A Zonal RUM Model to Value Recreation Sites with Aggregate Visitation Data

References

1. 1. Banzhaf, H. S.

   (2010). Consumer surplus with apology: A historical perspective on nonmarket valuation and recreation demand. Annual Review of Resource Economics 2, 183–207.

   OpenUrl
2. 1. Berry, S.,
   2. J. Levinsohn, and
   3. A. Pakes

   (1995). Automobile prices in market equilibrium. Econometrica 63(4), 841–890.

   OpenUrlCrossRefWeb of Science
3. 1. Bockstael, N. E.,
   2. W. M. Hanemann, and
   3. C. L. Kling

   (1987). Estimating the value of water quality improvements in a recreational demand framework. Water Resources Research 23(5), 951–960.

   OpenUrlCrossRefWeb of Science
4. 1. Boehm, L.,
   2. L. Chakraborti,
   3. R. T. Melstrom,
   4. N. Piccolo, and
   5. C. Reeling

   (2023). Measuring the economic value of deer hunting: comparing estimates from survey and harvest check-in data. Human Dimensions of Wildlife, 1–17.
5. 1. Connelly, N. A.,
   2. T. L. Brown, and
   3. B. A. Knuth

   (2000). Assessing the relative importance of recall bias and nonresponse bias and adjusting for those biases in statewide angler surveys. Human Dimensions of Wildlife 5(4), 19–29.

   OpenUrlCrossRef
6. 1. Dai, P.,
   2. S. Zhang,
   3. Y. Gong,
   4. Y. Zhou, and
   5. H. Hou

   (2022). A crowd-sourced valuation of recreational ecosystem services using mobile signal data applied to a restored wetland in China. Ecological Economics 192, 107249.

   OpenUrl
7. 1. Depro, B.,
   2. C. Timmins, and
   3. M. O’Neil

   (2015). White flight and coming to the nuisance: can residential mobility explain environmental injustice? Journal of the Association of Environmental and Resource Economists 2(3), 439–468.

   OpenUrl
8. 1. Dundas, S. J. and
   2. R. H. von Haefen

   (2020). The effects of weather on recreational fishing demand and adaptation: implications for a changing climate. Journal of the Association of Environmental and Resource Economists 7(2), 209–242.

   OpenUrl
9. 1. Dundas, S. J.,
   2. R. H. von Haefen, and
   3. C. Mansfield

   (2018). Recreation costs of endangered species protection: Evidence from Cape Hatteras national seashore. Marine Resource Economics 33(1), 1–25.

   OpenUrl
10. 1. English, E.,
    2. R. H. von Haefen,
    3. J. Herriges,
    4. C. Leggett,
    5. F. Lupi,
    6. K. McConnell,
    7. M. Welsh,
    8. A. Domanski, and
    9. N. Meade

    (2018). Estimating the value of lost recreation days from the deepwater horizon oil spill. Journal of Environmental Economics and Management 91, 26–45.

    OpenUrl
11. 1. Feather, P. and
    2. D. Hellerstein

    (1997). Calibrating benefit function transfer to assess the Conservation Reserve Program. American Journal of Agricultural Economics 79(1), 151–162.

    OpenUrlCrossRefWeb of Science
12. 1. Haener, M. K.,
    2. P. C. Boxall,
    3. W. L. Adamowicz, and
    4. D. H. Kuhnke

    (2004). Aggregation bias in recreation site choice models: resolving the resolution problem. Land Economics 80(4), 561–574.

    OpenUrlAbstract/FREE Full Text
13. 1. Hanemann, W.

    (1996). Welfare analysis with discrete choice models. Technical report.
14. 1. He, X. and
    2. G. L. Poe

    (2021). Exploring the shelf-life of travel cost methods of valuing recreation for benefits transfer. Resource and Energy Economics 63, 101123.

    OpenUrl
15. 1. Hindsley, P.,
    2. C. E. Landry, and
    3. B. Gentner

    (2011). Addressing onsite sampling in recreation site choice models. Journal of Environmental Economics and Management 62(1), 95–110.

    OpenUrl
16. 1. Ji, Y.,
    2. D. A. Keiser, and
    3. C. L. Kling

    (2020). Temporal reliability of welfare estimates from revealed preferences. Journal of the Association of Environmental and Resource Economists 7(4), 659–686.

    OpenUrl
17. 1. Johnston, R. J. and
    2. R. S. Rosenberger

    (2010). Methods, trends and controversies in contemporary benefit transfer. Journal of Economic Surveys 24(3), 479–510.

    OpenUrlWeb of Science
18. 1. Knoche, S. and
    2. F. Lupi

    (2007). Valuing deer hunting ecosystem services from farm landscapes. Ecological Economics 64(2), 313–320.

    OpenUrl
19. 1. Knoche, S. and
    2. F. Lupi

    (2013). Economic benefits of publicly accessible land for ruffed grouse hunters. The Journal of Wildlife Management 77(7), 1294–1300.

    OpenUrl
20. 1. Kolstoe, S. and
    2. T. A. Cameron

    (2017). The non-market value of birding sites and the marginal value of additional species: biodiversity in a random utility model of site choice by ebird members. Ecological Economics 137, 1–12.

    OpenUrl
21. 1. Leggett, C. G.,
    2. N. Scherer,
    3. T. C. Haab,
    4. R. Bailey,
    5. J. P. Landrum, and
    6. A. Domanski

    (2018). Assessing the economic benefits of reductions in marine debris at southern california beaches: a random utility travel cost model. Marine Resource Economics 33(2), 133–153.

