In essence, affiliated companies can set up intragroup pricing that differs from market mechanism. Suppose the cost of product P is 80, and the price of product P in the market is 100. In independent setting, selling P will result in 20 of profit.
Suppose A is selling P to B (A’s affiliate) for a transfer price of 85. B then sells it to an independent party for 100. A will pocket a profit of 5, and B 15. If the transfer price is 99, A will get 19 and B only 1; and so on.
Unlike independent parties’ behavior that each party will maximize its own interest/profit, affiliated companies can set price that may “hurt” one of them. In the example above, setting transfer price to 99 may indeed hurt B, for B only gets a meager profit of 1. The price of 99 may be too expensive for an independent party that it may not have entered such transaction.
[The above method uses price as a point of comparison. If there is no comparable transaction between independent party, for instance if the price of products comparable to product P is not available, we can compare the profit level instead. The party that conducts less complex functions – the one that only conducts routine production or distribution, bears risks that are not economically significant, and possess no unique and valuable intangibles – is designated as "tested party". The choice of simpler company as tested party is due to the consideration that the more complex a company is, the less likely it has comparable companies similar to it. If both parties assumes economically significant risks or contributes unique and valuable intangibles, then the profit is split to both parties.]
If A and B are in the same country and taxed with same rate, arguably, such arrangement does not pose a significant base erosion problem for tax authority. If tax authority failed to do a transfer pricing adjustment to A, they could do it to B with similar net gain in tax revenue.
But if A and B are in different countries with different tax rates/facilities, then the 20 profit can be arbitrarily shifted in whichever company that will pay smaller amount of tax. Or no tax at all. This is probably the reason of US, Australia, or India to deny transfer pricing benefit only in cross-border transactions. But other country, like Indonesia, allows tax authority to make a transfer pricing adjustment even when the affiliated parties are in Indonesia. There may be the case that two Indonesian companies may be affected by different tax regimes (for instance if one of them has a reduction of tax rate, tax holiday, loss carryforward, final income tax, etc.)
Tax authorities of the world thus have incentive to adjust transfer pricing done by their taxpayers. They will look up at transaction done by independent parties according to sound business practice, see how much the difference in price or profit is observed, attribute the difference, and then tax accordingly.
Accurately delineating the transaction, and then comparing affiliated transaction with independent transaction – so-called comparability analysis – is therefore the “heart” of transfer pricing adjustment. A comparable independent transaction (often called “comparable” for brevity) can be found within the company if one of the companies under scrutiny also transacts with independent party. This is called internal comparable, and generally considered to be more reliable than external comparable.
To find the appropriate comparable, there are 5 comparability factors that must be considered:
a. contractual terms;
b. functions, assets, risks;
c. characteristics of goods/services;
d. economic circumstances; and
e. business strategies.
A reliable comparable must be similar. Or, at least must not be materially different from the transaction under scrutiny so that reliable adjustment – if necessary – can be made.
Given that information in the market is imperfect – barring commodities or internal comparable, for instance – sometimes the comparables found are “inexact”. This most often happens with external comparables, such as those found via commercial database. Adjusting transfer price in Indonesian taxpayer may sometimes invoke comparables found in other countries, due to the lack of Indonesian comparables. Now recall that economic circumstances is one of comparability factor that must be considered. How to take this into account?
One of most common practice is disregarding such difference insofar as the comparables selected come from similar region. This assumes that the economic circumstances in Indonesia is more similar with other countries in Far East and Central Asia compared to, say, European Union. Of course, disagreement may arise whether this assumption holds.
The issue is therefore whether Far East and Central Asian comparables can be used, or whether there is significant difference with Indonesian comparables that, strictly speaking, only Indonesian comparables should be used in adjusting transfer price in Indonesia. There is also the issue whether some adjustments that may be implemented to improve reliability, such as working capital adjustment or country-risk adjustment, are warranted. (The latter adjustment is especially of our interest here.)
The case for European comparables has been tested by Meenan et al. (2004) and then updated by Peeters et al. (2016) as well as by Platform for Collaboration on Tax (2017). They all found that there are no material difference that comparables from other European countries can be used without adjustment.
What about Indonesia vis-a-vis Far East and Central Asian comparables? This short post is a rudimentary attempt to answer.
The Set-up
The procedure is similar to the works cited above: select companies with NACE v. 2 industry codes following Peeters, et al. (2016) and then group them into 4 industries (automotive manufacturing, electronics manufacturing, chemical distribution, and electronics distribution). We subsequently extract necessary financial information (Sales, Costs of Goods Sold/COGS, Other Operating Expenses/OPEX, Operating P/L, Tangible Fixed Assets, Stocks, Debtors, Cash and Cash Equivalents, and Total Assets) to compute some profitability ratios, which are:
a. Gross Profit Margin (GPM): (Sales – COGS) / Sales
b. Cost Plus Markup (CPM): (Sales – COGS) / COGS
c. Operating Profit Margin (OPM): Operating P/L / Sales
d. Return on Assets (ROA): Operating P/L / (Tangible Fixed Assets + Stocks + Debtors + Cash and Cash Equivalents), and
e. Full Costs Mark-up (FCMU): Operating P/L / (COGS + OPEX)
The periods used are 2014 – 2018 (5 years) using Orbis data. All are computed using five-year weighted average to smooth the earning profile that may be affected by business cycle.
