Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students

This paper examines how anti-corruption educational campaigns affect the attitudes of Russian university students toward corruption and academic integrity in the short run. About 2000 survey participants were randomly assigned to one of four different information materials (brochures or videos) about the negative consequences of corruption or to a control group. While we do not find important effects in the full sample, applying machine learning methods for detecting effect heterogeneity suggests that some subgroups of students might react to the same information differently, albeit statistical significance mostly vanishes when accounting for multiple hypotheses testing. Taking the point estimates at face value, students who commonly plagiarize appear to develop stronger negative attitudes toward corruption in the aftermath of our intervention. Unexpectedly, some information materials seem inducing more tolerant views on corruption among those who plagiarize less frequently and in the group of male students, while the effects on female students are generally close to zero. Therefore, policy makers aiming to implement anti-corruption education at a larger scale should scrutinize the possibility of (undesired) heterogeneous effects across student groups.