Now showing 1 - 2 of 2
  • Publication
    Mining for Mood Effect in the Field
    (University College Dublin. School of Economics, 2020-01) ;
    We conduct what we believe to be the most methodologically rigorous study of mood effect in the field so far to measure its economic impact and address shortcomings in the existing literature. Using a large dataset containing over 46 million car inspections in Sweden and England in 2016 and 2017, we study whether inspectors are more lenient on days when their mood is predicted to be good, and if car owners exploit the mood effect by selecting these days to inspect low quality cars. Different sources of good mood are studied: Fridays, sunny days, and days following unexpected wins by the local soccer team, with varying degrees of the car owner’s ability to plan for inspection, and hence the likelihood of selection bias. We find limited evidence to support the existence of mood effects in this domain, despite survey results showing belief to the contrary. There is some indication of selection effect on the part of car owners. Our findings cast doubt on previous mood effects found in the field.
      239
  • Publication
    Anchoring and Subjective Belief Distributions
    (University College Dublin. School of Economics, 2024-04) ; ; ;
    We investigate how the anchoring effect—a well-established cognitive bias—influences the full distribution of subjective beliefs. While prior research extensively examines the impact of anchoring and other biases on point estimates, their effect on higher moments of the distribution remains unexplored. Through a pre-registered online experiment (N=732), we find that anchoring impacts the mean, variance, and skewness of belief distributions. Notably, the anchoring effect diminishes when eliciting distributions rather than means. Furthermore, presenting anchors prior to eliciting beliefs reduces the variance in belief distributions compared to when elicited without anchors. Our study shows that cognitive biases may have important impacts beyond point estimates.
      14