Skip to main content

Estimating recreation benefits through joint estimation of revealed and stated preference discrete choice data

Abstract

We develop econometric models to estimate jointly revealed preference (RP) and stated preference (SP) models of recreational fishing behavior and preferences using survey data from the 2007 Alaska Saltwater Sportfishing Economic Survey. The RP data are from site choice survey questions, and the SP data are from a discrete choice experiment. Random utility models using only the RP data may be more likely to estimate the effect of cost on site selection well, but catch per day estimates may not reflect the benefits of the trip as perceived by anglers. The SP models may be more likely to estimate the effects of trip characteristics well, but less attention may be paid to the cost variable due to the hypothetical nature of the SP questions. The combination and joint estimation of RP and SP data seeks to exploit the contrasting strengths of both. We find that there are significant gains in econometric efficiency and differences between RP and SP willingness-to-pay estimates are mitigated by joint estimation. We compare a number of models that have appeared in the environmental economics literature with the generalized multinomial logit model. Naïve (1) scaled, (2) mixed logit, and (3) generalized multinomial logit models produced similar results to a generalized multinomial logit model that accounts for scale differences in RP and SP data. Willingness-to-pay estimates do not differ across these models but are greater than those in the mixed logit error components model that accounts for scale differences.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. In the case where the willingness-to-pay estimates are economically different, analysts should use both estimates separately in sensitivity analysis.

  2. The RP cost will be unbiased under the assumption that the travel cost variable is not measured with error (Randall 1994).

  3. Attribute non-attendance is not limited to cost (e.g., Colombo et al. 2013).

  4. See Krucien et al. (2015), Lancsar and Swait (2014), and Fifer et al. (2014) for discussion in the health and transportation literatures.

  5. The ability of the GMXL to separately estimate individual-level scale and preference heterogeneity has been challenged recently by Hess and Rose (2012), who argue that the utility specifications in GMXL models simply allow for more flexible distributions of the preference parameters.

  6. Lew et al. (2010) describe the development, content, and structure of the three survey versions, their implementation, and a summary of the data.

  7. We find similar results when only one RP observation is used in the jointly estimated model.

  8. Alternatively, we could randomly choose each of the k = 4 sites for respondents who take more than one trip, but this would cause us to diverge from the simulation of an on-site survey to measure site choice which is typically used in creel surveys.

  9. These rates were calculated over trips targeting any species, not just those targeting halibut or salmon.

  10. These results and others mentioned in this section are available upon request.

  11. All of these models are estimated with NLOGIT software (Greene 2012; Chang and Lusk 2011).

  12. Throughout the results section we refer to “statistical significance” indicating a coefficient that is statistically different from zero at the p < 0.05 level in a two-tailed test.

  13. We also attempted to estimate the θ coefficient by including the SP dummy variable in the estimation of \( \tau \). However, this model did not converge successfully.

  14. The model fails to converge when \( \tau \) is unconstrained while including the SP dummy variable. Similar results, other than a larger \( \theta \) parameter, are found with \( \tau \) fixed at 0.25. When \( \tau \) is fixed at 0.75 the model fails to estimate satisfactorily.

  15. This is also true in the RP model that uses all 2245 trips instead of the typical trip.

References

  • Abildtrup J, Olsen SB, Stenger A (2015) Combining RP and SP data while accounting for large choice sets and travel mode—an application to forest recreation. J Environ Econ Policy 4(2):77–201

    Article  Google Scholar 

  • Adamowicz W, Louviere J, Williams M (1994) Combining revealed and stated preference methods for valuing environmental amenities. J Environ Econ Manag 26:271–292

    Article  Google Scholar 

  • Adamowicz W, Swait J, Boxall P, Louviere J, Williams M (1997) Perceptions versus objective measures of environmental quality in combined revealed and stated preference models of environmental valuation. J Environ Econ Manag 32:65–84

    Article  Google Scholar 

  • Araña JE, León CJ (2013) Dynamic hypothetical bias in discrete choice experiments: evidence from measuring the impact of corporate social responsibility on consumers demand. Ecol Econ 87:53–61

    Article  Google Scholar 

  • Börjesson M (2008) Joint RP–SP data in a mixed logit analysis of trip timing decisions. Transp Res E Logist Transp Rev 44(6):1025–1038

    Article  Google Scholar 

  • Brownstone D, Bunch DS, Train K (2000) Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles. Transp Res B Methodol 34(5):315–338

    Article  Google Scholar 

  • Campbell D, Hensher DA, Scarpa R (2012) Cost thresholds, cut-offs and sensitivities in stated choice analysis: identification and implications. Resour Energy Econ 34(3):396–411

    Article  Google Scholar 

  • Carson RT, Flores NE, Martin KM, Wright JL (1996) Contingent valuation and revealed preference methodologies: comparing the estimates for quasi-public goods. Land Econ 72(1):80–99

    Article  Google Scholar 

  • Chang JB, Lusk JL (2011) Mixed logit models: accuracy and software choice. J Appl Econom 26(1):167–172

