Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students
Martin Huber (),
Elvira Leontyeva and
No 487, FSES Working Papers from Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland
This paper examines how anti-corruption educational campaigns affect the attitudes of Russian university students towards corruption and academic integrity. About 2,000 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. Using machine learning to detect effect heterogeneity, we find that various groups of students react to the same information differently. Those who commonly plagiarize, who receive excellent grades, and whose fathers are highly educated develop stronger negative attitudes towards corruption in the aftermath of our intervention. However, some information materials lead to more tolerant views on corruption among those who rarely plagiarize, who receive average or above average grades, and whose fathers are less educated. Therefore, policy makers aiming to implement anti-corruption education at a larger scale should scrutinize the possibility of (undesired) heterogeneous effects across student groups.
Keywords: Anti-Corruption Campaigns; Experiments; Corruption; Academic Integrity; University; Students; Russia (search for similar items in EconPapers)
JEL-codes: D73 I23 C93 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cis, nep-edu, nep-exp and nep-tra
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