The methods in detail
We apply 2 bottom-up statistical methods to estimate the medium business income tax gap estimate.
The method and results are outlined below and combined in Table 1.
- Calculation – medium business individuals
- Calculation – medium business companies
- Limitations
- Updates and revisions to previous estimates.
Calculation – medium business individuals
The following 5-step regression is applied to estimate the medium individual tax gap.
Step 1: Apply a logistic regression
A logistic regression is applied in our model in order to calculate the probability of each individual having a tax gap.
The results of ATO-initiated compliance activities as well as client-initiated amendments (positive amendments only) are used to estimate the unique probability that each individual has a tax gap. We analyse the income tax return data to identify relevant demographic and financial variables that would contribute to the prediction of whether or not individuals have a tax gap.
The observations for these variables are then weighted based on the individuals' propensities of being selected for compliance activity, before being included in the logistic regression that models the probability of an individual being non-compliant, resulting in a tax gap.
Step 2: Apply a poisson pseudo maximum likelihood regression
We identify individuals that have been subject to ATO-initiated compliance activity with a positive compliance result. We then determine the variables which are most highly associated with having a positive compliance result. This is based on analysing the correlation coefficients in the regression output, while also considering the collinearity of variables with each other.
After that, the observations for those variables are adjusted using the same weights in the logistic regression above to account for potential selection bias. We apply the poisson pseudo maximum likelihood regression because it better fits data with a high concentration of observations with zero values in the population.
The key difference between Steps 1 and 2 is that Step 1 calculates the likelihood of an individual having a tax gap while Step 2 calculates the size of each individual's potential tax gap.
Step 3: Combine the results from the 2 models
For each individual in the population and each financial year, the estimated unreported tax is multiplied by the estimated probability of non-compliance. These amounts are then summed on a financial year basis to arrive at the total estimated unreported tax for each year.
Step 4: Apply a non-detection uplift factor and non-pursuable debt
We uplift the estimates preceding this step to account for non-compliance that is not detected. This results in a final estimate that has a lower likelihood of understating the true size of gap in the system, which is not directly observable.
We also seek to quantify the tax gap effects of unreported offshore income by Australians. A main assumption of the unreported offshore income estimate method is that it is only borne by individuals (not companies). The estimate is allocated based on the share of net tax of individuals in each gap population. The estimated amount is then added to the non-detection uplift amount.
We also add in the value of non-pursuable debt. This is debt that the Commissioner of Taxation has assessed as:
- not legally recoverable
- uneconomical to pursue
- unable to be pursued due to another Act.
Step 5: Consolidate the tax gap estimates
We calculate the gross gap by adding the unreported amounts or total expected amendments from Step 3 to the non-detection uplift and non-pursuable debt from Step 4.
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.
|
Step |
Description |
2017–18 |
2018–19 |
2019–20* |
2020–21* |
2021–22* |
2022–23* |
|---|---|---|---|---|---|---|---|
|
1 |
Total population (count) |
6,822 |
6,607 |
6,725 |
7,446 |
7,759 |
8,134 |
|
2 |
Total expected amendments ($m) |
127 |
117 |
114 |
130 |
173 |
140 |
|
3.1 |
Non-detection ($m) |
77 |
70 |
71 |
80 |
103 |
90 |
|
3.2 |
Non-pursuable debt ($m) |
4 |
4 |
4 |
4 |
4 |
4 |
|
4.2 |
Gross gap ($m) |
208 |
191 |
190 |
214 |
280 |
233 |
|
4.3 |
Amendments ($m) |
33 |
48 |
27 |
34 |
67 |
32 |
|
4.4 |
Net gap ($m) |
175 |
143 |
163 |
180 |
213 |
201 |
|
4.5 |
Expected collections ($m) |
1,485 |
1,286 |
1,346 |
1,674 |
1,984 |
2,001 |
|
4.6 |
Total theoretical liability ($m) |
1,661 |
1,429 |
1,509 |
1,854 |
2,196 |
2,202 |
|
4.7 |
Gross gap (%) |
12.5% |
13.4% |
12.6% |
11.6% |
12.8% |
10.6% |
|
4.8 |
Net gap (%) |
10.6% |
10.0% |
10.8% |
9.7% |
9.7% |
9.1% |
Calculation – medium business companies
The following 5-step bottom-up regression is applied to estimate the medium company tax gap.
Step 1: Apply a logistic regression
We analyse the tax return data of companies that have been subject to amendment activities to identify relevant demographic and financial variables that would contribute to the prediction of whether or not businesses have a tax gap.
The observations for these variables are then weighted based on the businesses' propensities of being selected for compliance activity before being included in the logistic regression that models the probability of a company being non-compliant.
