Methodology

We use a 4 step bottom-up random enquiry approach to estimate the individuals not in business tax gap. Random sampling methods are highly credible and best practice. They are commonly used by international jurisdictions to estimate tax gaps for this type of population.

Step 1: Estimate unreported amounts

In each year we draw on a bundled sample of up to 3 years from the random enquiry program. The bundled sample is split into 2 key groups of those who are:

• progressed to manual review
• verified.

We combine the incidence rates and averages from these 2 groups. We then extrapolate to the population of individuals not in business to estimate the unreported tax liability.

We also estimate the impact of non-registration or non-lodgment of people outside the system. This estimate draws on comparisons of Australian Bureau of Statistics Census of Population and Housing data to income tax return data to estimate the number of non-lodging individuals who are not in business. We then estimate a dollar impact, drawing on the random sample data to determine the final amount. We discuss this further in Limitations.

Step 2: Estimate for errors not detected

We apply an uplift to the unreported tax liability estimate to correct for errors not identified through the random enquiry program. Uplift factors are based on the midpoint of international ranges and account for non-detected amounts relating to:

• income misreporting
• deductions and other issues.

We also apply an uplift for non-detected amounts that relate to hidden wages, consistent with our wider program for wages. We discuss this further in Limitations.

Step 3: Estimate for non-pursuable debt

We 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.

Debt trends show that it takes upwards of 5 years for non-pursuable amounts to crystallise or be considered finalised in any one financial year. As a result, we add a provisional amount of non-pursuable debt to the actual amount recorded in the most recent 4 years, based on historical amounts.

Step 4: Consolidate the gap estimates

We calculate the gross gap by adding the unreported amounts from Steps 1 to 3. 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. We derive both net and gross gap ratios by dividing the dollar amounts by the theoretical liability.

Summary of the estimation process

Table 5 provides a summary of each step of the estimation process and the results for each year.

Table 5: Summary of estimation process for individuals not in business income tax gap

Step	Description	2015–16	2016–17	2017–18	2018–19	2019–20	2020–21
1.1	Estimate unreported amounts and extrapolate to population (m)	7,267	7,306	7,482	6,920	7,361	7,206
1.2	Apply estimate for people outside the system ($m)	134	230	207	183	138	151
2.1	Apply estimate for non-detection (excluding hidden wages) ($m)	228	364	534	461	480	502
2.2	Apply estimate for hidden wages ($m)	2,356	2,439	2,637	2,819	2,960	2,937
3	Estimate for non-pursuable debt ($m)	162	147	145	145	145	145
4.1	Estimate the gross gap (by adding together the results of Steps 1 to 3) ($m)	10,147	10,486	11,005	10,528	11,084	10,940
4.2	Subtract compliance outcomes and voluntary disclosures ($m)	750	982	791	687	534	739
4.3	Net gap ($m)	9,397	9,504	10,213	9,840	10,550	10,201
4.4	Add expected collections ($m)	126,446	130,872	141,518	145,650	152,295	151,207
4.5	Theoretical liability ($m)	135,843	140,376	151,732	155,490	162,845	161,408
4.6	Gross gap (%)	7.5	7.5	7.3	6.8	6.8	6.8
4.7	Net gap (%)	6.9	6.8	6.7	6.3	6.5	6.3

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

Limitations

Limitations with estimating this tax gap include:

• The 2020–21 estimate uses 2 of the 3 finalised random enquiry program sample years– this will be updated in future estimates.
• The precision of the estimate is limited by the sample size – to maintain suitable confidence intervals over time, we use an ongoing bundled sample.
• To reduce compliance costs for the taxpayer, we applied materiality thresholds at the data-driven review stage– if a case develops into a manual review, all items in the tax return are investigated regardless of value.
• There is no independent data source that can provide a credible or reliable macroeconomic-based estimate, unlike for indirect taxes.
• A limitation of the random enquiry program and similar programs in other jurisdictions is uncertainty around the impact of the non-detection error– the enquiries we undertake do not discover the full extent of non-compliance.

