Australian Taxation Office, "Cash flow boost payment gap" (Internet Archive copy of Feb. 5, 2023)

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An Australian Taxation Office page, Cash flow boost payment gap, explaining how the office estimates the cash flow boost payment gap for 2019-20. It states that the cash flow boost supported small and medium businesses and not-for-profit organisations during the economic downturn associated with COVID-19 and was delivered as credits in the activity statement system. It estimates a net gap of 2.7% or $943 million for 2019-20, meaning over 97% of credits, totalling $34.5 billion, were correctly distributed. A table sets out credits distributed, recovered credits, final credits and revised credits, and the page says the estimate was assessed as reliable, defensible and credible.

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- Cash flow boost payment gap How we estimate the cash flow boost payment gap for 2019–20. On this page Overview The cash flow boost was introduced in the 2019–20 financial year. It supported small and medium businesses and not-for-profit organisations during the economic downturn associated with COVID-19. The cash flow boost was delivered as credits in the activity statement system. Taxpayers had to meet eligibility requirements to access credits. The gap represents the likely proportion of credits distributed to taxpayers that did not meet the eligibility criteria. As the cash flow boost program was temporary, we have only undertaken a single estimate. For 2019–20, we estimate a net gap of 2.7% or $943 million. This means over 97% of all credits, totalling $34.5 billion were correctly distributed. This gap forms a part of our overall tax performance program. Findings The cash flow boost gap for 2019–20 is $943 million or 2.7%. Table 1 shows the credits claimed, compliance and gross and net credits gap estimates for 2019–20 income tax year. Table 1: Cash flow boost – 2019–20 Element Total cash flow boost Credits distributed ($m) 35,400 Recovered credits ($m) 97 Final credits ($m) 35,303 Revised credits ($m) 891 Ineligible credits ($m) 943 Eligible credits ($m) 33,469 Gross payment gap ($m) 1,040 Net payment gap ($m) 943 Gross payment gap (%) 2.9 Net payment gap (%) 2.7 Methodology We use a 5-step top-down statistical modelling approach to estimate the gap. It uses pay as you go (PAYG) withholding trends to estimate behavioural changes caused by the cash flow boost announcement and to determine the behaviour drivers of those changes. We then use the distribution of credits to estimate the gap. Step 1: Split the PAYG withholding lodgers into 16 different groups based on their lodgment profile For each quarter between December 2015 and December 2020, we assigned PAYG withholding lodgers into 16 different groups based on their: salary and wages income tax withholding amounts. This produced a time series of 22 quarters for each of the 16 groups. Step 2: Predict the count of lodgers within each group in quarter 3 2019–20 We then predicted the expected number of lodgers in quarter 3, January to March 2020. The groups that see a significant departure from expected behaviour form part of the gap estimate. There are 11 of these groups. Step 3: Calculate the differences between expected and actual count Then we compare the predicted number of PAYG withholding lodgers for quarter 3 2019–20 to the actual lodgers. This identifies any changes in behaviour because of the cash flow boost stimulus measure. This difference then forms the basis for the gap estimate. Step 4: Estimate the net gap including revised credits Then we multiply the difference between predicted and actual lodgers as calculated in step 3 by the average cash flow boost credit made for each of the 11 groups. We then combine each of the gap estimates for each group to arrive at the net gap estimate (which includes the revised credits). Step 5: Estimate the net and gross gap Finally, we estimate the net gap by subtracting the revised credits from step 4. To estimate the gross gap, we add recovered credits to the net gap estimate. Summary of the estimation process The following table provides a summary of the steps in the gap estimation process. For illustrative purposes, we have only included only some of the 11 groups. Table 2: Summary of estimation process Step Description Group type A Group type B Group type C Group type K 1 and 2 Predicted count of lodgers 667,121 36,657 2,387 183,938 3a Actual count of lodgers 591,878 85,634 6,167 181,100 3b Difference in count -75,243 48,977 3,780 2,838 4a Net gap ($m) (including revised credits) -129 802 209 215 4b Total net gap ($m) (including revised credits) for all groups 1,834 5 Revised credits 891 5b Net gap ($m) 943 5c Recovered credits ($m) 97 5d Gross gap ($m) 1,040 5e Total credits distributed ($m) 35,400 5f Net gap (%) 2.7 5g Gross gap (%) 2.9 Limitations The following caveats and limitations apply when interpreting this estimate: We do not know if the behavioural changes are permanent and what the long-term impacts will be on the tax system. This is because cash flow boost and its incentives to participate in the tax system were temporary. We recognise a variety of techniques not included in this analysis may have been used to inappropriately obtain or increase the benefits including: restructuring to gain access to the cash flow boost inflating reported PAYG withholding amounts to maximise the cash flow boost resurrecting dormant entities or illegal phoenix activity fraudulent attempts to create an entitlement identity crime. Reliability We sought feedback and advice from external experts on our approach to estimating the cash flow boost gap. They have assessed our estimate as reliable, defensible and credible. How we estimate the cash flow boost payment gap for 2019–20. Last modified: 31 Oct 2022 QC 70672

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