Transfer pricing comparables: Preferring a close neighbor over a far-away peer?

Transfer pricing comparables: Preferring a close neighbor over a far-away peer?

^q

Bert Steens

^a,⇑

, Thibaut Roques

^b

, Sébastien Gonnet

^c

, Christof Beuselinck

^d

, Matthias Petutschnig

^e

aVU – Vrije Universiteit Amsterdam – School of Business and Economics – Department of Accounting & Control, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands

bTP qube, Village By CA, 11 Cours du 30 Juillet, 33000 Bordeaux, France

cTransfer Pricing Economists for Development, 56 Rue du Général Chanzy, 94130 Nogent-Sur-Marne, France

dIESEG School of Management, Univ. Lille, CNRS, UMR 9221 – LEM – Lille Economie Management, F-59000 Lille, France

eVienna University of Economics and Business – Department of Finance, Accounting and Statistics – Business Taxation Group, Welthandelsplatz 1/AD, A-1020 Vienna, Austria

a r t i c l e i n f o

Article history:

Available online 16 May 2022 Keywords:

Transfer pricing Corporate tax Comparables Country risk

Geographical proximity

a b s t r a c t

In a globalized economy, transfer pricing estimations are key in valuing international transactions between entities of multinational corporations (MNCs), and the use of uncon- trolled peer group comparison methods are widespread. In the absence of uniform guide- lines on the optimal identification for comparable companies, however, it remains a concern that poor selection choices may lead to biased estimates. This may systematically bias cross-jurisdictional revenue flows. The current approach employed by tax practition- ers and implicitly endorsed by several tax administrations worldwide commonly relies on comparables from neighboring countries. We employ a global sample of over 11,000 man- ufacturing firms located across 84 countries over the period 2012–2016. We find evidence that the risk level of the country where companies are incorporated is highly correlated with their profitability and that geographical closeness is less relevant for explaining prof- itability when controlling for country risk. Our findings suggest that the search for foreign comparables is better guided by country risk rather than geographic proximity and that insufficiently controlling for country-level sovereign risk biases high-risk countries’ corpo- rate tax revenues downwards. We conclude that the accuracy of comparables is likely to benefit from expanding the scope of observations to a global level, while controlling for country risk.

Ó2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

https://doi.org/10.1016/j.intaccaudtax.2022.100471 1061-9518/Ó2022 The Author(s). Published by Elsevier Inc.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

qThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

⇑Corresponding author.

E-mail addresses: h.b.a.steens@vu.nl (B. Steens), thibaut@tpqube.com (T. Roques), sebastien.gonnet@tped.eu (S. Gonnet), c.beuselinck@ieseg.fr (C. Beuselinck),matthias.petutschnig@wu.ac.at(M. Petutschnig).

Contents lists available atScienceDirect

Journal of International Accounting,

Auditing and Taxation

1. Introduction

For decades, transfer pricing calculations for transactions between business entities that belong to the same corporate group and that are located in different geographical areas rank among the most important taxation and operational chal- lenges of multinational companies (MNCs).¹Thetaxation challengeoriginates from the difficulty to substantiate that intercom- pany prices comply with the ‘‘arm’s length” principle and from the related concern that MNCs can shift profits from high-tax countries to low-tax countries by selectively adjusting cross-border transfer prices (Grubert, 2003; Huizinga & Laeven, 2008;

Beuselinck et al., 2015; De Simone, 2016; Klassen et al., 2017; Henry & Sansing, 2018; Wier, 2020). Not surprisingly, the Organ- isation for Economic Cooperation and Development (OECD) launched the Base Erosion and Profit Shifting (BEPS) Action Plan (2013) and has issued guidelines to ensure that profits are taxed where economic activities are generated. With transfer pricing theoperational challengearises from the fact that, even absent tax incentives, unbiased transfer prices are difficult to identify in countries where comparable companies are not available or are only available in limited amounts.

While there exists evidence that intra-firm trade prices deviate from outside (arm’s length) prices and that such choices are often consistent with tax-minimizing behavior (e.g.,Grubert & Mutti, 1991; Clausing, 2003; De Simone, 2016; Klassen et al., 2017), we have few insights in how the paucity of local comparable data affects the arm’s length nature of transfer prices. Moreover, we are short of insights in whether a lack of local company comparables potentially results in a systematic bias in transfer prices across institutional risk levels.

The internationally agreed standard for the determination of an acceptable transaction price within multinational enter- prises (MNEs) is the arm’s length principle.²This principle relies on various methods of which the most commonly used is the Transactional Net Margin Method (‘‘TNMM”)³. The TNMM seeks to compare the net profit relative to an appropriate base (e.g., costs, sales, assets) in controlled transactions (transactions between related entities) with the net profit relative to the same base in uncontrolled transactions (transactions between independent entities) (OECD, 2022, p. 113). Net profit is defined as the earnings minus operating costs (OECD, 2022, p. 120) relevant for the compared transaction (OECD, 2022, pp. 119–121).

The net profitability ratios used in the TNMM-approach (e.g., return on sales, return on capital employed) are classical indicators for measuring and managing the financial performance of a company’s profit and investment centers and for setting their trans- fer prices (e.g.,Cools & Slagmulder, 2009; Plesner Rossing, 2013; Merchant & Van der Stede, 2017).

However, not all companies apply TNMM (-principles) for both tax purposes and management purposes. Cools and Slagmulder (2009)find that MNCs also use TNMM ratios to set transactional transfer prices of their revenue centers and cost centers prioritizing tax compliance over management control.Bouwens and Steens (2016)report the case of an MNC that integrates a TNMM-consistent return on capital mark-up in the full cost transfer prices of its manufacturing cost centers for both management control and taxation purposes.

The practical implementation of the TNMM relies on finding independent companies with similar functions, assets, and risks whose net profitability levels consequently serve as a relevant benchmark (Plesner Rossing, 2013, pp. 182-186; OECD, 2022, pp. 147-149). However, the search for comparable independent companies is fraught with complexities, in particular in countries where financial information at the firm level is scarce. When domestic financial information is not available in a particular country, the current practice consists of using foreign comparable companies and performing adequate adjust- ments (Petutschnig & Chroustovsky, 2018). However, this practice is not well defined and to our knowledge there exist no universal guidelines on the identification of ‘optimal’ foreign comparable companies, nor on how acceptable adjustments can be motivated in case of intrinsic differences across countries.⁴Also, the concern exists that the availability of local com- parables is especially low in developing and emerging economies (EuropeAid, 2011).Ribeiro et al. (2010)for instance report that the coverage of all active companies based in non-OECD countries in publicly available financial reporting databases is only 26.6% of all covered firms. The proportion of active companies based in Far Eastern, Middle Eastern, Central Asian, and African countries is even far lower and reaches only 0.4%. This suggests that especially for transactions with entities in these countries, there may be a particular concern regarding the lack of domestic comparables.

