Methodology

We use a 3 step model-based bottom-up methodology with channel analysis and micro-analytical techniques to estimate the petroleum resource rent tax (PRRT) gap. We use this method because we know and monitor the taxpayers involved. We use operational intelligence and subject matter expertise to inform any necessary assumptions.

Step 1: Determine the scope of the PRRT population

We use operational data to determine the size and scope of the PRRT population and the amount of PRRT expected to be collected.

Step 2: Estimate the PRRT gap

We identify the key PRRT risk areas. We then use our operational data and subject matter expertise to estimate the impacts. This gives us risk rates. We apply these rates to the population in step 1, to produce an estimate of the PRRT gap.

Step 3: Estimate the theoretical liability

We add the tax gap calculated in step 2 to the amount of PRRT expected to be collected. This gives us the theoretical liability. Then we divide the tax gap by the theoretical liability to determine the gap as a percentage.

Summary of estimation process

The steps for the estimation process and the results for each year as a dollar amount and percentage are shown in Table 2.

Table 2: Applying the methodology, PRRT gap, 2015–16 to 2020–21

Step	Description	2015–16	2016–17	2017–18	2018–19	2019–20	2020-21
1	PRRT expected collections ($m)	998	1,026	1,207	1,039	970	927
2	Tax gap ($m)	17	15	18	18	16	19
3.1	Theoretical liability ($m)	1,016	1,040	1,224	1,057	987	946
3.2	Tax gap (%)	1.7%	1.4%	1.4%	1.7%	1.7%	2.0%

Find out more about our overall methodology, data sources and analysis used for creating our tax gap estimates.

Limitations

The following limitations apply to PRRT gap estimate:

• We assume all project participants are registered and lodge PRRT returns as required, and do not participate in the shadow economy.
• We assume expert judgment (informed by our outcomes and engagement activities) is a reliable indicator of levels of non-compliance and the impact of law interpretation risks in respect of lodged PRRT returns for    
  • assessable receipts
  • applied deductible expenditure
  • exploration expenditure
  • transferred exploration expenditure.
• The extent of non-detection is unknown and challenging to measure. We use operational data to inform the level of non-detection.
• We assume our final adjustments represent the correct outcome at law.

Accounting for non-detection in the gap

We do not detect all errors through audit and assurance activity. We account for this by applying a non-detection uplift to derive the estimate.

We apply different uplift rates depending on the level of assurance we have over the tax reported in each PRRT return. Where we have a high level of assurance, we apply a lower uplift rate to account for the confidence we have in that return. We apply a higher uplift to PRRT returns that we have not reviewed in detail.

Updates and revisions to previous estimates

Each year we refresh our estimates in line with the annual report. Changes from previously published estimates occur for a variety of reasons, including:

• improvements in methodology
• revisions to data
• additional information becoming available.

This year our revision saw a reduction in the gap estimates across all years. This is due to our improved understanding of risk specific to PRRT resulting in a reduction in the risk rates used to help derive the gap estimate. The estimates continue to remain low over the years.

Figure 2 shows the tax gap from our current model compared to the previous estimates.

Figure 2: Current and previous PRRT gap estimates, 2013–14 to 2020–21
 

The data used in Figure 2 is presented in Table 3 below.

Table 3: Summary of published tax gap percentages for PRRT, 2013–14 to 2020–21

	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20	2020-21
2023 Program	n/a	n/a	1.7%	1.4%	1.4%	1.7%	1.7%	2.0%
2022 Program	n/a	1.9%	1.4%	1.3%	1.4%	1.8%	1.3%	n/a
2021 Program	2.8%	2.5%	1.7%	1.7%	1.9%	2.2%	n/a	n/a
2020 Program	2.8%	2.5%	1.7%	1.7%	1.7%	n/a	n/a	n/a
2019 Program	3.2%	3.1%	2.1%	2.1%	n/a	n/a	n/a	n/a
2018 Program	2.1%	2.5%	2.0%	n/a	n/a	n/a	n/a	n/a