Multinational tax avoidance: Is it all about profit shifting?

Christof Beuselinck, IESEG School of Management and LEM

Jochen Pierk, Erasmus University Rotterdam

ABSTRACT The standard perception of international tax planning strategies is that multinational companies (MNCs) avoid taxes via cross-jurisdictional income shifting. In the current paper, we exploit MNC parent and subsidiary entity level data to study this allegation by investigating the importance of within-country (local) tax avoidance, which we measure as the abnormal GAAP effective tax rate (AETR) relative to the country-industry-year average GAAP ETR. For a large sample of over 150,000 domestic and foreign affiliate observations pertaining to more than 7,600 European MNCs, we observe that time-invariant MNC (group) fixed effects explain close to 80 percent of the total explained variation in subsidiary local tax avoidance. This evidence supports the idea that the MNC corporate style is largely responsible for the design and orchestration of subsidiary local tax avoidance strategies. Further, we document that the level of subsidiary local tax avoidance is positively related to group tax avoidance suggesting that not all tax avoidance pertains to income shifting. Moreover, this group-subsidiary level association has also more than doubled over the period of study (2006-2014), confirming that MNCs rely increasingly more on local tax avoidance strategies in more recent years, i.e., when income shifting has landed in the eye of the debate on ethical tax planning. Finally, the focus on local tax avoidance is largest in domestic subsidiaries and in vertically integrated subsidiaries. While the former result suggests that the familiarity with the headquarters’ local tax administration gives rise to larger local tax avoidance opportunities, the latter result supports the idea that subsidiary local tax avoidance becomes more important when transfer prices can be challenged more by tax authorities as it is the case in vertically integrated transactions.

Draft: September 8, 2017

This paper has benefited from comments by Kathleen Andries and Anna Alexander. We thank workshop participants at University of Gothenburg (Sweden), University of Bristol (UK), University of Paderborn (Germany), the Research Day in Accounting hosted by the University of Antwerp (Belgium), the 1st ERIM Accounting Day at Erasmus University Rotterdam (Netherlands), and conference participants at the 40th Annual Congress of the European Accounting Association in Valencia (Spain) for their valuable comments.

Corresponding author: Jochen Pierk, Burgemeester Oudlaan 50, 3062 PA Rotterdam, Netherlands, E-mail: [email protected], Phone: +31/10/4082248

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1. Introduction

The interest in corporate tax avoidance has reached an all-time high level and the financial and academic perspective is dominated by the idea that cross-jurisdictional income and debt shifting is the primary source of tax gains (e.g., Atwood et al., 2012; Beuselinck et al., 2015;

Collins et al., 1998; Klassen et al., 1993; Klassen and Laplante, 2012; Markle, 2015; Newberry and Dhaliwal, 2001; Rego, 2003). In line with the increasing demand for a fairer corporate taxation game for global multinational corporations (MNCs) versus domestic-only corporations, where such shifting opportunities are non-existing, the Base Erosion and Profit Shifting (BEPS) action plan by the OECD (2013) is working on several proposals and guidelines to ensure that profits are taxed where economic activities are generated. This attention seems warranted and is in line with the common perception that excessive income shifting activities should no longer be part of contemporary sustainable business strategies as is evidenced in the rise to the term “tax shaming” (Barford and Hold, 2013).

However, recent academic evidence by Dyreng et al. (2017) suggests that over the 25 year period 1988 - 2012 the effective tax rates (ETRs) for U.S. corporations have declined for both multinational as well as domestic firms. This suggests that even for domestic firms a wide range of tax avoidance opportunities can have become available, for instance, by income shifting across states (e.g., Dyreng et al., 2013); by intra-company transactions between business group members within a specific jurisdiction (Beuselinck and Deloof, 2014; Gramlich et al., 2004); by focusing on specific locally available tax planning strategies such as investments in tax favored assets, usage of accelerated depreciation schemes, tax credits, and allowances for corporate equity

(Anning et al., 2015); or via optimizing tax schemes that are temporarily available within one specific tax jurisdiction (Shevlin et al., 2012). These observations seem to suggest that the focus

2 on income shifting to capture MNCs tax avoidance behavior is potentially understating the full spectrum of tax avoidance strategies that international corporations have at their disposal.

Our study on the more complete picture of MNC tax avoidance is important because local tax planning opportunities are not only available in the multinationals’ parent country, but also in all its foreign subsidiary countries. Moreover, local tax avoidance is asymptotically equivalent to income shifting and potentially less costly because it does not suffer from cross-jurisdictional shifting costs. In the current paper, we investigate this issue in more detail by observing subsidiary entity-level as well as MNC group-level GAAP ETRs for a sample of 7,660 European

MNCs (34,111 observations) that are headquartered in one of 27 EU Member States and their

42,115 domestic and foreign affiliates (158,749 observations) across the globe.1, 2

To do so, we conceptually follow Kohlhase and Pierk (2017) and we distinguish between tax avoidance across countries (income shifting) and tax avoidance within countries (local tax avoidance). While prior studies (e.g., Atwood et al., 2012; Markle, 2015) have shown that MNCs headquartered in worldwide tax systems shift income to a lesser extent across countries compared to MNCs headquartered in territorial tax system countries, Kohlhase and Pierk (2017) additionally show for an international panel of observations that MNCs from worldwide tax systems are also less tax aggressive compared to their industry peers within foreign affiliate countries. More in particular, they find that subsidiaries owned by investors from worldwide tax systems (like the U.S.) have a higher average GAAP effective tax rate (ETR) compared to subsidiaries owned by foreign investors from countries with a territorial tax system. This finding is consistent with the claim in Scholes et al. (2015) that the incremental repatriation tax under a

1 The sample includes 27 out of the 28 EU Member States as Italy has a regional tax that is based on the value of all produced goods. In this case, the standard proxies for tax avoidance, e.g. the effective tax rate, cannot be interpreted. 2 We focus on GAAP ETR because the majority of our sample firms, especially private firms, do not publish cash flow statements. 3 worldwide tax system reduces the incentive of worldwide parent companies to be tax aggressive in foreign subsidiaries.

In the current paper, we further build on the MNC parent-subsidiary tax avoidance associations to investigate whether and if so to what extent MNCs achieve lower consolidated

GAAP ETRs by local tax avoidance or rather shift income across countries. In particular, we study abnormal GAAP ETRs defined as deviations from country-industry-year average GAAP

ETRs for both MNC groups and subsidiaries as our main proxy for tax avoidance. Then, we identify the proportion of MNC group level tax avoidance that stems from subsidiary-level local tax avoidance. Empirically, we regress the abnormal ETR of the group on the (pretax-income) weighted abnormal ETR of all its subsidiaries. This approach is attractive because it can distinguish between tax avoidance that is realized entirely via income shifting (where the association is predicted to be zero) and tax avoidance that originates from 100% local strategies

(where the association would equal one) or from a combination of both (where the association is between zero and one). 3

Because of the paucity of insights in parent and subsidiary country local tax avoidance, we start our analyses by gauging the relative importance of MNC time-invariant factors that can explain subsidiary local tax avoidance behavior. After these descriptive insights, we investigate the time-series pattern as well as the cross-sectional determinants of subsidiary local tax avoidance. First, we find that MNC time-invariant fixed effects explain almost 80% of the total explained variation in subsidiary abnormal GAAP ETR, which is far above the 6% (27%) that stems from the MNC parent country (parent/subsidiary country pairs) fixed effects. We interpret these results as evidence that MNC origin and MNC-affiliate country bilateral relationships only

3 We explain our research method and design in more detail in Sections 3.1 and 3.2. 4 capture a fraction of the subsidiary tax avoidance and that it is rather the MNC fixed effect (i.e., the “corporate style”) that is largely responsible for the design and orchestration of subsidiary local tax avoidance behavior.

In further analyses on the association between MNC group and subsidiary-level local tax avoidance, we find that after controlling for the standard GAAP ETR determinants identified in prior tax research, tax avoidance of the average MNC is positively related to the local subsidiary tax avoidance. The observation of a significantly positive association between parent and subsidiary tax avoidance is consistent with the conjecture that MNCs’ tax avoidance is not the result of profit shifting alone. Furthermore, we find in a time trend analysis that this association increases steadily with about one percent per year over the study period (2006-2014), suggesting that MNCs have increasingly relied more on local tax avoidance in more recent years.

Next, in cross-sectional and within-group analyses, we show that the association between subsidiary local tax avoidance and MNC tax avoidance is similar for publicly listed MNCs and privately-held MNCs. Also, we observe that the focus on local tax avoidance is largest in domestic subsidiaries, suggesting that the familiarity with the headquarters’ local tax administration gives rise to larger local tax avoidance opportunities. Finally, we show that the association between subsidiary local tax avoidance and MNC group level tax avoidance is most pronounced in vertically integrated subsidiaries (i.e., where the subsidiary operates in a different sector of activity than its parent), confirming the idea that in cases when transfer prices can potentially be challenged more by tax authorities, MNCs focus more on subsidiary local tax avoidance.