Our study provides empirical evidence relevant for finding TNMM comparable companies for firms in countries where local comparables are lacking. As in many cases the recommended practice is to consider companies in neighboring coun- tries or the same region (Bloomberg, 2016; Cooper et al., 2016; Ernst & Young, 2019), we evaluate determinants for compa- rability that enable practitioners to go beyond the traditional practice of prioritizing geographical proximity. We argue that if a country-specific factor other than geographic proximity contributes to explaining the profitability of independent compa-

1See e.g.,Ernst & Young (2009), ‘‘Since 1995, Ernst & Young has surveyed multinational companies (MNEs) on international tax matters, with special emphasis on what continues to be the number one international tax issue of interest to them – transfer pricing” (p. 1), andErnst & Young (2019), ‘‘Transfer pricing that is rooted in operational reality and globally consistent will prove to be defensible to tax authorities and in that way, even amid an era of heightened controversy, represent the absolute lowest levels of tax risk.” (p. 3), and‘‘Tax risk is by far the most critical issue driving respondents’ transfer pricing strategies” (p. 5).

2The arm’s length principle is set forth in article 9 of the OECD Model Tax Convention as follows: ‘‘where conditions are made or imposed between the two enterprises in their commercial or financial relations which differ from those which would be made between independent enterprises, then any profits which would, but for those conditions, have accrued to one of the enterprises, but, by reason of those conditions, have not so accrued, may be included in the profits of that enterprise and taxed accordingly” (OECD, 2022).

3See for instance the International Manual of theUK HM Revenue & Customs (2016). Country-specific support is found inMartin and Bettge (2020)for Advanced Pricing Agreements (APA’s) closed in the US and inTambunan et al. (2020)for China and India.

4TheTransfer Pricing Forum (2016)by the Bloomberg BNA Tax and Accounting Center has one of the largest collections of country surveys and shows a wide variety of national criteria and decision-making in standard practice for selecting comparables.

nies, it could be used in determining the most appropriate country panel in which to search for foreign comparable compa- nies. In particular, we study whether the implicit riskiness of the country of incorporation can help fine-tuning the ex post profitability comparison of independent companies.

Our empirical evaluation relies on a large panel of over 11,000 manufacturing companies over a 5-year period from 2012 to 2016. Our tests confirm the conjecture that profitability is positively related with country risk and that regional differ- ences in company profit levels are less relevant after controlling for country risk. In economic terms, our results suggest that – ceteris paribus and relative to the countries with the lowest risk level (AAA rating) – firms located in AA-rated (A-rated) countries have a 6.2 (4.6) percentage points higher return on sales (ROS, defined as earnings before interest and taxes divided by sales) and this increases to be 8.7, 11.4, 12.2, and 9.8 percentage points higher, respectively, for BBB, BB, B, and CCC-rated countries. A mediation analysis further shows that the disproportionally larger number of smaller firms located in riskier countries is not the single driving factor of the direct positive relationship between firm profitability and country risk.

The outcomes of our study suggest that country risk provides better guidance for the search for foreign independent com- parable companies than geographic proximity. Our results imply that the quality of transfer pricing comparables is likely to gain from extending the scope of observations to a global level, while simultaneously controlling for country risk level.

Our study is timely and important for several reasons. First, there is substantial lack of insights in how to properly adjust profitability metrics of comparable companies when they face different economic risks than the tested party (BEPS Actions 8–10;OECD, 2013, 2015; Bloomberg, 2016; Cooper et al., 2016; Ernst & Young, 2019). Second, because economic risks are inherently higher in emerging versus more mature markets, our study can help emerging and developing countries’ govern- ments, as their tax administrations would become better equipped with an economically sound approach towards reviewing transfer pricing arrangements of multinationals. This is essential in times when revenue mobilization is a key development priority and essential to finance investments in human capital and infrastructure (e.g.,Crivelli et al., 2016; Cobham & Jansky´, 2018, 2019).

While there exists widespread academic evidence that government’s tax revenue loss is often the result of tax-efficient income and debt shifting and technical tax avoidance schemes (e.g.,Collins et al., 1998; Newberry & Dhaliwal, 2001;

Clausing, 2003; Hebous & Ruf, 2017), there is very little evidence on the root causes. The use of downward-biased peer com- parable profit levels within the application of the TNMM could be one of these root causes (Fuest et al., 2011). Yet at the same time, there are concerns that developing countries are at a comparative disadvantage in collecting tax revenues compared to mature economies as there are fewer databases for comparable transactions for verifying transfer prices between related parties. Moreover, developing countries tend to have less documentation requirements and tend to be less strict in enforcing existing requirements (EuropeAid, 2011). In the first part of our study, we add relevant new insights that may help in curing one part of the problem, namely improving the current TNMM estimation methodology.

The remainder of the study is organized as follows.Section 2presents the development of our hypothesis on the influence of the riskiness of countries on their companies’ profitability.Section 3describes the data and research design.Section 4pre- sents the empirical findings andSection 5concludes.

2. Background and hypothesis

2.1. Empirical evidence in transfer pricing literature

The academic empirical literature on transfer pricing is extensive and has focused largely on profit shifting using transfer (mis)pricing. Early work, includingGrubert and Mutti (1991), Harris et al. (1993), Hines and Rice (1994), andCollins et al.

(1998), provided indirect evidence for tax-motivated profit shifting by MNCs, showing that their pre-tax profits are system- atically correlated with tax differentials across countries.Wier (2020)provides the first direct systematic evidence of profit shifting through transfer mispricing in a developing country. Using South African transaction-level customs data, the paper directly tests for transfer price deviations from arm’s-length pricing and finds that multinational firms in South Africa manipulate transfer prices in order to shift taxable profits to low-tax countries. That paper further shows that transfer mis- pricing in South Africa is not more severe than in advanced economies.

In recent years, the literature increasingly focuses on the analysis of transfer pricing rules and on the impact of tax enforcement on the profit shifting behavior of firms. Relying on a large international panel of firm-specific (micro) estimates, Beer and Loeprick (2015), Riedel et al. (2015), andMarques and Pinho (2016)find evidence that the implementation of trans- fer pricing rules and the increase in transfer pricing strictness is associated with a general reduction in tax-motivated profit shifting.Beuselinck et al. (2015)reach similar conclusions for a sample of European multinational groups and additionally observe that privately-held groups shift more income out of the home country to save on taxes compared to publicly listed groups, suggesting that non-tax costs shape the shifting intensity.

