The Amount B Consultation: Perspectives and Recommendations
Updated: Nov 24
By: Mary Ongore
The OECD/G20 Inclusive Framework on Base Erosion and Profit Shifting has been working on addressing challenges arising from the digitalisation of the economy. The blueprint for addressing this was released on 14 October 2020, according to which Amount B was to be used to simplify the pricing of baseline marketing and distribution activities in accordance with the arm’s length principle and assist low-capacity jurisdictions. The 8th October 2021 statement set out the two-pillar solution and mandated work to be conducted on Amount B. A public consultation was carried out in December 2022, and this further developed the work in this area.
In July 2023, the OECD disseminated a public consultation document on Pillar One Amount B. The Public consultation set out qualifying criteria, scoping criteria and accompanying commentary. It also set out the Transactional Net Margin Method (TNMM) as the most appropriate method to price in-scope transactions. This work is timely, given the fact that the African Tax Administration Forum (ATAF) reports that between 30-70% of the transfer pricing disputes for their member countries are based on distribution activities for which no local comparables are available.
This blog interrogates public consultation.
The first section will interrogate the scoping criteria, the second the pricing framework, the third the wholesale distribution of goods, and the fourth the country uplifts within geographic markets.
1. Scoping criteria
Alternative A or Alternative B
The public consultation proposed two different scoping criteria. Alternative A is a quantitative scoping criterion, while Alternative B has an additional qualitative scoping criterion.
Alternative B favours jurisdictions with higher technical capacity, while Alternative A would be beneficial for jurisdictions that have limited transfer pricing resources and capacity.
Proponents of Alternative B argue that the inclusion of qualitative criteria would prevent MNEs from manipulating their economic arrangements in order to benefit from Amount B, especially because the Amount B margins are low. In addition, they argue that the reliance on operating expenses to annual net sales is not based on empirical evidence and further qualitative tests are needed to ensure the accurate identification of baseline and non-baseline activities.
Proponents of Amount A, however, argue that the whole aim of Amount B is to introduce simplicity, and the use of further qualitative criteria would be subjective and would result in increased disputes over whether transactions are in-scope or not.
The use of a hybrid approach is suggested. This would entail the use of quantitative criteria as well as qualitative criteria and would best be reflected through the use of Alternative B. However, Alternative B, as it is in the consultative document, could be better designed. It adopts an examples-based approach whose main limitation is the fact that it cannot be exhaustive. As such, the aim of attaining tax certainty would not be met, given that this approach cannot cover all scenarios.
Reliance on Operating Expenses
In identifying qualifying transactions, reliance is placed on operating expenses as a percentage of annual net sales. Baseline activities are identified as those that fall within the range of 3%-30% under Alternative A and 3%-50% under Alternative B. There is, however, no empirical evidence that establishes a link between operating expenses/sales and functionality. This is, therefore, purely a simplification measure that does not provide any further indication of functional intensity for the effective characterisation of baseline/non-baseline activities. Functionality is usually tested on a facts and circumstances basis, and a formulaic approach would need to be based on empirical evidence.
Empirical analysis should be used to give a rationale for the adoption of this ratio as a measure of identifying baseline versus non-baseline activities.
The public consultation document proposes to introduce a segmentation guardrail. According to this guardrail, qualifying transactions would be out of scope when the tested party performs both distribution and non-distribution activities, and the proportion of indirect operating expenses allocated between the distribution and non-distribution business (using allocation keys) exceeds 30% of the total cost.
This formula is complex, burdensome and theoretically incorrect. This is because the formula requires the conduct of an entity-wide test on the amount of indirect operating expenses. This would mean that even if the distribution activities account for just 20% of the revenue, the formula requires you to check the total indirectly allocated operating expenses divided by the total cost for all activities. This is unnecessary as it will be difficult for tax administrators to be able to devote resources to identify all items of costs in all the other segments that are not even covered under Amount B. In addition, when an entity-wide test is done, it allows the manner of cost allocation in out-of-scope transactions to play a role in determining whether the qualifying transactions pass or fail the test. A simplified formula should only consider indirectly allocated operating expenses in the distribution segment vis-a-vis total operating expenses in the distribution segment.
