• ## Methodology

We selected a model-based bottom-up methodology, employing channel analysis and micro-analytical techniques to estimate the petroleum resource rent tax (PRRT) gap.

We selected this method given the lack of suitable external data to produce a top-down estimate and the size of the taxpayer base. Our estimates are produced using assumptions based on operational intelligence and subject matter expertise.

The three steps to our approach are explained below, followed by a summary of the overall estimate.

### 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 paid.

### Step 2: Estimate the PRRT gap

We identify the key areas of risk which are relevant to the PRRT. We then use our operational data and subject matter expertise to estimate the impacts of the risks, resulting in corresponding risk rates. These risk rates are applied to the population as defined 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 paid (determined in Step 1) to estimate the theoretical liability. We then divide the tax gap by the theoretical liability to determine the tax 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, 2013–14 to 2017–18

Step

Description

2013–14

2014–15

2015–16

2016–17

2017–18

1

PRRT paid (\$m)

1,827

1,244

983

1,033

1,212

2

Tax gap (\$m)

53

32

17

18

21

3.1

Theoretical liability (\$m)

1,880

1,277

1,000

1,051

1,233

3.2

Tax gap (%)

2.8

2.5

1.7

1.7

1.7

### Limitations

The following limitations apply to the three core components of the model used to estimate the PRRT gap:

• We assume all project participants are registered and lodge PRRT returns as required, and do not participate in the non-observed 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 is extremely challenging to measure. We use a figure based on operational data to estimate the impact of non-detection.
• We assume our final adjustments represent the correct outcome at law.

#### Accounting for non-detection in the gap

Not all errors are detected through audit and assurance activity, so we account for this by applying a non-detection uplift to the unreported tax 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 particular PRRT return. We apply a higher uplift to PRRT returns that we have not reviewed in detail.

### Updates and revisions to previous estimates

The PRRT gap estimates have been revised, which is standard practice in gap estimates. Revisions occur as a result of using updated information, including updated risk rates. The estimates continue to remain low over the years.

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

Figure 2: Current and previous PRRT gap estimates, 2013–14 to 2017–18 The data used in Figure 2 is presented in Table 2 below.

Table 3: Summary of published net tax gap percentages for PRRT, 2013–14 to 2017–18

Gap release year

2013–14

2014–15

2015–16

2016–17

2017–18

2017–18

2.1%

2.5%

2.0%

n/a

n/a

2018–19

3.2%

3.1%

2.1%

2.1%

n/a

2019–20

2.8%

2.5%

1.7%

1.7%

1.7%