A reduction in tax-motivated profit shifting due to strict tax regulations is also consistent with the findings ofChen et al.

(2021). They find that the tax motives of tax departments determine their relative focus on tax planning or on compliance.

Tax departments focusing on planning have a greater association with tax avoidance and a higher tax risk, while depart- ments focusing on compliance have an incrementally lower tax risk but higher tax rates.

Interestingly, almost none of the empirical work on tax enforcement, profit shifting, tax planning, and tax compliance explicitly addresses the availability of comparable information internationally and how this may impact profit shifting flex-

ibility. An exception that deals with the topic indirectly isDe Simone (2016), who used the adoption of International Finan- cial Reporting Standards (IFRS) in the European Union (EU) in 2005 to examine how the introduction of a single set of com- mon accounting principles allows firms to rely on a broader range of comparables and to select the more tax-beneficial end- of-the-range comparable. Her results suggest an 11.3% tax-motivated change in reported pre-tax profits following affiliate IFRS adoption, indicating firms use IFRS to motivate tax-favorable comparables selection. Similarly,Murra et al. (2013) observe for a sample of Italian firms that foreign comparable selection is geared by tax purposes. To our knowledge, the cur- rent study is the first to explicitly and thoroughly analyze the selection process of foreign comparables and its validity in an international context. The following sections describe uncontrolled comparable selection choices and risk adjustments in more detail.

2.2. Controlled transactions and the need for uncontrolled comparables

A fundamental feature of transfer pricing rules is to distinguish between ‘‘controlled transactions” and ‘‘uncontrolled transactions”. The former refers to transactions between two enterprises that are associated – i.e., they are members of the same group of companies. The latter refers to transactions between independent enterprises. Such transactions may involve the sale or transfer of goods, or anything else of value, such as physical and financial assets, intangibles (including rights), or services as well as rights to services.

The conditions of a controlled transaction are established, or tested, by reference to the conditions observed in compa- rable uncontrolled transactions, commonly referred to as the ‘‘arm’s length principle” (OECD, 2022). An uncontrolled trans- action is comparable to a controlled transaction when there are no differences between them that could materially affect the pricing being examined; or when such differences exist, if reasonably accurate comparability adjustments can be made in order to eliminate the effects of such differences (OECD Transfer Pricing Guidelines, 2022, chapter II, para. 2.15). The OECD Transfer Pricing Guidelines (2022)and theUnited Nations (UN) Practical Manual on Transfer Pricing for Developing Countries (2021)each set out a framework of five economically relevant characteristics or comparability factors to be con- sidered when determining whether a controlled transaction is comparable to an uncontrolled transaction. These are the con- tractual terms of the transaction, the functions performed by each of the parties to the transaction (taking into account assets used and risks assumed), the characteristics of the property transferred or services provided, the economic circumstances of the parties and the market in which the parties operate, and the business strategies pursued by the parties.

There are two broad types of comparables: internal and external. There is no hierarchy between internal and external comparables so that the most reliable available comparables should be sought. An internal comparable exists where there is a comparable transaction between one party to the controlled transaction and an independent party. TheOECD Transfer Pricing Guidelines (2022)note that where they exist, ‘‘Internal comparables may have a more direct and closer relationship to the transaction under review than external comparables. [. . .] On the other hand, internal comparables are not always more reli- able and it is not the case that any transaction between a taxpayer and an independent party can be regarded as a reliable com- parable for controlled transactions carried on by the same taxpayer”.⁵

In practice, however, application of the arm’s length principle is often heavily reliant on external comparables (Deloitte, 2015). An external comparable exists where there is a comparable transaction between two enterprises that are indepen- dent, and neither of which is a party to the controlled transaction. The most common source of information on external com- parables is commercial databases (European Commission (EC), 2016). However, in 2017, ThePlatform for Collaboration on Tax (2017)published a study finding a scarcity of domestic information that can be used for comparability analysis in many countries. The study summarizes information in several databases available to transfer pricing practitioners globally for the year 2013. Only local companies that are independent and for which revenue and net margin information is available (for the possible application of the arm’s length principle using the TNMM) were considered. Although close to 9 million corporate records with revenue and net margin information were available, only half met the basic independence requirement. Fur- thermore, for the vast majority of countries worldwide (164 out of 196 countries) <1,000 local independent company obser- vations with information on revenues and net margins were available for 2013. In a similar vein,Torslov et al. (2020) conclude that international tax studies based on firm-specific (micro) estimates suffer from data unavailability because either public registries do not exist or because entity-level income figures are not reported. While ongoing efforts of com- mercial providers of financial information to increase coverage are improving the situation, it may be necessary to look for alternative, non-domestic information sources in many countries.

ThePlatform for Collaboration on Tax (2017)recommends starting a search for comparables with information available concerning the local (national) market of the tested party using the logic that such a search criterion automatically rules out local market differences. However, in case local comparables are scarce or unavailable, data from other geographic markets (potential ‘foreign comparables’) may be considered (Deloitte, 2015). In some cases, it is considered that the geographic mar- ket may be less relevant than other characteristics, meaning that the most reliable comparables available are those from a foreign market. According to thePlatform for Collaboration on Tax (2017, p. 43)‘‘it generally makes sense to consider potential comparables from the same geographic market as the tested party in the first instance as this will minimise any potential

5See Paragraphs 3.27–3.28 ofOECD Transfer Pricing Guidelines (2022)or Section B.3.5.2.3 to B.3.5.2.6 of theUN Practical Manual on Transfer Pricing (2021) for a complete discussion on internal versus external comparables.

differences that could have a material effect on the comparison. Where the market is considered to be a key comparability factor, it may be appropriate for this to be defined as a country, a region, or group of countries that are considered to be either (a) a single or largely integrated market; or (b) sufficiently similar to the market of the tested transactions”.