Among the indicia above, only OPM and ROA are computed in the original studies by Meenan et al. (2004) and Peeters, et al. (2016). These indicators are on net profit level, which is only useful for transactional method such as Transactional Net Margin Method (TNMM). I expand the choice to include GPM and CPM to account for traditional methods such as Cost-Plus and Resale Price methods. I also include FCMU to account for the manufacturing remuneration.
We, however, relaxed some of the screening criteria because in some industries there is no Indonesian company that fits in. We, for example, do not limit based on arbitrary threshold of sales. We also do not limit the samples to only include companies whose operating profit margin is around -5% to 15% and ROA around -10% to 20% (by assuming the five-year weighted average and the usage of interquartile range will smooth out the outliers). On the other hand, we implement stricter criteria in other place, such as excluding companies that do not have financial data for at least 3 years.
After we compute the indicia, we divide the Indonesian samples and Asian samples (which include Indonesia) in each industry group by median. Then, we apply Chi-square test to each lower quartile and upper quartile.
The aggregated cumulative distribution function of comparables' profitability is denoted by πΉ(π), which gives the share of comparables with a profitability ratio of our choice smaller than or equal to r. Our interest is interquartile range, and subsequently πΉ(π) for each industry is measured using the critical values defining the 1st and 3rd quartile of the cumulative distribution as πΉ(π∗) = 0.25 and πΉ(π∗∗) = 0.75.
Given the values π∗ and π∗∗, the number of firms in Indonesia with profitability ratios below and above this benchmark profitability were recorded. Formally, the analysis defines ππ1=πΉπ(π∗)ππ, ππ2= [πΉπ(π∗∗) − πΉπ(π∗)]ππ, and ππ3= [1 − πΉπ(π∗∗)]ππ
where i denote Indonesian specific comparable. Under the null-hypothesis, 25% of the Indonesian-specific comparables in both ππ1and ππ3, and 50% in the middle group ππ2 are expected.
Accordingly, a joint test statistic defines the variable
where πππ denotes the expected number of firms in each country-specific group.
The chi-square statistic π2 increases as πππ - πππ increases, which indicates that the observed distribution of comparables is significantly different from expected.
The Result and Conclusion
No significance difference exists between Indonesian comparables' lower and upper quartiles and Far East and Central Asian comparables' lower and upper quartiles in the industries tested. We repeat the calculation with Fischer exact test which is more conservative than chi-squared test in small samples (even in sample size less than 5) and the results still hold. Changing the denominator of ROA with Total Assets also does not change the result.
What does this mean? First, this does not mean that comparables from outside of Indonesia are as reliable as Indonesian comparables. This only means that the profitability level of Indonesian and other Far East and Central Asian comparables are drawn from similar distribution function, in this case a Chi-squared. Again, a thorough comparability analysis – with consideration to all five comparability factors – is more paramount than a simple similar-country-therefore-more-reliable approach.
So, does this mean that country risk adjustment is unnecessary?
In our opinion, any kind of adjustment to increase reliability must ultimately be proven to be justifiable. If, for instance, taxpayer proposes Working Capital Adjustment, she must first demonstrate that the comparables she obtains do not have a high degree of reliability. But then again, why does she knowingly choose less reliable comparables to begin with? If, for example, the level of inventory-to-total assets of the tested party is significantly different from the comparable companies the taxpayer choose, and that difference may significantly affect price/profit, then it begs the question of why the taxpayer does not apply a quantitative screening using inventory-to-total assets as a criterion.
The similar reasoning is used in multiple-year data vs. single year data. OECD Transfer Pricing Guideline 2017 para. 3.75 does not put emphasis on the usage of multiple year data as a default or systematic requirement. But only when the usage of multiple year data adds value to the analysis (for example if there is an effect from business life cycle) then it may be justifiable.
Ultimately, referring to OECD Transfer Pricing Guideline 2017 para. 3.59, at least in the industries referred here there are no substantial deviation in the interquartile range that indicates unreliability due to country-level difference. Therefore, unadjusted comparables may be used if other comparability factors are indeed similar enough that the comparables are suitable for inclusion.
Reference:
Meenan, P., Dawid, R., and HΓΌlshorst, J. (2004) Is Europe One Market? A Transfer Pricing Economic Analysis of PanEuropean Comparables Sets. European Commission, Brussels Taxud/C1/LDH/WB
Peeters, R., Noben, S., and Laurent, I. (2016) Study on Comparable Data Used or Transfer Pricing in the EU. European Commission, Brussels TAXUD/2014/CC/126 doi: 10.2778/657328
Platform for Collaboration on Tax (2017) A Toolkit for Addressing Difficulties in Accessing Comparables Data for Transfer Pricing Analyses. IMF - OECD - UN - World Bank
OECD (2017) OECD Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations 2017. OECD Publishing, Paris. http://dx.doi.org/10.1787/tpg-2017-en
