WILLINGNESS-TO-PAY ESTIMATES AND GEOGRAPHIC EMBEDDED SAMPLES: CASE STUDY OF ALASKAN STELLER SEA LION

Abstract

This paper examines willingness to pay for an endangered species protection program across geographically embedded samples. The samples range from the boroughs that contain critical habitat for the Steller sea lion to the state that contains these boroughs to the entire United States. The analysis is done using the contingent valuation method and comparing mean willingness to pay estimates using the Method of Convolution. When comparing willingness-to-pay estimates among paired samples, results indicate significantly different values across geographic samples, which may lead to dramatically different policy implications. Respondent motivating factors also vary from sample to sample, which may be of interest to the management agencies, especially federal agencies and those with a national scope.

Keywords:

• Contingent Valuation
• Geographically Embedded
• Willingness to Pay
• Endangered Species
• Steller Sea Lion
• Alaska

The authors would like to thank Ron Dearborn of the Alaska Sea Grant for providing funding and research support for this project. Scott Smiley and Quentin Fong helped with focus group facilitation. John Loomis and Joseph Cooper contributed to survey design. Scott Bates, Stephanie Moreland, and Lovro Valcic assisted in survey mailing and data organization. Finally, the authors would like to thank Douglas Larson and John Loomis for their constructive methodological suggestions.

Notes

1 Threatened fish and wildlife; change in listing status of Steller sea Lions under the Endangered Species Act, 62 Federal Register 24345–24355 (1997).

2 In November 2000 renamed NOAA Fisheries (National Oceanic and Atmospheric Administration Fisheries), MMPA Bulletin, 2nd/3rd quarter 2000, NMFS, Office of Protected Resources, Silver Spring, MD.

3 For more, see U.S. Code of Federal Regulations, Fisheries of the Exclusive Economic Zone off Alaska, 50 Code of Fed. Reg. 3437–7815 (1999).

4 Steller Sea Lion Protection Measures for the Groundfish Fisheries Off Alaska, 66 Fed. Register 7275–7327 (2001).

5 U.S. Code, Title 16, Chapter 35.

6 J.B. Loomis & D. White, Economic Benefits of Rare and Endangered Species: Summary and Meta Analysis, 18 Eco. Econ. 97–206 (1996).

7 R.J. Sutherland & R.G. Walsh, Effect of Distance on the Preservation Value of Water Quality, 61 Land Econ. 281–291 (1985); J. Pate, J. Loomis, The effect of distance on willingness to pay values: A case study of wetlands and salmon in California, 20 Eco. Econ. 199–207 (1997).

8 W.M. Hanemann, Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses, 66 Am. J. Agr. Econ. 332–341(1984).

9 See M. Ben-Akiva, S.R. Lerman, Discrete Choice Analysis, Theory & Application to Travel Demand (1985).

10 Hanemann, supranote 10, at 332–341.

11 If we wish to rule out negative values of mean WTP, we can truncate the estimate of expected WTP at zero. Given model (7), mean WTP can then be calculated as follows:

The disadvantage of doing this is that we may overestimate true WTP. It can also be theoretically inconsistent because during the stage of estimating parameters it is assumed that WTP can undertake both negative and positive values. In the stage of calculating mean WTP it is assumed that WTP can undertake only positive values. T. Haab & K.K. McConnell, Referendum Models and Negative Willingness to Pay: Alternative Solutions, 32 J. Envtl. Econ. & Mgmt. 251–270 (1997). Nevertheless, this approach is often used in practice as a means to solve the “problem” of negative mean WTP. Restricted mean WTP for the United States sample is U.S. $130.55; for the Alaska sample, restricted mean WTP is U.S. $98.88; and for Boroughs sample, the restricted mean WTP is U.S. $110.10.

12 The services of Survey Sampling, Inc. in Fairfield, CT were employed to obtain a representative sample.

13 K. Arrow et al., Report of the NOAA Panel on Contingent Valuation, U.S. Department of Commerce, 58 Fed. Register 4602–4614 (1993).

14 Scale ranged from 1 to 5, where 1 was “strongly disagree,” 3 was “neutral,” and 5 was “strongly agree.”

15 R.G. Cummings, D.S. Brookshire, & W.B. Schulze, Valuing Environmental Goods (1986).

16 K.L. Giraud, J.B. Loomis, & R.L. Johnson, Internal and External Scope in Willingness to Pay Estimates for Threatened and Endangered Wildlife, 56 J .Envtl. Econ. & Mgmt. 221–229 (1999).

17 D. Dillman Mail & Internet Surveys: The Tailored Design Method (2nd ed. 2000). Announcement letter, 1st survey mailing, reminder postcard, 2nd survey mailing, 3rd survey mailing via Priority Mail.

18 Coefficients assigned ∗ were significant at α < 0.01; coefficients assigned † were significant at α < 0.05; other coefficients were significant at α > 0.054.

^a Coefficients assigned

∗were significant at α < 0.01; coefficients assigned

^†were significant at α < 0.05; other coefficients were significant at α > 0.05.

19 G. Poe, E. Severance-Lossin, & M. Welsh, Measuring the Difference (X-Y) of Simulated Distributions: A Convolutions Approach, 76 Am. J. Agric. Econ. 904–915 (1994).

20 T. Park, J. Loomis, & M. Creel, Confidence Intervals for Evaluating Benefits from Dichotomous Choice Contingent Valuation Studies, 67 Land Econ. 64–73 (1991).

21 I. Krinsky & A. Robb, Approximating the Statistical Properties of Elasticities, 68 Rev. Econ. & Statistics 715–719 (1986).

22 Sutherland & Walsh, supranote 9, at 281–291 (1985); Pate & Loomis, supranote 9, 199–207.

23 For the exact wording of the Yes follow-up question, please refer to Appendix A, infra.

24 For the exact wording of the No follow-up question, please refer to Appendix A, infra.