The use of foreign comparables in practice is very common, particularly due to inadequate information being available (e.g.,Cooper et al., 2016). In a survey conducted by the World Bank in Eastern European and Central Asian Economies in 2013, tax practitioners in the region indicated that when domestic comparables are not available, the most common approach adopted is to use ‘‘foreign comparables” (Cooper et al., 2016, p. 147). Among the 51 practitioners surveyed, 67 per- cent reported that they regularly observe challenges in obtaining domestic comparable information, and 57 percent reported that they either often, very often, or always use ‘‘foreign comparables” (Cooper et al., 2016, p. 147).⁶

Tax administrations’ position regarding the acceptability of foreign comparables varies among countries. However, in most countries using foreign comparables is generally accepted where no local comparables are available and provided that the foreign comparables meet the applicable comparability standard (Ernst & Young, 2019). Examples of foreign comparable firm’s selection are numerous. In Europe for instance, the reliance on regional (pan-European) comparables is widespread and is endorsed in the Council of the European Union’s Code of Conduct on transfer pricing documentation for associated enterprises in the EU.⁷Selection based on smaller geographic regions or ‘‘sub-regions” is promoted in the case of Nordic, Ibe- rian, and Benelux countries. A similar approach is followed in China where the tax administration accepts pan-Asian compara- bles samples in the absence of Chinese publicly listed comparables, preferring the pan-Asian sets of publicly listed companies to the sets of private Chinese companies. Also, Australia and New Zealand accept comparables from each other. In South Africa, the South African Revenue Service (SARS) formally acknowledged that there is a lack of local comparable information available, and provided specific guidance on the use of foreign comparables in section 12 of Practice Note 7.⁸In a study on international tax policies of African economies,Beer and Loeprick (2021)indirectly also draw attention to the fact that micro-level firm-level information on MNEs remains very limited in commercial databases for many developing countries, including most economies in Sub-Saharan Africa. This scarcity of firm-level financial reporting information on MNEs in Sub-Saharan Arica is supported by Lee and Swenson (2016) and Thiart (2021).

The validity of relying on foreign market data, however, has not been comprehensively analyzed.Meenan et al. (2004) investigate whether arm’s length ranges differ across the EU. Their analysis supports the assumption of homogenous prof- itability distributions and, therefore, endorses the use of pan-European data. An update of the study for the EC byPeeters et al. (2016)also concludes that pan-European searches provide for a reliable representation of local profit expectations, but only for the period 2010–2014. However, when the more volatile years of the financial crisis are included (2008–

2014), substantial heterogeneity in profitability ratios also exists in the European context (Peeters et al., 2016, p. 194 et seq.). We are unaware of other studies investigating the validity of foreign comparables outside the EU.

2.3. Comparables and fundamental risk adjustments

While the approach in practice to finding suitable comparables often looks at similar-type peer firms in neighboring countries, thereby emphasizing the geographic proximity, academic studies identify the importance of the effects of country-specific characteristics – such as country risk or population characteristics – on the profitability of companies (e.g.,Hawawini et al., 2004; Makino et al., 2004). This logic relates to the observation that profitability and risk are inter- twined. Entrepreneurs seeking higher profit levels have to accept higher risk levels. Consequently, high-risk enterprises will need to achieve higher returns in order to keep investors interested. The Capital Asset Pricing Model (CAPM) predicts that the required return on equity of an asset is a function of the risk free rate and the asset’s sensitivity (expressed by the risk factor Beta) to the systematic market risk premium (Treynor, 1962; Sharpe, 1964; Lintner, 1965; Mossin, 1966). Various extensions of the model were introduced to consider additional explanatory factors, such as a firm’s size portfolio and firm’s value ver- sus growth perspectives (Fama & French, 1992, 1993). A common factor that appears relevant in international valuation studies is that country-related risks have become an important return component (e.g.,Diamonte et al., 1996; Bekaert &

Harvey, 2000).

In general, political and economic risk factors are seen as relevant factors explaining firm profitability (Lintner, 1965). On the one hand, stable (less risky) countries are associated with a higher entrepreneurial risk appetite (Ketelhöhn &

Quintanilla, 2012; Boubakri et al., 2013), which is expected to translate into higher profit levels. On the other hand, countries with higher intrinsic risks (due to political, economic, societal, technological, ecological, and demographical developments) can only attract and keep investors when their returns compensate for these higher risk profiles. For instance,Harvey (2004) documents that country risk profiles are associated with future equity returns only in emerging economies, suggesting that these particular markets are segmented from the rest of the world.

6Similarly, according toEuropeAid (2011), Kenyan taxpayers report relying on European databases given the limitations in domestic information sources when searching for comparable uncontrolled transactions.

7SeeResolution of the Council and of the Representatives of the Governments of the Member States, Meeting with the Council, on a Code of Conduct on transfer pricing documentation for associated enterprises in the European Union (EU TPD), FISC 74 OC 405 (2006), para 25.

8See SARS Practice note 7, available athttps://www.sars.gov.za/AllDocs/LegalDoclib/Notes/LAPD-IntR-PrN-2012–11%20-%20Income%20Tax%20Practice%

20Note%207%20of%201999.pdf.

For companies operating in emerging economies,Koller et al. (2015, p. 724, 2020, p. 745) assert that the cost of capital will get closer to the global cost of capital after adjusting for local inflation and capital structures, but recommend adding a country risk premium to the weighted average cost of capital for valuing enterprises under a business-as-usual scenario.

Although analysts typically apply the sovereign risk premium as a proxy for the country risk premium,Koller et al. (2015, 2020)warn of its tendency to overestimate the country risk premium. This can be the case for certain industries, such as the consumer goods industry and raw materials industry. Typical cash flow patterns of firms operating in these industries have lower volatility and a low correlation with local government bond payments. The sovereign risk premium is adequate if the cash flow patterns of the firms involved are highly correlated with the local government bond payments. Alternatively, the country risk premium can be based on the probability of lower cash flows and the potential cash flow reduction as a result of the riskiness (Koller et al., 2015, pp. 705-728, 2020, pp. 735–751).

When political, economic, and other country-specific conditions require adding a country risk premium, the same eco- nomic value can only be created when higher profitability levels are achieved. Economic value is created when the return on capital employed (ROCE) exceeds the weighted average cost of capital after tax (WACC). This condition for economic value creation is expressed by equation(1)below.

ROCE¼NOPAT

CE >WACC ð1Þ

ROCEis defined as net operating profit after tax (NOPAT) divided by capital employed (CE).NOPATequalsEBIT (earnings before interest and taxes)minus taxes (

s

^{) onEBIT.CE}equals fixed operating assets plus working capital. Therefore, equation (1)can be rewritten to equation(2).

NOPAT>WACCCE ð2Þ

Incorporating a country risk premium for a company in a high-risk country increases theWACCthat would apply to a similar company in a country with the lowest risk level byd∙WACC(d> 0). As a consequence, in order to generate economic value,NOPATnow needs to satisfy the condition expressed by equation(3).