    Article  Google Scholar 

  • Christie M, Gibbons J (2011) The effect of individual ‘ability to choose’ (scale heterogeneity) on the valuation of environmental goods. Ecol Econ 70:2250–2257

    Article  Google Scholar 

  • Colombo S, Christie M, Hanley N (2013) What are the consequences of ignoring attributes in choice experiments? implications for ecosystem service valuation. Ecol Econ 96:25–35

    Article  Google Scholar 

  • de-Magistris T, Pascucci S (2014) The effect of the solemn oath script in hypothetical choice experiment survey: a pilot study. Econ Lett 123(2):252–255

    Article  Google Scholar 

  • de-Magistris T, Gracia A, Nayga RM (2013) On the use of honesty priming tasks to mitigate hypothetical bias in choice experiments. Am J Agric Econ 95(5):1136–1154

    Article  Google Scholar 

  • Fiebig DG, Keane MP, Louviere J, Wasi N (2010) The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Mark Sci 29(3):393–421

    Article  Google Scholar 

  • Fifer S, Rose J, Greaves S (2014) Hypothetical bias in stated choice experiments: is it a problem? and if so, how do we deal with it? Transp Res A Policy Pract 61:164–177

    Article  Google Scholar 

  • Freeman AM (2003) The Measurement of environmental and resource values: theory and methods, 2nd edn. Resources for the Future, Washington

    Google Scholar 

  • Greene WH (2012) NLOGIT 5.0, Econometric Software, Plainview

  • Greene WH, Hensher DA (2007) Heteroscedastic control for random coefficients and error components in mixed logit. Transp Res E Logist Transp Rev 43(5):610–623

    Article  Google Scholar 

  • Greene WH, Hensher DA (2010) Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models. Transportation 37(3):413–428

    Article  Google Scholar 

  • Haab T, McConnell KE (2002) Valuing environmental and natural resources: the econometrics of non-market valuation. Edward Elgar, Cheltenham

    Book  Google Scholar 

  • Haab T, Hicks R, Schnier K, Whitehead JC (2012) Angler heterogeneity and the species-specific demand for marine recreational fishing. Mar Resour Econ 27(3):229–251

    Article  Google Scholar 

  • Hensher DA (2008) Empirical approaches to combining revealed and stated preference data: some recent developments with reference to urban mode choice. Res Transp Econ 23(1):23–29

    Article  Google Scholar 

  • Hensher DA (2010) Hypothetical bias, choice experiments and willingness to pay. Transp Res B Methodol 44(6):735–752

    Article  Google Scholar 

  • Hensher DA (2012) Accounting for scale heterogeneity within and between pooled data sources. Transp Res A Policy Pract 46(3):480–486

    Article  Google Scholar 

  • Hensher DA, Bradley M (1993) Using stated response choice data to enrich revealed preference discrete choice models. Mark Lett 4(2):139–151

    Article  Google Scholar 

  • Hensher DA, Rose JM, Greene WH (2008) Combining RP and SP data: biases in using the nested logit ‘trick’—contrasts with flexible mixed logit incorporating panel and scale effects. J Transp Geogr 16(2):126–133

    Article  Google Scholar 

  • Hensher DA, Beck MJ, Rose JM (2011) Accounting for preference and scale heterogeneity in establishing whether it matters who is interviewed to reveal household automobile purchase preferences. Environ Resour Econ 49:1–22

    Article  Google Scholar 

  • Hensher DA, Rose JM, Greene WH (2015) Applied choice analysis: a primer, 2nd edn. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Hess S, Rose JM (2012) Can scale and coefficient heterogeneity be separated in random coefficients models? Transportation 39(6):1225–1239

    Article  Google Scholar 

  • Hoyos D (2010) The state of the art of environmental valuation with discrete choice experiments. Ecol Econ 69(8):1595–1603

    Article  Google Scholar 

  • Keane MM, Wasi N (2013) Comparing alternative models of heterogeneity in consumer choice behavior. J Appl Econom 28:1018–1045

    Google Scholar 

  • Koetse MJ (2017) Effects of payment vehicle non-attendance in choice experiments on value estimates and the WTA–WTP disparity. J Environ Econ Policy 6(3):225–245

    Article  Google Scholar 

  • Kragt ME (2013) The effects of changing cost vectors on choices and scale heterogeneity. Environ Resour Econ 54:201–221

    Article  Google Scholar 

  • Krucien N, Gafni A, Pelletier-Fleury N (2015) Empirical testing of the external validity of a discrete choice experiment to determine preferred treatment option: the case of sleep apnea. Health Econ 24(8):951–965

    Article  Google Scholar 

  • Lancsar E, Swait J (2014) Reconceptualising the external validity of discrete choice experiments. PharmacoEconomics 32(10):951–965

    Article  Google Scholar 

  • Larson DM, Lew DK (2013) How do harvest rates affect angler trip patterns? Mar Resour Econ 28(2):155–173

    Article  Google Scholar 

  • Lew DK, Larson DM (2011) A repeated mixed logit approach to valuing a local sport fishery: the case of southeast Alaska salmon. Land Econ 87(4):712–729

