Methodology for estimating small business income gap

The bottom-up method

We use a random enquiry program bottom-up method to estimate the small business tax gap. This comprises an estimate of the income tax gap for individuals in business and an estimate for small companies. These are combined to give us the overall small business income tax gap.

We use the same overall steps, but because they have different characteristics, we calculate them separately. We step through the method below and combine the estimates in Table 1.

Step 1: Estimate unreported amounts

We calculate the average amendment and amendment rate for reviewed taxpayers for each population based on sample data from up to three years. These averages are then extrapolated to the relevant population to estimate the base unreported tax liability.

To estimate people outside the system, we compare Australian Bureau of Statistics (ABS) Census of Population and Housing (Census) data with tax return data. This provides an estimate of the number of non-lodging individuals in business.

We then estimate a dollar impact, drawing on the random sample data to determine the final unreported amount.

Step 2: Estimate for non-detection (including hidden wages)

We adjust for undetected non-compliance that our audit processes may miss, to avoid understating the true tax gap. To do so, we apply uplifts based on the midpoint of international benchmarks.

We also apply an uplift for hidden wages to the individuals in business population. This is consistent with our wider program for hidden wages.

Applying uniform non-detection uplifts to the estimate would exaggerate the size of the final gap. A different uplift is applied to the deduction labels of small business tax returns.

The impact of non-detection across the tax gap is also different across the 2 populations represented in this estimate:

• individuals in business
• small companies.

Individuals in business

Within the individuals in business population, the following 3 areas require an estimate to account for non-detection:

• business income
• deductions
• hidden wages.

The uplift for business income forms the largest component of non-detection. This recognises the limitations in detecting omitted income where little or no third-party reporting systems are available.

We apply uplifts to observed results based on the midpoint of international ranges.

Non-detection of deductions in the individuals in business population is applied differently. This is because there is no incentive for taxpayers to under-claim deductions on their tax return. So the uplift for non-detection for deductions is confined to the capacity to detect errors in tax returns where deductions have been claimed.

Actual wages received by individuals in business can be difficult to validate in a random enquiry program. We used a macro estimate based on the hidden wages element used in the pay as you go (PAYG) withholding and super guarantee gap estimates.

An estimate for wages not detected in the random enquiry program and for people operating outside the system was reconciled to the hidden wages analysis undertaken in the PAYG withholding gap estimate. The result provides an estimate for hidden wages in the individuals in small business population.

Small companies

The small companies element has only 2 areas that require an estimate to account for non-detection:

• business income
• deductions.

Like individuals in business, the income non-detection uplift for small companies forms the largest component of the non-detection estimate.

We recognise limitation with detecting omitted income for small companies. This is because no, or limited, third-party reporting systems are available to compare to. We apply uplifts to observed results based on the midpoint of international ranges.

Non-detection of deductions and other issues in the small companies population is applied in the same way as individuals in small business. There is also no incentive for these taxpayers to under-claim deductions on their tax return. So the uplift for deductions non-detection focuses on the capacity to detect errors in tax returns where deductions have been claimed.

Combined impact of non-detection

Table 2 shows a summary of the combined impact of non-detection on the small business income tax gap, for the 2022–23 estimate and revisions to prior years.

Table 2: Summary of the impact of non-detection on the gap ($ million)

Source of non-detection	2017–18	2018–19	2019–20	2020–21	2021–22	2022–23
Business income	4,025	4,953	4,658	5,662	7,302	8,934
Deductions and other issues	113	138	131	211	231	274
Hidden wages	606	607	692	783	894	1,074
Total non-detection	4,743	5,698	5,481	6,656	8,427	10,281

 

Step 3: Estimate for non-pursuable debt

We add in the value of non-pursuable debt. This is debt the Commissioner of Taxation has assessed as:

• not legally recoverable
• uneconomical to pursue, or
• unable to be pursued due to another Act.

Debt trends show that it takes more than 5 years for non-pursuable amounts to crystallise for any one income year. To account for this, we add a provisional amount of non-pursuable debt to the actual amount recorded, based on historical amounts. These figures are revised as we refresh and move these estimates forward.

A revised debt methodology is under development that considers external economic factors and internal changes to provide a realistic estimate of the amount of debt that will likely never be paid.