NOPAT>ð1þdÞ WACCCE ð3Þ

Accordingly, we hypothesize that profitability levels in high risk countries are on average higher than for low risk countries.

Hypothesis:Profitability levels in high-risk countries are higher than in low-risk countries.

3. Data, research method, and results 3.1. Data

Our empirical analysis relies on data that are commonly accessible to transfer pricing analysts and build on current prac- tices followed by transfer pricing practitioners. To this end, we use a firm-level dataset covering 11,459 manufacturing com- panies from 84 countries over a 5-year period from 2012 to 2016. FollowingHorobet et al. (2019), we select companies that perform activities covered by the C-Manufacturing NACE primary code, referring to the European Classification of Economic Activities (Eurostat, 2008). The dataset was retrieved in July 2018 from the ORBIS database provided by Bureau van Dijk (BvD), which is one of the most commonly used databases by taxpayers and tax administrations for comparable companies’

searches. We select the relevant companies covered by the ORBIS database using the following criteria, which are also com- monly applied for these searches (Bloomberg, 2016; Horobet et al., 2019):

Active companies– The companies selected for our research need to be active. Applying this criterion avoids including in our sample inactive companies or companies in bankruptcy procedures as our hypothesis focuses on sustainable profitability.

Independent companies– Our sample should only comprise companies that are not controlled by other companies. The profitability levels of companies belonging to a group structure may be influenced by intragroup transactions and are, there- fore, not suitable for evaluating the arm’s-length nature of transfer pricing practices. The ORBIS database includes for each company an independence indicator that reflects the level of ownership concentration and which is widely used in practice and research (e.g.,Klassen et al., 2017; Horobet et al., 2019). For our study, we only retain companies with the independence indicator of A+, A or A-, representing the lowest levels of ownership concentration. These companies have recorded share- holders, each having <25% of direct or total ownership of the company. In addition, we exclude companies with only uncon- solidated accounts available and with subsidiaries owned between 50% and 100% or with an unknown percentage.

Availability of financial data– In order to compute the profitability ratios for at least three years, companies must have the requiredEBITand turnover data, for all years in the period 2014–2016.

Size– Companies must have a turnover>2 million EURO for each year of the period 2012–2016.

Descriptive information– All firms must have a website and overview information. This step is required for transfer pric- ing analyses, in order for practitioners to review in detail potential comparable companies.

We exclude the top and bottom 0.5% observations with the highest and lowest values for our set of independent and dependent variables and obtain a sample of 51,402 firm-year observations. Sensitivity analyses reveal that our conclusions are robust for threshold values at the 0.1% level.⁹Our analysis focuses on the differences in corporate profitability across coun- tries and whether these differences are systematically related to a country’s risk profile. The profitability metric we employ is the return on sales (ROS), a commonly used proxy for comparing profitability levels in transfer pricing settings (e.g.,Grubert, 2003; Beuselinck et al., 2015).ROSis the operating profit margin defined asEBITdivided by the company’s turnover, which equals net sales (S). Return on total operating costs (ROTC) is also often used in transfer pricing studies for comparable company searches.ROSequalsROTCmultiplied by total costs divided by turnover. Therefore,ROSandROTCare derived from the same basic variables (turnover and total operating costs) and are equivalent.

The ORBIS data for the companies in our sample comprises the required financial metrics to computeROSand common financial profitability drivers serving as control variables (relating to turnover, fixed and current assets and liabilities, and financing structure). For all these variables we use an unbalanced panel dataset of 51,402 observations for 11,459 companies over a 5 year period. For each company we add proxies for the corresponding country risk level as suggested byHarvey (2004), Bekaert et al. (2016, p. 4), andKoller et al. (2015, pp. 711–725, 2020, pp. 736–751). Our main proxy is a dummy vari- able for the country sovereign rating of the country where the company is located. This avoids the risks associated with incorporating a (scalar) country risk premium in the cost of capital warned for byBekaert et al. (2016, p. 3) and Koller et al. (2015, pp. 724–725, 2020, pp. 736–738). They point to the risk of overestimating country risk levels and including diversifiable risks in the cost of capital. Instead, we use a binary (0/1) dummy variable for each rating expressing the appli- cability of the rating for each company (0 = not applicable, 1 = applicable, and for each company only one of these dummies can equal 1). We use the sovereign ratings published by Standard and Poor’s (S&P).Table A.1inAppendix Asummarizes the country ratings used for the companies in our sample. We now turn to the empirical model employed for our analyses before reporting key data descriptive statistics.

3.2. Research method

From equations(1),(2), and(3)it can be derived thatROSiof companyiin a high-risk country needs to satisfy the con- dition expressed by equation(4)in order to create economic value.

ROSi>ð1þdiÞ WACC_iCEi

Si^ð1

s

^{iÞ ð4Þ}

This can be shown by the derivation expressed by equation(5).

ROSi¼EBITi

Si ¼ NOPATi

Si ð1

s

iÞ¼ð1þdiÞ WACC_iCEiþRIi

Si^ð1

s

^{iÞ ð5Þ}

The residual incomeRIiis the profit in excess of the profit demanded by investors (RIi= CE∙(ROCEi– (1 +di)∙WACCi)). There- fore, it measures the economic value created (or, in other words, the economic profit realized) during a certain period.

We investigate whether differences betweenROSiandROSjof comparable firmsiandjbased in countries with different ratings are associated with differences betweendi∙WACCianddj∙WACCjattributable to the non-diversifiable country risk¹⁰. We do this while controlling for the size of the company (expressed by the natural logarithm of its turnover,Si), common finan- cial value drivers determiningCEi/Si(fixed to total assets,FTA; creditors on turnover,APT; debtors on turnover,ART; and inven- tories on turnover,INVT), and the financing structure (gearing [leverage],G) that also influencesWACCi.

The percentages for the companies’ taxes onEBITfor the individual firms (

s

i) are not observed. Taking the standard (nom- inal) corporate income tax rate or the percentage of corporate incomes taxes actually paid by the company would not account for the probably existing company specific tax burdens and differences between the fiscal treatment of operating and non-operating results. Consequently, we perform our analyses with and without the standard country tax rates. Further- more, we use binary dummy variables that allow for comparing profitability levels of the regions (Region_i,g) and ownership forms (Publici,p). Based on this we test our hypothesis by estimating the following regression model (equation(6)).