    Article  Google Scholar 

  • Lew DK, Larson DM (2012) Economic values for saltwater sport fishing in Alaska: a stated preference analysis. North Am J Fish Manag 32(4):745–759

    Article  Google Scholar 

  • Lew DK, Larson DM (2014) Is a fish in hand worth two in the sea? evidence from a stated preference study. Fish Res 157:124–135

    Article  Google Scholar 

  • Lew DK, Wallmo K (2017) Temporal stability of stated preferences for endangered species protection from choice experiments. Ecol Econ 131:87–97

    Article  Google Scholar 

  • Lew DK, Lee J, Larson DM (2010) Saltwater sportfishing in Alaska: a summary and description of the Alaska saltwater sportfishing economic survey, 2007. U.S. Department of Commerce, NOAA Technical Memorandum NMFS-AFSC-214

  • Loomis J (2011) What’s to know about hypothetical bias in stated preference valuation studies? J Econ Surv 25(2):363–370

    Article  Google Scholar 

  • Lusk JL (2003) Effects of cheap talk on consumer willingness-to-pay for golden rice. Am J Agr Econ 85(4):840–856

    Article  Google Scholar 

  • Lusk JL, Schroeder TC (2004) Are choice experiments incentive compatible? A test with quality differentiated beef steaks. Am J Agric Econ 86(2):467–482

    Article  Google Scholar 

  • McConnell KE, Tseng WC (1999) Some preliminary evidence on sampling of alternatives with the random parameters logit. Mar Resour Econ 14(4):317–332

    Article  Google Scholar 

  • Metcalfe PJ, Baker W, Andrews K, Atkinson G, Bateman IJ, Butler S, Carson RT, East J, Gueron Y, Sheldon R, Train K (2012) An assessment of the nonmarket benefits of the water framework directive for households in England and Wales. Water Resour Res 48:W03526

    Article  Google Scholar 

  • Mitchell RC, Carson RT (1989) Using surveys to value public goods: the contingent valuation method. Resources for the Future, Washington

    Google Scholar 

  • Randall A (1994) A difficulty with the travel cost method. Land Econ 70(1):88–96

    Article  Google Scholar 

  • Ready RC, Champ PA, Lawton JL (2010) Using respondent uncertainty to mitigate hypothetical bias in a stated choice experiment. Land Econ 86(2):363–381

    Article  Google Scholar 

  • Scarpa R, Thiene M, Hensher DA (2012) Preferences for tap water attributes within couples: an exploration of alternative mixed logit parameterizations. Water Resour Res 48:W01520

    Article  Google Scholar 

  • Swait J, Louviere J (1993) The role of the scale parameter in the estimation and comparison of multinomial logit models. J Mark Res 30(3):305–314

    Article  Google Scholar 

  • Train KE (2003) Discrete choice methods with simulation. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • von Haefen RF, Phaneuf DJ (2008) Identifying demand parameters in the presence of unobservables: a combined revealed and stated preference approach. J Environ Econ Manag 56:19–32

    Article  Google Scholar 

  • Whitehead J (2011) Multiple choice discrete data joint estimation. In: Whitehead J, Haab T, Huang JC (eds) Preference data for environmental valuation: combining revealed and stated approaches. Routledge, New York, pp 73–83

    Google Scholar 

  • Whitehead JC, Pattanayak SK, Van Houtven GL, Gelso BR (2008) Combining revealed and stated preference data to estimate the nonmarket value of ecological services: an assessment of the state of the science. J Econ Surv 22(5):872–908

    Article  Google Scholar 

  • Whitehead J, Haab T, Huang JC (eds) (2011) Preference data for environmental valuation: combining revealed and stated approaches. Routledge, New York

    Google Scholar 

  • Yeh CY, Haab TC, Sohngen BL (2006) Modeling multiple-objective recreation trips with choices over trip duration and alternative sites. Environ Resour Econ 34(2):189–209

    Article  Google Scholar 

Download references

Acknowledgements

A previous version of this paper was presented at the Society for Benefit–Cost Analysis annual meeting in Washington, DC, March 2015 and seminars at Appalachian State University, Resources for the Future and Ohio State University. The authors thank Trudy Cameron, Steve Kasperski, Kristy Wallmo, seminar participants, and two anonymous journal referees for numerous comments and David Hensher for sharing his NLOGIT code for the generalized mixed logit. Funding for this research was provided by the Alaska Fisheries Science Center, National Marine Fisheries Service. Opinions expressed are those of the authors and do not reflect those of NMFS, NOAA, or the U.S. Department of Commerce.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to John C. Whitehead.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Whitehead, J.C., Lew, D.K. Estimating recreation benefits through joint estimation of revealed and stated preference discrete choice data. Empir Econ 58, 2009–2029 (2020). https://doi.org/10.1007/s00181-019-01646-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-019-01646-z

Keywords

  • Discrete choice experiment
  • Generalized multinomial logit model
  • Hypothetical bias
  • Revealed preference
  • Stated preference
  • Travel cost method

JEL Classification

  • Q51
  • Q22
  • Q26