Periodic Reporting for period 1 - INSPiRE (The Influence of Information Search on Preference Formation and Choice)
Reporting period: 2018-11-01 to 2020-10-31
Cost-benefit analyses are routinely used by policy makers when designing environmental policy to weigh the costs and benefits of different policy alternatives against one another. Not all costs and benefits have market values and stated choice experiments are one non-market valuation technique used to elicit values in the absence of markets. Standard practice in stated choice experiments is to create a hypothetical market in which people choose among competing policy alternatives under the assumptions that they have complete information about all available policy alternatives, and that they are perfectly rational and maximize utility based on a clearly defined set of preferences, which can be retrieved when needed in any situation. In reality, these assumptions are questionable. Drawing on accumulating evidence from economics, psychology, and marketing, the INSPiRE project sought to understand how searching for information about policy alternatives affects stated preference formation, learning, and choice, and to what extent this could reduce hypothetical bias. The project developed a novel experimental design procedure where participants had to actively search for information by deciding at each decision point whether they wanted to see another alternative or make a choice between the alternatives already seen. This allowed us to track the information search process. Importantly, the decision to continue to search or not also reveals information about people’s preferences. Using this extra preference information when estimating people’s utility functions can lead to better and more precise estimates. With better estimates for how people value the non-marketed goods and services affected by a given policy decision, the better is the information for policy decision makers.
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far
Over the period of the project, we gathered a large and rich dataset comprising more than 4000 respondents drawn at random from a large representative internet panel. The data includes 10 experimental treatments, which separately and together allow us to test many hypotheses related search, information processing, learning and fatigue, decision rules, and choice. Research from the project has been presented at 9 different occasions at international conferences, workshops and invited seminars. So far, we have published 1 peer reviewed journal article, 2 open source R packages and 1 popular science article; 3 working papers are in the process of being submitted for peer review in leading field journals. We have made available on the project website an online tool to present the experimental treatments and the complete survey instrument programmed in Shiny. All source code, including for the survey, is made freely available under a GPL-3 license from GitHub.
Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)
The rich dataset will allow us to continue to test hypotheses related to search, decision rules and choice, and to continue publishing high-quality papers. The data will be made available through the University of Stirling’s open access research data repository DataSTORRE following the FAIR principles, which should allow other researchers to test their hypotheses. The project outcomes should inspire stated preference researchers to consider the importance of search in shaping preferences and that the preference information inherent in a search process can be used to elicit better estimates for people’s preferences. This will contribute to push research and methods beyond the state-of-the-art in the coming years. Through the broad uptake and use of the experimental methods proposed here by stated preference researchers and better understanding of their importance by policy makers, the improved values derived should lead to better policy decisions.