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Fisheries management for different angler types


  • John Curtis
  • Benjamin Breen


On-site survey data from coarse and game angling sites in Ireland is used to estimate count data models of recreational angling demand. To investigate the existence of preference heterogeneity across angler-types, three demand functions are estimated according to angler type; coarse, game and a combination of both. Comparison of these demand functions indicates that the fishery characteristics which drive demand differ depending on angler-specific characteristics. For example treating all anglers as an homogeneous group led to results suggesting angling demand is higher where there is a greater provision of angling services (such as guide-hire and tackle shops). While this relationship pertained for the game angling demand function, angling service levels had no effect on coarse angling demand. Water quality, which was not found to be significant in driving demand in the combined case, was identified as a significant determinant of angling demand in game fisheries. Overall the results strongly support the need to specifically address angler characteristics when analysing angler preferences. Improved survey design that attains more detailed information such as anglers' quarry-type, skill level, etc. will improve the ability of analysts to understand angler preferences and provide more effective policy recommendations.

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  • John Curtis & Benjamin Breen, 2016. "Fisheries management for different angler types," Papers WP529, Economic and Social Research Institute (ESRI).
  • Handle: RePEc:esr:wpaper:wp529

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    References listed on IDEAS

    1. Curtis, John & Stanley, Brian, 2015. "Water Quality and Recreational Angling Demand in Ireland," Papers WP521, Economic and Social Research Institute (ESRI).
    2. Englin, Jeffrey & Shonkwiler, J S, 1995. "Estimating Social Welfare Using Count Data Models: An Application to Long-Run Recreation Demand under Conditions of Endogenous Stratification and Truncation," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 104-112, February.
    3. Shaw, Daigee, 1988. "On-site samples' regression : Problems of non-negative integers, truncation, and endogenous stratification," Journal of Econometrics, Elsevier, vol. 37(2), pages 211-223, February.
    4. Daniel Hellerstein & Robert Mendelsohn, 1993. "A Theoretical Foundation for Count Data Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 75(3), pages 604-611.
    5. John A. Curtis, 2002. "Estimating the Demand for Salmon Angling in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 33(3), pages 319-332.
    6. Grogger, J T & Carson, Richard T, 1991. "Models for Truncated Counts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(3), pages 225-238, July-Sept.
    7. Timothy C. Haab & Kenneth E. McConnell, 2002. "Valuing Environmental and Natural Resources," Books, Edward Elgar Publishing, number 2427.
    8. Mario du Preez & Stephen Hosking, 2011. "The value of the trout fishery at Rhodes, North Eastern Cape, South Africa: a travel cost analysis using count data models," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 54(2), pages 267-282.
    9. Sutinen, Jon G. & Johnston, Robert J., 2003. "Angling management organizations: integrating the recreational sector into fishery management," Marine Policy, Elsevier, vol. 27(6), pages 471-487, November.
    10. Shrestha, Ram K. & Seidl, Andrew F. & Moraes, Andre S., 2002. "Value of recreational fishing in the Brazilian Pantanal: a travel cost analysis using count data models," Ecological Economics, Elsevier, vol. 42(1-2), pages 289-299, August.
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