Step 1: Estimate probability of entity being selected
We identify characteristics of FTC claimants 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 data.
Step 2: Estimate probability of entity incorrectly claiming
We analyse the business activity statement (BAS) data of FTC claimants that had an interaction with us, as well as those that didn't.
We apply a logistic regression to estimate the probability of an FTC claimant being found to have overclaimed and another to estimate the probability of an FTC claimant being found to have underclaimed. To adjust for selection bias in the operational data, we apply the sample weights calculated in step 1 to the observations.
Step 3: Estimate the average incorrectly claimed amount
We analyse the BAS data of FTC claimants who had an interaction with us, to identify characteristics that would contribute to predicting the size of overclaims and underclaims. We apply a Poisson Pseudo Maximum Likelihood (PPML) regression to estimate the overclaim amount for each entity predicted to have overclaimed and another to estimate the underclaim amount for each entity predicted to have underclaimed. Due to the non-normal distribution of amended BAS, arising from a large share returning a null result, a PPML regression better fits the data and incorporates the null results of claimants who are audited yet claimed correctly.
The results are again weighted to adjust for selection bias. The PPML regressions are then applied to each entity in the population to estimate the potential size of overclaims and underclaims.
The key difference between steps 2 and 3 is that step 2 estimates the likelihood of an entity overclaiming or underclaiming, while step 3 estimates the size of each entity's potential overclaim or underclaim amount.
Step 4: Apply regressions results to each entity to estimate overclaims
We combine the regression results from steps 2 and 3 to estimate overclaims. We estimate overclaims by taking the average of the results from 1,000 simulations. This amount includes amendment results.
Step 5: Apply regressions results to each entity to estimate underclaims
We combine the regression results from steps 2 and 3 to estimate underclaims. We estimate underclaims by taking the average of the results from 1,000 simulations. This amount includes amendment results.
Step 6: Estimate net incorrect claims
We subtract total underclaims from step 5 from total overclaims from step 4 to arrive at net incorrect claims.
Step 7: Estimate for non-detection
We multiply the amount from step 6 by the uplift factor to account for non-detection.
Step 8: Estimate for non-pursuable debt
We add non-pursuable debt to account for overclaims not repaid which is estimated for the current year based on long-term trends.
Step 9: Estimate the gross gap, net gap and theoretical fuel tax credit
We add steps 6, 7 and 8 to estimate the gross gap. We then subtract amendments from it to estimate the net gap.
Then we estimate the theoretical fuel tax credit by subtracting the gross gap from the voluntary credits. We derive the gap percentages by dividing the gap amounts by the theoretical credit.
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 |
2017–18 |
2018–19 |
2019–20 |
2020–21 |
2021–22 |
2022–23 |
|---|---|---|---|---|---|---|---|
|
1 to 6 |
Estimate net incorrect claims ($m) |
75.0 |
115.7 |
95.7 |
182.5 |
265.6 |
273.0 |
|
7 |
Add estimate for non-detection ($m) |
18.8 |
28.9 |
23.9 |
45.6 |
66.4 |
68.2 |
|
8 |
Add claims made incorrectly and not paid back ($m) |
7.4 |
7.4 |
7.4 |
7.4 |
7.4 |
7.4 |
|
9.1 |
Gross gap ($m) |
101.1 |
152.0 |
127.0 |
235.5 |
339.4 |
348.6 |
|
9.2 |
Amendments ($m) |
34.9 |
10.7 |
7.1 |
2.7 |
101.4 |
26.3 |
|
9.3 |
Net gap ($m) |
66.2 |
141.3 |
119.9 |
232.8 |
237.9 |
322.3 |
|
9.4 |
Total credit ($m) |
6,846 |
7,185 |
7,412 |
7,548 |
6,904 |
7,753 |
|
9.5 |
Theoretical credit ($m) |
6,780 |
7,044 |
7,292 |
7,315 |
6,666 |
7,430 |
|
9.6 |
Gross gap (%) |
1.5% |
2.2% |
1.7% |
3.2% |
5.1% |
4.7% |
|
9.7 |
Net gap (%) |
1.0% |
2.0% |
1.6% |
3.2% |
3.6% |
4.3% |
Find out more about our overall research methodology, data sources and analysis for creating our tax gap estimates.
Limitations
The gap estimates are subject to the following limitations.
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 lower coverage levels of compliance activity and transitory changes have resulted in us adopting a split regression approach. We split the data across two estimation periods, 2016–17 to 2019–20 and 2020–21 to 2022–23, to estimate the coefficients for each period separately. This implicitly assumes the relationships between variables do not change much within each period and vary between periods. While deriving standalone regression results for each income year would be ideal, it is not feasible due to limited data across the fuel tax credit population.
The extent of non-detection is unknown and extremely challenging to measure.
This estimate does not include the population that may be entitled to fuel tax credits but has not registered for fuel tax credits.
This population does not include a small number of taxpayers who claimed credits for domestic electricity generation.
Non-pursuable debt is provisional and based on an average of historical amounts.
Updates and revisions to previous estimates
Each year we refresh our estimates in line with our 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 shows the net gap from our current estimate, compared to our previously published estimates.
Figure 2: Current and previous net fuel tax credit gap estimates, 2011–12 to 2022–23.
The data used in Figure 2 is presented in Table 3 below.
Table 3: Current and previous fuel tax credits net gap estimates, 2011–12 to 2022–23
|
Year published |
2011–12 |
2012–13 |
2013–14 |
2014–15 |
2015–16 |
2016–17 |
2017–18 |
2018–19 |
2019–20 |
2020–21 |
2021–22 |
2022–23 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
2023 |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
1.0% |
2.0% |
1.6% |
3.2% |
3.6% |
4.3% |
|
2022 |
n/a |
n/a |
n/a |
n/a |
n/a |
1.8% |
1.4% |
1.5% |
1.5% |
2.3% |
2.6% |
n/a |
|
2021 |
n/a |
n/a |
n/a |
−0.3% |
−0.6% |
0.7% |
1.9% |
0.5% |
0.2% |
n/a |
n/a |
n/a |
|
2020 |
n/a |
n/a |
n/a |
–0.1% |
−0.2% |
−0.1% |
0.0% |
–0.1% |
n/a |
n/a |
n/a |
n/a |
|
2019 |
n/a |
n/a |
n/a |
−0.1% |
−0.2% |
−0.1% |
−0.1% |
−0.1% |
n/a |
n/a |
n/a |
n/a |
|
2018 |
n/a |
–0.2% |
–0.1 |
0.0% |
−0.1% |
−0.1% |
−0.1% |
n/a |
n/a |
n/a |
n/a |
n/a |
|
2017 |
0.7% |
0.7% |
0.8% |
−0.4% |
−0.2% |
−0.3% |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
|
2016 |
0.7% |
0.7% |
0.7% |
−0.5% |
–0.3% |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
|
2015 |
0.8% |
0.6% |
0.6% |
–0.4% |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |