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Spatial regulations and endogenous consideration sets in fisheries

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Author Info

  • Hicks, Robert L.
  • Schnier, Kurt E.

Abstract

The implementation of spatial regulations has become a mainstay in fisheries management. These regulations have generated a sizable economics literature focused on the spatial behavior of fishermen. Fundamental to these studies is the consideration set (spatial alternatives) assumed by the researcher to be possessed by the decision agent. Often times this consideration set is assumed to be the entire spatial extent of the fishery. This research proposes the use of finite mixture modeling to endogenously estimate the formation of consideration sets and the method is applied to a unique spatial decision environment, the Atka mackerel fishery in the Aleutian Islands. Consideration sets are modeled using different macro-definitions of spatial regions to focus the micro-level spatial decision making within the fishery and to investigate the sensitivity of the results to alternative macro-level spatial definitions. Results illustrate the biases associated with traditional consideration set assumptions by estimating fishermen's valuations for different high-value sites within the Aleutian Islands under alternative consideration set assumptions. Furthermore, our results demonstrate how a model that assumes some structure on potential consideration sets reduces the dimensionality problems associated with other endogenous approaches to choice set definition.

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Bibliographic Info

Article provided by Elsevier in its journal Resource and Energy Economics.

Volume (Year): 32 (2010)
Issue (Month): 2 (April)
Pages: 117-134

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Handle: RePEc:eee:resene:v:32:y:2010:i:2:p:117-134

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Web page: http://www.elsevier.com/locate/inca/505569

Related research

Keywords: Location choice Consideration sets Spatial fisheries management;

References

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  1. George R. Parsons & GAndrew J. Plantinga & GKevin J. Boyle, 2000. "Narrow Choice Sets in a Random Utility Model of Recreation Demand," Land Economics, University of Wisconsin Press, vol. 76(1), pages 86-99.
  2. Hicks, Robert L. & Schnier, Kurt E., 2006. "AJAE Appendix: Dynamic Random Utility Modeling: A Monte Carlo Analysis," American Journal of Agricultural Economics Appendices, Agricultural and Applied Economics Association, vol. 88(4), November.
  3. Roger Haefen, 2008. "Latent Consideration Sets and Continuous Demand Systems," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 41(3), pages 363-379, November.
  4. Swait, Joffre, 2001. "Choice set generation within the generalized extreme value family of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 643-666, August.
  5. Smith, Martin D. & Wilen, James E., 2003. "Economic impacts of marine reserves: the importance of spatial behavior," Journal of Environmental Economics and Management, Elsevier, vol. 46(2), pages 183-206, September.
  6. Daniel S. Holland & Jon G. Sutinen, 2000. "Location Choice in New England Trawl Fisheries: Old Habits Die Hard," Land Economics, University of Wisconsin Press, vol. 76(1), pages 133-149.
  7. Smith, Martin D. & Zhang, Junjie, 2007. "Sorting Models in Discrete Choice Fisheries Analysis," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 9681, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
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  9. Krinsky, Itzhak & Robb, A Leslie, 1986. "On Approximating the Statistical Properties of Elasticities," The Review of Economics and Statistics, MIT Press, vol. 68(4), pages 715-19, November.
  10. George R. Parsons & A. Brett Hauber, 1998. "Spatial Boundaries and Choice Set Definition in a Random Utility Model of Recreation Demand," Land Economics, University of Wisconsin Press, vol. 74(1), pages 32-48.
  11. Haab, Timothy C. & Hicks, Robert L., 1997. "Accounting for Choice Set Endogeneity in Random Utility Models of Recreation Demand," Journal of Environmental Economics and Management, Elsevier, vol. 34(2), pages 127-147, October.
  12. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-90, July.
  13. Krinsky, Itzhak & Robb, A Leslie, 1990. "On Approximating the Statistical Properties of Elasticities: A Correction," The Review of Economics and Statistics, MIT Press, vol. 72(1), pages 189-90, February.
  14. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, December.
  15. George R. Parsons & Mary Jo Kealy, 1992. "Randomly Drawn Opportunity Sets in a Random Utility Model of Lake Recreation," Land Economics, University of Wisconsin Press, vol. 68(1), pages 93-106.
  16. repec:ags:mareec:28102 is not listed on IDEAS
  17. repec:ags:mareec:28103 is not listed on IDEAS
  18. Hicks, Robert L. & Schnier, Kurt E., 2008. "Eco-labeling and dolphin avoidance: A dynamic model of tuna fishing in the Eastern Tropical Pacific," Journal of Environmental Economics and Management, Elsevier, vol. 56(2), pages 103-116, September.
  19. Robert L. Hicks & Ivar E. Strand, 2000. "The Extent of Information: Its Relevance for Random Utility Models," Land Economics, University of Wisconsin Press, vol. 76(3), pages 374-385.
  20. Rita Curtis & Robert L. Hicks, 2000. "The Cost of Sea Turtle Preservation: The Case of Hawaii's Pelagic Longliners," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(5), pages 1191-1197.
  21. repec:ags:mareec:28100 is not listed on IDEAS
  22. Timmins, Christopher & Murdock, Jennifer, 2007. "A revealed preference approach to the measurement of congestion in travel cost models," Journal of Environmental Economics and Management, Elsevier, vol. 53(2), pages 230-249, March.
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Cited by:
  1. Zolfaghari, Alireza & Sivakumar, Aruna & Polak, John, 2013. "Simplified probabilistic choice set formation models in a residential location choice context," Journal of choice modelling, Elsevier, vol. 9(C), pages 3-13.

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