INSIGHT: Transfer Pricing Implications of Covid-19 for the Asia-Pacific Region and the EU

A significant amount has been written about the impact of the Covid-19 pandemic on the global economy and the expected slow-down or contraction in 2020. The impact is seen by some experts as the most severe since the 2008–09 financial and economic crisis and, in certain countries, is expected to be the largest recession since the end of the Second World War.

There is little doubt that the Asia-Pacific (APAC) region, which was more sheltered than other regions during the 2008–09 crisis, will be more severely impacted this time, but is also already showing signs of recovery. In fact, according to the Economic and Social Survey of Asia and Pacific 2020 report, economic growth in many APAC countries was already slowing since 2019, led by the large economies, China and India, while several other economies weakened more than expected—the Hong Kong SAR due to internal social unrest, and Singapore and Thailand due to weak business sentiment and exports amid geo-political trade tensions. Recent oil price declines also adversely affect fuel-exporting countries such as Malaysia.

General Transfer Pricing Overview—Limited Risk Model

A transfer pricing (TP) analysis begins with the delineation of transactions, i.e. assessing the actual behavior of the parties against the provisions of the contracts. Assessing the actual behavior involves understanding the transaction nature and, more importantly, functional and risk profiles of the transacting entities. Thereafter, the analysis involves the application of the most appropriate method from the five methods prescribed by the Organization for Economic Co-operation and Development (OECD) Guidelines. One of the most common methods applied in TP analyses is the transactional net margin method (TNMM).

The TNMM seeks to ascertain the appropriate arm’s-length net margin a taxpayer should be earning from controlled transactions. In applying the TNMM, the net profit of the taxpayer against an appropriate base-line of sales, costs and assets employed is compared with those of independent companies involved in comparable transactions. The TNMM is most appropriate if one of the parties in the transaction does not make valuable or unique contributions.

The TNMM is often applied to the limited risk model adopted by many multinational companies (MNCs) in their global operations. Under the limited risk model, the transactions are usually designed such that one party performs all the strategic functions, and controls and assumes the material risks such as the market risk, as well as owning valuable intangible assets. The party is identified as the principal or the entrepreneur. The other party only performs the functions it is contracted to do, does not own valuable intangible assets, and the risks it assumes are limited. In most instances, limited risk entities are arranged as contract/toll manufacturers, limited risk distributors and (contract) service providers, whereby they perform routine business activities for their group related parties in the local market—although variations to these models exist.

In determining the arm’s-length price of the related party transactions of limited risk entities by applying the TNMM, benchmarking studies are performed using databases to identify comparable companies which perform similar functions, utilize similar assets and assume similar risks as the limited risk entities, based on certain quantitative and qualitative criteria. The financial results of a selected number of comparable companies are subsequently grouped to form a statistical interquartile range (IQR) as the arm’s-length range of the economic study. The IQR is referenced to establish the TP policies of the limited risk entities.

The general expectation of many, if not all, tax authorities is that limited risk entities (and entities that the tax authorities deem limited risk) should maintain a routine profit in line with their TP policies based on the periodic update of their TNMM benchmarking studies. Losses in the limited risk entities are usually frowned upon. The argument is that the principals who bear all the material risks and rewards should also bear the downside risk of the transactions and thus bear the losses when they do arise.

Adjusting Transfer Pricing Policies

This brings us to the question of whether and how MNCs deploying the limited risk model review and update their TP policies in situations where an array of dysfunctional events caused by Covid-19 impose tremendous challenges to their TP systems.

Ideally, updating the TNMM TP policy to factor in the impact of Covid-19 requires real-time data. However, this may not be possible due to the non-contemporaneous nature of the data especially those found in databases.

In view of this, taxpayers may need to make reference to data relevant to companies in similar economic circumstances in the past, and one of the options is to refer to the financial trend experienced by comparable companies that existed during the 2008–09 recession. Looking into the profitability trend of different industries during the last major economic downturn could provide taxpayers with insights into the possible level of contraction of the industry they operate in during a global economic downturn, enabling them to make more informed decisions on whether to adjust TP policies, and by what extent.

