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Methodology

What method we use to estimate the small super funds income tax gap.

Published 28 November 2023

To estimate the small super funds income tax gap we utilised a 3-step bottom-up illustrative method for historical years, and a random enquiry program (REP) for 2020–21.

Step 1: Estimate unreported tax

Unreported tax consists of the additional tax expected to be raised if we undertook compliance activity on the portion of the tax base not covered. It arises from incorrectly reported tax returns and has 2 components:

  • unreported tax from the lodged population
  • unreported tax from the non-lodged population.

We use the random enquiry program compliance outcomes to determine the average amendment amount and average rate of amendments. These results are then extrapolated to the whole of the small super funds population to calculate the estimated unreported tax amount.

Step 2: Estimate non-detection

We take the estimated unreported tax from Step 1 and uplift it to account for amounts that are not detected.

Step 3: Calculate the gross gap and net gap

We combine the amounts determined above for amendments, unreported tax, and non-detection with non-pursuable debt to obtain the gross and net tax gaps. The net gap is equal to the gross gap less amendments.

We then add the gross gap to the tax voluntarily paid amount to estimate the theoretical tax liability. We calculate the gap percentages by dividing the gap estimate by the theoretical tax liability.

Summary of the estimation process

Table 3 provides the results at each step of the estimation process for each year from 2015–16 to 2020–21.

Table 3: Applying the methodology – small super funds income tax gap

Step

Description

2015–16

2016–17

2017–18

2018–19

2019–20

2020–21

1.1

Lodged population

533,399

546,551

550,870

547,738

539,988

520,799

1.2

Registered population

549,620

557,043

557,114

559,647

563,890

572,675

1.3

Voluntary expected collections ($m)

1,324

1,433

1,708

1,565

1,745

2,124

2.1

Amendments ($m)

21

27

20

20

20

20

2.2

Expected collections ($m)

1,346

1,460

1,728

1,585

1,765

2,144

3.1

Unreported tax: lodged ($m)

9

12

9

9

9

20

3.2

Unreported tax: not lodged ($m)

<1

<1

<1

<1

<1

9

4

Non-detection ($m)

28

36

26

26

26

38

5.1

Non-pursuable debt ($m)

3

3

4

3

3

3

5.2

Gross gap ($m)

62

78

59

58

58

90

5.3

Net gap ($m)

41

51

39

38

38

70

6.1

Theoretical liability ($m)

1,386

1,511

1,767

1,623

1,803

2,214

6.2

Gross gap (%)

4.4

5.2

3.3

3.6

3.2

4.1

6.3

Net gap (%)

2.9

3.4

2.2

2.3

2.1

3.1

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

Limitations

Estimating the tax gap for small super funds is difficult, with inherent uncertainty. Tax issues and the tax law are complex and contestable.

Also, our estimates do not account for differences where there are alternative views on tax law interpretation. In these circumstances, differences can exist between reasonably arguable positions presented by us and taxpayers. Non-detection estimates are also extremely challenging to measure.

The current methodology provides an aggregated estimate of the small super funds tax gap. This may allow generalised comparisons with other taxes.

The gap estimate is a lagging measure, as compliance results take several years to flow through. This is due to the complexity of the market and the elapsed time associated with finalising our compliance activities.

The assumptions we use to construct our estimate are informed by actual data and expert opinion. The insights from the aged random enquiry program are less relevant in the current period and require extensive review.

The following caveats and limitations apply when interpreting this tax gap estimate.

  • For funds that we don't audit or review, we assume
    • a certain degree of non-compliance with tax law occurs
    • the degree of non-compliance in these groups is less than those we do audit or review due to our risk-based approaches to engagement.
  • For funds that we do audit or review, we assume
    • adjustments to their tax liabilities are representative of the value of non-compliance with tax law
    • we don't detect all instances of non-compliance
    • adjustments to their tax liabilities from completed audits and reviews are correct at law, at the time of estimation.

Accounting for non-detection in the gap

We account for non-detection by applying an uplift factor to the amendments amount. The uplift factor is based on international rates. The impact of non-detection on the small super funds income tax gap is $38 million in 2020–21.

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.

We have updated our knowledge of the small super population through a new random enquiry program (REP). However we have retained the existing method for years prior to 2020–21.

For all years we have updated all data that is reported and known. This includes the registered population, lodged population, amendments and voluntary tax paid.

The updated REP has allowed us to improve the reliability of our small super gap estimates, leading to an improved rating of medium.

Figure 3: Current and previous small super funds net tax gap estimates, 2015–16 to 2020–21 (Rollover component distinguished)

Figure 3 displays our previous and current net gap estimates from 2015–16 to 2020–21, as outlined in Table 4.

This data is set out in Table 4 as a percentage.

Table 4: Current and previous small super funds net tax gap estimates (percentage), 2014–15 to 2020–21

Year published

2014–15

2015–16

2016–17

2017–18

2018–19

2019–20

2020-21

2023

2.8%

2.9%

3.4%

2.2%

2.3%

2.1%

3.1%

2022

2.1%

2.6%

2.9%

2.1%

2.3%

2.1%

n/a

2021

1.9%

2.4%

2.6%

2.9%

2.8%

n/a

n/a

2020

1.6%

1.8%

2.4%

2.5%

n/a

n/a

n/a

2019

1.1%

1.5%

2.0%

n/a

n/a

n/a

n/a

2018

3.2%

n/a

n/a

n/a

n/a

n/a

n/a

 

 

 

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