Publication details for Professor Nigar HashimzadeHashimzade, N., Myles, G. D. & Rablen, M. D. (2016). Predictive Analytics and the Targeting of Audits. Journal of Economic Behavior and Organization 124: 130-145.
- Publication type: Journal Article
- ISSN/ISBN: 0167-2681
- DOI: 10.1016/j.jebo.2015.11.009
- Keywords: Tax compliance, Social network, Agent-based model.
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
The literature on audit strategies has focused on random audits or on audits conditioned only on income declaration. In contrast, tax authorities employ the tools of predictive analytics to identify taxpayers for audit, with a range of variables used for conditioning. The paper explores the compliance and revenue consequences of the use of predictive analytics in an agent-based model that draws upon a behavioral approach to tax compliance. The taxpayers in the model form subjective beliefs about the probability of audit from social interaction, and are guided by a social custom that is developed from meeting other taxpayers. The belief and social custom feed into the occupational choice between employment and two forms of self-employment. It is shown that the use of predictive analytics yields a significant increase in revenue over a random audit strategy.