We note that the 2008–09 recession occurred under different economic circumstances and it may not be appropriate to assume that the same economic pattern will apply to the current economic downturn. Specifically, the impact of Covid-19 is certainly expected to be more extensive (for example, a majority of countries experienced lockdowns, thereby halting all types of economic activity, and there is a fear of subsequent second or third waves of outbreak which will further wreak havoc). Such a widespread shutdown will undoubtedly hit the profitability of the majority of taxpayers. Nonetheless, it is not all doom and gloom for every industry. Companies operating in certain industries, such as healthcare companies selling medical devices and personal protection equipment, are benefiting greatly from the pandemic, as are those in the e-commerce sector.

Two Approaches

In this article, we demonstrate two approaches that can be used to compare and analyze the profitability trend of comparables between 2008–09 and 2018, the latest available data from public databases.

Approach 1: Manual Financial “Back-date” of Selected Comparables

Under this approach, we sampled several commonly used APAC and European comparable sets prepared recently for TP documentation and audit purposes and plotted out their historical data from 2004–2018 for the APAC region and 2007–2016 for the European region. The long data period provides an overview that includes economic cycles.

Our review includes the financial “back-dates” of benchmarks for the following industries:

• APAC region back-dates using BvD’s Osiris database
  • Manufacturers of automobile parts and original equipment manufacturers (OEM)
  • Wholesalers of electrical parts and equipment industry
  • Providers of administrative services

• European back-dates using BvD’s Amadeus database
  • Manufacturers of automotive components
  • Distributor of electronic components
  • Providers of administrative services

The sets we analyzed represent operations that do not involve significant intellectual property, i.e., their functional profiles are similar to those of limited risk entities.

The results or the trends derived from the analysis are to illustrate the recessionary impact for reference purposes and should not be taken to be applicable to every industry or every tested party without considering the facts and circumstances on a case-by-case basis. Detailed analysis taking into account each taxpayer’s circumstances and the industry/competitive landscape ought to be carried out.

Statistically speaking, back-dating of the comparable sets may create survivorship bias in the data. These comparables have survived the last recession to be successfully operating in the present market. Thus, survivorship bias tends to create conclusions that are overly optimistic, and they may not be representative of the actual market environment. The effect of survivorship bias, however, is negated by the fact that the comparables identified in the recent benchmarking exercises could well be loss making during the 2008–09 recession, but our back-dating approach means that they still remain in the sets. These comparables would otherwise fail the loss makers quantitative screen typically applied to TNMM benchmarking exercises for limited risk entities. Having said that, the sample sets analyzed below have applied the loss makers quantitative screen, which arguably should not have been applied, as loss-making comparables should not be excluded unless they display an array of erratic behaviors that point towards insolvency.

APAC Manufacturers—Automobile

The APAC automobile parts manufacturers in Figure 1 below showed a general downward trend from 2007, a year before the recession set in, while the full-cost mark-up (FCMU) IQR narrowed from 2006 onward. The median yielded by the comparable companies declined 44% in 2008 compared to 2007 and a further 11% in 2009. The decline was a substantial 51% between 2007 and 2009 followed by an extremely strong rebound in 2010—the lower quartile FCMU jumped from 1.0% in 2009 to 4.8% in 2010, while the upper quartile FCMU soared from 5.4% to 9.9%. Thereafter, due to the calibration of the market, the FCMU normalized to the pre-2007 level.

Meanwhile, the IQR of Automobile OEMs in APAC also showed a similar trend as that of parts manufacturers discussed above. During the recession period, we observed a decline in the IQR, i.e. lower and upper quartiles declined 70% and 19% respectively, but we observed a recovery from 2010 onwards and continued improvement thereafter. An anomaly observed for this particular comparable set is that the median actually increased from an FCMU of 1.8% in 2008 to 3.6% in 2009 and continued to increase until 2016. There is an obvious reduction in the IQR after the height of 2016 which reflected the general APAC market conditions for automobile OEMs whereby the rapid growth of new car sales softened significantly, impacting their margin.

APAC Distributors—Electric and Electronics

The median and lower quartile operating margin (OM) or earnings before interest and tax margin (EBIT margin) of electrical parts and equipment distributors also showed a decline from 2007–2009, as seen in Figure 3 below. The upper quartile also declined between 2008 and 2009. The decline was steep between 2008–2009, i.e., a decline of 34% in the median while the lower quartile OM was almost eroded to nil in 2009. Similar to what was observed in the manufacturing sets, the distributors also showed a strong rebound in 2010 and a gradual growth in the years after; however, the median oscillated between 4% and 7.5% and the IQR narrowed after 2013.

