Citation

Silva TC, Laiz MT, Tabak BM. Accid. Anal. Prev. 2020; 146: e105694.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.aap.2020.105694

PMID

32980658

Abstract

We use a controlled experiment to analyze the impact of watching different types of educational traffic campaign videos on overconfidence of undergraduate university students in Brazil. The videos have the same underlying traffic educational content but differ in the form of exhibition. We find that videos with shocking content (Australian school) are more effective in reducing drivers' overconfidence, followed by those with punitive content (American school). We do not find empirical evidence that videos with technical content (European school) change overconfidence. Since several works point to a strong association between overconfidence and road safety, our study can support the conduit of driving safety measures by identifying efficient ways of reducing drivers' overconfidence. Finally, this paper also introduces how to use machine learning techniques to mitigate the usual subjectivity in the design of the econometric specification that is commonly faced in many researches in experimental economics.

Language: en

Keywords

Machine learning; Econometrics; Video; Overconfidence; Traffic campaigns