Abstract
Choice experiments, a survey methodology in which consumers face a series of choice tasks requiring them to indicate their most preferred option from a choice set containing two or more options are used to generate estimates of consumer preferences to determine the appropriate allocation of public resources to competing projects or programs. The analysis of choice-experimental data typically relies on the assumptions that choices of the non-status quo option are demand-revealing and choices of the status quo option are not demand-revealing, but, rather, reflect an underlying behavioral bias in favor of the status quo. This paper reports the results of an experiment demonstrating that both of those assumptions are likely to be invalid. We demonstrate that choice experiments for a public good are vulnerable to the same types of strategic voting that affect other types of multiple-choice voting mechanisms. We show that owing to the mathematics of choice-set design, what actually is strategic voting often is misinterpreted as a behavioral bias for the status quo option. Therefore, we caution against using current choice-experimental methodologies to inform policy making about public goods.
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Notes
By the “mathematics of combinatorial choice set design”, we mean the method by which individual choice options with different levels of attributes are combined into groups of options, termed choice sets. During a choice experimental survey, respondents are presented with a series of tasks, in which they are asked to choose one option from each choice set. Their selection usually is interpreted to indicate the most preferred option in the set. .
A proof of the generalized result is available from the authors.
If the number of subjects who showed up was not divisible by nine, the unassigned subjects were invited to participate in a different experimental session at a later time. Prior to starting the experiment, all subjects completed an informed consent process. Subjects were free to leave at any time.
The experiment was conducted under the oversight of the university’s Institutional Review Board (IRB). All experimental instructions are available in the online supplementary materials. Experimental data are available from the corresponding author upon request.
As a check, we re-calculated all standard errors and p-values by controlling for clustering at the group level instead of at the subject level. Generally speaking, standard errors and p-values when controlling for clustering at the group level are the same or smaller than when controlling for non-independence at the individual subject level. Controlling for non-independence at the group level does not change any of the conclusions reported below. .
The overall rate of demand revelation is significantly less in the UBAL treatment than the OOD treatment (p = 0.02). No other significant differences in rates of demand revelation are evident across methods to create fractional factorial choice experiment designs.
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Hutchinson: Employed as a consultant by Northern Ireland Electricity Networks Ltd in connection with the preparation of the 6th Price Control Agreement (RP6) with The Office of the Utility Regulator NI (Final Determination 30th June 2017). Study is cited herein as Queen’s University Belfast and Perceptive Insight (2015). Scarpa: Served as lead consultant in the design of the survey instruments and the choice data analysis for the study for the Australian Energy Market Operator cited herein.
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This research involves human participants. The experiments reported herein were conducted under the oversight of the United States Air Force Academy Institutional Review Board, protocol number FAC20130036H.
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This research is funded by Queen’s University, Belfast, under a cooperative research and development agreement with the United States Air Force Academy. The funding was for payment of experimental subjects only. Sponsor had no role in study design; collection, analysis, and interpretation of data; writing of the report; or decision to submit the article for publication. The opinions expressed herein are solely those of the authors and do not necessarily reflect the views of the authors’ respective institutions.
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Carson, K.S., Chilton, S.M., Hutchinson, W.G. et al. Public resource allocation, strategic behavior, and status quo bias in choice experiments. Public Choice 185, 1–19 (2020). https://doi.org/10.1007/s11127-019-00735-y
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DOI: https://doi.org/10.1007/s11127-019-00735-y