valuing bag limits in the nc charter boat fishery with rp and sp data n.
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Valuing Bag Limits in the NC Charter Boat Fishery with RP and SP Data

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Valuing Bag Limits in the NC Charter Boat Fishery with RP and SP Data

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  1. Valuing Bag Limits in the NC Charter Boat Fishery with RP and SP Data John C. Whitehead Department of Economics Appalachian State University Boone, North Carolina 28608 Phone: (828) 262-6121 whiteheadjc@appstate.edu

  2. Co-authors • Chris Dumas, UNCW • Craig Landry, ECU • Jim Herstine, UNCW

  3. Motivation

  4. Outline • Background • Literature • Theory • Data • Methods • Results

  5. NC For-Hire Fleet • 750 vessels • 97% are charter trips • 431,000 passengers • 303,000 charter boat trips • 70,000 fishing trips • $65 million annually in fishing fees • $55 million annually for charter boats • 1445 captain and crew jobs

  6. Literature • Most marine recreational demand literature focuses on the private boat mode • Most charter sector literature considers economic impacts (e.g. Bohnsack et al., 2002) • Some recent studies consider bioeconomicmodelling (e.g., Abbot and Wilen, 2009)

  7. Estimating Benefits • Poor and Breece (2006) • Chesapeake Bay • Travel cost method • Water quality • WTP per angler per trip = $225 • WTP/angler/trip for quality improvement = $85 • Limitation of using the TCM is that many/most charter trips are overnight trips

  8. Value of catch • Large literature, e.g.: • Schuhman (1998) uses RUM • Whitehead and Haab (1999) use RUM • Gillig et al. (2003) use CVM and TCM for red snapper • The problem with using the value of catch rate changes for bag limits is that not all anglers are affected by bag limit.

  9. Household production model • McConnell, Strand and Blake-Hedges (1995): • HPM: q=f(x, hcr) • RUM: site selection depends on predicted q • Welfare: v(q, tc) - v(min[q,b], tc)}/Muy • Problems: data intensive • Is q related to hcr? WHHS (2010) • Is site selection related to predicted q? • Not likely to work with charter boats

  10. Stated Preference Studies • Carson, Hanemann and Steinberg (1990) use CVM for Alaskan salmon • Oh et al. (2005) use choice experiments for Texas red drum • Stoll and Ditton (2006) use CVM for NC bluefin tuna • Whitehead (2006) uses MSB-H approach with CVM

  11. Combined RP/SP data • Allows consideration of situations beyond the range of historical experience • While grounding stated behavior in revealed behavior

  12. Theory / Application $ SP with higher fees RP and SP baseline trips SP with tighter bag limits Trips

  13. 2007 NC For-Hire Survey • Dockside passenger survey produced 1204 usable surveys • Primary purpose, n = 597 • Secondary purpose, n = 607 • 84% are charter trips

  14. Locations • 20% at Roanoke Island • 29% on Outer Banks • 7% on Central Coast • 31% in New Hanover County • 14% in Brunswick County

  15. Characteristics • Most are male • Average age is 40 • Income = $75,000 • Nights away from home • Primary purpose = 3 • Secondary purpose = 6-7 • Charter trips • Primary purpose = 3 • Secondary purpose = 2

  16. Charter boat target species • Primary purpose anglers • Dolphin – 34% • Tuna – 22% • Wahoo – 17% • Secondary purpose anglers • Dolphin – 34% • Spanish mackerel – 20% • Billfish - 13% • Tuna - 13%

  17. Head boat target species • Primary purpose anglers • Snapper – 7% • Grouper – 6% • Wahoo – 17% • Secondary purpose anglers • Bluefish – 13% • Grouper – 8% • Dolphin – 7% • Snapper 5%

  18. RUM Results: Primary purpose

  19. RUM Results: Secondary Purpose

  20. Willingness to pay

  21. Follow-up telephone survey • N = 296, we use 244 anglers who plan to take a future charter trip • More likely to be primary purpose anglers (60% vs 46% of nonrespondents) • More avid • More likely to have been intercepted on the Outer Banks (4 trips vs 3 trips for nonrespondents)

  22. RP Trips (n = 296)

  23. SP Higher Fee Questions

  24. SP Bag Limit Questions

  25. SP Charter Trips (n = 244)

  26. Debriefing Questions

  27. Model

  28. Count Data Models • Y = 0, 1, 2, 3 • Poisson: • E(x) = var(x) =  • Negative binomial: • E(x) =  • Var(x) = (1 + α)

  29. Panel data models

  30. Something I haven’t read

  31. Poisson Models

  32. Negative Binomial Models

  33. Welfare estimates • Willingness to pay per trip • WTPtrip = -1/Higher fee • Willingness to pay to avoid a one fish reduction in the bag limit per trip • WTPbag = -bag /Higher fee

  34. Willingness to Pay

  35. Questions?