1 / 39

Estimating the Economic Value of Beach Nourishment

Estimating the Economic Value of Beach Nourishment. John Whitehead, ASU; Dan Phaneuf, NCSU; Chris Dumas, UNCW; Jim Herstine, UNCW; Jeff Hill, UNCW; Bob Beurger, UNCW.

jett
Download Presentation

Estimating the Economic Value of Beach Nourishment

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Estimating the Economic Value of Beach Nourishment John Whitehead, ASU; Dan Phaneuf, NCSU; Chris Dumas, UNCW; Jim Herstine, UNCW; Jeff Hill, UNCW; Bob Beurger, UNCW

  2. Convergent Validity of Revealed and Stated Recreation Behavior with Quality Change: A Comparison of Multiple and Single Site Demands John Whitehead, ASU; Dan Phaneuf, NCSU; Chris Dumas, UNCW; Jim Herstine, UNCW; Jeff Hill, UNCW; Bob Beurger, UNCW

  3. Beach Nourishment

  4. NC Beach Nourishment: 1961-2006 • Annual costs = $4.37 million (2004 $) • Annual cost of nourishing all 138 miles = $831 million • The number of NC beaches annually nourished ranges from one to seven (a small fraction of total beaches)

  5. Market for Beach Width? Price S D Beach Width

  6. Implicit Market: Travel cost method Travel cost D’ D Trips

  7. TCM estimate of ΔCS, ΔX Market measure of ΔCS, ΔX Construct Validity • The degree to which inferences can be made from the measures of a theoretical construct to the theoretical construct

  8. Construct validity tests • Length of stay: Loomis, ERE, 1993 • Site selection: Grijalva, Berrens, Bohara and Shaw, AJAE, 2002 • Hurricane evacuation: Whitehead, ERE, 2005

  9. Single-site TCM estimate of ΔCS, ΔX Multi-site TCM estimate of ΔCS, ΔX Convergent validity • The degree to which a measure is similar to other measures for which it should also be similar. Revealed Preference Stated Preference

  10. Convergent validity tests • WTP and implicit market values: Carson et al., Land Econ, 1996 • RP and SP number of trips: Jeon and Herriges, working paper, 2005

  11. Hypothetical bias • Experimental WTP: List and Gallet, ERE, 2001 • Experimental WTP: Murphy et al. ERE, 2005 • Whitehead et al., JES, forthcoming

  12. A comparison of three TCM models over ΔCS and Δx • Single-site RP and SP model • Linked multiple-site RP site-selection and trip frequency model • Kuhn-Tucker RP seasonal demand system

  13. Issues • Single-site models are less capable of incorporating substitutes • RP data may not be able to forecast accurately beyond the range of historical experience • SP data is subject to hypothetical bias

  14. Current Application: NC Beaches

  15. Model 1: Single Site Model Travel cost D = RP, SP D’ = SP D’ D Trips: X = Σxj

  16. Model 1: Welfare • lnμit = b0+ b1p + b2p + b3y + b4q + b5SP + ui + eit • CS per trip [SP=0] = -[x(q’) – x(qo)]/b1 • CS = CS per trip × [X + DX]

  17. Step 1: Discrete choice site selection model Model 2: Linked Model

  18. Step 1: Nested Logit

  19. Nested Logit details

  20. Negative binomial trip frequency model Step 2: Linked Model X(IV,y,z) IV(q) Trips: X = Σxi

  21. Linked Model: Welfare • WTP per trip = -[IV(q’) – IV(qo)]/d • WTP = WTP per trip × [X + DX]

  22. Model 3: Kuhn-Tucker Model Travel costj Dj = RP Dj’= RP Dj’ Dj Trips: xj

  23. via Maximum Likelihood

  24. Kuhn-Tucker Model: Welfare • Welfare analysis and demand prediction in the KT model relies on Monte Carlo integration in which the unobserved heterogeneity (error) terms are drawn conditionally so that behavior at baseline travel cost and site conditions is replicated in the simulated outcomes. Given multiple simulated error vectors for each person compensating surplus is calculated for each error draw, and the average over people and draws provides an estimate of E(CS) … • See appendix and references for details

  25. Data • 2003 Survey • n = 1000+ beach goers • n = 800+ with complete RP data • n = 600+ with complete SP data • n = 351 with X = Σxj • Single and multi-day trips

  26. Σxj -X • Mean = 2.24 (n = 535) • STD = 21.52 • Quantiles • 100% = 260 • 95% = 10 • 90% = 3 • 10% = -1 • 5% = -3 • 0% = -98

  27. Single Site Trips

  28. Multiple-Site Data

  29. Travel cost • Single Site minimum distance TC = $94 • Outer banks TC = $203 • Mean multiple site TC = $125 (n = 17 × 351 = 5967)

  30. Single Site Models

  31. Linked Model

  32. Kuhn-Tucker Model

  33. Convergent Validity

  34. Policy Implications: Benefits • 1.58m households in the study region • 64% participants • Aggregate benefit = $772 million • The annual recreation benefit of increased width = $60 million

  35. Policy Implications: Costs • The annual cost to replace one foot of eroded beach ≈ $32,000 per mile • 60 miles • 100 feet • Every 4 years • Annual cost ≈ $48,000,000

  36. Net benefits • NB = $60m – $48m • NB < 0 in linked model • Property benefits are not included … • Environmental costs are not included …

  37. Conclusions • Single-site and multi-site models are statistically convergent valid for quality change [not economically convergent valid] • Hypothetical bias: can be adjusted with SP = 0 • Joint estimation of RP and SP is not needed in this application • RP data is able to forecast beyond the range of historical experience

  38. Additional Research • Compare CS and WTP per trip • Is it hypothetical bias? • Consider those with mismatched trips • Consider sample selection effects • Compare with other multiple site models (e.g., participation model)

More Related