Accounting for non-detection in the gap

Not all errors are detected through the random enquiry program. We account for these by applying a non-detection uplift to the unreported tax liability estimate.

The 3 sources of non-detection for this tax gap relate to:

• income misreporting
• deductions and other issues
• hidden wages.

The unreported tax liability is divided into the above elements, with an appropriate non-detection factor then applied to each portion.

Table 6: Summary of the impact of non-detection on the individuals not in business income tax gap

Source of non-detection	2015–16 ($m)	2016–17 ($m)	2017–18 ($m)	2018–19 ($m)	2019–20 ($m)	2020–21 ($m)
Income misreporting (excluding hidden wages)	188	335	342	305	252	285
Deductions and other issues	40	29	192	156	228	216
Hidden wages	2,356	2,439	2,637	2,819	2,960	2,937
Total non-detection	2,584	2,803	3,171	3,280	3,440	3,439

Accounting for the shadow economy

The shadow economy concerns economic activity not declared, which may be a result of attempts to avoid tax obligations. We account for the shadow economy in this tax gap by considering the impacts of:

• hidden wages
• people outside the system
• undisclosed business activity.

Table 7: Summary of the impact of the shadow economy on the individuals not in business income tax gap ($ million)

Element	2015–16	2016–17	2017–18	2018–19	2019–20	2020–21
Hidden wages	2,356	2,439	2,637	2,819	2,960	2,937
People outside the system	134	230	207	183	138	151
Undisclosed business income	0	0	0	0	0	0
Total shadow economy impact	2,490	2,669	2,844	3,002	3,098	3,088

Confidence in the random sample findings

A confidence interval quantifies the precision of the estimate from a random sample relative to the true value from the population.

A 95% confidence level is considered industry best practice in terms of statistical analysis. It is the most used level by researchers, including His Majesty's Revenue and Customs in the UK in its equivalent tax gap program.

Using a 95% confidence level means we are 95% confident that the true value of the net gap for 2020–21 lies in the confidence interval 5.2% to 7.4%, or $8.4 billion to $12.0 billion. The upper and lower bounds of the 95% confidence intervals follow. The gap estimates we make public reflect the mid-point of the lower and upper bound estimates.

Figure 5: 95% confidence interval – upper and lower bound estimates – individuals not in business income tax gap, 2015–16 to 2020–21

 

The estimate from the random enquiry program is not the only component of this tax gap estimate. To establish the overall gap, we also draw on operational data for specific compliance risk areas. For example, we looked at the:

• failure by employers to withhold tax and report wage income of their employees
• non-lodgment of income tax returns
• non-payment of debts.

We combined the operational and random enquiry program findings to produce an overall gap estimate.

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 2018 and has been revised 5 times annually. In 2019 we realigned the population and estimate to be consistent with the wider tax gap research program. In both 2022 and 2023 we increased the uplift for hidden wages non-detection, resulting in increases to historically revised estimates.

Figure 6 displays the net gap from our current model compared to all previously published estimates.

Figure 6: Current and previous individuals not in business income tax gap estimates, 2013–14 to 2020–21

 

The data used in Figure 6 is presented in Table 8 below.

Table 8: Summary of published net tax gap percentages for individuals not in business, 2013–14 to 2019–20

Gap release year	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20	2020-21
2022-23	n/a	n/a	6.9%	6.8%	6.7%	6.3%	6.5%	6.3%
2021–22	n/a	6.4%	6.5%	6.4%	6.4%	5.9%	5.6%	n/a
2020–21	5.4%	6.1%	6.2%	6.1%	5.9%	5.6%	n/a	n/a
2019–20	5.6%	6.1%	6.2%	6%	5.6%	n/a	n/a	n/a
2018–19	5.6%	6.2%	6.4%	n/a	n/a	n/a	n/a	n/a
2017–18	5.8%	6.4%	n/a	n/a	n/a	n/a	n/a	n/a