoretical model to describe how the shadow value of individual quota plays a role in a harvester’s decision and propose an empirical model that captures the dynamic effect of the seasonal quota usage. Our study finds support for the existence of dynamic bycatch avoidance: harvesters use the security provided by quota allocations to reduce harvesting around periods of high bycatch. Our policy simulation demonstrates that opening the season earlier could reduce bycatch while the main target catch is maintained due to temporal shift of quota usage.

Suggested Citation

  • Keita Abe & Christopher M. Anderson & Matthew N. Reimer, 2022. "Catch More to Catch Less: Estimating Timing Choice as Dynamic Bycatch Avoidance Behavior," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 82(4), pages 953-984, August.
    • Handle: RePEc:kap:enreec:v:82:y:2022:i:4:d:10.1007_s10640-022-00699-6
    DOI: 10.1007/s10640-022-00699-6
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    References listed on IDEAS

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    More about this item

    Keywords

    Bycatch; Dynamic avoidance; Policy simulation; Prohibited species catch; Shadow value of individual quota;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q22 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Fishery
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy

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