Our study contributes to the developing literature that addresses international tax avoidance behavior by observing MNC groups and subsidiary level data (e.g., Beuselinck et al., 2015; De

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Simone et al., 2017; Dharmapala and Riedel, 2013; Huizinga and Laeven, 2008; Johannesen et al., 2017; Kohlhase and Pierk, 2017; Markle, 2015). Our study also makes a methodological contribution in that it allows for the identification of subsidiary local tax avoidance by MNCs both cross-sectionally and within-groups. While prior work on within-country tax avoidance so far was mainly based on single-country data (e.g., Beuselinck and Deloof, 2014; Dyreng et al.,

2013; Gramlich et al., 2004) or tackled specific features of the tax code within a particular setting

(e.g., Hebous and Ruf, 2017; Shevlin et al., 2012), the current study provides new large-sample international insights in the importance of local tax avoidance. The combined evidence suggests that subsidiary local tax avoidance is a non-negligible component of international MNC tax planning, and that this local tax avoidance has gained in popularity in more recent years after the global financial crisis when income shifting has been labelled more and more as an unethical tax avoidance strategy (Hazra, 2014). The fact that we observe within-group differences in the importance of subsidiary local tax avoidance further deepens our understanding of the tax avoidance behavior of MNCs.

Our findings therefore may be particularly interesting for policy makers who are debating on how to curb tax base erosion and profit shifting (OECD, 2013) and also for public economists who often consider tax avoidance as income shifting only when studying international transfers of goods and services. For the BEPS action plan to be effective, it is crucial to know to what extent multinationals rely on within-country tax avoidance and perhaps use this as a substitute for across-country income shifting, especially so in more recent years. Finally, these results should interest lobbying groups and the financial press as it is one of the first studies showing that MNC tax avoidance behavior may go beyond income shifting and that MNCs seem to rebalance their tax avoidance behavior after the recent increased press attention and public scrutiny.

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The remainder of the paper is as follows. In Section 2, we elaborate hypotheses based upon related literature and theoretical predictions. We discuss the research design in Section 3. Section

4 presents the sample and results while section 5 discusses within-group variation. Section 6 presents robustness tests and we conclude in Section 7.

2. Hypotheses Development

The tax debate has centered around the idea that multinational firms (MNCs) are saving most on their tax bill because they can shift income from high-tax to low-tax jurisdictions, including tax havens (e.g., Dharmapala and Riedel, 2013; Dyreng and Markle, 2016; Dharmapala, 2014).

This may be especially true for MNCs that can arrange their cross-border transactions on intangible assets which are by nature more difficult to value and can be more flexibly relocated de jure across borders (Grubert, 2003; De Simone et al., 2014). However, recent U.S. based evidence suggests that also purely domestic firms, just like MNCs, seem to have reduced their effective tax rates (ETRs) with similar speed and magnitude (Dyreng et al., 2017). Such an observation raises the question whether recent international tax reform guidance like the Base

Erosion and Profit Shifting (BEPS) initiative at the OECD (OECD 2013) that has focused mainly on MNCs is sufficiently considering tax avoidance opportunities. Dyreng et al. (2017) conclude that their findings may be originating from the increasing opportunities to reduce ETRs, either via careful and intensifying organized tax planning or from changing provisions in the local tax laws.

Another question that emerges from this observation relates to the dominance of local tax reduction opportunities in MNC tax strategies and its relative importance compared to income shifting. This is relevant because decisions to shift income may not only cause the tax bill to go down, it can also bear significant costs. First, income shifting decisions create administrative costs because it can only be accomplished with the creation of the well-developed professional

7 tax support system and the hiring of tax experts. Second, shifted income can be trapped abroad, especially in contexts of worldwide tax regimes where MNCs may decide to leave the cash in their foreign subsidiaries to avoid the marginal tax cost upon dividend repatriation (Graham et al.,

2011; Markle 2015). Also, because ex post repatriation decisions of ex ante shifted income may yield a tax expense without corresponding pre-tax earnings in the same period it may lead to important nontax costs which may inhibit firms from shifting income ex ante (Blouin et al.,

2012).

The non-negligible costs that accompany income shifting decisions lead to the conjecture that

MNCs may also reside to other, potentially less costly, tax bill reducing techniques. In line with the race to the bottom argument where emerging and developed countries are not only competing via tax rates, but also via offering specific tax-favorable schemes that impact the tax base such as investment in tax favored assets, accelerated depreciation schemes, tax credits (e.g., research investment credits), or allowance for corporate equity, we expect to observe that MNCs exploit local tax reducing opportunities. This behavior is expected to manifest in the focus of MNCs on local tax avoidance that can vary across groups. We therefore conjecture that subsidiary local tax avoidance behavior is largely influenced by the corporate group and explains a positive fraction of the MNC group tax avoidance. Therefore, our first hypothesis is built out of two sub- hypotheses that go as follows:

H1a: MNC fixed effects (i.e., MNC corporate styles) largely explain subsidiary local tax avoidance strategies.

H1b: Subsidiary local tax avoidance is positively associated with MNCs tax avoidance.

However, MNC tax avoidance strategies may also have changed over time. This may be particularly true because of the changing public opinion about tax bill reducing decisions. One

8 example is the negative reputational effects that were recently evidenced in the high-profile cases of Amazon, Facebook, Google UK and Starbucks against the UK appeals court and where the corporate press often blames large corporations of “…shifting profits around the world and paying small tax bills.” (Goodley et al., 2012).4 Discussions of the ethics of tax avoidance are now observable on different layers of society while a few years ago, it was more a ‘gaggle’ of activists and campaign groups that were protesting against MNC tax avoiding behavior.5 In line with the increasing demand about a fairer corporate taxation game, the Base Erosion and Profit

Shifting (BEPS) action plan by the OECD (2013) is also working on several proposals and guidelines to ensure that profits are taxed where economic activities are generated. More and more, the common perception that excessive income shifting activities should no longer be part of contemporary sustainable business strategies as evidenced in the rise to the term “tax shaming”

(Barford and Hold, 2013).

Because of the ever-increasing attention on income shifting especially after the global financial crisis as a tax-aggressive strategy (e.g., Anning et al., 2015), MNCs may see local tax avoidance strategies progressively as the more cost-efficient tax strategy compared to income shifting. Consequently, we conjecture that MNCs in their continuous search for tax-minimizing planning may have switched more to local tax avoidance strategies as compared to income

4 An example of how corporate tax strategy decisions may ultimately impact customer behavior is evidenced in the following example, mentioned on the BBC news article entitled, “Google, Amazon, Starbucks: The rise of 'tax shaming'” (accessible on: http://www.bbc.com/news/magazine-20560359): “Another impact of tax shaming is that some people, such as 45-year-old self-employed businessman Mike Buckhurst, from Manchester, boycott brands. "I've uninstalled Google Chrome and changed my search engine on all my home computers. If I want a coffee I am now going to go to Costa, despite Starbucks being nearer to me, and even though I buy a lot of things online, I am not using Amazon. "I'm sick of the 'change the law' comments, I can vote with my feet. I feel very passionate about this because at one point in my life I was a top rate tax payer and I paid my tax in full," he says.” 5 Examples of sprouting protests in the public opinion arise right after the global financial crisis as in the small-scale student protests mentioned in the corporate press against tax avoiding behavior from the corporations of Sir Philip Green, efficiency adviser of the UK government. (https://www.theguardian.com/world/2010/nov/29/philip-green- protest-alleged-tax-avoidance) and the creation of the protest group called UK UnCut, mobilizing its protesters via the hastag #taxmeet (https://www.theguardian.com/business/2011/jan/19/tax-avoidance-uk-uncut-boots). 9 shifting in more recent years to avoid the negative media attention associated with income shifts.

Therefore, we hypothesize that the association between subsidiary local tax avoidance and MNC group tax avoidance has increased in more recent years. This results in hypothesis H2:

H2: The positive association between subsidiary local tax avoidance and MNC group tax avoidance has increased over time.

Recently, tax-aggressive income shifting strategies from high to low-tax country countries have received a lot of media attention and this had led to poor reputational effects for the companies that received tax investigation (Anning et al., 2015). This concern may be particularly valid for listed (public) companies since minority investors can have value-based concerns about tax avoidance strategies which may impact long-term value. This negative value impact can come from direct tax settlement lawsuits like in the following examples: GSK ($3.4bn settlement; U.S. lawsuit in 2006), AstraZeneca (US$1.1bn: U.S. in 2010) and £550m (UK in 2010), or Vodafone

(£1.25bn: UK in 2010).6 However, the longer term negative value impact can also come from purely reputational costs (Hazra, 2014). Due to the increased public scrutiny, listed corporations might be incentivized to engage less in tax avoidance, including local subsidiary tax avoidance.

However, prior literature also suggests that public firms are also less likely to shift income from high to low-tax countries compared to private firms (Lin et al. 2012, Beuselinck et al. 2015) and that the nontax costs of future repatriations may at least partly explain this behavior. If local tax avoidance, however, is judged to be a suitable and efficient alternative tax avoidance tool, public firms may in fact have a preference for avoiding taxes locally because shifting is costlier for

6 Full reference to these lawsuits and settlements are available at: https://www.wsj.com/articles/SB115798715531459461 (GSK, 2006), https://www.theguardian.com/business/2010/feb/23/astrazeneca-tax-uk-pharmaceuticals (AstraZeneca 2010) and http://www.telegraph.co.uk/news/politics/8875360/Taxman-accused-of-letting-Vodafone-off-8-billion.html (Vodafone in 2010). 10 them. This substitution argument for local tax avoidance to compensate for the reduced incentives to shift income in listed firms, may seem warranted given the recent evidence in Pierk

(2016) who finds that listed EU firms on average are more tax aggressive than private EU firms.

