Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students
- Denisova-Schmidt, Elena School of Humanities and Social Sciences, University of St.Gallen
- Huber, Martin Department of Economics, University of Fribourg
- Leontyeva, Elvira School of Social Studies and Humanities, Pacific National University, Russia
- Solovyeva, Anna NORC at the University of Chicago
-
27.01.2020
Published in:
- Empirical Economics. - 2020, p. 1-24
English
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.
- Faculty
- Faculté des sciences économiques et sociales et du management
- Department
- Département d'économie politique
- Language
-
- English
- Classification
- Economics
- License
- License undefined
- Identifiers
-
- RERO DOC 330174
- DOI 10.1007/s00181-020-01827-1
- Persistent URL
- https://folia.unifr.ch/unifr/documents/309233
Statistics
Document views: 94
File downloads:
- combiningexperimentalevidencewithmachinelearningtoassesanti-corruption.pdf: 306