Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/198976 
Year of Publication: 
2019
Series/Report no.: 
CESifo Working Paper No. 7616
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
Tax administrations use machine learning to predict risk scores as a basis for selecting individual taxpayers for audit. Audits detect noncompliance immediately, but may also alter future filing behavior. This analysis is the first to estimate compliance effects of audits among high-risk wage earners. We exploit a sharp audit assignment discontinuity in Norway based on individual tax payers risk score. Additional data from a random audit allow us to estimate how the audit effect vary across the risk score distribution. We show that the current risk score audit threshold is set far above the one that maximizes net public revenue.
Subjects: 
tax audits
tax revenue
tax reporting decisions
income tax
machine learning
risk
profiling
JEL: 
D04
H26
H83
Document Type: 
Working Paper
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