Eventually, it remains an empirical question as to whether private or public MNC engage more in local tax avoidance. This results in hypothesis H3, formulated in its null form:

H3: Public MNCs within-country tax avoidance behavior is not different from private MNCs within-country tax avoidance behavior.

Tax-strategic decisions, however, may not be uniformly applied across subsidiaries. Based upon a similar sample as ours of EU multinational group and subsidiary accounts, De Simone et al. (2017) show a different ROA responsiveness to tax incentives between profitable and unprofitable affiliates in high-tax jurisdictions, suggesting that loss affiliates are treated separately in cross-border transfer pricing decisions. Another characteristic that may be non- trivial in the possibility to avoid a high tax bill is the closeness to and familiarity with the local tax system. MNCs that operate globally may be focusing first on domestic subsidiaries to reduce the tax bill and only afterwards resort to local tax avoidance in foreign affiliates. Also, avoiding taxes domestically may be preferable above shifting taxable income out of the home country and repatriating it back at a cost.

Also, subsidiary local tax avoidance is expected to pay off more than income shifting practices in contexts where transfer prices can be contested more. One example where more uncertainty arises is for global MNCs that are vertically integrated. BEPS Action Plan 10, for instance, names the lack of a suitable comparable unit price (CUP) one of the primary concerns

11 for tax authorities to contest applied transfer prices.7 This is true because transfers within large vertically integrated corporations cannot be regarded as equivalent to transactions between unrelated parties. Consequently, in cases of vertical-type value chain transfers, it may be more efficient to focus on subsidiary local tax avoidance than to rely on tax-reducing transfer pricing since the latter has a higher risk of being challenged by the (local) tax authorities.

Both the local proximity argument as the vertical integration perspective discussed above lead to the expectation that the focus on subsidiary local tax avoidance may vary within MNC groups and result in hypotheses H4a and H4b:

H4a: Subsidiary local tax avoidance behavior is more associated with MNC group tax avoidance behavior in domestic versus foreign subsidiaries.

H4b: Subsidiary local tax avoidance behavior is more associated with MNC group tax avoidance behavior in vertically integrated subsidiaries versus horizontally integrated subsidiaries.

3. Research Method

In many MNC tax avoidance studies, the traditional view is that shifting income from high-tax affiliates to low-tax affiliates reduces worldwide taxes. This paper suggests that the observed

MNC tax avoidance is not necessarily entirely dominated by income shifts and that subsidiary local tax avoidance can be an important tax objective which eventually can contribute to the

MNC group tax avoidance strategy. In Section 3.1 below we provide a numerical example to illustrate the logic of how the local (within-country) tax avoidance can be gauged from observing

7 The OECD Base Erosion and Profit Shifting (BEPS) Action Plan 10 relates to transactional profit split methods and aims to “…establish arm’s length outcomes or test reported outcomes for controlled transactions by determining the division of profits that independent enterprises would have expected to realise from engaging in a comparable transaction or transactions.” For more information refer to: https://www.oecd.org/ctp/transfer-pricing/Revised- guidance-on-profit-splits-2017.pdf 12 subsidiary local tax avoidance patterns and relating these to MNC group tax avoidance behavior.

Section 3.2 provides an overview of the empirical model specifications.

3.1. Local Tax Avoidance versus Income Shifting

To illustrate the rationale applied for our empirical tests and model specifications, consider an observation where a specific 3-digit SIC industry (e.g., 345: Fabricated Structural Metal

Products) in a specific country (e.g., Germany) has N country-industry rivals that face an average effective tax rate (ETR) of 20 percent for any given year. Also assume that within SIC 345, we observe 2 German-origin MNCs Alpha (A) and Beta (B) that have an identical aggregate taxable income (100,000) and both have two equal-sized subsidiaries (proxied by Sales) spread over 2 affiliate countries, C1 and C2, and where the subsidiaries are labelled as follows: SubA_C1 and

SubA_C2 (both majority-owned and incorporated for tax reasons by Alpha) versus SubB_C1 and

SubB_C2 (both majority-owned and incorporated for tax reasons by Beta). Also, assume that the respective peers’ effective tax rates in country C1 and C2 are 10 percent and 30 percent, respectively. For simplicity, we assume that the peers’ effective tax rate equals the statutory tax rate.

On the surface, it is clear from a tax planning perspective that both groups have incentives to record higher taxable income in C1 as this affiliate country has the lowest statutory tax rate among the two affiliate countries. In line with a tax-minimizing planning strategy, Group Alpha records taxable income of 60,000 in country C1 and 40,000 in country C2, leading to a combined tax burden of 18,000 (=60k*0.10+40k*0.30). This makes Group Alpha tax aggressive relative to its industry-country-year peer group as its realized ETR equals 18 percent which is 2 basis points below that of its peers. Group Beta, however, realizes a similar ETR of 18 percent but achieves this via exploiting local tax advantages bringing its affiliate ETR under the statutory tax rate and

13 by locating its taxable income equally (i.e., 50-50) across-country C1 and C2. The way how Beta achieved this is via affiliate-country local tax planning strategies (e.g., local tax loopholes exploitation) leading to a reduction by 10 percent in ETR compared to the STR in C1 (9% instead of 10%) as well as C2 (27% instead of 30%). The combined tax burden for Beta is also 18,000

(=50k*0.09+50k*0.27). In other words, while both groups Alpha and Beta achieved an identically lower group ETR compared to their peers, Alpha realized this via income location decisions consistent with a tax-efficient shifting strategy (income shifting), while Beta realized this via a focus on subsidiary country local tax avoidance.

When we summarize these opposite tax planning strategies in the example below, we observe that the abnormal group ETR (AETRg) relative to the country/industry/year SIC 345 peer group is minus 2 percent in both cases. The difference between the groups is apparent in the abnormal ETR across the subsidiaries (AETRs). While Alpha has a zero deviation from the affiliate country STR in its local ETR realizations (=60k*[10%-10%] + 40k*[30%-30%] = 0.0),

Beta realizes a 10 percent deviation (=50k/100k*[10%-9%]/10% + 50k/100k*[30%-27%]/30% =

0.10). By weighting local (within-country) tax avoidance by the respective taxable income, one can calculate the weighted abnormal ETR combined over all affiliate countries (wAETRs). In the case of Alpha – who is realizing the lower tax bill via income shifts – the group ETR differential

(AETRg) relative to the relevant peer group (-0.02) is unrelated to the weighted subsidiary ETR differential (wAETRs, 0.00) while for Beta – who is realizing the lower tax bill via local tax avoidance – the group ETR differential (-0.02) is identical to the weighted subsidiary ETR differential (-0.02).

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Exhibit 1: Numerical Example of Local (Within-country) vs. Across-Country (Income Shifting) Tax Avoidance Group Alpha Group Beta Consolidated SubA-C1 SubA-C2 Consolidated SubB-C1 SubB-C2 PTI 100,000 60,000 40,000 100,000 50,000 50,000 Tax expense 18,000 6,000 12,000 18,000 4,500 13,500 ETR (group) 0.18 0.18 AETR (group) -0.02 -0.02 ETR (subs) 0.10 0.30 0.09 0.27 AETR (subs) 0.00 0.00 -0.01 -0.03 wAETR (subs) 0.00 -0.02 PTI is pretax income. ETR(group) is the groups’ effective tax rate as documented in the consolidated statement. AETR(group) is the groups abnormal effective tax rate defined as ETR(group) minus the country-industry-year average of 20%. STR is the statutory tax rate of the respective subsidiary country (which is assumed to be equal to the peers’ effective tax rate). ETR(subs) is the subsidiaries’ effective tax rate as documented in the unconsolidated (individual) statement. AETR(subs) is the subsidiaries’ abnormal effective tax rate defined as ETR(subs) minus the country-industry-year average. wAETR(subs) is the by pretax income weighted average of abnormal effective tax rates of the groups’ subsidiaries (AETR(subs)).

In these extreme cases, it becomes apparent that no matter how much income is located in low tax jurisdictions, the correlation between AETRg and wAETRs will always remain zero (0.00) if group Alpha is not able to deviate its affiliate ETR from the local STR in one of its subsidiary countries via affiliate within-country tax avoiding strategies. One the other hand, the perfect correlation of one (1.00) that is observed in Beta is only observed in cases where group tax avoidance is perfectly correlated with the income-weighted local subsidiary tax avoidance. In reality, we can expect intermediate cases where groups do shift income for tax purposes to lower

STR countries yet are also locally tax-aggressive in their affiliate countries. Under these scenarios, the association between AETRg and wAETRs will be positive and between zero and one. In our empirical analyses, we are interested to observe whether MNCs do apply within- subsidiary country tax-aggressive planning strategies. Second, we aim to identify in cross- sectional variations in the AETRg and wAETRs based upon characteristics that may explain why groups rely more on income shifting (zero or low correlation between parent and weighted

15 subsidiary abnormal ETRs) versus within-country tax avoidance (correlation closer to one between parent and weighted subsidiary abnormal ETRs).