Table 3 shows a summary of the actual and provisional amounts of non-pursuable debt.

Table 3: Summary of non-pursuable debt for small business ($ million)

Description	2017–18	2018–19	2019–20	2020–21	2021–22	2022–23
Actual non-pursuable debt	228	166	133	135	110	25
Provisional non-pursuable debt	153	215	248	246	271	356
Total non-pursuable debt	381	381	381	381	381	381

Step 4: Estimate gross gap

Next, we add the results of steps 1 to 3 to arrive at the gross gap estimate. The gross gap is equal to the sum of the base unreported tax liability, non-detection, and non-pursuable debt.

Step 5: Estimate net gap

We deduct compliance outcomes and voluntary disclosure amounts (amendments) from the gross gap in step 4 to arrive at the net gap.

Given ATO compliance actions can take many years to complete, an uplift is applied to amendment amounts in later years to reflect expected final outcomes. Where actual amendment outcomes exceed this estimate, the actual amendments are used instead.

Amendments of $100 million and above are treated as outliers and excluded from the amendments estimate. These amendments may belong to entities in other tax gap populations, such as high wealth or medium business. We will reassess their allocation in future estimates.

Step 6: Estimate the theoretical liability

The expected collections amount is calculated by adding voluntary expected collections and amendments. The net gap is added to the expected collections amount to estimate the theoretical tax liability.

Summary of estimation process

Table 4 shows the dollar value (in millions) at steps 1 to 6.2 for the individuals in business element. Steps 6.3 and 6.4 show percentage figures for the gross and net gaps.

Table 4: Applying the methodology – individuals in business element ($ million)

Step	Description	2017–18	2018–19	2019–20	2020–21	2021–22	2022–23
1.1	Estimate unreported amounts for sample and extrapolate to population ($m)	5,467	7,193	6,636	8,476	9,888	11,941
1.2	Apply estimate for people outside the system ($m)	1,433	1,822	1,692	1,646	1,563	1,880
2.1	Apply estimate for non-detection (excluding hidden wages) ($m)	3,675	4,736	4,385	5,102	6,463	7,913
2.2	Apply estimate for hidden wages ($m)	606	607	692	783	894	1,074
3	Add non-pursuable debt ($m)	237	237	237	237	237	237
4	Equals gross gap ($m)	11,419	14,595	13,643	16,245	19,045	23,045
5.1	Subtract amendments ($m)	763	741	605	726	709	709
5.2	Equals net gap ($m)	10,655	13,854	13,038	15,519	18,337	22,336
6.1	Add expected Collections ($m)	71,023	73,528	75,450	85,706	98,299	108,284
6.2	Equals theoretical tax liability ($m)	81,678	87,383	88,488	101,225	116,635	130,620
6.3	Gross gap (%)	14.0	16.7	15.4	16.0	16.3	17.6
6.4	Net gap (%)	13.0	15.9	14.7	15.3	15.7	17.1

Table 5 shows the dollar value (in millions) at steps 1 to 6.2 for the small companies element. Steps 6.3 and 6.4 show percentage figures for the gross and net gaps.

Table 5: Applying the methodology – small companies element ($ million)

Step	Description	2017–18	2018–19	2019–20	2020–21	2021–22	2022–23
1.1	Estimate unreported amounts for sample and extrapolate to population ($m)	1,311	1,135	1,229	2,155	2,960	3,602
1.2	Apply estimate for people outside the system ($m)	n/a	n/a	n/a	n/a	n/a	n/a
2.1	Apply estimate for non-detection (excluding hidden wages) ($m)	463	355	404	771	1,070	1,295
2.2	Apply estimate for hidden wages ($m)	n/a	n/a	n/a	n/a	n/a	n/a
3	Add non-pursuable debt ($m)	144	144	144	144	144	144
4	Equals Gross gap ($m)	1,917	1,633	1,777	3,069	4,174	5,041
5.1	Subtract amendments ($m)	207	221	188	205	205	205
5.2	Equals Net gap ($m)	1,710	1,413	1,589	2,864	3,969	4,836
6.1	Add Expected Collections ($m)	14,846	14,734	14,922	18,198	19,885	20,508
6.2	Equals Theoretical tax liability ($m)	16,556	16,146	16,510	21,062	23,854	25,343
6.3	Gross gap (%)	11.6	10.1	10.8	14.6	17.5	19.9
6.4	Net gap (%)	10.3	8.7	9.6	13.6	16.6	19.1

For more information about our research methodology, data sources and analysis, see creating our tax gap estimates.

