Method to estimate the WET gap
To estimate the wine equalisation tax (WET) gap, we use both logistic and linear regressions in a bottom-up, multi-stage regression model.
We estimate the net gap which is the difference between the payable and refundable amounts. For a whole of program view, we include all entities in the WET system.
Step 1: Calculate a risk score
We consult with the WET compliance area to identify logic and variables for risk selection in ATO compliance activity.
We then calculate a risk score for all WET entities in the population. The score is a key explanatory variable used in subsequent regressions.
Step 2: Apply logistic regression for selection probability
Apart from the risk score calculated in step 1, we identify other characteristics of WET entities to help us predict the probability of them being selected for compliance activity.
We then estimate the probability of selection using a logistic regression. This step determines the sample weights for correcting selection bias in the operational audit data.
Step 3: Apply a weighted logistic regression
We analyse the business activity statement (BAS) data of WET entities that were subject to amendment activities, as well as those which were not.
We apply a logistic regression to estimate the probability of a WET entity being found non-compliant as a result of a compliance activity. To adjust for selection bias in the operational audit data, we apply the sample weights calculated in step 2 to the observations.
Step 4: Apply a Poisson Pseudo Maximum Likelihood regression
We analyse the BAS and case data of WET entities which had an interaction with the ATO to identify characteristics that would contribute to the prediction of amended tax size. We apply a Poisson Pseudo Maximum Likelihood (PPML) regression to estimate the amended tax size for each entity found to be non-compliant. Due to the non-normal distribution of amended tax returns, arising from a large share returning a null result, a PPML regression better fits the data and incorporates the null results of taxpayers who are audited yet compliant.
The result is again weighted to adjust for selection bias. The PPML regression is then applied to each entity in the population to estimate the potential size of amended tax.
The key difference between steps 3 and 4 is that step 3 calculates the likelihood of an entity having a tax gap, while step 4 calculates the size of each entity's potential amended tax.
Step 5: Apply the combined results
We combine the regression results from steps 3 and 4 to estimate unreported tax. We estimate total unreported tax by taking the average of the results from 20,000 simulations. This amount includes amendment results.
Step 6: Apply a non-detection uplift factor
We uplift the estimates before this step 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 7: Consolidate the tax gap estimates
We calculate the gross gap by adding the unreported amounts from step 5 to the non-detection uplift and non-pursuable debt from step 6.
We calculate the net gap by subtracting the total amendment amount from the gross gap. Then we add the net gap to the expected collections to estimate the total theoretical liability.
Summary of the estimation process
Table 2 shows a summary of each step of the estimation process and the results for each year.
|
Step |
Description |
2016–17 |
2017–18 |
2018–19 |
2019–20 |
2020–21 |
2021–22 |
|---|---|---|---|---|---|---|---|
|
1 to 5 |
Estimate unreported tax ($m) |
17.2 |
23.3 |
21.0 |
19.7 |
16.2 |
16.4 |
|
6.1 |
Apply estimate for non-detection ($m) |
8.5 |
8.3 |
7.8 |
7.7 |
7.8 |
7.8 |
|
6.2 |
Add non-pursuable debt ($m) |
13.3 |
13.3 |
13.3 |
13.3 |
13.3 |
13.3 |
|
7.1 |
Gross gap ($m) |
50.1 |
49.3 |
46.9 |
46.5 |
47.1 |
47.1 |
|
7.2 |
Amendments ($m) |
11.1 |
4.4 |
4.8 |
5.9 |
9.8 |
9.6 |
|
7.3 |
Net gap ($m) |
38.9 |
44.9 |
42.1 |
40.7 |
37.3 |
37.5 |
|
7.4 |
Expected collections ($m) |
879 |
918 |
1,023 |
1,020 |
1,087 |
1,104 |
|
7.5 |
Total theoretical liability ($m) |
918 |
962 |
1,065 |
1,061 |
1,124 |
1,141 |
|
7.6 |
Gross gap (%) |
5.5% |
5.1% |
4.4% |
4.4% |
4.2% |
4.1% |
|
7.7 |
Net gap (%) |
4.2% |
4.7% |
4.0% |
3.8% |
3.3% |
3.3% |
Find out more about our overall research methodology, data sources and analysis for creating our tax gap estimates.
Limitations
These limitations are associated with the WET gap estimation:
- There is considerable delay after a financial year ends and the completion of our compliance activities relating to that year. This means gap estimates may be subject to revisions for several years.
- The extent of non-detection is not directly observable and extremely challenging to measure. We use a figure based on expert opinion and operational data.
- The calculation of the shadow economy's impact on WET revenue is difficult to measure. For this estimate, we allocate a small amount within the non-detection estimate. This is in keeping with expert opinion that shadow economy activity is infrequent and irregular with a negligible impact on the WET gap.
- The lower coverage levels of compliance activity have resulted in us adopting a pooled regression approach. We draw all data across 2015–16 to 2021–22 together to estimate the coefficients for all years. This implicitly assumes the relationships between variables do not change much over that period. While deriving standalone regression results for each income year would be ideal, it is not feasible due to limited data across the WET population.
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 many reasons, including:
- different methodologies used
- improvements in methodology
- revisions to data
- additional information becoming available.
Figure 2 shows the gross and net gaps from our current model compared to our previous estimates.
Figure 2: Current and previous WET tax gap estimates, 2010–11 to 2021–22
The data used in Figure 2 is presented in Table 3 below.
|
Program year |
2010–11 |
2011–12 |
2012–13 |
2013–14 |
2014–15 |
2015–16 |
2016–17 |
2017–18 |
2018–19 |
2019–20 |
2020–21 |
2021–22 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
2024 |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
4.2 |
4.7 |
4.0 |
3.8 |
3.3 |
3.3 |
|
2023 |
n/a |
n/a |
n/a |
n/a |
n/a |
3.6 |
3.4 |
3.9 |
3.2 |
3.1 |
2.8 |
n/a |
|
2022 |
n/a |
n/a |
n/a |
n/a |
3.0 |
3.1 |
2.8 |
3.4 |
3.1 |
3.0 |
n/a |
n/a |
|
2021 |
n/a |
n/a |
n/a |
5.3 |
3.0 |
2.8 |
2.7 |
3.2 |
2.9 |
n/a |
n/a |
n/a |
|
2020 |
n/a |
n/a |
12.8 |
5.3 |
3.0 |
2.8 |
2.6 |
3.1 |
n/a |
n/a |
n/a |
n/a |
|
2019 |
n/a |
6.1 |
12.6 |
5.3 |
3.0 |
2.6 |
2.5 |
n/a |
n/a |
n/a |
n/a |
n/a |
|
2018 |
4.1 |
4.9 |
0.5 |
5.9 |
0.1 |
0.5 |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
|
2017 |
4.1 |
4.9 |
0.5 |
5.9 |
0.6 |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
|
2016 |
4.1 |
4.9 |
0.5 |
5.9 |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
|
2015 |
2.3 |
4.3 |
3.3 |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
Notes: Gap estimates published in 2017–18 and earlier are WET payable gaps only for 2010–11 to 2013–14.