APAC Service Providers—Administrative Services

The median and lower quartile FCMU of the APAC administrative services providers in Figure 4 below showed an overall downward trend from 2004, and eventually reached the respective lowest points (i.e. 1.8% and -0.1% respectively) in 2009. In the same year, the IQR diverged significantly. Similarly to the general patterns discovered from the above manufacturers and distributors sets, the performance of the selected services providers began to recover from 2010 onwards while the upper quartile further diverged since 2014 and reached the maximum 12.7% in 2018.

European Manufacturers—Automotive Components

The impact of the 2009 recession is clearly visible in the lower quartile and the median of the full cost mark-ups realized by a set of European automotive components manufacturers. The lower quartile even got into negative territory in 2009 and 2010. This reflects the heavy impact of the recession on the European automotive sector. The upper quartile, however, was above its prior year value in 2009 and increased further in 2010, higher than the upper quartiles of 2011 and 2012. We interpret the diametrical behavior of the quartiles as being caused by an increase in volatility which could potentially be caused by higher levels of business uncertainty.

European Distributors—Electrical Components

A financial back-date for distributors of electrical components in the European market shows a similar picture as the manufacturing of automotive components comparable set. The lower quartile of the full cost mark-ups declined into the negative. In addition, the median in 2009 was even lower than the lower quartile in the year prior to the recession. The median and the lower quartile resemble similar dynamics, but the upper quartile remained relatively stable over the observation period. It can only be speculated whether another decline in the lower quartile in 2012 was caused by the European debt crisis.

European Service Providers—Back-Office Services

It is unclear why the IQR of the back-office services set already experienced a downward shift in 2008 but there was a further marked decline in 2009. The lower quartile almost touched 0% in 2009, which shows that there were service providers in the market which accepted providing back-office services at or below their full cost for a limited period of time. It is also remarkable that the recovery of the firms in the lower part of the distribution was very slow. It took a few years, until 2016, before the lower quartile reached the same level as the pre-crisis year.

Approach 2: Simulation Approach

Under this approach, 2,000 random final sets were sampled from a large data set of companies. For the simulation, we analyzed selected profit level indicators of companies between the recession period, i.e. the years from 2008–10, and a period without major macroeconomic fluctuations, i.e. 2014–16. The analysis of multi-year periods by application of the weighted average can be described as global “best practice” and is used to address fluctuations in profitability caused by, for example, product life cycles or minor business cycles.

It is not unusual that indicative ranges can be determined using large data sets for TP purposes. One prominent example is the seminal paper by Verlinden, Boone and Dunn which laid the empirical foundation for the de facto safe harbor mark-up rate of 5% firstly mentioned by the EU’s Joint Transfer Pricing Forum and later on by the OECD with respect to low value-adding services. The data set analysis determines ranges using a large number of companies by function across different sectors (identified by industry codes) and countries over a period of several years.

Conversely, a specified benchmark analysis (as identified in Approach 1), in many instances comprises a smaller number of comparable companies in the final sets, usually in the lower two-digit count. This is because of the application of qualitative screenings, as postulated by the OECD Guidelines, to the database search in order to narrow the numbers of companies for analysis and selection. Experience shows that the latter benchmark analyses display wider IQR ranges. As can be seen from the sets analyzed in Approach 1, the IQR ranges of those benchmarking sets formed by a smaller number of comparables display a more volatile behavior in times of economic stress.

Therefore, to reduce the possible variance created from small sample sets, our analysis under Approach 2 combines the variance inherent in classical benchmarks with cross-sector effects of large database analysis. This means that a final set with a sizeable number of companies is randomly drawn from large data sets. The analyses are performed based on three main functions—distribution, manufacturing, and provision of services.

For each of these 2,000 “synthetic” benchmarks with a synthetic final set of 15 companies, the IQR of weighted average full cost mark-ups for the periods between 2014 and 2016 and between 2008 and 2010 are generated. It yields 2,000 data points of lower quartile, 2,000 data points of median and 2,000 data points of upper quartile for each of the functions. The medians are then calculated for each of the quartiles so that they form IQR points of both periods.

Oriana Results for APAC Region

For the APAC region we simulated 2,000 synthetic final data sets based on a sample of company data from the BvD’s Oriana database. The data sample jointly comprises up to 15,292 independent companies that have the BvD independence indicators of A+ to B-, in the APAC region.

