<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Denisova-Schmidt, Elena</dc:creator>
  <dc:creator>Huber, Martin</dc:creator>
  <dc:creator>Leontyeva, Elvira</dc:creator>
  <dc:creator>Solovyeva, Anna</dc:creator>
  <dc:date>2017-08-01</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">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.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://folia.unifr.ch/global/documents/305953</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/305953/files/WP_SES_487.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>http://www.unifr.ch/ses/wp</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Anti-Corruption Campaigns</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">Experiments</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">Corruption</dc:subject>
  <dc:subject xmlns:ns4="xml" ns4:lang="en">Academic Integrity</dc:subject>
  <dc:subject xmlns:ns5="xml" ns5:lang="en">University</dc:subject>
  <dc:subject xmlns:ns6="xml" ns6:lang="en">Students</dc:subject>
  <dc:subject xmlns:ns7="xml" ns7:lang="en">Russia</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/33</dc:subject>
  <dc:title xmlns:ns8="xml" ns8:lang="en">Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_18ws</dc:type>
</oai_dc:dc>