3.2. Empirical Model – Group Fixed Effects

A growing body of literature has identified the importance of controlling for time-invariant factors to explain corporate behavior. Bertrand and Schoar (2003), for instance, find that manager fixed effects explain a substantial proportion of corporate activities including investments, leverage, and cash holdings. More recently, Graham et al. (2012) show that firm and especially manager fixed effects explain close to 55% of the variation in executive compensation packages.

Recently, Law and Mills (2017) have identified manager fixed effects also to be explaining around 50% of the variation in corporate ETRs.

In our context, it is relevant to examine the importance of group (MNC) time-invariant fixed effects for subsidiary tax avoidance behavior. This is relevant because subsidiary decisions are orchestrated by strategic impulses from corporate headquarters and also tax strategies are designed at the top level. Consequently, and in line with the argumentation in hypothesis H1a, we start by identifying how much of the local subsidiary tax avoidance variation can be explained by

MNC time-invariant components. This proportion can be interpreted as the MNC corporate headquarters ‘style’ that is manifested into the local subsidiary tax avoidance behavior. To empirically quantify this MNC style, we utilize an approach similar to the one developed in

Abowd et al. (1999) and applied in Graham et al. (2012) and Law and Mills (2017). The approach is providing a relatively simple to interpret (yet computationally demanding) calculation technique that allows capturing the relative contribution of each set of fixed effects

2 (FEk) to the respective model R by summing up the ratio cov(AETRg, FEk)/var(AETRg) for all

16 fixed effects. This ratio effectively captures the fraction of the model R2 that is attributable to each set of fixed effects.

3.3. Empirical Model – Correlation of Subsidiary and Group Tax Avoidance

To identify the proportion of tax avoidance that is coming from local (within-country) tax avoidance versus across-country income shifting, we analyze the relationship between the MNC consolidated abnormal effective tax rate (AETRg) and the weighted AETR of their domestic and foreign subsidiaries based on unconsolidated data (wAETRs). First, the effective tax rate (ETR) is calculated as GAAP tax expense divided by GAAP pretax income. In our empirical quantification, we start by computing the abnormal effective tax rate for each group and each subsidiary, which is the deviation from the respective country-industry-year average. We use “t” as a year subscript, “s” as a subsidiary subscript, and the subscript “g” relates to the respective group. The AETR for the subsidiaries are computed as follows.

1 n (1) AETRs,t  ETRs,t  *ETR j,c,t n i1

AETRs,t can be interpreted as the subsidiary-specific ETR deviation from the country- industry-year average. In other words, it captures the relative tax-avoidance for each MNC subsidiary entity, relative to its subsidiary country-industry-year peer group. We interpret positive values as less tax avoidance while negative values represent more tax avoidance. An AETR of zero is expected to correspond to a subsidiary which ETR is identical to the country-industry-year average ETR.

We can perform this type of analysis, since our dataset (as described in more detail below) allows us to observe unconsolidated (subsidiary-entity) financial statements of domestic and

17 foreign affiliates that are majority-owned by global MNCs. The pretax income that is reported in unconsolidated financial statements is the source-country income that is subject to local tax.

Notably, this is the income that is reported in a country after potential profit shifting activities into or out of that specific country. Since our dependent variable for tax avoidance is a ratio, it is a suitable indicator of an affiliate’s local tax burden that does not directly reflect the impact of income shifting transactions. Next, we compute the weighted average (by pretax income, PTI) of the AETR for all subsidiaries (s) of a given multinational to obtain one measure of tax avoidance of all its subsidiaries in year t. This measure can be interpreted as the weighted local tax avoidance within jurisdictions where the subsidiaries are located (wAETRs) and where the weight is formed by the level of the subsidiary taxable income.

1 m wAETRs,t  m *  AETRs,t * PTI s,t s1 (2)  PTI s,t s1

Next, we define the abnormal effective tax rate of the group based on consolidated statements. The calculation is the same as for subsidiaries as shown in Formula 1 with the exception the data is based on the groups’ consolidated statement.

1 n (3) AETRg,t  ETRg,t  *ETR j,c,t n i1

We then regress the abnormal ETR of the group (AETRg,t) on the weighted tax avoidance of the subsidiaries (wAETRs,t) to investigate how the parent’s tax avoidance is associated with the subsidiaries avoidance. A coefficient of zero would indicate that there is no association between the ex post realized MNC tax avoidance and the local tax avoidance in subsidiaries. This result of

18 a zero correlation in the case of tax-aggressive MNC group is indicative of tax avoidance that is realized via income shifting as it is not related to any subsidiary country tax avoidance.8 A coefficient of one would indicate that the parent’s tax avoidance is explained completely by the subsidiaries’ local tax avoidance instead of via profit shifting. A significantly positive coefficient indicates that MNC group tax avoidance is explained by a proportion of within affiliate country tax avoidance where the proportion is summarized in the value of the coefficient. The model of interest goes as follows:

AETRg,t  0  1 * wAETRs,t  controls g,t  i,t (4)

We insert a battery of tax determinants that prior research has identified to be important drivers of tax avoidance and tax sheltering (e.g., Gupta and Newberry, 1997; Chen et al., 2010;

Desai and Dharmapala, 2009). First, we control for a firm’s size (SIZE) proxied by the natural logarithm of firm assets. In line with Mills et al. (1998) and Rego (2003), we expect SIZE to be negatively related to ETRs since large firms are expected to do more effective tax planning.

However, in line with the political cost argument as in Zimmerman (1982), SIZE may also be positively related to ETRs. Second, we control for a firm’s pretax profitability. Following the arguments in Gupta and Newberry (1997), we expect that under the condition of stable tax preferences and for a given level of total assets, ETR is negatively related to ROA. This result is also predicted from the perspective that MNCs with higher levels of pre-tax income have more opportunities to reduce their overall tax burdens through tax-planning activities (e.g., Rego,

2003). Third, we control for the level of capital intensity (PPE) and interpret this variable as a

8 The opposite could also be true, namely that subsidiaries are very tax aggressive but this is not observed in the MNC group avoidance as this would also result in a zero/insignificant association. The likelihood of this outcome, however, as most MNCs strive for tax minimization at the consolidated level and is also less likely to appear as we will show in the empirical results section. 19 proxy for a firm’s asset mix. In line with the idea that tax benefits are associated with capital investments, we expect that capital-intensive firms should face lower ETRs (see e.g., Gupta and

Newberry, 1997). Fourth, we control for the level of capitalized intangibles (INTANG) as more intangible firms can benefit from favorable tax treatments for research and development (e.g.,

Patent Boxes).9 Fifth, we include LEV to control for a firm’s financing policy. The tax codes generally accord differential treatment to the capital structure of firms because interest expenses are deductible for tax purposes, whereas dividends are not, leading to the expectation that firms with higher leverage would have lower ETRs. However, a positive relation between ETRs and leverage is possible if firms with high marginal tax rates are more likely the ones that can attract and use debt financing (Gupta and Newberry, 1997). Sixth, we include a dummy which is coded one if the respective group had a loss in the previous years (LAGLOSS). As tax-loss carryforwards are not observable but apply in most of the observed institutional settings under study, LAGLOSS captures these to some extent. Seventh, we include #SUBS which is the number of subsidiaries that belong to the respective group to control for the number of available options for avoiding taxes locally. Eighth, to control for the tax attractiveness we include ΔTAXINDEX, which is the difference between the tax attractiveness index of the location of the headquarters as proposed by Keller and Schanz (2013) and the average tax attractiveness indices of the respective subsidiaries. MNCs with subsidiaries located in more tax attractive subsidiaries relative to their peer firms are expected to benefit from these tax features via a lower ETR resulting in a predicted positive coefficient for ΔTAXINDEX. Ninth, we include PUBLIC which is a dummy variable equal one if the group is publicly listed, and zero otherwise. Prior research has shown that private

9 Note that if R&D is expensed rather than capitalized like is the case in many GAAP worldwide then we do not expect to observe a significant relationship between capitalized intangible assets and ETR as the true intangibility then is not reliably represented on the firm’s balance sheet. 20 and public firms have different costs and benefits associated with tax planning leading to the expectation that public firms may be more tax-efficient (e.g. Beatty and Harris, 1998; Beuselinck et al., 2015; Pierk 2016).

Because the variables AETRg and wAETRs are both demeaned at the country-year-industry level, there are no separate country-industry-year dummies included in the model. However, we do additionally include subsidiary-country fixed effects to further control for differences in profit shifting opportunities. These fixed effects are a battery of dummies that take on the value of one for all countries the respective MNC operates in.

3.4. Time-series Variation and Within-Group Difference Testing

In additional tests, we investigate whether the association between AETRg,t on wAETRs,t shows some time-series patterns (H2) and/or differs across cross-sectional and within-group sample splits based on listing status (H3), domestic/foreign location (H4a) and vertical/horizontal integration (H4b). As discussed above, profit shifting is getting more and more in the eye of the storm and receives considerably larger attention by the financial press and news media as well as by national governments and supranational organizations recently. The listing status split serves to identify whether listed/private MNC groups prefer local tax avoidance above income shifting.

The within-group difference testing further allows for identification of settings that are more apt for subsidiary local tax avoidance.

