Motivations to grow energy crops: the role of crop and contract attributes

Perennial energy crops are a promising source of bioenergy whose production involves production risks, long‐term commitment of land and need for crop‐specific investments without the coverage of crop insurance potentially available for conventional crops. We conduct a choice experiment in five states in the Midwestern and South‐central regions of the U.S. to examine the effect of crop‐contract attributes on the joint discrete‐continuous choice decisions to adopt an energy crop and convert acres to it from a status quo use, while controlling for the effect of various farmers’ risk and time preferences, sociodemographic characteristics, and availability of crop insurance for conventional crops. We find robust evidence that high discount rates, high upfront establishment costs and need for crop‐specific investments create disincentives for adoption and allocation of land to energy crop production. The effects of riskiness of returns and risk aversion are less robust across specifications. The effect of conventional crop insurance on the energy crop adoption decision differs across types of insurance; in particular, farmers with revenue insurance are statistically significantly less likely to adopt an energy crop. Our results have implications for the design of effective contracts and policy incentives to induce the production of energy crops.     

Modeling the effects of including/excluding attributes in choice experiments on systematic and random components

This paper examines the impact of attribute presence/absence in choice experiments using covariance heterogeneity models and random coefficient models. Results show that attribute presence/absence impacts both mean utility (systematic components) and choice variability (random components). Biased mean effects can occur by not accounting for choice variability. Further, even if one accounts for choice variability, attribute effects can differ because of attribute presence/absence. Managers who use choice experiments to study product changes or new variants should be cautious about excluding potentially essential attributes. Although including more relevant attributes increases choice variability, it also reduces bias.     

Experimental analysis of choice

Experimental choice analysis continues to attract academic and applied attention. We review what is known about the design, conduct, analysis, and use of data from choice experiments, and indicate gaps in current knowledge that should be addressed in future research. Design strategies consistent with probabilistic models of choice process and the parallels between choice experiments and real markets are considered. Additionally, we address the issues of reliability and validity. Progress has been made in accounting for differences in reliability, but more research is needed to determine which experiments and response procedures will consistently produce more reliable data for various problems.Sawtooth SoftwareDecision Research     

Combining Sources of Preference Data for Modeling Complex Decision Processes

We review current state-of-the-art practices for combining preference data from multiple sources and discuss future research possibilities. A central theme is that any one data source (e.g., a scanner panel source) is often insufficient to support tests of complex theories of choice and decision making. Hence, analysts may need to embrace a wider variety of data types and measurement tools than traditionally have been considered in applied decision making and choice research. We discuss the viability of preference-stationarity assumptions usually made when pooling data, as well as random-utility theory-based approaches for combining data sources. We also discuss types of models and data sources likely to be required to make inferences about and estimate models that describe choice dynamics. The latter discussion is speculative insofar as the body of literature on this topic is small.     

Retaining the visitor,enhancing the experience: identifying attributes of choice in repeat museum visitation

• How people make choices in relation to cultural and leisure consumption has been explored from the viewpoint of motivation, lifestyle segmentation, and lifecycle. Little is known about the specific characteristics associated with choices to visit, re‐visit, or not to visit a museum. Understanding characteristics of choice, developing incentives, bundled packages, and levels of pricing is an essential element in marketing strategies for museums operating in a competitive leisure marketplace. However, determining what really matters to cultural consumers is complex and methodologies to assist in unraveling such complexities are not easily identified. This study aimed to address ways in which people respond to specific incentives as influences in choosing museum visitation. The study was conducted in two major museums in Australia to determine how useful choice modeling is in identifying features that matter to cultural consumers. The results suggest that choice modeling has much to offer in relation to understanding the benefits people are seeking from a museum experience as well as offering strategic insight into potential collaborative ventures and re‐combinations of existing museum products and services.

Copyright © 2008 John Wiley & Sons, Ltd.