ROS_i;t¼b0þX⁶

r¼1

b_1;rCountryRating_i;t;rþb2LNðSi;tÞ þb3FTA_i;tþb4APT_i;tþb5ART_i;tþb6INVT_i;tþb7G_i;t

þX⁶

g¼1

b8;gRegion_i;gþX²

p¼1

b9;pPublici;pþ

e

i ð6Þ

9The threshold value of 0.1% corresponds with a confidence level that exceeds the common under limit of 3∙rfor outliers, while 5.0% corresponds with a common under limit of 2∙rfor unusual observations (e.g.,Doane & Seward, 2019, pp. 129–130;Hair et al., 2019, pp. 85-93).

10Arguably, the non-diversifiable country risk will influence the cost of equity (e.g., as a separate factor extending the classical CAPM model) and the cost of debt (as an element determining the spread between the risk free rate and the market interest rate).

The definitions of the variables, coefficients, and indexes are included inTable 1. In accordance with the hypothesized positive relationship between firm profitability and country risk level, we expect thatb1,6>b1,5>b1,4>b1,3>b1,2>b1,1> 0 (b1,1–b1,6are the coefficients for the dummy variables representing firms in countries rated AA – CCC; AAA is the base line case). In addition, we expect that regional profitability differences will either emphasize or retain the hypothesized relation- ship, i.e.,b8,g0, forg= 1, 4 (Europe and North America) andb8,g0, forg= 2, 3, 5, 6 (Latin America, Middle East and North Africa, South Asia, and Sub-Saharan Countries). We expect that company size is positively associated with profitability (i.e., b2> 0), while the relationship between profitability and the other control variables will largely depend on the business mod- els of the manufacturing companies involved (not predicted, np).

First, we test a simplified version of the model capturing the explanatory variablesCountryRatingi,t,randLN(Si,t) using ordi- nary least squares (OLS) regression without industry fixed effects and year fixed effects (Model 1). We also test the full model with industry fixed and year fixed effects as well as additional covariates using robust clustered standard errors (Model 2). As we aim to find evidence for the extent to which country risk levels influence the profitability of companies and not to develop a model that explains or predict profitability, we anticipate confined goodness of fit levels, but require significant estimates.

Table 1

Operationalization of variables.

Name Abbreviation Definition Expected effect onROSi,t

Return on Sales ROSi,t Return on Sales of companyiin fiscal yeart N/A

Country risk rating CountryRatingi,t,r Binary variable (0,1) for the applicability of country ratingrfor companyiin fiscal yeart

+ Sales Si,tandLN(Si,t) Sales of companyiin fiscal yeart.To control for

non-linearities the natural logarithm (=loge) of Si,t(LN(Si,t)) is used for the analyses; this variable also proxies size

+

Fixed to Total Asset ratio FTAi,t Fixed to Total Assets ratio of companyiin fiscal yeart

not predicted (np) Accounts Payable on Turnover ratio APTi,t Accounts Payable (A/P or Creditors) on

Turnover ratio of companyiin fiscal yeart np Accounts Receivable on Turnover ratio ARTi,t Accounts Receivable (A/R or Debtors) on

Turnover ratio of companyiin fiscal yeart np Inventories on Turnover ratio INVTi,t Inventories on Turnover ratio of companyiin

fiscal yeart

np Gearing [Leverage] Gi,t Sum of non-current liabilities and loans

divided by shareholder funds of companyiin fiscal yeart

np

Region Regioni,g Binary variables (0,1) for the applicability of

geographical regiongfor companyi

0/– forg= 1, 4 0/+ forg= 2, 3, 5, 6

Public Publici,p Binary variables (0,1) for whether companyiis

publicly listed or not (p= 1) and whether companyiis private or not (p= 2)

np

Residual ei,t Residual (error) for companyiin yeart np

Coefficient for Country risk rating b1,r Regression coefficient for the applicability of country ratingrfor companyiin fiscal yeart (CountryRatingi,t,r)

b1,6>b1,5>b1,4>b1,3>b1,2>b1,1> 0

Coefficient for Region b8,g Regression coefficient for the applicability of regiongfor companyi(Regioni,g)

b8,g0, forg= 1, 4 b8,g0, forg= 2, 3, 5, 6 Coefficients for other variables b∙ Regression coefficients for the other variables

in equation(6)

np

Company index i Index for company,i= 1,.., 11,459 N/A

Year index t Index for the fiscal year,t= 1 (2012),.., 5

(2016); fiscal year 2012 is the reference year for variableFYt

N/A

S&P rating index r Index for the S&P rating,r= 1 (AA), 2 (A), 3 (BBB), 4 (BB), 5 (B), 6 (CCC); AAA is the reference index category

N/A

Region index g Index for geographical region;g= 1,.., 6, (for

Europe, Latin America, Middle East and North Africa, North America, South Asia and Sub- Saharan Africa). East Asia and Pacific is the reference index category

N/A

Ownership form index p Index for ownership form,p= 1 (publicly listed company), 2 (private); delisted is the reference index category

N/A

Notes:Fixed effects are tested for 5 fiscal years (t= 1,. . .,5) and all 24 manufacturing subindustries (captured by NACE codes C10 – C33). The data for the financial variables in equation(6)(ROSi,t,Si,t,FTAi,t,APTi,t,ARTi,t,INVTi,t, andGi,t) for each companyiand each yeart(2012 – 2016) come from the ORBIS database. The binary variableCountryRatingi,t,ris based on the sovereign ratings published by Standard and Poor’s (Appendix A summarizes the sovereign ratings used).

Additionally, we employ mediation analysis to distinguish between the direct effect, the indirect effect, and the total effect (=direct plus indirect) of the companies’ country rating onROSi,t. The direct effect is measured by the estimates for the coefficientsCountryRatingi,t,rand is expected to be negative (the lower the country rating for companyiin yeart, the higherROSi,t). Yet, the riskier countries in our sample have smaller companies than the less risky countries. To the extent that the company size impacts profitability, the direct effect only partly explains the influence of the country risk levels on their companies’ profitability. Therefore, the indirect impact of the country rating onROSi,tthroughLN(Si,t) is also mea- sured. This indirect effect could result in a total effect that deviates from the direct effect. If the smaller companies are the least (most) profitable companies, the direct effect could overestimate (underestimate) the total impact of the country risk. Mediation analysis is applied to shed light on this.