4. Sample and Results

4.1. Sample

The sample is based on non-financial groups from 27 EU Member States and their global subsidiaries. The data is gathered from Bureau van Dijk © Orbis database covering the period

2006 to 2014. This database contains information on the (most recent) ultimate owner of each

21 corporation, which we use to construct corporate groups. Groups are considered in our sample when they have at least one foreign subsidiary. We do not consider purely national groups since these firms can only avoid taxes locally and cannot engage in cross-jurisdictional income shifting. For each EU Member State, we download the consolidated parent financial data and the unconsolidated subsidiary level data to calculate the group-level ETR, resp. affiliate-level ETR.10

Subsidiaries are defined as such if the parent company directly or indirectly owns at least 50% of the shares. This search strategy allows us to combine all unique subsidiary observations to their ultimate parent. We exclude observations with missing data on pretax income and total assets and for which we have missing data on control variables; for firm-years with a negative pretax income, firm-years with a negative tax expense; firm-years with a tax rate above 100% of pre-tax income; and subsidiaries with net income of exactly zero (in this case firms have a profit transfer agreement). The final dataset of the subsidiaries consists of 158,749 subsidiary-year observations from 69 different countries. This sample corresponds to 34,111 group-year observations from the

10 Note that the use of Orbis database which has information on accounting data to study tax avoidance poses some challenges that all other studies using this dataset also suffer from. We explain the three most important limitations and the way how we address these. First, accounting profits are not identical to taxable profits and book-tax differences may vary systematically over time and across countries. However, the use of country-time fixed effects that we introduce in our empirical design capture country/time-varying book-tax differences. Moreover, since we focus on EU multinationals of which we observe domestic and foreign subsidiary observations, the 4th and 7th EU Directive apply in the large majority of our sample cases. In most EU Member States, taxable income is based on reported accounting income and is adjusted with specific tax law regulations. Second, our study could suffer from measurement error in the tax avoidance measurement due to imperfect coverage of the Orbis database. If the database coverage is particularly low in specific countries because of the low level of local disclosure, like is the case in tax havens, our results may be biased. However, Johannson et al. (2016) show that Orbis scores relatively well in the coverage of tax haven presence, and correctly identifies tax haven presence in 70 percent of the cases. Third, since we cover 69 countries, it is hard to identify country-specific tax treatments that may be put in place at one point in time and that explain the relative weight that specific MNCs may want to place on within- versus across-country tax avoidance strategies. To the extent that the treatments are available for all MNCs operating in the specific jurisdiction, the subsidiary-country-year fixed effects again are capturing this effect. In all other cases where only specific MNCs are able to negotiate tax deals locally (for instance, only very large MNCs are able to negotiate advance pricing agreements (APSs) with local authorities or can set up structures to take advantage of tax loopholes), the empirical tests are expected to capture the cross-sectional variation. 22

European Union. Table 1 shows the location of the subsidiaries (rows) and the origin of the respective group (columns).

***INSERT TABLE 1 HERE***

For expositional purposes, we separately show the MNC parent/subsidiary observations only for these countries where we observe more than 1,000 subsidiary-year observations. The countries for which this is the case are Austria, Belgium, Germany, Denmark, Spain, Finland,

France, United Kingdom, Ireland, Luxembourg, the Netherlands, Poland, Portugal and Sweden.

In the interest of readability, the observations of all other countries (N=12) are pooled in the final column (Other). As shown in Table 1, we observe most subsidiary-locations (rows) in the United

Kingdom (GB, 19,049), followed by Spain (ES, 17,011) and France (FR, 15,624). In terms of the

MNC parent-origin (column), we observe that MNCs from Germany (DE) have the highest number of subsidiaries (41,252), followed by Great Britain (GB, 22,210) and Spain (IT, 15,042), respectively. Further, a large fraction of the observed subsidiaries is located domestically. For example, the highest fraction of local subsidiaries is observed in Great Britain (GB/GB: 10,807).

Thus, our sample includes 10,807 subsidiary observations for subsidiaries located in Great

Britain majority owned by British-origin MNCs.

4.2. Descriptive Statistics and Results – Subsidiary Level

In Table 2, we observe that the mean (median) subsidiary-level ETR is 24.7% (25.1%) and the interquartile range lies between 17.1% and 30.6%. While average and median ETRs are consistent with rates reported in prior research in a U.S. setting (e.g., Dyreng et al., 2017), the top quartile of observed ETRs are significantly higher. One potential explanation for some extreme

ETRs may lie in the fact that we observe tax expenses, not cash tax payments, and we have some countries in our sample that had high tax rates during our sample period (e.g. Germany above

23

38% before 2008). By definition, the mean abnormal effective tax rate (AETRs) of subsidiaries is zero. The median is also zero indicating that approximately half of the subsidiary observations sample is labelled as avoiding tax (left-tail of the distribution) and the other half is labelled as not avoiding tax (right tail).

***INSERT TABLE 2 HERE***

In Table 3, we investigate whether subsidiary local tax avoidance is determined by the group. The dependent variable is the abnormal effective tax rate of subsidiaries (deviation from the respective country-year-industry average). First, we do not include any additional fixed effects and the R2 is around 3.3%. Next, we want to know whether the origin of the parent has additional explanatory power and we include parent-country fixed effects (26 fixed effects). The parent-

2 country fixed effects account for 0.2 % of the total R (row: cov(AETR , FEgroup) / var(AETR)).

In Column (3), we include fixed effects for each parent-country/subsidiary-country combination

(787 fixed effects). These fixed effects account for 1.2 % of the total R2. Lastly, we include fixed effects for each group (7,659 fixed effects). The group fixed effects account for 10.9 % increase in R2. Also the adjusted R2 has increased from 3.2 % to 9.5 %. The 10.9 % increase in R2 in

Column (4) is equivalent to 80% of the total variation, which is far above the (6%) 27% that stems from the MNC (parent-country) parent-country/subsidiary-country pairs fixed effect. In line with Hypothesis 1a, we interpret these results as evidence that MNC origin and MNC- affiliate country bilateral relationships only capture a portion of the subsidiary tax avoidance and that rather the MNC fixed effect (i.e., the “corporate style”) is largely responsible for the design and orchestration of subsidiary local tax avoidance behavior.

***INSERT TABLE 3 HERE***

24

4.3. Descriptive Statistics and Results – Group Level

Table 4 includes the summary statistics of the groups. We observe that the average ETR (tax expense/pre-tax income) is 28.4%. The median ETR is slightly lower (27.0%). Interestingly, only

25% of the MNC groups realized an ETR below 20.7%. By design, the abnormal effective tax rates of groups (AETRg) is zero. With respect to wAETRs, the pretax income-weighted abnormal

ETR of the groups’ subsidiaries, we find that the average group displays a slightly tax aggressive

11 strategy in its subsidiaries (p50=-0.004). The average group has 4.654 subsidiaries (#SUBSg) in the final sample. In terms of profitability (ROAg), the groups are on average highly profitable

(mean=9.7%; median=7.4%). The average group has 9.1% of its balance sheet total in capitalized intangibles and the maximum level of intangibility is 83.6%. Mean (median) level of PPE is

24.4% (20.9%). The average group has a balance sheet total of about € 128.8 million and a financial leverage (short and long-term) of 57.7%. Finally, 6.5% of the observations had a negative income in the pre-observation year and 24.5% of the MNCs in the sample are publicly listed.

***INSERT TABLE 4 HERE***

The correlation table (Table 5) gives first evidence that the group-level tax avoidance, measured as abnormal effective tax rates (AETRg), is positively correlated with the tax avoidance of its subsidiaries (wAETRs). The Pearson correlation between AETRg and wAETRs is 0.11 and the

Spearman rank correlation is 0.14 (both statistically significant at the 1% level). Furthermore, the

Table 5 suggest that the consolidated ETR is positively related to INTANGg (0.08; p<0.01) and

LEVg (0.12; p<0.01). At the same time, ETRg is significantly negatively related to ROAg (-0.20; p<0.01), and negatively to SIZEg (-0.02; p<0.01).

11 The mean of wAETRs is not equal to zero due to the pretax weighting. 25

***INSERT TABLE 5 HERE***

Table 6 reports the regression results for the variables of interest. The columns quantify the association between the group tax avoidance (AETRg) and the pretax income-weighted abnormal effective tax rate (wAETRs) within subsidiary affiliate countries. Recall that a zero correlation is expected to arise if parents realize tax savings that are totally independent from the subsidiary within-country tax avoidance and that a significantly positive correlation indicates that groups realize tax savings that are explained to a specific extent by the subsidiary within-country tax avoidance. In all specifications, we find that group tax avoidance is positively related to the subsidiary within-country tax avoidance. These findings allow us to reject the null hypothesis

(H1b) of no within-country tax avoidance.

***INSERT TABLE 6 HERE***

In Table 7 we investigate whether there is a general time trend in within-country tax avoidance. Panel A includes graphical evidence. The left-hand side graph shows the yearly coefficient when regression AETRg on wAETRs. The graph indicates that there is an overall time trend and within-country tax avoidance is getting more important over time. The right-hand side shows this general time trend, based on a regression of wAETRs on a time trend. Panel B includes the respective regression results. In line with our second hypothesis, we find that the association between AETRg and wAETRs increases steadily with about one percent per year, suggesting that

MNCs have increasingly relied more on local (within-country) tax avoidance in more recent years.

