Evaluating the Public Perception of a Feebate Policy in California through the Estimation and Cross-Validation of an Ordinal Regression Model

Authors: Elliot Martin, PhD, Susan Shaheen, PhD, Timothy Lipman, PhD, Madonna Camel

Date: May 2014

Abstract: Understanding the roots of policy perception can be critical for informing the design and implementation of innovative public policies. Feebates is one such innovative policy and in the context of new vehicles can be designed to offer buyers a rebate for the purchase of low-emission vehicles and a fee for the purchase of high-emission vehicles. Because feebates is a policy that directly impacts the consumer, understanding the dynamics of public perception, support, and opposition is important. This study explores the public perception of a feebate policy within California and evaluates the robustness of ordinal regression models to predict policy sentiment. The authors conducted a series of 12 focus groups throughout the State, which were followed by a computer-assisted telephone interview (CATI) survey of 3,072 California residents in Fall 2009. The survey results were used to gain insights into the consumer response to a feebate policy, while focus groups gauged participant understanding of the feebate concept and overall response in preparation for the statewide survey. The survey data was weighted to match key demographics of the population and probed respondents on sentiments towards climate change, foreign oil dependence, policy fairness as well as perceptions of a potential California feebates policy. The results suggested that roughly three quarters (~76%) of the population would have supported a feebate policy, while one-in-five (~22%) would have opposed it. To evaluate how key factors simultaneously influence policy support/opposition, the authors estimated an ordinal regression model on policy support, which could correctly re-predict 89.4% of the sample’s policy support or opposition. To further assess the model’s robustness, it was validated through a series of re-estimations with 10,000 randomly drawn subsamples. Models estimated using these subsamples were then applied to predict policy opinion on the remaining hold-out sample. The model performed very well, as hold-out sample opinions could be predicted with an average accuracy of 89.1%, with little variance in performance. The authors conclude with a discussion of the implications of these results on public support for feebates and comment on the use of ordinal regression to predict policy opinion.

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January 8, 2019