What are the taxpayer savings from cancelling the visas of organised crime offenders?


This study estimates the taxpayer savings from cancelling or refusing the visas of organised crime offenders. Using data from the Australian Criminal Intelligence Commission and the Department of Home Affairs, the research produced a statistical model of the known lifetime offending by organised crime offenders in Australia and applied this to the 184 organised crime offenders whose visas were cancelled / refused between December 2014 and May 2018. The results show organised crime offenders are a prolific group. They show a level of activity, and persistence in offending, unlikely to be observed in any general population of offenders. Offending by outlaw motorcycle gang (OMCG) members is more serious than other organised crime offenders. Cancelling or refusing the visas of 184 organised crime offenders (139 of whom were OMCG members) is estimated to save the community $116 million as a result of the crimes prevented and savings to the prison system.


  • Summary
    • What did we do?
    • What did we find?
  • Introduction
    • What was the purpose of this study?
    • How did we do it?
    • What's not included in these estimates?
  • Results
    • How many crimes are committed by organised crime offenders?
    • What do offences committed by organised crime offenders cost the community?
    • How much time do organised crime offenders spend in prison?
    • What does it cost to imprison organised crime offenders?
    • How much could be saved by cancelling or refusing the visas of organised crime offenders?
    • Could the savings be higher if more prolific offenders were targeted?
    • Which offenders are more likely to be in the high-offending group?
  • Conclusion
  • References
  • Appendix A: Detailed methodology and results
    • Limitations and excluded costs
    • Crime cost calculations and sensitivity analysis
    • Sensitivity analysis of estimated prison costs
    • Adjustments to estimated costs and savings
    • Group-based trajectory analysis
    • Model predicting likelihood of being in the prolific, high-offending group