***INSERT TABLE 7 HERE***

26

5. Cross-Sectional and Within-Group Evidence

In Table 8 we identify MNC-level characteristics that we expect to be correlated with the incentives and opportunities to focus more on within-country tax avoidance. In line with

Hypothesis 3, we observe in Column (1) that public firms, on average, do employ less within- country tax avoidance compared to private firms (coefficient of the interaction of wAETRs and

PUBLICg: -0.017). The coefficient, however, is not statistically significant. In Column (2) we apply a propensity score matching where the first stage models the likelihood of being publicly listed. The coefficient of the interaction term of wAETRs and PUBLICg is insignificantly positive.

Overall, the results of Table 8 indicate that there are no significant differences between public and private multinationals.

***INSERT TABLE 8 HERE***

In Table 9, we investigate differences within groups, i.e. we want to know for which subsidiaries the correlation between AETRg on wAETRs is more pronounced. In Panel A, we compare domestic subsidiaries with foreign subsidiaries. Thus, we compute the pretax weighted abnormal effective tax rate separately for domestic subsidiaries (wAETRdomestic) and for foreign subsidiaries (wAETRforeign). The sample size is reduced as we require each group to have at least one foreign and one domestic subsidiary in the final sample. Column (1) shows that we find significantly positive coefficients for domestic and foreign subsidiaries, but the effect is more pronounced for domestic subsidiaries. To rule out that this is simply driven by the economic importance of the domestic subsidiaries, we match both types of subsidiaries based on pretax income. Thus, Column (2) includes observations where the foreign pretax income is within a

25% range of the domestic pretax income. The results show that only the coefficient for domestic subsidiaries is statistically significant (0.106, P<0.001). Thus, we conclude that the focus on local

27 tax avoidance is largest in domestic subsidiaries, suggesting that the familiarity with the headquarters’ local tax administration gives rise to larger local tax avoidance opportunities.

Similarly, we split subsidiaries into being in the same industry as the group based on a 2-digit

SIC code to proxy for vertical integration. The coefficients of wAETRsame_industry and wAETRdifferent_industry are both statistically significant in Column (1), but the more pronounced for subsidiaries that are in different industries. If we match on pretax income (similar as in Panel A), only subsidiaries in a different industry show a statistically positive coefficient. This finding is consistent with the argument that vertical transfers of goods and services (so, from connected group members but at different layers in the value chain and where comparable price units may be challenged more by tax authorities) are context where MNCs may focus more on local tax avoidance rather than tax- reducing transfer prices. Overall, the results are in line with Hypothesis 4a and Hypothesis 4b.

***INSERT TABLE 9 HERE***

6. Robustness Tests

A potential concern is that we might not observe all subsidiaries of the groups. For example, we do not observe U.S. subsidiaries as data on U.S. private firms is usually not available.

Although we have no prediction how this could potentially affect our results, we limit the sample to groups where the sum of all subsidiaries pretax profits are at least 50% of the group’s pretax profits. This way, we ensure that we capture significant parts of the taxable profits. The results displayed in Column (1) of Table 10 show that the coefficients are stronger when focusing on groups where we have significant part of the pretax profits. This indicates that data availability is diluting our results and our findings can be understood as the lower boundary of the real importance of within-country tax avoidance. Similarly, we restrict the sample to firms where we

28 observe at least 3 subsidiaries per group. The coefficient of wAETRs in Column (2) is slightly larger compared to the coefficient observed in the full sample (Table 6).

When computing abnormal effective tax rates for groups and subsidiaries, we compare the effective tax rate with the country-industry-year average. One potential concern is that this measure is not robust if there are only one or two observations in the respective cluster.

Therefore, we repeat our analyses and limit the sample to observations where we observe at least seven observations in the respective cluster, both for the computation of abnormal effective tax rates of groups and subsidiaries. The results are displayed in column (3) of Table 10 and they show qualitatively the same results.

Finally, we use all data restrictions of the previous columns in Column (4). The sample size is here reduced to 6,247 group observations. Even here we find that the coefficient is higher compared to the full sample. Overall, we conclude that data limitations are likely to underestimate the real effect of within-country tax avoidance and the findings of Table 6 can be seen as a lower bound of the real effect.

***INSERT TABLE 10 HERE***

Our sample includes a high number of observations from specific countries, e.g. Great-

Britain. In untabulated results, we re-run the analyses of Table 6 and exclude Great-Britain. The results stay qualitatively the same. We also repeat this procedure for all other 26 parent-countries

(27 times in total). Overall, the results are not driven by observations from a specific country.

7. Conclusion

The purpose of the current study is to investigate whether and if so to what extent MNCs achieve lower consolidated effective tax rates (ETRs) via within versus across-country tax avoidance. We first show that the parents of subsidiaries are an important determinant of 29 subsidiary tax avoidance. Next, after controlling for the standard ETR determinants identified in prior tax research, we show that the consolidated tax avoidance of the average MNC in our sample is related to the subsidiaries’ tax avoidance. This finding is consistent with the conjecture that MNCs’ tax avoidance is partly explained by its domestic and foreign-affiliate country tax avoidance and is not originating exclusively from cross-jurisdictional income shifting. This finding indicates that the nearly exclusive attention on MNC cross-jurisdictional income shifting strategies may be understating the totality tax planning actions of MNCs.

To investigate whether within-country tax avoidance acts as a substitute rather than a complement for cross-country tax avoidance (i.e., income shifting), we perform additional tests based on MNC characteristics and the reliance on within-country tax avoidance. A time trend analyses shows that while firms rely more on the within-country tax avoidance in more recent years. Furthermore, within-country tax avoidance is concentrated among domestic subsidiaries and subsidiaries that are in a different industry than the corporate group.

Our findings have important policy implications. In line with recent U.S. evidence by Dyreng et al. (2017) which shows that over the last 25 years domestic-only firms experienced a similar decrease in cash ETRs compared to multinationals, the current study suggests that the almost exclusive focus on multinational income shifting for tax avoidance may be misplaced and in fact is underestimating the complete focus of MNCs in tax avoidance strategies. Instead, tax regulators may want to focus also on within-country tax avoidance and how this helps MNCs in lowering their overall tax bill. As such, we invite future research that investigates specific features in national tax systems that allows MNCs to reduce their tax bill. Also, our findings suggest that in an era characterized by austerity and government deficits and where the pressure

30 for a fairer tax game is growing, MNCs respond quickly in updating their most preferable tax planning strategies.

31

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9. Tables and Figures

Table 1. Location of Groups and Subsidiaries AT BE DE DK ES FI FR GB IE LU NL PL PT SE Other Total AE 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 4 AL 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 4 AT 1,471 72 1,495 93 62 77 10 264 21 14 130 0 3 110 26 3,848 AU 1 7 165 18 6 29 0 154 20 5 41 0 0 20 1 467 BA 2 1 62 7 11 8 2 8 0 0 8 3 0 10 92 214 BB 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 6 BE 76 5,796 1,699 213 150 118 429 787 347 130 2,573 5 8 384 3 12,718 BG 59 72 367 35 59 10 7 71 22 2 90 5 4 63 297 1,163 BR 2 5 24 2 41 3 0 10 2 2 7 0 18 4 0 120 CH 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 12 CI 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2 CN 26 39 554 40 12 44 8 111 44 0 44 0 0 79 2 1,003 CO 13 59 213 8 179 10 4 176 45 11 33 0 5 28 0 784 CY 0 2 5 2 0 0 0 0 0 0 0 0 0 0 12 21 CZ 559 218 2,453 161 153 143 25 371 92 187 493 91 6 278 644 5,874 DE 595 472 9,721 483 236 250 119 1,086 202 83 1,099 58 17 401 73 14,895 DK 28 42 423 2,236 27 129 5 181 41 18 176 9 3 519 7 3,844 DZ 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 3 EE 5 8 42 30 8 253 2 11 1 1 17 3 5 85 70 541 ES 98 297 2,726 268 10,277 149 306 1,219 226 34 792 5 392 205 17 17,011 FI 27 59 491 248 15 2,919 22 228 79 17 69 0 0 983 9 5,166 FR 135 1,837 3,957 303 623 195 5,230 1,554 279 101 876 18 18 468 30 15,624 GB 155 460 3,303 472 489 313 180 10,807 968 71 1,120 22 9 604 76 19,049 GR 4 45 244 26 107 14 9 100 32 1 98 0 0 48 568 1,296 HR 132 36 391 49 13 24 2 55 11 4 32 8 5 30 276 1,068 HU 221 125 1,120 98 80 83 13 177 51 49 85 4 4 102 199 2,411 IE 0 37 305 71 91 36 1 605 265 17 119 0 17 60 3 1,627 IL 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 2 IN 9 8 120 13 9 15 4 71 9 6 23 0 0 24 3 314 IS 0 0 14 34 4 9 0 12 9 0 0 0 2 0 4 88 JP 4 0 51 2 0 11 0 27 11 3 7 0 0 11 0 127 KN 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 5 KR 14 48 500 62 14 44 7 144 46 9 37 0 0 83 0 1,008 KZ 0 0 4 0 0 0 0 0 0 1 12 0 0 0 3 20 LK 0 0 2 0 0 0 0 3 0 0 0 0 0 0 0 5 To be continued