    OpenUrl
22. 1. Lloyd-Smith, P.,
    2. J. K. Abbott,
    3. W. Adamowicz, and
    4. D. Willard

    (2019). Decoupling the value of leisure time from labor market returns in travel cost models. Journal of the Association of Environmental and Resource Economists 6(2), 215–242.

    OpenUrl
23. 1. Lupi, F.,
    2. J. P. Hoehn, and
    3. G. C. Christie

    (2003). Using an economic model of recreational fishing to evaluate the benefits of sea lamprey (Petromyzon marinus) control on the St. Marys River. Journal of Great Lakes Research 29, 742–754.

    OpenUrlCrossRefWeb of Science
24. 1. Lupi, F.,
    2. D. J. Phaneuf, and
    3. R. H. von Haefen

    (2020). Best practices for implementing recreation demand models. Review of Environmental Economics and Policy 14(2), 302–323.

    OpenUrl
25. 1. McFadden, D.

    (1974). The measurement of urban travel demand. Journal of Public Economics 3(4), 303–328.

    OpenUrlCrossRef
26. 1. McFadden, D.

    (1978). Modeling the choice of residential location. Transportation Research Record 673, 72–77.

    OpenUrl
27. 1. Melstrom, R. T.,
    2. D. H. Jayasekera,
    3. T. A. Boyer, and
    4. C. Jager

    (2017). Scale heterogeneity in recreationists’ decision making: Evidence from a site choice model of sport fishing. Journal of Outdoor Recreation and Tourism 18, 81–87.

    OpenUrl
28. 1. Melstrom, R. T. and
    2. F. Lupi

    (2013). Valuing recreational fishing in the Great Lakes. North American Journal of Fisheries Management 33(6), 1184–1193.

    OpenUrlCrossRef
29. 1. Morey, E. R.,
    2. W. S. Breffle,
    3. R. D. Rowe, and
    4. D. M. Waldman

    (2002). Estimating recreational trout fishing damages in Montana’s Clark Fork River basin: Summary of a natural resource damage assessment. Journal of Environmental Management 66(2), 159–170.

    OpenUrlCrossRefPubMedWeb of Science
30. 1. Murdock, J.

    (2006). Handling unobserved site characteristics in random utility models of recreation demand. Journal of Environmental Economics and Management 51(1), 1–25.

    OpenUrlCrossRefWeb of Science
31. 1. Naidoo, R. and
    2. W. L. Adamowicz

    (2005). Biodiversity and nature-based tourism at forest reserves in uganda. Environment and Development Economics 10(2), 159–178.

    OpenUrl
32. 1. Newbold, S. C.,
    2. S. Lindley,
    3. S. Albeke,
    4. J. Viers,
    5. G. Parsons, and
    6. R. Johnston

    (2022). Valuing satellite data for harmful algal bloom early warning systems. RFF working paper WP 22-23.
33. NPS (1949). The Economics of Public Recreation: An Economic Study of the Monetary Evaluation of Recreation in the National Parks. Land and Recreational Planning Division, National Park Service, Washington, DC.
34. 1. Phaneuf, D. J. and
    2. V. K. Smith

    (2005). Recreation demand models. In K. Maler and J. Vincent (Eds.), Handbook of Environmental Economics, pp. 671–761. North Holland Publishing.
35. 1. Reeling, C.,
    2. D. Erickson,
    3. Y. Kim,
    4. J. G. Lee, and
    5. J. O. Widmar,
    6. Nicole

    (2023). Combining aggregate demand and discrete choice data with application to deer license demand in Indiana. Land Economics 99(4), 477–489.

    OpenUrlAbstract/FREE Full Text
36. 1. Rosenberger, R. S.

    (2016). Recreation Use Values Database. https://recvaluation.forestry.oregonstate.edu/database. Online; accessed 4 January 2023.
37. 1. Smith, V. K.,
    2. G. Van Houtven, and
    3. S. K. Pattanayak

    (2002). Benefit transfer via preference calibration: “prudential algebra” for policy. Land Economics 78(1), 132–152.

    OpenUrlAbstract/FREE Full Text
38. 1. Tanner, S.,
    2. F. Lupi, and
    3. C. Garnache

    (2019). Estimating the impact of fires on recreation in the angeles national forest using combined revealed and stated preference methods. Paper presented at the 2019 Meeting of the Agricultural and Applied Economics Association.
39. 1. Timmins, C. and
    2. J. Murdock

    (2007). A revealed preference approach to the measurement of congestion in travel cost models. Journal of Environmental Economics and Management 53(2), 230–249.

    OpenUrlCrossRefWeb of Science
40. USDOI (1993). 1991 National Survey of Hunting, Fishing and Wildlife-Associated Recreation: Indiana. https://www2.census.gov/programs-surveys/fhwar/publications/1991/fhw91-in.pdf. Online, accessed 4 January 2023.
41. USDOI (2018). 2011 National Survey of Hunting, Fishing and Wildlife-Associated Recreation: Indiana. https://www2.census.gov/programs-surveys/fhwar/publications/2011/fhw11-in.pdf. Online, accessed 1 March 2023.
42. 1. von Haefen, R. H. and
    2. D. J. Phaneuf

    (2008). Identifying demand parameters in the presence of unobservables: A combined revealed and stated preference approach. Journal of Environmental Economics and Management 56(1), 19–32.

    OpenUrlCrossRefWeb of Science
43. 1. Whitehead, J. C.

    (2006). A comparison of contingent valuation method and random utility model estimates of the value of avoiding reductions in king mackerel bag limits. Applied Economics 38(15), 1725–1735.

    OpenUrl