An approach to obtaining comparable data that needs to be formulated which will be acceptable to SARS is the focus of this article.
By Gerdi Van der Westhuysen and Karen Miller
From a transfer pricing perspective, comparability is at the heart of the arm’s-length principle. This heart is being worn out by the challenge experienced by taxpayers in Africa to find comparable data in their specific country (or even from the African region) that can be used for transfer pricing comparability purposes. This scarce data challenge will remain until African countries introduce a statutory requirement for private companies to file annual accounts. Multinationals continue to ask what comparability data can be used to support intragroup pricing with their operations in Africa and what comparables will the African revenue authorities typically accept.
Foreign versus local
We have finally some degree of comfort following the recent release of a toolkit which provides practical guidance to developing countries on how to address comparability challenges. Part II of this toolkit focuses on making the best use of available data. Some key points to note:
- The toolkit reminds us that the selection of the most appropriate transfer pricing method, on the basis of a detailed factual analysis, is central to the application of the arm’s-length principle. The selection of the wrong transfer pricing method is likely to have a greater impact on the outcome than the accuracy of the data used in the method’s application. Performing a thorough functional analysis is critical to ensure the selection of the most appropriate transfer pricing method.
- The toolkit provides practical tools such as commonly used profit level indicators (PLIs) for particular types of businesses, a sample functional analysis questionnaire, and a step-by-step template which could be used to screen for potential comparables, as well as information to help tax administrations to critically analyse comparability studies presented by taxpayers.
While ‘perfect’ or ideal comparables are scarce in Africa, commonly the data used from other regions will still allow a reasonably reliable analysis to be performed and a satisfactory approximation of an arm’s-length outcome to be determined. We understand that SARS will consider the use of comparable data from regions other than Africa provided careful consideration is given to the following:
- The comparable data reflects similar markets to South Africa and is from other developing countries, for example Brazil, Russia, India or China (BRICS).
- The comparable data considers sovereign credit ratings in selecting countries from which to source data.
- The comparable data considers economically similar regions in an industry context.
While not providing complete certainty, the above developments do enable an approach to obtaining comparable data to be formulated which should be acceptable to SARS.
Timing is key
It is not only the source of the data which creates challenges. A question which continuously arises is whether the data used should be on a multiple-year or single-year basis. The OECD guidelines state that the use of multiple-year data can be useful but is not a systematic requirement. It is fact dependent. In addition, the OECD guidelines do not prescribe the number of years for which data should be used in multiple-year analyses.
For multinationals which have had the misfortune of experiencing an audit and transfer pricing adjustment in South Africa, experience has indicated that SARS adopts an annual approach to testing the pricing of the South African entity. However, this is changing, and SARS is adopting a more flexible approach, recognising that the availability of data remains a challenge. SARS has recently indicated that where multiple-year data is used, it must be justified by the taxpayer. One example is the effect of business cycles or other relevant economic circumstances on the operating results of the taxpayer. In the event there is no justification for using a multiple-year approach, SARS will effect adjustments on a year-on-year basis. Therefore, it is important for taxpayers to consider and record the reason for using multiple-year data.
This can be problematic in itself, as data availability creates timing problems. For instance, at the time the company prepared its transfer pricing support for the submission of its tax return, it is unlikely that comparable data for the year being tested and supported is available. However, when SARS commences an audit several years later, the data is available. This begs the question as to whether the data relating to the year of assessment being tested should be used or the data available at the time the transfer pricing support for that year was prepared.
This question of timing has also been considered by the OECD. In the OECD guidelines it is stated that there is no need for a company to conduct new benchmarking studies annually where the underlying business activities or transactions have not changed. However, taxpayers should update the financial data of the comparable companies annually to derive an updated arm’s-length range. As such, a taxpayer would only be required to perform a new benchmarking study every three years, while updating the financial results annually. Importantly, the OECD also states that the comparable data which should be used is that which is available at the time the tax return is lodged.
