A Latent Class Nested Logit Model for Rank-Ordered Data with Application to Cork Oak Reforestation
- 295 Downloads
We analyze stated ranking data collected from recreational visitors to the Alcornocales Natural Park (ANP) in Spain. The ANP is a large protected area which comprises mainly cork oak woodlands. The visitors ranked cork oak reforestation programs delivering different sets of environmental (reforestation technique, biodiversity, forest surface) and social (jobs and recreation sites created) outcomes. We specify a novel latent class nested logit model for rank-ordered data to estimate the distribution of willingness-to-pay for each outcome. Our modeling approach jointly exploits recent advances in discrete choice methods. The results suggest that prioritizing biodiversity would increase certainty over public support for a reforestation program. In addition, a substantial fraction of the visitor population are willing to pay more for the social outcomes than the environmental outcomes, whereas the existing reforestation subsidies are often justified by the environmental outcomes alone.
KeywordsDiscrete choice Stated preference Willingness-to-pay Forest Land use
JEL ClassificationC33 C35 C51 Q23 Q51 Q57
We thank Alejandro Caparrós and Pablo Campos for allowing us to access the data used in this study. We wish to thank Editor Christian Vossler and two anonymous referees for helpful and constructive comments. All views expressed herein are our own.
Funding Oviedo’s involvement in this study was funded by the Spanish Ministry of Economy and Competitiveness (VEABA ECO2013-42110-P, I + D National Plan).
Compliance with Ethical Standards
Conflict of interest
We declare that we have no conflict of interest.
- Akaich F, Nayga RM, Gil JM (2013) Are results from non-hypothetical choice-based conjoint analyses and non-hypothetical recoded-ranking conjoint analyses similar? Am J Agric Econ 95:946–963Google Scholar
- Ben-Akiva M, Lerman SR (1985) Discrete choice analysis: theory and application to travel demand. MIT Press, CambridgeGoogle Scholar
- Clark SL, Muthén B (2009) Relating latent class analysis results to variables not included in the analysis. mimeo. https://www.statmodel.com/download/relatinglca. Cited 24 Mar 2015
- Doblas-Miranda E, Martínez-Vilalta J, Lloret F, Álvarez A, Ávila A, Bonet FJ, Brotons L, Castro J, Curiel Yuste J, Díaz M, Ferrandis P, Garca-Hurtado E, Iriondo JM, Keenan TF, Latron J, Llusiá J, Loepfe L, Mayol M, Moré G, Moya D, Peñuelas J, Pons X, Poyatos R, Sardans J, Sus O, Vallejo VR, Vayreda J, Retana J (2014) Reassessing global change research priorities in Mediterranean terrestrial ecosystems: how far have we come and where do we go from here? Glob Ecol Biogeogr 24:25–43CrossRefGoogle Scholar
- European Commission (2014) Comission Regulation (EU) N0 702/2014 of 25 June 2014. Off J Eur Union 57:193Google Scholar
- Giergiczny M, Hess S, Dekker T, Chintakayala PK (2013) Testing the consistency (or lack thereof) between choices in best-worst surveys. Paper presented at the 3rd international choice modelling conference, Sydney, 3–5 July 2013Google Scholar
- Hess S, Ben-Akiva M, Gopinath D, Walker J (2011) Advantages of latent class over continuous mixture of logit models. mimeo. http://www.stephanehess.me.uk/papers/Hess_Ben-Akiva_Gopinath_Walker_May_2011. Cited 3 Mar 2016
- Hole AR (2015) MIXLOGITWTP: Stata module to estimate mixed logit models in WTP space. Statistical Software Components S458037, Boston College, Department of EconomicsGoogle Scholar
- Hoyos D, Mariel P, Pascual U, Etxano I (2012) Valuing a Natura 2000 Network site to inform land use options using a discrete choice experiment: an Illustration from the Basque Country. J For Econ 18:329–344Google Scholar
- Keane MP, Wasi N (2013) Comparing alternative models of heterogeneity in consumer choice behavior. J Appl Econ 28:1018–1045Google Scholar
- Krawczyk M (2012) Testing for hypothetical bias in willingness to support a reforestation program. J For Econ 18:282–289Google Scholar
- Layton DF, Lee ST (2006a) From ratings to rankings: the econometric analysis of stated preference ratings data. In: Halvorsen R, Layton DF (eds) Explorations in environmental and natural resource economics: essays in honor of Gardner M. Brown, Jr. Edward Elgar Publishing, CheltenhamGoogle Scholar
- Loomis J (2005) Economic values without prices: the importance of nonmarket values and valuation for informing public policy debates. Choices 20:179–182Google Scholar
- McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New YorkGoogle Scholar
- McFadden D (1978) Modeling the choice of residential location. In: Karlqvist A, Lundqvist L, Snickars F, Weibull J (eds) Spatial interaction theory and planning models. North Holland, AmsterdamGoogle Scholar
- Mogas J, Riera P, Bennett J (2006) A comparison of contingent valuation and choice modeling with second-order interactions. J For Econ 12:5–30Google Scholar
- Ovando P, Campos P, Montero G (2007) Forestaciones con Encinas y Alcornoques en el Área de la Dehesa en el Marco del Reglamento (CEE) 2080/92 (1993–2000). Rev Española Estudio Agrosoc Pesq 214:173–186Google Scholar
- Pacifico D, Yoo HI (2013) lclogit: a Stata command for fitting latent-class conditional logit models via the expectation-maximization algorithm. Stata J 13:625–639Google Scholar
- Train KE, Weeks M (2005) Discrete choice models in preference space and willingness-to-pay space. In: Alberini A, Scarpa R (eds) Applications of simulation methods in environmental resource economics. Springer, DordrechtGoogle Scholar
- Yan J, Yoo HI (2014) The seeming unreliability of rank-ordered data as a consequence of model misspecification. MPRA Paper No. 56285. https://mpra.ub.uni-muenchen.de/56285/. Cited 3 Mar 2016