We apply several robustness tests to further examine the soundness of the observed relationship.First, besides the coun- try rating, we employ alternative proxies for country risk.Second, we estimate the model including the standard (nominal) country tax rate as additional explanatory variable.Third, we test the relevant assumptions for regression analysis (ho- moscedastic and not autocorrelated residuals, independent regressors exhibiting a non-linear relationship with the depen- dent variable, and normally distributed residuals). As we use robust clustered standard errors, ruling out heteroscedasticity and autocorrelation issues, we verify whether our results suffer from multicollinearity, non-linearity, and non-normal resid- uals. Multicollinearity is tested by the statistic based on the generalized variance inflation factor (GVIF) proposed byFox and Monette (1992)for models which use indicator regressors for the same categorical variable (GVIF^1/(2∙df)). Impacts of non- linearities are controlled for by applying log-transformation. Only log-transformingSi,tyields stronger results. Therefore, we useLN(Si,t) instead ofSi,t. Regression analysis is commonly considered to be robust to violations of the assumption of nor- mally distributed residuals when the sample size exceeds 200 (Hair et al., 2019, p. 291). This assumption can be relaxed for large enough samples because the Central Limit Theorem assures that the sampling distribution of the estimates will con- verge towards a normal distribution (Pek et al., 2018). For a sample size of 10,000 observations,Osborne (2013)shows that estimates are not substantially influenced by violating the assumption of normally distributed regression residuals. Never- theless, formal tests are recommended even when the sample size is large (Pek et al., 2018, p. 4; Hair et al., 2019, p. 291).

Therefore, we test this assumption using two of the most common formal normality tests, the Shapiro-Wilk test and the Kolmogorov-Smirnov test (Razali & Wah, 2011, p. 22). In addition, we followOsborne (2013) and Pek et al. (2018)and val- idate the results of the multivariate regression analysis by using bootstrapping.

3.3. Descriptive statistics

InTable 2, we report the descriptive statistics for our dependent variableROSpooled over the full sample as well as by country rating. The mean (median) pooledROSof our sample observations is 3.6% (5.0%). The interquartile range spans the observed values from 1.7% to 9.8%, indicating a substantial variation. This is also the case for the individual ratings. The means, the medians, and the upper and lower limits of the interquartile ranges show a consistent pattern of increasing ROSvalues for lower rated countries. This is consistent with the expectation that profitability levels are positively associated with the level of country risk borne.

Table 3reports descriptive statistics for the key independent variables besides the country risk rating. On average the turnover of the companies in our sample amounts to 432 million EURO while the median is substantially smaller: 37 million EURO. This reveals the right-skewed pattern of the turnover data, which is strongly confirmed by the skewness statistic in Table 3. This suggests that even after trimming, our sample still includes companies with extremely high turnover levels. The representativeness of this pattern is supported by our sensitivity analysis showing that our conclusions remain unaltered for trimming threshold values between 5.0% and 0.1%.¹¹

Also, the financial ratios described inTable 3show a great variety of companies in our sample. The interquartile range of the fixed assets intensity of the business models in our sample spans from 26.2% to 55.1%, while the mean and median are just over 40%. The extent to which companies employ working capital also varies substantially. For instance, on average the companies have approximately 20 cents for each EURO invoiced outstanding, while their outstanding payments amount to approximately 12.5 cents for each EURO of costs. Again, overall values have a wide interquartile range (for accounts receiv- able: 11.4%27.3%; for accounts payable: 5.7%16.6%). The average (median) gearing ratio equals 80% (43%) and roughly 25 percent of the firms have a gearing above 100%.

3.4. Results

Table 4summarizes the pairwise correlations between the financial metrics (ROS_i,t,S_i,t,FTA_i,t,APT_i,t,ART_i,t,INVT_i,t, andG_i,t).

As the skewness and kurtosis of the variables reported inTable 3do not support normally distributed variables, the non- parametric Spearman correlations need to be relied upon. These correlations show thatROSi,thas significant negative rela- tions withG_i,tandAPT_i,t, and significant positive relations withS_i,t(as expected),FTA_i,t,andART_i,t. However, the correlation coefficients are all very small (the absolute value of these coefficients are all below 0.25), except for the correlation between

11Excluding even >5.0% of our observations and therefore relaxing common lower limits applied for defining outliers and even just unusual values (e.g.,Hair et al., 2019, pp. 85-93;Doane and Seward, 2019, pp. 129-130) is not justified given selection criteria already applied to arrive at a dataset of independent companies with revenues exceeding 2 million EURO that were active during the entire 5 years’ time window.

Table 2

Descriptive statistics for dependent variable Return on Sales (ROS) per S&P rating.

Rating N Mean Min Q1 Median Q3 Max SD

AAA 6,299 4.4% 319% 1.5% 4.9% 10.5% 45.9% 39.5%

AA 20,236 4.0% 319% 1.5% 5.3% 10.6% 46.3% 19.7%

A 5,710 4.3% 317% 2.1% 5.2% 9.2% 45.8% 17.1%

BBB 15,264 5.4% 319% 1.9% 4.5% 8.6% 46.2% 9.0%

BB 1,723 7.0% 171% 2.5% 5.8% 11.5% 44.4% 10.8%

B 1,423 6.7% 131% 2.5% 5.5% 9.8% 45.3% 9.4%

CCC 747 6.3% 87% 2.7% 6.5% 10.6% 38.8% 9.9%

Total for all ratings 51,402 3.6% 319% 1.7% 5.0% 9.8% 46.3% 20.4%

Table 3

Descriptive statistics for main independent variables.

Variable N Mean Min Q1 Median Q3 Max Kurtosis Skewness

Turnover (S) 51,402 432 2 11 37 163 25,313 80.5 8.0

Gearing (G) 51,402 80 0 13 43 101 756 12.5 2.7

Fixed on total assets (FTA) 51,402 41.0% 0.9% 26.2% 40.5% 55.1% 91.9% 2.4 0.1

A/P on turnover (APT) 51,402 12.5% 0.0% 5.7% 10.3% 16.6% 97.0% 11.3 2.1

A/R on turnover (ART) 51,402 21.0% 0.0% 11.4% 18.4% 27.3% 114.6% 8.0 1.7

Inventories on turnover (INVT) 51,402 18.8% 0.0% 8.9% 15.0% 23.6% 144.8% 13.3 2.5

Notes:The total number of observations (N) refers to all data retrieved for the 2012–2016 period. After trimming with a 1% threshold. Turnover is reported in million EURO.

Table 4 Correlation table.