This therefore should restrict SARS to only apply comparable data which was available to the taxpayer at the time the tax return is lodged even though data specific to the year under audit is available at a later date.
What constitutes a range?
Transfer pricing analyses requires the identification of an arm’s-length range. The OECD guidelines talks of degrees of comparability and when a range should be refined through the use of statistical tools. The most common statistical tool applied is to narrow the range by adopting the interquartile data set. However, this should not be an automated approach and in considering the data set obtained, cognisance should be had for the quality of the comparable sources, the number of data points and the overall spread of the range. A range that is already quite small will not be improved by being reduced to an interquartile range.
SARS endorses this view and has indicated that where there is an equal degree of comparability for all the data points in a particular dataset, the full range from the minimum to the maximum of such a range can be viewed as the arm’s-length range.
While this is likely in the event internal comparable data is used, it is rarely the case where external comparable databases are used to source data. This is due to the limited quality of the data provided. As such, aligned to the OECD’s view, SARS considers that statistical tools that take account of central tendency to narrow the range (for example the interquartile range) will improve the reliability of the range and should be adopted where external comparable databases are used to conduct benchmarking studies. This is because such analyses typically yield ranges that include a sizeable number of observations. This approach is not only endorsed by SARS but is applied by tax authorities across Africa to enhance the reliability of the comparable analyses.
And finally, what point in the range should be used?
Having determined the arm’s-length range, the question remains as to what point represents the most reliable point. This question is critical as it is inevitably the point of dispute with the revenue authority when negotiating in an audit situation.
The OECD guidelines are a little evasive in this area suggesting a point of central tendency where there are comparability defects in the data used. SARS adopts the approach that, in the absence of persuasive evidence for the selection of a particular point in the range, any adjustment should be made to the mid-point of the range. This is done in order to minimise the risk of error due to unknown or unquantifiable remaining comparability defects. This approach is also adopted by many African tax authorities.
To adjust or not?
Comparability adjustment is continuously advocated but rarely used. The most common are working capital adjustments which aim to adjust for risk variances between the tested party and the comparables for the carrying of inventory, debtor and creditor risk. For practitioners who have used such adjustments, the impact on the results is often marginal and often has little impact on the overall range of results. The need for comparability adjustments should be considered on a case by case basis. SARS has concurred that practical experience indicates that working capital adjustments seldom have a material impact on the comparable results. To the extent they are deemed necessary SARS would entertain them. One area they can prove useful is testing the profitability of a limited risk distributor against comparable distributors. Because it is not always possible to confirm the risk profile of the comparables, adjusting for working capital risk to that of the tested party can improve the reliability of the analysis undertaken.
The most controversial adjustment is without doubt the use of country risk adjustments. They are both extremely complex calculations and questionable in their reliability as such these adjustments are rarely seen on both the tax authority and taxpayer side. Broad-based approaches can be adopted such as selecting comparables from countries within similar sovereign credit ratings (as discussed earlier) but making adjustments to the results of comparables to account for country risk based on government bond yield rates and other such data can often lead to anomalous results.
And in the rest of Africa?
There have also been recent TP developments in Africa which impact comparability analysis to be conducted by taxpayers which may be of interest to readers. Some of these are highlighted below:
Tanzania 2018 TP regulations
- The 2018 TP regulations confirm that where domestic comparable data cannot be obtained, external comparable data may be used.
- Where four or less comparable data points are used, the average is the arm’s-length result, while in the case of more than four comparable data points, the arm’s-length result shall be the data point between the 35th percentile and 60th percentile. If the result falls outside the arm’s-length range, the price should be adjusted to the median point of range.
Zambia 2018 TP regulations
Zambia has included the low value-adding intragroup services safe harbour in its TP legislation.
Nigeria 2018 TP regulations
To counter the comparability data challenges relating to intangible property transactions, Nigeria introduced a limitation of royalty deduction (deduction for tax purposes shall not exceed 5% of earnings before interest, tax, depreciation, amortisation, and that consideration) in its new TP regulations.
This article was originally published in ASA.