We then undertake a Monte Carlo simulation to determine each company's binary status of being either compliant or non-compliant.
Step 2: Apply a linear regression
We analyse the tax return data of known non-compliant companies to identify characteristics of businesses that would contribute to the prediction of the tax gap size if the company were found to be non-compliant.
We apply weights to account for selection bias. Then we apply the linear regression to each company to estimate the potential size of the tax gap.
The key difference between Steps 1 and 2 is that Step 1 calculates the likelihood of a company having a tax gap while Step 2 calculates the size of each company's potential tax gap.
Step 3: Combine the results from the 2 regressions
We calculate the estimated unreported tax amount for each simulation by adding together the non-compliance amounts from Step 2 for all non-compliant businesses predicted in step 1.
We estimate total unreported tax (including amendments) by taking an average of the results from 20,000 simulations.
Step 4: Apply a non-detection uplift factor and non-pursuable debt
We uplift the estimates preceding this step to account for non-compliance that is not detected. This results in a final estimate that has a lower likelihood of understating the true size of gap in the system, which is not directly observable.
We also add in the value of non-pursuable debt. This is debt that the Commissioner of Taxation has assessed as:
- not legally recoverable
- uneconomical to pursue
- unable to be pursued due to another Act.
Step 5: Consolidate the tax gap estimates
We calculate the gross gap by adding the unreported amounts from Step 3 to the non-detection uplift and non-pursuable debt from Step 4.
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.
|
Step |
Description |
2017–18 |
2018–19 |
2019–20* |
2020–21* |
2021-22* |
2022–23* |
|---|---|---|---|---|---|---|---|
|
1–2 |
Total population (count) |
32,809 |
33,824 |
34,603 |
36,584 |
39,630 |
42,944 |
|
3 |
Total expected amendments ($m) |
677 |
658 |
723 |
844 |
961 |
1,013 |
|
4.1 |
Non-detection ($m) |
338 |
329 |
362 |
422 |
480 |
506 |
|
4.2 |
Non-pursuable debt ($m) |
59 |
59 |
59 |
59 |
59 |
59 |
|
5.1 |
Gross gap ($m) |
1,074 |
1,046 |
1,144 |
1,325 |
1,500 |
1,578 |
|
5.2 |
Amendments ($m) |
158 |
179 |
105 |
90 |
102 |
90 |
|
5.3 |
Net gap ($m) |
916 |
866 |
1,039 |
1,235 |
1,399 |
1,488 |
|
5.4 |
Expected collections ($m) |
11,725 |
11,127 |
11,797 |
14,341 |
15,852 |
16,536 |
|
5.5 |
Total theoretical liability ($m) |
12,641 |
11,993 |
12,836 |
15,576 |
17,251 |
18,024 |
|
5.6 |
Gross gap (%) |
8.5% |
8.7% |
8.9% |
8.5% |
8.7% |
8.8% |
|
5.7 |
Net gap (%) |
7.2% |
7.2% |
8.1% |
7.9% |
8.1% |
8.3% |
Find out more about our research methodology, data sources and analysis used for creating our tax gap estimates.
Limitations of the medium business tax gap
The following caveats and limitations apply when interpreting this tax gap estimate:
- There is a considerable delay between an income year and the completion of our compliance activities for that year. This means gap estimates are subject to revisions for a considerable period. Amendment results for companies and individuals are projected for 2019–20 to 2022–23 and are subject to revisions overcoming years before eventually being replaced by actual amendments.
- Provisions are made for non-pursuable debt for all years, excluding 2017–18.
- There is no independent data source that can provide a credible or reliable macroeconomics-driven estimate (unlike indirect taxes).
- The true extent of non‑detection is unknown and is extremely challenging to measure. There is no international proxy we can apply to the individuals or companies in this 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 a variety of reasons, including:
- improvements in methodology
- revisions to data
- additional information becoming available.
Figure 2: Comparison of previous and current net tax gap estimates (%), 2012–13 to 2022–23
This data is presented in Table 4 as a percentage.
|
Program year |
2017–18 |
2018–19 |
2019–20 |
2020–21 |
2021–22 |
2022–23 |
|---|---|---|---|---|---|---|
|
2025 |
7.6% |
7.5% |
8.4% |
8.1% |
8.3% |
8.4% |
|
2024 |
7.2% |
7.2% |
7.5% |
7.1% |
7.3% |
n/a |
|
2023 |
7.1% |
7.4% |
7.5% |
7.2% |
n/a% |
n/a |
|
2022 |
6.4% |
6.9% |
7.0% |
n/a |
n/a |
n/a |
|
2021 |
6.0% |
6.2% |
n/a |
n/a |
n/a |
n/a |
|
2020 |
6.2% |
n/a |
n/a |
n/a |
n/a |
n/a |