To estimate the fringe benefits tax (FBT) gap, we combine judgment based analysis with logistic and linear regressions in a bottom-up, multi-stage regression model. We selected this method as it draws on our ongoing engagement activities and operational intelligence.
Step 1: Identify employer population and their engagement activities
We identify the employer population through PAYG withholding records. We put these into subpopulations that align to our engagement activities. Key to this is the identification of FBT registered employers and those not registered for FBT. Those employers not registered for FBT include:
- employers that declare employee contributions or had reportable fringe benefits amounts in their employees’ PAYG withholding records, but do not lodge an FBT return
- employers that do not lodge an FBT return, report employee contributions or reportable fringe benefits, but claim motor vehicle expenses in their income tax return
- all other employers that do not lodge an FBT return but do not fit into the previous categories.
Step 2: Estimate the probability of selection for engagement
For relevant employers identified in step 1, we identify characteristics that contributed to their probability of being selected for FBT compliance. We use these characteristics to estimate the probability of selection for the broader population via a logistic regression. This step determines the sample weights for correcting selection bias in our FBT compliance data.
Step 3: Estimate the probability of non-compliance
For relevant employers subjected to compliance engagement, we identify characteristics that contributed to their probability of being non-compliant with FBT. We use these characteristics to estimate the probability of being non-compliant with FBT for the broader population via another logistic regression. To adjust for selection bias in our FBT compliance data, we apply sample weights calculated from step 2 to the observations in step 3.
Step 4: Estimate unreported amounts
For relevant employers that were non-compliant, we identify characteristics that help predict their amount of unreported FBT. We then apply a linear regression to estimate the potential unreported FBT for the broader population. The data is again weighted to adjust for selection bias using the results of step 2.
For large employers, we skip steps 2 and 3, instead accounting for selection bias with a judgment based correction to average unreported FBT from large employers in our FBT compliance data.
Step 5: Apply a non-detection uplift factor and non-pursuable debt
We uplift the estimate from step 4 to account for non-compliance that is not detected. This ensures the final estimate is not understated.
We also add in the value of non-pursuable debt. This is debt the Commissioner of Taxation has assessed as:
- not legally recoverable
- uneconomical to pursue
- unable to be pursued due to another Act.
Step 6: Consolidate tax gap estimates and adjust for deductibility
We calculate the gross gap by adding the unreported amounts from Step 4 to the non-detection uplift and non-pursuable debt from Step 5. We calculate the net gap by subtracting the total amendment amount from the gross gap. Then we add the net gap to expected collections to estimate the total theoretical liability.
Employers can claim an income tax deduction for the fringe benefits they provide. We determine the size of the forgone income tax deduction that would have been realised if FBT were correctly reported. We adjust the estimates by the relevant income tax rate and subtract from the sum of unreported tax, non-detection and non-pursuable debt amounts to produce the net revenue effect amounts.
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.
|
Step |
Description |
2016–17 |
2017–18 |
2018–19 |
2019–20 |
2020–21 |
2021-22 |
|---|---|---|---|---|---|---|---|
|
1.1 |
Population |
848,840 |
852,807 |
855,751 |
898,049 |
932,724 |
947,857 |
|
1.2 |
Amendments ($m) |
31 |
30 |
25 |
19 |
36 |
26 |
|
2.1 |
Add estimated unreported amounts ($m) |
1,806 |
1,821 |
1,789 |
1,937 |
1,985 |
2,010 |
|
2.2 |
Add non-pursuable debt ($m) |
5 |
5 |
5 |
5 |
5 |
5 |
|
2.3 |
Add non-detection ($m) |
483 |
485 |
473 |
511 |
529 |
531 |
|
3.1 |
Equals Gross gap ($m) |
2,326 |
2,341 |
2,292 |
2,472 |
2,555 |
2,573 |
|
3.2 |
Subtract estimated impact of income tax deductions ($m) |
634 |
638 |
624 |
677 |
659 |
664 |
|
3.3 |
Equals Net revenue effect gross gap ($m) |
1,691 |
1,703 |
1,668 |
1,795 |
1,897 |
1,908 |
|
3.4 |
Subtract amendments ($m) |
31 |
30 |
25 |
19 |
36 |
26 |
|
3.5 |
Equals Net revenue effect net gap ($m) |
1,660 |
1,673 |
1,643 |
1,776 |
1,861 |
1,882 |
|
3.6 |
Add Expected collections ($m) |
4,160 |
3,859 |
3,911 |
3,913 |
3,261 |
3,524 |
|
3.7 |
Equals Theoretical tax liability ($m) |
5,820 |
5,532 |
5,554 |
5,689 |
5,122 |
5,407 |
|
3.5 |
Net revenue gross gap (%) |
29.1 |
30.8 |
30.0 |
31.6 |
37.0 |
35.3 |
|
3.6 |
Net revenue net gap (%) |
28.5 |
30.2 |
29.6 |
31.2 |
36.3 |
34.8 |
Find out about our overall research methodology, data sources and analysis for creating our tax gap estimates.
Limitations
The following caveats and limitations apply when interpreting this tax gap release:
- There is no independent data source which can provide a credible or reliable macroeconomic-based estimate (unlike transaction-based taxes).
- The data available does not always indicate whether amendments processed are due to our action or taxpayers correcting their own errors.
- There is a high level of uncertainty around the level of non-detection – the current factor used to account for non-detection is based on factors used in other gaps, and other jurisdictions.
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 gap was first published in October 2020 and has been revised for the 2023–24 Annual Report. This year we have implemented an improvement in methodology which has resulted in noticeable revisions to estimates for previously published tax years. The impact of revisions for each published estimate is shown in the figure below.
Figure 3: Current and previous net revenue effect net gap estimates, 2014–15 to 2021–22
This data is present in Table 3 as a percentage.
|
Program year |
2014–15 |
2015–16 |
2016–17 |
2017–18 |
2018–19 |
2019–20 |
2020–21 |
2021-22 |
|---|---|---|---|---|---|---|---|---|
|
2024 |
n/a |
n/a |
28.5% |
30.2% |
29.6% |
31.2% |
36.3% |
34.8% |
|
2023 |
n/a |
28.3% |
23.4% |
26.1% |
24.1% |
20.1% |
28.2% |
n/a |
|
2022 |
26.7% |
28.4% |
21.1% |
22.1% |
22.0% |
20.3% |
n/a |
n/a |
|
2021 |
25.1% |
26.7% |
22.0% |
22.4% |
22.6% |
n/a |
n/a |
n/a |
|
2020 |
25.2% |
26.8% |
22.0% |
21.2% |
n/a |
n/a |
n/a |
n/a |