As can be seen from Figure 8, we found a clearly pronounced recessionary effect in the 2008–10 period even for the less affected APAC region. The trend is consistent across manufacturing, distribution and services. The effect at the lower quartile is relatively large for manufacturing and services (almost two percentage points), so are their medians and upper quartiles. There is, however, a less clear recessionary impact on distributors in the APAC region.

Amadeus Results for the European Region

For the European region we simulated 2,000 synthetic final data sets based on a sample of company data from the BvD’s Amadeus database. The data sample jointly comprises up to 50,550 independent companies that have the BvD independence indicators of A+ to B- in Europe.

Figure 9 summarizes the results of the simulation exercise. As for the APAC region, a clear recessionary effect is visible. Again, the impact of the recession on the lower quartile is relatively large for manufacturing and distribution. Similarly to the APAC region, the simulated impact of the recession on the operating margins of European distributors is less pronounced. For the simulation at hand, the upper quartile is even higher in the period 2008–2010 compared to the non-crisis period. It is an interesting topic for future research to analyze whether such behavior of the interquartile range is caused by the increased uncertainty in the business environment.

Observations and Conclusions

Our analyses above provide a clear case for taxpayers to carry out more detailed comparability analyses specific to their fact pattern and industry. The current available data from public databases is not suitable to be used as a benchmark for limited risk entities in view of the current challenges of a massive global economic downturn. Our analyses can be interpreted to conclude that a disparity is likely to exist between the “arm’s-length values” determined by applying currently available financial data and the economic reality faced by many MNCs around the world.

The approaches serve as approximations of the effects of pandemic on “routine” companies’ net profit indicators, albeit our results suffer to some extent from a survivorship bias. Additionally, from the sets identified in Approach 1, a “V” shape recovery is generally observed from the lows of 2009. The same cannot necessarily be expected in the case of the Covid-19 pandemic. That said, some further adjustments, such as those based on the degree of gross domestic product (GDP) contractions (some countries announced more positive or less than expected contraction in GDP data than others) which are available on a quarterly basis, may be considered to improve the insights one derives from the analyses of historical data during the 2008–2009 recession.

Planning Points

It is worth noting that any changes in the TP policies that result in profit reduction or incurring of losses are likely to be scrutinized by tax authorities.

Despite the pandemic, the tax authorities may still have certain expectations of profits remaining in limited risk entities. On the other hand, tax authorities in the entrepreneur’s tax jurisdiction might rightly claim that the group companies should assume losses for some time. That claim is not completely unfounded, given that we are talking about low risk and not about “no risk” models.

Having said that, some tax authorities have, in one form or another, acknowledged the risks Covid-19 has created on the operations and profitability of MNCs, which provides some welcome flexibility and room for discussions with tax authorities on the TP policy changes taxpayers may choose to make. To mitigate any major risks, taxpayers can choose to adjust their TP policies in phases; for example, taxpayers could adjust downwards based on anecdotal data for now, but readjust upwards once more concrete data becomes available.

In the longer term, however, MNCs should revisit their TP systems and recalibrate them to take into consideration future economic shocks to avoid future tax disputes on their business operations in the developing countries in APAC. As an immediate takeaway, specific terms and conditions should be put forward in intercompany contracts to allow for adjustments to the ‘‘normal’’ TP system under specific circumstances, but still respecting the arm’s-length principle.

Above all, MNCs should regularly stay up to date with information being released by individual tax authorities who will certainly form their own opinions on this matter.

Tony Gorgas is Partner, Global Transfer Pricing Services, KPMG Australia; Cheng Chi is Partner, Global Transfer Pricing Services, KPMG China; Michel Braun is Director, Global Transfer Pricing Services, KPMG Germany; Choon Beng Teoh is Director, Global Transfer Pricing Services, KPMG China; Sebastian Hoffmann is Senior Manager, Global Transfer Pricing Services, KPMG Germany; and Lino Lv is Manager, Global Transfer Pricing Services, KPMG China.

The authors may be contacted at: tgorgas@kpmg.com.au; cheng.chi@kpmg.com; mbraun@kpmg.com; choonbeng.teoh@kpmg.com; shoffmann2@kpmg.com; lino.lv@kpmg.com

This column does not necessarily reflect the opinion of The Bureau of National Affairs, Inc. or its owners.