2. The pricing framework
The pricing matrix has been developed based on a “global dataset.” The global dataset that has been used is Orbis, a commercial database containing global company financial data. This database, however, only contains comparables from around five countries, 70% of which are from Europe and North America. In addition, the dataset does not have granular data, and as a result, a lot of quantitative analysis is not possible.
Given that the pricing matrix, if adopted in its current form, will be here to stay in the long term, it is recommended that more effective quantitative analysis is done. In any case, the rationale for the use of the figures in the dataset should be given so that it can be shown that it has been developed based on robust empirical analysis.
The pricing matrix is heavily based on industry groupings. The segmentation of industries is based on the three industry groupings and depends on statistically significant differences in levels of return between the industry categories.
Annex C illustrates the basis of the methodology used, and the majority of the companies fall under group 2, which consists of industry categorisations that do not show statistically significant relationships to the levels of return. This highlights one of the biggest weaknesses of the matrix: it is largely arbitrary.
The aim that the matrix attempts to achieve is a tall order. It is nearly impossible to categorize distribution in all possible sectors with precision. Some studies have been conducted on specific sectors, and even then, those have shown that there can be a wide range of possible comparables. It is, therefore, a Herculean task to conduct searches for a wide range of distribution activities in different industries and come up with an accurate outcome.
The work on the industry groupings should be refined with a better methodology adopted. Failure to do this would result in an arbitrary result when applying the methodology.
The use of operating expenses as a proxy for functionality
The second characteristic of the pricing matrix is its reliance on operating expenses as a percentage of annual net sales as a measure of factor intensity. As we have indicated earlier, there is no empirical evidence of a consistent relationship between the two. According to the pricing matrix, the operating expenses to sales ratio only becomes significant at functional intensity D and E, i.e. where the net operating assets to revenue is low. Where there are higher levels of operating assets, that relationship breaks down.
More empirical evidence should be used to determine the return on sales determined by the pricing matrix, and if this has already been done, it needs to be made publicly available before it can be adopted in the pricing of distribution activities.
While the Berry Ratio has been included as simply a corroborative mechanism, the practical implication is that even if you get a Profit Level Indicator, this will be subjected to the Berry Ratio cap and collar regime.
The Berry Ratio compares gross profit to operating expenses. Distributors, however, expect rewards based on sales rather than operating expenditure. It is, therefore, unclear why the Berry Ratio should be used as the final determinant of the return on sales.
The use of the Berry Ratio should be abandoned in its entirety.
3. Wholesale distribution of digital goods
The public consultation proposes the inclusion of digital goods within the scope of Amount B.
The distribution of digital goods is a complex area, as it is difficult to draw the line between them and intangibles. Moreover, the economically relevant characteristics of distributors of tangible goods and digital goods are innately different. As such, the use of the scoping criteria for the two should not be the same.
Digital goods should not be included in Amount B.
4. Country uplifts within geographic markets
There are three suggested approaches to dealing with geographic differences and their ability to influence profitability. These are the modified pricing matrix, the data availability mechanism and the use of a local database. These will be considered in turn.
The Modified Pricing Matrix
This accounts for observed geographic differences based on available local data. It will apply to a small number of jurisdictions for which relevant data is available. For those qualifying jurisdictions, the modified pricing matrix in Figure 4.2 supersedes the pricing matrix in Figure 4.1.
This is a welcome approach, given that geographical differences can have a significant impact on profit margins. There, however, needs to be robust statistical analysis to determine what a “qualifying jurisdiction” is.
The Data Availability Mechanism
This mechanism will apply where there is no/insufficient data in the global dataset for the tested party, but there is evidence of country risk that may influence arms-length returns. In these cases, it is suggested that the sovereign credit rating be used as a proxy for profitability. There, however, is a cap of 85% of net operating asset intensity when computing the adjusted return on sales for qualifying transactions of qualifying jurisdictions.
We welcome the use of country risk adjustments, given that the global dataset used does not have local African comparables. We, however, disagree with the 85% OAS cap, which will unnecessarily limit developing countries’ returns. This is because there is no reason given for it.
Utilising a local database
The use of local datasets where they exist has been included in the Amount B consultation. This has been pushed for by jurisdictions that have local comparables.
This is consistent with Transfer Pricing theory. While it may not immediately apply to developing countries in Africa, we do not oppose this since it is in accordance with the long-term aim of getting local comparables.