The Populist Impulse: Assessing The Relationship Between Cognitive Reflection, And Populist Attitudes And Voting Behaviour. - Info and Reading Options

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In this project, I examine the role of cognitive reflection (that is the extent to which an individual is likely to suppress their spontaneous intuition and engage in higher order cognition) may explain this rise both in terms of attitudes and voting behaviour. To do this, I will field an original survey with a sample of approximately 1,000 respondents representative of the UK population. In doing so, I will avail of the cognitive reflection test (CRT) as a measure of cognitive reflection (Frederick, 2005) while measuring populist attitudes using an eight-item scale proposed Van Hauwaert and Van Kessel (2018) and measuring populist voting behaviour using the results of a conjoint analysis. The CRT is composed of three questions which are designed to prompt an intuitive but incorrect answer, whereas the correct answer demands more careful thinking. In designing the questions this way, it is possible to observe which mode of thinking an individual has used when attempting to solve the question. For example, A bat and a ball cost £1.10 in total. The bat costs £1.00 more than the ball. How much does the ball cost? Here, the intuitive answer that comes to mind is £0.10. However, it takes a moment of reflection to realise that if this were true, the bat would cost £1.10 and together, the bat and ball would cost £1.20. Therefore, given the conditions set out in the question, the ball cannot cost £0.10 and it is up to the respondent to reason the correct answer of £0.05. For this survey I will ask respondents the CRT in a randomised order along with two decoy questions. Like the CRT, the decoy questions are also intended to prompt an intuitive answer from the respondent. However in contrast to the CRT, these intuitive responses are indeed correct. These decoy questions are used in additional robustness tests in lieu of the actual CRT scores as placebo. Once responses from the CRT (and decoys) are collected, I will code correct responses 1 and incorrect responses 0. In additional robustness checks, I will recode these 1 for all unintuitive responses, and 0 for all intuitive ones (thus excluding respondents who did not give any of these two responses). After the data has been coded, I will then aggregate the responses into a single measure of reflectiveness using an item response theory graded response model. Such a specification can account for differences in the difficulty associated with each item and thus construct a measure of reflectiveness as a latent variable. Measuring attitudes, the scale proposed by Van Hauwart and Van Kessel will be applied with respondents being asked to respond to each of the eight items on a five-point Likert scale (with an option to indicate “Don’t know”). Once this data are collected, I will combine these responses into a single measure of populism, using an item response theory graded response model, similar to that described above. On this constructed latent variable, I will estimate the marginal effect of a one-unit increase of the respondent’s CRT score using a series of OLS regressions with and without controls for the respondent’s demographic characteristics. To assess populist voting behaviour, I adopt the conjoint design first used by Bakker et al. (2021). In this design, respondents are faced with two hypothetical candidates with attributes randomly determined across a set of political relevant dimensions. Of particular interest for this study are the following dimensions and their associated attributes: i) anti-establishment sentiment, ii) people centrism, and iii) conflict/cooperation. These dimensions represent multiple facets of populism and can be used to assess its appeal. For each of these dimensions, there are two attributes: one populist and one non-populist. For each set of two attributes, one is randomly attributed with equal probability and independent of any other factors. In addition, a number of attributes associated with dimensions unrelated to populism are also presented with each candidate. These pertain to immigration, taxation, and personal background and will be presented (along with the populist dimensions) in a randomised order. Presented with two candidates, each with a full list of attributes as described above, the respondent is then asked from whom they would vote for in a hypothetical election between the two. From this, the respondent’s vote choice and the attributes associated with each candidate are recorded and used for analysis. I will repeat this process three times. Once these data are collected, I will run a series of OLS regressions to estimate the determinants of the linear probability of a respondent’s vote choice in this hypothetical scenario. In particular, I will estimate the interaction between a candidate’s populist attributes and the respondent’s CRT score. In doing so, I will be able to estimate the marginal component effect of populist attributes (anti-elitist, people-centric, and conflict seeking) on candidate vote choice at different levels of cognitive reflection. For robustness, I will do this with and without controlling for the remaining randomly determined attributes . Moreover, I will cluster the standard error of these estimates by the individual respondent.

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