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Table 1 continued LT 9 30 163 95 0 169 5 43 6 0 27 29 0 108 171 855 LU 7 229 263 2 13 4 13 173 11 144 55 0 0 5 2 921 LV 11 11 142 129 5 187 0 55 8 0 37 22 0 220 237 1,064 MA 0 10 83 3 72 1 31 8 0 0 5 0 0 2 0 215 MD 0 0 3 0 0 0 0 0 0 0 0 0 0 0 1 4 ME 2 0 3 0 1 0 0 0 0 0 4 1 0 0 17 28 MK 6 1 3 2 0 0 0 2 0 0 0 0 0 0 19 33 MT 6 0 112 1 5 0 0 53 12 3 6 5 1 12 96 312 MU 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 NL 58 226 1,039 121 188 30 19 776 128 41 2,383 0 23 145 17 5,194 NO 32 83 623 662 10 579 4 352 82 32 265 7 0 1,655 25 4,411 NZ 0 1 159 37 3 4 0 98 23 0 25 0 0 14 0 364 PA 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 3 PE 0 8 22 2 36 0 2 28 0 0 20 0 3 0 4 125 PH 0 9 147 25 6 11 1 49 17 7 18 0 0 13 1 304 PK 0 0 22 0 0 0 0 5 0 0 0 0 0 0 0 27 PL 205 326 2,419 407 246 270 145 576 187 30 633 1,571 67 515 142 7,739 PT 25 126 556 87 1,312 62 118 223 46 14 204 8 1,469 67 9 4,326 PY 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 5 RO 236 198 996 108 156 45 107 304 87 15 385 69 27 73 263 3,069 RS 82 27 229 23 53 5 6 24 3 0 67 15 0 38 143 715 RU 60 107 649 84 43 191 22 310 10 13 227 64 5 71 195 2,051 RW 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 SE 93 145 1,263 773 51 1,049 25 529 117 17 419 35 8 7,228 31 11,783 SG 3 0 186 38 0 13 3 116 28 0 84 0 0 24 7 502 SI 103 23 373 38 4 32 5 43 6 0 12 9 1 32 201 882 SK 288 86 961 131 117 75 60 122 84 17 195 48 5 83 212 2,484 TH 0 0 2 1 0 0 0 0 0 0 1 0 0 0 0 4 TR 7 17 111 10 21 3 7 27 3 0 32 0 0 16 1 255 TT 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 8 TW 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 TZ 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 2 UA 34 16 251 10 27 26 0 33 30 3 130 72 5 22 41 700 UY 0 3 4 0 2 2 0 2 0 2 1 0 0 0 0 16 Sum 4,904 11,428 41,252 7,763 15,042 7,644 6,959 22,210 3,711 1,109 13,218 2,186 2,130 14,943 4,250 158,749 This table provides the locations of the subsidiaries (rows) and the origin of the respective parents (columns).

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Table 2. Summary Statistics - Subsidiaries Variable n Mean Sd Min P25 P50 P75 Max

ETRs 158,749 0.247 0.139 0.001 0.171 0.251 0.306 0.802

AETRs 158,749 0.000 0.124 -0.527 -0.053 0.000 0.043 0.702

ROAs 158,749 0.147 0.147 0.002 0.046 0.102 0.195 0.795

PPEs 158,749 0.189 0.247 0.000 0.011 0.072 0.284 0.965

INTANGs 158,749 0.020 0.064 0.000 0.000 0.000 0.006 0.433

LEVs 158,749 0.557 0.270 0.002 0.353 0.576 0.773 1.091

SIZEs 158,749 9.259 2.043 4.573 7.902 9.157 10.508 14.832

LAGLOSSs 158,749 0.079 0.269 0.000 0.000 0.000 0.000 1.000 This table presents the summary statistics for the subsidiaries. ETR is the GAAP effective tax rate. AETR is the abnormal effective tax rate defined as ETR minus the country-industry-year average. ROA is pretax income divided by total assets. LEV, PPE, and INTANG are total debt, PPE, and intangible assets deflated by total assets. SIZE is the natural logarithm of total assets. LAGLOSS equals one if the firm had negative pretax income in the previous year. All non-dichotomous variables are winsorized at the 1% and 99% level.

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Table 3. Regression Results - Subsidiaries

Dep. Var.: AETRs (1) NO FE (2) Parent-Country (3) Parent-Sub. Pairs (2) GROUP FE

ROAs -0.119*** -0.120*** -0.122*** -0.132*** (53.74) (54.11) (54.20) (54.67)

PPEs 0.004*** 0.004*** 0.005*** 0.003** (3.09) (3.16) (3.52) (2.13)*

INTANGs 0.035*** 0.036*** 0.039*** 0.04*** (7.21) (7.53) (7.92) (7.62)

LEVs 0.023*** 0.023*** 0.024*** 0.027*** (19.63) (19.33) (19.78) (20.58)

SIZEs -0.007*** -0.007*** -0.007*** -0.008*** (41.63) (41.49) (40.23) (41.40)

LAGLOSSs -0.025*** -0.025*** -0.025*** -0.024*** (22.00) (22.02) (22.01) (20.57) Subs. Country-FE Yes Yes Yes Yes Parent-Subsidiary FE No Parent-Country Group Country N 158,749 158,749 158,749 158,749 R2 – adj. 0.032 0.033 0.040 0.095 R2 0.033 0.034 0.045 0.138 cov(AETR,FE)/var(AETR) 0.002 0.012 0.109 R2 explained by FE in % 0.058 0.267 0.789 This table provides OLS regression results. The dependent variable is AETR which is the subsidiaries’ abnormal effective tax rate defined as ETR minus the country-industry-year average ROA is pretax income divided by total assets. LEV, PPE, and INTANG are total debt, PPE, and intangible assets deflated by total assets. SIZE is the natural logarithm of total assets. LAGLOSS equals one if the firm had negative pretax income in the previous year. The models include fixed-effects for subsidiary countries. Model 1 includes no group fixed effects, Model 2 includes 26 parent-country fixed effects, Model 3 includes 787 parent-country/subsidiary-country pairs fixed effects, and Model 4 includes 7759 MNC group fixed effects. All non-dichotomous variables are winsorized at the 1% and 99% level. /* marks significance at the 1% level, according to two-sided tests.

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Table 4. Summary Statistics - Groups Variable n Mean Sd Min P25 P50 P75 Max

ETRg 34,111 0.284 0.142 0.013 0.208 0.270 0.333 0.839

AETRg 34,111 0.000 0.126 -0.550 -0.063 -0.004 0.043 0.650 wAETRs 34,111 -0.009 0.099 -0.423 -0.054 -0.004 0.031 0.677

#SUBSg 34,111 4.654 9.774 1.000 1.000 2.000 4.000 248.000

#SUBSforeign 34,111 2.786 7.563 0.000 1.000 1.000 2.000 207.000

ΔTAXINDEXg 34,111 0.035 0.128 -0.479 0.000 0.000 0.087 0.516

ROAg 34,111 0.097 0.083 0.005 0.041 0.074 0.125 0.467

PPEg 34,111 0.244 0.194 0.001 0.080 0.209 0.359 0.836

INTANGg 34,111 0.091 0.144 0.000 0.004 0.025 0.109 0.672

LEVg 34,111 0.577 0.195 0.121 0.443 0.590 0.717 1.000

SIZEg 34,111 11.766 1.968 7.922 10.368 11.511 12.969 17.265

LAGLOSSg 34,111 0.065 0.246 0.000 0.000 0.000 0.000 1.000

PUBLICg 34,111 0.245 0.430 0.000 0.000 0.000 0.000 1.000 This table presents the summary statistics for the subsidiaries in Panel A and for the groups in Panel B. ETR is the GAAP effective tax rate. AETR is the abnormal effective tax rate defined as ETR minus the country- industry-year average. wAETR is the by pretax income weighted average of abnormal effective tax rates (AETR) of the groups’ subsidiaries. MNC equals one if the group has at least one foreign subsidiary. #SUBS is the number of subsidiaries. #SUBSforeign is the number of foreign subsidiaries. ΔTAXINDEX is the difference between the parents’ tax attractiveness index as proposed by Keller and Schanz (2013) and the average tax attractiveness indices of the respective subsidiaries. ROA is pretax income divided by total assets. LEV, PPE, and INTANG are total debt, PPE, and intangible assets deflated by total assets. SIZE is the natural logarithm of total assets. LAGLOSS equals one if the firm had negative pretax income in the previous year. PUBLIC is an indicator variable coded one if the respective group is publicly listed, and zero otherwise. All non-dichotomous variables are winsorized at the 1% and 99% level.