N= 51,402 Turnover Return

on Sales

Gearing Fixed on Total Assets

Creditors (AP) on Turnover

Debtors (AR) on Turnover Return on Sales (ROS)

Pearson 0.05***

Spearman 0.18***

Gearing (G)

Pearson 0.04*** 0.04***

Spearman 0.09*** 0.18***

Fixed on total assets (FTA)

Pearson 0.13*** 0.00 0.13***

Spearman 0.23*** 0.02*** 0.24***

A/P on turnover (APT)

Pearson 0.02*** 0.12*** 0.11*** 0.11***

Spearman 0.04*** 0.13*** 0.14*** 0.11***

A/R on turnover (ART)

Pearson 0.06*** 0.03*** 0.01** 0.21*** 0.40***

Spearman 0.07*** 0.04*** 0.01 0.22*** 0.42***

Inventories on turnover (INVT)

Pearson 0.06*** 0.04*** 0.05*** 0.11*** 0.18*** 0.15***

Spearman 0.03*** 0.01 0.07*** 0.10*** 0.13*** 0.14***

Note:***, **, and * denote 0.01, 0.05, and 0.10 significance levels, respectively.

Table 5

Univariate test on equal means and interquartile ranges.

Panel A: Means and medians

Variable AAA AA A BBB BB B CCC Kruskal-Wallis Chi square

(p-value) Return on Sales

(ROS)

Mean Median

4.4%

4.9%

4.0%

5.3%

4.3%

5.2%

5.4%

4.5%

7.0%

5.8%

6.7%

5.5%

6.3%

6.5%

191 (0.000) Panel B: Interquartile ranges

Variable AAA A A BBB BB B CCC Chi square (p-value)

Return on Sales (ROS) [1.5% – 10.5%]

[1.5% – 10.6%]

[2.1% – 9.2%]

[1.9% – 8.6%]

[2.5% – 11.5%]

[2.5% – 9.8%]

[2.7% – 10.6%]

1,035 (0.000)

APT_i,tandART_i,t, which is only weak to moderate. This does not suggest substantial impacts of these variables on explaining return on sales without including country rating, region, and (other) control variables.

Table 5repeats the means, medians, and interquartile ranges ofROSper country rating ofTable 2, which are largely consis- tent with our hypothesis.Table 5adds the outcomes of non-parametrically testing of the equality of medians (by the Kruskal- Wallis test) and the equality of the interquartile ranges (Chi-square test). Based on these outcomes the null-hypothesis of equality can be rejected, offering some statistically significant support for the pattern of an increasingROSwith decreasing country rating. Only the median value of theROSfor the companies in BBB rated countries clearly deviate from this pattern.

Table 6reports the regression results for estimating Models 1 and 2. The results of estimating the models support the relevance of the main explanatory variables, notwithstanding the anticipated confined proportion of the variance of the ROSthey explain (the adjusted R²equals 6.7% for Model 1 and 13.6% for Model 2). The F-statistics support the significance of the models as correspondingp-values are well below 1%. As expected, both models result in significant positive estimates for all dummy variables expressing the companies’ country rating and (log-transformed) sales. In addition, Model 2 shows significant estimates for gearing (Gi) and accounts receivable and payable ratios (ARTandAPT), while the fixed asset intensity (FTA) and the inventory on turnover ratio (INVT) do not seem to significantly impactROS. With regards to regional differ- ences, only the coefficients for North America and South Asia are statistically significant at a 1% level.

Both models result in estimates for the coefficients forCountryRatingthat are smaller for companies in AA-rated or A- rated countries than for companies in countries rated BBB, BB, B, or CCC, which is in accordance with our hypothesis. Fur- thermore, these coefficients are all significantly positive, meaning that the contributions of all risk levels exceeding AAA are positive, which is also in accordance with our hypothesis. This outcome is emphasized by the substantial and significantly negative coefficient (-0.072***) for the dummy variable for North America (CountryRatingi,t,4) as both countries in that region are AAA-rated. Furthermore, the only other significant geography dummy variable refers to South Asia (CountryRatingi,t,5), a region where the two main countries, India and Pakistan, are respectively rated BBB and B/CCC throughout the period. The positive coefficient for this dummy variable (0.050***) is also consistent with the hypothesized positive relationship between country risk andROS. None of the other four geography dummies are significant.

Fig. 1presents the confidence intervals for the estimated coefficients and shows that this difference between the two groups of estimated coefficients is significant. The difference is also substantial as the overlap of the two groups of confi- dence intervals is relatively small.

The values of the Welcht-statistic reported inTable 7provide strong support for this pattern. They show that the coef- ficients for companies in AA and A rated countries are significantly smaller than the estimates for the companies located in countries with a BBB, BB, or B rating, while the estimated coefficient corresponding with a BBB-rating is significantly smaller than the coefficients for the companies in BB or B rated countries. These findings are consistent with the hypothesized neg- ative relation betweenROSand country rating. Minor deviations from the hypothesized pattern are the numerically but not significantly larger estimate for AA compared to A and the not significantly lower estimate for CCC compared to B and BB.¹² The outcomes of the analyses confirm the expectation that larger (smaller) companies have higher (lower) values for the ROS. As the riskier countries in our sample have a higher share of smaller companies, the country rating also indirectly affects ROSthrough size (measured byLN(Si,t)). For that reason, we apply a mediation analysis in order to establish the total effect of the country ratings onROScomprising the direct effect (measured by the estimates reported inTable 6) and indirect effect.

We report the findings inTable 8.

The significantly negative indirect effects reveal that the smaller companies negatively impactROS. Consequently, the total effect of the country rating is smaller than only its direct effect. But still the total effect is significant for all ratings.

The pattern of the total effects per country rating is similar to the pattern of direct effects, supporting our hypothesis.

3.5. Robustness tests

As a first robustness test, we run the regressions with two alternative proxies for country risk. FollowingBoubakri et al.

(2013) and Bekaert et al. (2016), we use the political and economic ratings from the International Country Risk Guide (ICRG).

The second alternative approach is based on the Worldwide Governance Indicators (WGI), consisting of six composite indi- cators of broad dimensions of governance covering over 200 countries since 1996: Voice and Accountability, Political Stabil- ity and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption (Kaufmann et al., 2011). For all the alternative variables, larger values correspond to lower country risk, leading to the expectation of a negative association between (the inverse of) country risk and firm profitability. We use Model 2 and replace the dummies for the country rating by the alternative proxies. The main results regarding the alternative proxies and size (LN(S_i,t)) are reported inTable 9.

Except for ICRG’s economic risk rating, the results confirm our findings. For the other country risk indicators the negative relation between country risk andROSis supported: lower levels of country risk correspond to a statistically significant smaller ROS.

12As we cannot fully rule out dependencies between the estimates, a Wald-test using an asymptotic chi-square statistic is used to test the equality of the estimated values of the coefficients. This test results in rejecting the null-hypothesis of equal values, which also supports the statistical significance of the pattern ofFig. 1.