Limitations

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

• The 2023 preliminary estimate uses outcomes finalised to date from the 2021 and 2022 random enquiry program samples. We will revise the 2023 estimates with additional outcomes from the 2022 and 2023 samples.
• The 2018, 2019 and 2020 sample years were reduced due to the need to support the community during natural disasters and COVID-19.
• The precision of our estimate is limited by the sample size of the random enquiry program. By using an ongoing bundled sample, we seek to maintain suitable confidence intervals over time.
• We are working to develop non-detection estimates for random enquiry programs in the Australian environment. In the interim, we use the midpoint estimate for credible international estimates used by the United Kingdom and United States.
• Estimates for the tax impact of people outside the system are difficult to estimate. This estimate will always be subject to significant uncertainty.
• There is no external data set that has the same view of the small business population that we have.

Accounting for the shadow economy

For tax gap purposes we focus on the shadow economy definition covers activities that are productive and legal but are deliberately concealed to avoid paying taxes or complying with regulations (or both).

The shadow economy estimate within the small business income tax gap is also separated into the calculations for individuals in business, and companies. Within these 2 broad categories, there are 3 main elements:

• deliberate non-disclosure of business income and deliberate over-claiming of business deductions
• hidden wages, predominantly individuals in the population receiving cash-in-hand wages – we estimate this using a top-down model approach drawing on random enquiry observations
• people outside the system – where we use an ABS Census comparison approach.

When analysing the reasons for non-compliance, we sought to identify aspects of behaviour that indicated a deliberate intention to hide business activity.

We added a component to the individuals in business population gap estimate that is not included in non-detection, which is to account for people outside the tax system. This element seeks to estimate the amount of omitted income from these people. To quantify this element, we assumed that the incidence and relative magnitude of income non-compliance in the random enquiry sample is also representative of people outside the system.

The tax effect of the shadow economy for small business in 2022–23 is estimated to be $17.1 billion. The majority of this, $14.2 billion, is associated with under-reported business income and over-claimed business deductions.

We outline the impacts of the shadow economy on the community and how we address them in tax and small business.

Table 6 shows a summary of the shadow economy impact on the gross tax gap. This amount has increased from 51% of the overall gross gap in 2017–18 to 61% in 2022–23. For this estimate we assume the same percentage applies to the net gap.

Element	2017–18	2018–19	2019–20	2020–21	2021–22	2022–23
Hidden wages	606	607	692	783	894	1,074
People outside the system	1,433	1,822	1,692	1,646	1,563	1,880
Undisclosed business income and over-claimed business deductions	4,732	6,637	7,452	9,255	11,731	14,150
Total shadow economy impact	6,771	9,065	9,836	11,684	14,188	17,103

Updates 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:

• improvements in methodology
• revisions to data
• additional information becoming available.

We refreshed our previous estimate to consider updates in underlying data and refinements to the methodology, so the results between years remain comparable.

The increase in the updated estimates for 2020–21 and 2021–22 is mainly due to additional completed sample cases since the previous publication.

Figure 5 shows the net gap from our current model compared to the previous estimate, done in 2024.

Figure 5: Current and previous small business net tax gap estimates, 2015–16 to 2022–23

 

The data is set out as a percentage in Table 7.

Table 7: Current and previous small business net tax gap estimates, 2017–18 to 2022–23

Program year	2017–18	2018–19	2019-20	2020–21	2021–22	2022–23
2025	12.6%	14.7%	13.9%	15.0%	15.9%	17.4%
2024	12.6%	14.5%	14.0%	14.6%	12.6%	n/a
2023	12.4%	14.4%	13.1%	12.8%	n/a	n/a
2022	12.6%	12.7%	11.6%	n/a	n/a	n/a
2021	11.7%	12.7%	n/a	n/a	n/a	n/a
2020	11.5%	n/a	n/a	n/a	n/a	n/a
2019	n/a	n/a	n/a	n/a	n/a	n/a