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Table 5. Correlations - Groups (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

(1) ETRg 0.82* 0.140* 0.02* 0.00 0.00 -0.17* -0.02* 0.12* 0.11* -0.01 0.00 -0.08*

(2) AETRg 0.89* 0.12* 0.02* 0.00 -0.01 -0.17* 0.00 0.10* 0.08* 0.00 0.01 -0.02*

(3) wAETRs 0.11* 0.11* -0.12* -0.07* 0.02* 0.03* -0.01 -0.08* -0.02* -0.10* -0.01 -0.11*

(4) #SUBSg -0.01 -0.02* -0.07* 0.6* 0.000 -0.08* 0.07* 0.29* 0.04* 0.53* -0.05* 0.34*

(5) #SUBSg,foreign -0.01 -0.01 -0.05* 0.91* 0.28* 0.03* 0.03* 0.20* -0.02* 0.38* -0.03* 0.27*

(6) ΔTAXINDEXg -0.01 0.00 0.02* 0.00 0.06* 0.02* 0.02* -0.03* -0.01 0.04* 0.02* -0.03*

(7) ROAg -0.20* -0.18* 0.02* -0.06* -0.03* -0.01 -0.14* -0.09* -0.32* -0.21* -0.17* -0.05*

(8) PPEg -0.02* 0.00 0.00 0.03* 0.01 0.03* -0.18* -0.13* -0.05* 0.16* 0.00 -0.01

(9) INTANGg 0.08* 0.08* -0.05* 0.17* 0.15* -0.05* -0.09* -0.24* 0.09* 0.33* 0.02* 0.38*

(10) LEVg 0.12* 0.10* -0.01 0.07* 0.04* 0.00 -0.27* -0.02* 0.05* 0.06* 0.09* -0.06*

(11) SIZEg -0.02* -0.02* -0.08* 0.46* 0.42* 0.02* -0.22* 0.16* 0.25* 0.08* -0.04* 0.42*

(12) LAGLOSSg 0.03* 0.04* -0.02* -0.03* -0.02* 0.02* -0.12* 0.01 0.03* 0.10* -0.04* 0.01

(13) PUBLICg -0.08* -0.04* -0.08* 0.28* 0.25* -0.04* -0.06* 0.00 0.36* -0.05* 0.44* 0.01 This table presents Pearson correlations in the lower triangle and Spearman correlations in the upper triangle. ETR is the GAAP effective tax rate. AETR is the abnormal effective tax rate defined as ETR minus the country-industry-year average. wAETR is the by pretax income weighted average of abnormal effective tax rates (AETR) of the groups’ subsidiaries. MNC equals one if the group has at least one foreign subsidiary. #SUBS is the number of subsidiaries. #SUBSforeign is the number of foreign subsidiaries. ΔTAXINDEX is the difference between the parents’ tax attractiveness index as proposed by Keller and Schanz (2013) and the average tax attractiveness indices of the respective subsidiaries. ROA is pretax income divided by total assets. LEV, PPE, and INTANG are total debt, PPE, and intangible assets deflated by total assets. SIZE is the natural logarithm of total assets. LAGLOSS equals one if the firm had negative pretax income in the previous year. PUBLIC is an indicator variable coded one if the respective group is publicly listed, and zero otherwise. All non-dichotomous variables are winsorized at the 1% and 99% level. /* marks significance at the 1% level, according to two-sided tests

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Table 6. Regression Results

Dep. Var.: AETRg (1) (2) (3) wAETRs 0.138*** 0.139*** 0.139*** (0.016) (0.016) (0.016)

SIZEg -0.004*** (0.001)

ROAg -0.280*** (0.032)

PPEg 0.002 (0.007)

INTANGg 0.079*** (0.022)

LEVg 0.030*** (0.005)

LAGLOSSg 0.005 (0.005)

#SUBSg -0.000** (0.000)

ΔTAXINDEXg -0.008** (0.003)

PUBLICg -0.017*** (0.003) Constant 0.001*** -0.004*** 0.052*** (0.000) (0.001) (0.010) Subs. Country-FE No Yes Yes N 34,111 34,111 34,111 R-squared 0.012 0.018 0.066 This table provides OLS regression results. The dependent variable is AETR which is the groups’ abnormal effective tax rate defined as ETR minus the country-industry-year average. wAETR is the by pretax income weighted average of abnormal effective tax rates (AETR) of the groups’ subsidiaries. MNC equals one if the group has at least one foreign subsidiary. #SUBS is the number of subsidiaries. ΔTAXINDEX is the difference between the parents’ tax attractiveness index as proposed by Keller and Schanz (2013) and the average tax attractiveness indices of the respective subsidiaries. ROA is pretax income divided by total assets. LEV, PPE, and INTANG are total debt, PPE, and intangible assets deflated by total assets. SIZE is the natural logarithm of total assets. LAGLOSS equals one if the firm had negative pretax income in the previous year. PUBLIC is an indicator variable coded one if the respective group is publicly listed, and zero otherwise. The models include fixed-effects for subsidiary countries when indicated. Standard errors are clustered at investor (group) country level and are provided within the brackets below the coefficients. ***/**/* marks significance at the 1/5/10% level, respectively, according to two-sided tests.

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Table 7. Time Trend Panel A: Graphical Evidence

The figure on the left-hand side shows the yearly coefficient when regressing AETRg on wAETRs in line with Model (4). The dependent variable is AETRg which is the groups’ abnormal effective tax rate defined as ETR minus the country-industry-year average. wAETRs is the by pretax income weighted average of abnormal effective tax rates (AETR) of the groups’ subsidiaries. The figure on the right-hand side shows the respective time trend based on a regression of wAETRs on a time trend.

Panel B: Regression Results

Dep. Var.: AETRg (1) wAETRs 0.082*** (0.022) wAETRs *TREND 0.010*** (0.003) Controls Yes Subs. Country-FE Yes N 34,111 R-squared 0.067 This table in Panel B provides OLS regression results. The dependent variable is AETR which is the groups’ abnormal effective tax rate defined as ETR minus the country-industry-year average. TREND is a time trend computed as the current year minus 2005. Control variables are included in line with Table 7. The models include fixed-effects for subsidiary countries. Standard errors are clustered at investor (group) country level and are provided within the brackets below the coefficients. ***/**/* marks significance at the 1/5/10% level, respectively, according to two-sided tests.

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Table 8. Public vs. Private Firms

Dep. Var.: AETRg (1) (2) PSM wAETRs 0.143*** 0.129*** (0.020) (0.027)

PUBLICg -0.017*** -0.018*** (0.004) (0.004) wAETRs *PUBLICg -0.017 0.011 (0.023) (0.020) Controls Yes Yes Subs. Country-FE Yes Yes N 34,111 9,260 R-squared 0.066 0.075 This table provides OLS regression results. The dependent variable is AETR which is the groups’ abnormal effective tax rate defined as ETR minus the country-industry-year average. PUBLIC is an indicator variable coded one if the respective group is publicly listed, and zero otherwise. Model 2 shows observations based on a propensity score matched sample where the first stage models the likelihood to be a public firm. Control variables are included in line with Table 7. The models include fixed-effects for subsidiary countries. Standard errors are clustered at investor (group) country level and are provided within the brackets below the coefficients. ***/**/* marks significance at the 1/5/10% level, respectively, according to two-sided tests.

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Table 9. Within-Group Variation Panel A: Foreign vs Domestic Subsidiaries

Dep. Var.: AETRg (1) (2) Match wAETRdomestic 0.086*** 0.106** (0.023) (0.050) wAETRforeign 0.042*** 0.059 (0.010) (0.044) Controls Yes Yes Subs. Country-FE Yes Yes N 12,509 9,260 R-squared 0.066 0.075

Panel B: Same Industry vs Different Industry

Dep. Var.: AETRg (1) (2) Match wAETRsame_industry 0.028** 0.047 (0.013) (0.075) wAETRdifferent_industry 0.064*** 0.194*** (0.015) (0.047) Controls Yes Yes Subs. Country-FE Yes Yes N 8,954 853 R-squared 0.073 0.188 This table provides OLS regression results. The dependent variable is AETR which is the groups’ abnormal effective tax rate defined as ETR minus the country-industry-year average. wAETRdomestic is the groups’ abnormal effective tax rate of domestic subsidiaries. wAETRforeign is the groups’ abnormal effective tax rate of foreign subsidiaries. wAETRsame_industry is the groups’ abnormal effective tax rate of subsidiaries that operate in the same industry as the parent based on two digits sic codes. wAETRdifferent_industry is the groups’ abnormal effective tax rate of subsidiaries that operate in a different industry as the parent. The second model of both Panels limit the sample to groups that have their pretax-income approximately equally distributed in domestic and foreign subsidiaries (same industry and different industry). Control variables are included in line with Table 7. The models include fixed-effects for subsidiary countries. Standard errors are clustered at investor (group) country level and are provided within the brackets below the coefficients. ***/**/* marks significance at the 1/5/10% level, respectively, according to two-sided tests.

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Table 10. Robustness Tests

Dep. Var.: AETRg (1) min 50% (2) min 3 subs. (3) min 7 cluster (4): (1) & (2) & (3) wAETRs 0.282*** 0.155*** 0.140*** 0.191*** (0.037) (0.028) (0.019) (0.047) Controls Yes Yes Yes Yes Subs. Country-FE Yes Yes Yes Yes N 14,920 14,489 26,998 6,247 R-squared 0.100 0.100 0.100 0.100 This table provides OLS regression results. The dependent variable is AETR which is the groups’ abnormal effective tax rate defined as ETR minus the country-industry-year average. Model 1 limits the sample to groups where the subsidiaries pretax-profits exceeds 50% of the group’s pretax-profits. Model 2 limits the sample to groups where we observe at least 3 subsidiaries. Model 3 limits the sample to groups where we observe at least 7 observations for the respective country-industry-year cluster. Model 4 uses the restrictions of all previous models. Control variables are included in line with Table 7. The models include fixed-effects for subsidiary countries when indicated. Standard errors are clustered at investor (group) country level and are provided within the brackets below the coefficients. ***/**/* marks significance at the 1/5/10% level, respectively, according to two- sided tests.

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