This paper reports on the experimental design process and considerations of a choice experiment conducted in collaboration with farmers in northern Australia. The purpose of the research is to inform the design of effective and efficient payments-for-ecosystem services schemes to safeguard north Australia's biodiversity values. It promotes the contractual provision of biodiversity conservation services by farmers, in particular pastoralists operating in Australia's tropical savannas. The paper focuses on the discrete choice experimental (DCE) aspects. The DCE is employed to estimate farmers' preference heterogeneity for supplying ecosystem services, specifically their willingness to accept remuneration for the on-farm conservation of biodiversity, based on potential programme attributes. The design of the choice experiment draws on best practice standards (Hoyos, 2010), a recognition of the benefits of embedding design in a consultative process (Klojgaard et al., 2012) and recent advances in accounting for response certainty (Brouwer et al., 2010; Hensher et al., 2012). DCE design decisions relating to attribute selection, attribute levels, alternatives and choice tasks are explained based on literature, focus group discussions, expert input and an iterative process of Bayesian D-efficient DCE design. Additional design aspects include measuring choice certainty and stated attribute attendance, embedding the DCE within a discrete-continuous approach, capturing relevant respondent-related attributes with socio-economic-psychological questions and scales, and devising appropriate data collection logistics.
Survey of north Australian graziers and pastoralists with choice experiment regarding participation in contractual biodiversity conservation
Greiner, R. (Creator), Charles Darwin University - Datasets, 2015
Greiner, R., Bliemer, M., & Ballweg, J. (2014). Design considerations of a choice experiment to estimate likely participation by north Australian pastoralists in contractual biodiversity conservation. Journal of Choice Modelling, 10, 34-45. https://doi.org/10.1016/j.jocm.2014.01.002