Enhanced anonymity in tax experiments does not affect compliance

  • Christoph Kogler (Contributor)
  • Jerome Olsen (Contributor)
  • Rebecca Bogaers (Contributor)



In the domain of classical economic games, it has previously been suggested that deviations from purely rational behavior could be explained by a lack of experimenter-subject anonymity. In fact, some experiments show that contributions and prosocial behavior increase when participants feel observed. In the present study, we investigate whether measures of enhanced anonymity, beyond a conventional standard, are necessary in the particular case of tax behavior experiments. This issue might be pivotal for both the validity and generalizability of existing published studies as well as for designing future studies. We suspect social desirability to be even more relevant in experiments on tax compliance, which often apply a context-rich setting, entailing a strong ethical component. Interestingly, certain common experimental practices reflect potential breaches of anonymity during sign-up, the actual task, and the payment phase. Accordingly, we tested whether (1) tax compliance is higher under conditions of regular anonymity compared to enhanced anonymity, and (2) whether this anonymity manipulation moderates established effects of tax-related parameters, such as audit probability and fine rate. Despite an enhanced perception of anonymity due to our manipulation, we did not observe a difference in relative tax compliance between the regular and enhanced anonymity conditions. Additionally, enhanced anonymity did not interact with the effects of tax rate, audit probability, and fine level on tax compliance. We conclude that commonly used procedures in tax experiments are sufficient to guarantee a satisfactory level of anonymity. The first author received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 798824) for the drafting of this paper.
Date made available2020
Date of data production2019

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