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  • Methodology

    Wine equalisation tax (WET) applies to wine consumed in Australia. It is generally applied at the last wholesale sale (where the sale is to a retailer) but it also applies to direct sales from wine producers to consumers.

    WET payable on wholesale sales is calculated by applying the rate of 29% to the taxable value. It is usually paid by wholesalers, producers or importers. Overpaid amounts are refundable.

    The WET system also includes a producer rebate scheme. This entitles wine producers to a rebate of 29% of the taxable wholesale value of eligible domestic sales. The maximum rebate amount is $350,000 per financial year. This effectively makes the first $1.2 million of domestic wholesale sales exempt from WET.

    The maximum producer rebate was reduced from $500,000 from the 2018–19 financial year.

    The WET gap is the difference between WET payments and refunds and the amounts that should have been reported if all taxpayers were fully compliant with the law.

    Here we outline the methodology we have selected to estimate the WET gap. We detail our assumptions, limitations, data sources and reliability rating as assessed by our independent expert panel.

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    Selecting the methodology

    The 2016–17 WET gap has been estimated using a statistical based bottom-up approach, referred to as extreme value theory (EVT). We can estimate WET refundable in addition to WET payable, enabling estimation of the net WET gap.

    We previously used the results of a random enquiry program (REP). However, as it was conducted for 2014–15 these results are considered to no longer be sufficiently current. As another REP has not been conducted, we have chosen to use EVT instead.

    This approach has been endorsed by the independent expert panel. It is considered the most appropriate given the nature of the market, the design of the tax and the data available.

    Applying the methodology

    We used six steps in applying the bottom-up methodology to estimate the WET gap, as shown in Figure 3. We then describe teach step in detail.

    Figure 3: Steps to estimate the WET tax gap

    This image is a pictorial representation of the six calculation steps outlined in the text following this image. This image provides the names of the steps only.

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    Step 1: Identify extreme values

    We first calculate the sum of all negative amendments. We sort the positive amendments in ascending order and calculate the cumulative sum. We identify the amendment where the cumulative sum is just equal to or less than the total of negative amendments. The remaining amendments which are greater than this amendment are the extreme values.

    Step 2: Establish linear regression trend and extrapolate to population

    We then transform the amendment data. This allows us to estimate the equation that describes the relationship between the value and rank of the amendments. We estimate the expected number of amendments in the population if every taxpayer was reviewed. We then extrapolate the relationship to the WET population to estimate the amount of under-reporting.

    Step 3: Apply estimate for non-detection

    For the resulting gross gap in Step 2, we calculate an uplift based on the non-detection uplift factor.

    Step 4: Apply estimate for non-pursuable debt

    We add in an estimate for non-pursuable debt.

    Step 5: Estimate gross gap

    Next, we add the results of Step 2, Step 3 and Step 4 to arrive at the gross gap estimate.

    Step 6: Estimate net gap and theoretical liability

    The final step takes the gross gap from Step 5 and deducts amendments to arrive at the net gap estimate. We then add the net gap to the tax paid amount to estimate the theoretical tax liability.

    Summary of the estimation process

    Table 2: Applying the methodology – WET gap

    Step

    Description

    2011–12

    2012–13

    2013–14

    2014–15

    2015–16

    2016–17

    1 & 2

    Identify values, establish linear regression and extrapolate to population ($m)

    38

    88

    32

    17

    13

    13

    3

    Apply estimate for non-detection error ($m)

    11

    26

    10

    5

    4

    4

    4

    add non-pursuable debt ($m)

    14

    12

    16

    12

    12

    12

    5

    equals gross gap ($m)

    63

    126

    58

    34

    29

    29

    6.1

    subtract amendments ($m)

    18

    21

    16

    11

    7

    9

    6.2

    equals net gap ($m)

    45

    105

    42

    24

    22

    21

    6.3

    add tax paid ($m)

    700

    730

    743

    778

    816

    817

    6.4

    equals total theoretical liability ($m)

    746

    835

    784

    801

    839

    838

    6.5

    Gross gap (%)

    8.5

    15.1

    7.3

    4.3

    3.5

    3.5

    6.6

    Net gap (%)

    6.1

    12.6

    5.3

    3.0

    2.6

    2.5

    Limitations

    The limitations associated with estimation of the WET gap are listed as follows:

    • There is considerable lag after a financial year ends and the completion of ATO compliance activities relating to that year. This means that gap estimates may remain subject to revision for a considerable period.
    • The compliance activities undertaken by the ATO vary in ‘intensity’ and completeness. There is an inherent trade-off that all gap modelling approaches must consider when determining which activity to include.
    • The extent of non-detection is unknown and is extremely challenging to measure. We use a figure based on expert opinion and operational data.
    • The calculation of the impacts of the black economy on WET revenue is difficult to measure. For this estimate, we have included an amount within the non-detection estimation for black economy activity. This amount however is small, and is in keeping with expert opinion that black economy activity, where present, is infrequent and irregular, and would have a negligible impact on the WET gap.

    Updates and revisions to previous estimates

    Figure 4 displays the gross gap and net gap from our current model compared to the previous estimate.

    Figure 4: Current and previous WET tax gap estimates, 2010–11 to 2016–17

    This graph provides a visual representation of the previous and current net gap estimates provided at Table 3.

    The current estimates use EVT to produce gap estimates for net WET for all years. The previous estimates used different methods and bases for different years. The nature of issues investigated in compliance activities in 2012-13 resulted in higher amendments and therefore in turn, a high gap estimate for that financial year.

    Table 3: Summary of published WET net gap percentages

    Gap release year

    2010–11
    (%)

    2011–12
    (%)

    2012–13
    (%)

    2013–14
    (%)

    2014–15
    (%)

    2015–16
    (%)

    2016–17
    (%)

    2016

    4.1

    4.9

    0.5

    5.9

    na

    na

    na

    2017

    4.1

    4.9

    0.5

    5.9

    0.6

    na

    na

    2018

    4.1

    4.9

    0.5

    5.9

    0.1

    0.5

    na

    2019

    na

    6.1

    12.6

    5.3

    3.0

    2.6

    2.5

    Data sources

    We used the following ATO data sources to estimate the WET gap:

    • ATO compliance data of WET compliance cases
    • ATO data of WET reported in lodged business activity statements.

    Reliability

    Our estimate of the WET gap has been assessed by an independent expert panel, as described in Principles and approaches to measuring gaps.

    Based on advice from the independent expert panel, the reliability rating for the WET gap estimate is medium.

    We apply a bottom-up extreme value theorem (EVT) approach. The relationship between the size and rank of amendments is estimated and extrapolated to the population. The WET gap incorporates WET payable and WET refundable. The characteristics of the WET population align with the assumptions of EVT.

    Figure 5: Reliability rating scale from very low to very high – WET gap

    This image is a graphical representation of the reliability rating for the current wine equalisation tax gap estimate. It graphically represents a rating of medium, which is a score between 16 and 20. The maximum score is 30.

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      Last modified: 17 Oct 2019QC 57167